Articles | Volume 15, issue 7
https://doi.org/10.5194/gmd-15-2773-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-2773-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
06 Apr 2022
Development and technical paper |
| 06 Apr 2022
| 06 Apr 2022
Implementation of an ensemble Kalman filter in the Community Multiscale Air Quality model (CMAQ model v5.1) for data assimilation of ground-level PM2.5
Soon-Young Park
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Institute of Environmental Studies, Pusan National University, Busan,
46241, Republic of Korea
Uzzal Kumar Dash
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Jinhyeok Yu
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Keiya Yumimoto
Research Institute for Applied Mechanics, Kyushu University, Fukuoka,
816-8580, Japan
Itsushi Uno
Research Institute for Applied Mechanics, Kyushu University, Fukuoka,
816-8580, Japan
Chul Han Song
CORRESPONDING AUTHOR
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju, 61005, Republic of
Korea
Related authors
Kyunghwa Lee, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, Jhoon Kim, Younha Kim, Jung-Hun Woo, Sang-Woo Kim, and Chul H. Song
Geosci. Model Dev., 13, 1055–1073, https://doi.org/10.5194/gmd-13-1055-2020, https://doi.org/10.5194/gmd-13-1055-2020, 2020
Short summary
Short summary
For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2) by preparing the initial conditions for model simulations via data assimilation using satellite- and ground-based observations. The performance of the developed air quality prediction system was evaluated using ground in situ data during the KORUS-AQ campaign period.
Soon-Young Park, Dong-Hyeok Kim, Soon-Hwan Lee, and Hwa Woon Lee
Atmos. Chem. Phys., 16, 3631–3649, https://doi.org/10.5194/acp-16-3631-2016, https://doi.org/10.5194/acp-16-3631-2016, 2016
Short summary
Short summary
In order to improve the predictability of air quality, we optimize initial ozone state throughout the 4D-Var data assimilation. Previously developed code for the data assimilation has been modified to consider background error in matrix form, and various numerical tests are conducted. A surface observational assimilation is conducted and the statistical results for the 12 h assimilation periods show a 49.4 % decrease in RMSE and a 59.9 % increase in IOA.
Kiyeon Kim, Chul Han Song, Kyung Man Han, Greg Yarwood, Ross Beardsley, and Saewung Kim
EGUsphere, https://doi.org/10.5194/egusphere-2025-23, https://doi.org/10.5194/egusphere-2025-23, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Despite the crucial role of halogen radicals in the atmosphere, the current CMAQ model does not account for multi-phase halogen processes. To address this issue, we incorporated 177 halogen reactions, together with anthropogenic and natural halogen emissions into the CMAQ model. Our findings reveal that incorporation of these halogen processes significantly improves model performances compared to ground observations. In addition, we emphasize the influence of halogen radicals on air quality.
Kiyeon Kim, Kyung Man Han, Chul Han Song, Hyojun Lee, Ross Beardsley, Jinhyeok Yu, Greg Yarwood, Bonyoung Koo, Jasper Madalipay, Jung-Hun Woo, and Seogju Cho
Atmos. Chem. Phys., 24, 12575–12593, https://doi.org/10.5194/acp-24-12575-2024, https://doi.org/10.5194/acp-24-12575-2024, 2024
Short summary
Short summary
We incorporated each HONO process into the current CMAQ modeling framework to enhance the accuracy of HONO mixing ratio predictions. These results expand our understanding of HONO photochemistry and identify crucial sources of HONO that impact the total HONO budget in Seoul, South Korea. Through this investigation, we contribute to resolving discrepancies in understanding chemical transport models, with implications for better air quality management and environmental protection in the region.
Peng Xian, Jeffrey S. Reid, Melanie Ades, Angela Benedetti, Peter R. Colarco, Arlindo da Silva, Tom F. Eck, Johannes Flemming, Edward J. Hyer, Zak Kipling, Samuel Rémy, Tsuyoshi Thomas Sekiyama, Taichu Tanaka, Keiya Yumimoto, and Jianglong Zhang
Atmos. Chem. Phys., 24, 6385–6411, https://doi.org/10.5194/acp-24-6385-2024, https://doi.org/10.5194/acp-24-6385-2024, 2024
Short summary
Short summary
The study compares and evaluates monthly AOD of four reanalyses (RA) and their consensus (i.e., ensemble mean). The basic verification characteristics of these RA versus both AERONET and MODIS retrievals are presented. The study discusses the strength of each RA and identifies regions where divergence and challenges are prominent. The RA consensus usually performs very well on a global scale in terms of how well it matches the observational data, making it a good choice for various applications.
Bok H. Baek, Rizzieri Pedruzzi, Minwoo Park, Chi-Tsan Wang, Younha Kim, Chul-Han Song, and Jung-Hun Woo
Geosci. Model Dev., 15, 4757–4781, https://doi.org/10.5194/gmd-15-4757-2022, https://doi.org/10.5194/gmd-15-4757-2022, 2022
Short summary
Short summary
The Comprehensive Automobile Research System (CARS) is an open-source Python-based automobile emissions inventory model designed to efficiently estimate high-quality emissions. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road-link-level network information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced inventory for policymakers, stakeholders, and the air quality modeling community.
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Pradeep Khatri, Atsushi Shimizu, Hitoshi Irie, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev., 14, 2235–2264, https://doi.org/10.5194/gmd-14-2235-2021, https://doi.org/10.5194/gmd-14-2235-2021, 2021
Short summary
Short summary
This study compares performance of aerosol representation methods of the Japan Meteorological Agency's regional-scale nonhydrostatic meteorology–chemistry model (NHM-Chem). It indicates separate treatment of sea salt and dust in coarse mode and that of light-absorptive and non-absorptive particles in fine mode could provide accurate assessments on aerosol feedback processes.
Mayumi Yoshida, Keiya Yumimoto, Takashi M. Nagao, Taichu Y. Tanaka, Maki Kikuchi, and Hiroshi Murakami
Atmos. Chem. Phys., 21, 1797–1813, https://doi.org/10.5194/acp-21-1797-2021, https://doi.org/10.5194/acp-21-1797-2021, 2021
Short summary
Short summary
We developed a new aerosol satellite retrieval algorithm combining a numerical aerosol forecast. This is the first study that utilizes the assimilated model forecast of aerosol as an a priori estimate of the retrieval. Aerosol retrievals were improved by effectively incorporating both model and satellite information. By using the assimilated forecast as an a priori estimate, information from previous observations can be propagated to future retrievals, thus leading to better retrieval accuracy.
Arman Pouyaei, Yunsoo Choi, Jia Jung, Bavand Sadeghi, and Chul Han Song
Geosci. Model Dev., 13, 3489–3505, https://doi.org/10.5194/gmd-13-3489-2020, https://doi.org/10.5194/gmd-13-3489-2020, 2020
Short summary
Short summary
This paper introduces a novel Lagrangian model (Concentration Trajectory of Air pollution with an Integrated Lagrangian model, C-TRAIL) for showing the source and receptor areas by following polluted air masses. To investigate the concentrations and trajectories of air masses simultaneously, we use the trajectory-grid (TG) Lagrangian advection model. The TG model follows the concentrations of representative air
packetsof species along trajectories determined by the wind field.
Sojin Lee, Chul Han Song, Kyung Man Han, Daven K. Henze, Kyunghwa Lee, Jinhyeok Yu, Jung-Hun Woo, Jia Jung, Yunsoo Choi, Pablo E. Saide, and Gregory R. Carmichael
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-116, https://doi.org/10.5194/gmd-2020-116, 2020
Revised manuscript not accepted
Kyunghwa Lee, Jinhyeok Yu, Sojin Lee, Mieun Park, Hun Hong, Soon Young Park, Myungje Choi, Jhoon Kim, Younha Kim, Jung-Hun Woo, Sang-Woo Kim, and Chul H. Song
Geosci. Model Dev., 13, 1055–1073, https://doi.org/10.5194/gmd-13-1055-2020, https://doi.org/10.5194/gmd-13-1055-2020, 2020
Short summary
Short summary
For the purpose of providing reliable and robust air quality predictions, an operational air quality prediction system was developed for the main air quality criteria species in South Korea (PM10, PM2.5, CO, O3 and SO2) by preparing the initial conditions for model simulations via data assimilation using satellite- and ground-based observations. The performance of the developed air quality prediction system was evaluated using ground in situ data during the KORUS-AQ campaign period.
Kazuyuki Miyazaki, Kevin W. Bowman, Keiya Yumimoto, Thomas Walker, and Kengo Sudo
Atmos. Chem. Phys., 20, 931–967, https://doi.org/10.5194/acp-20-931-2020, https://doi.org/10.5194/acp-20-931-2020, 2020
Short summary
Short summary
We introduce a multi-model, multi-constituent chemical data assimilation framework that directly accounts for model error in transport and chemistry by integrating a portfolio of forward chemical transport models. The assimilation was able to reduce ensemble forward model spread and bias relative to independent measurements. Diagnostic information readily available from the framework has the potential to improve chemical predictions through relationships such as emergent constraints.
Yongjoo Choi, Yugo Kanaya, Seung-Myung Park, Atsushi Matsuki, Yasuhiro Sadanaga, Sang-Woo Kim, Itsushi Uno, Xiaole Pan, Meehye Lee, Hyunjae Kim, and Dong Hee Jung
Atmos. Chem. Phys., 20, 83–98, https://doi.org/10.5194/acp-20-83-2020, https://doi.org/10.5194/acp-20-83-2020, 2020
Short summary
Short summary
The relationship between black carbon (BC) and carbon monoxide (CO) can differ by the different structure of fuel consumption. By investigating the representativeness of the BC and CO emission inventory for real-world comparison with reliable observations, this study suggested that accurate CO emissions should be preferentially investigated to enhance the accuracy of the BC emission rate over East Asia.
Hyun S. Kim, Inyoung Park, Chul H. Song, Kyunghwa Lee, Jae W. Yun, Hong K. Kim, Moongu Jeon, Jiwon Lee, and Kyung M. Han
Atmos. Chem. Phys., 19, 12935–12951, https://doi.org/10.5194/acp-19-12935-2019, https://doi.org/10.5194/acp-19-12935-2019, 2019
Short summary
Short summary
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. In general, the accuracies of the LSTM-based predictions were superior to the 3-D CTM-based predictions. Based on this, we concluded that the LSTM-based system could be applied to daily operational PM forecasts in South Korea. We expect that similar AI systems can be applied to the predictions of other atmospheric pollutants.
Xiaole Pan, Baozhu Ge, Zhe Wang, Yu Tian, Hang Liu, Lianfang Wei, Siyao Yue, Itsushi Uno, Hiroshi Kobayashi, Tomoaki Nishizawa, Atsushi Shimizu, Pingqing Fu, and Zifa Wang
Atmos. Chem. Phys., 19, 219–232, https://doi.org/10.5194/acp-19-219-2019, https://doi.org/10.5194/acp-19-219-2019, 2019
Yu Tian, Xiaole Pan, Tomoaki Nishizawa, Hiroshi Kobayashi, Itsushi Uno, Xiquan Wang, Atsushi Shimizu, and Zifa Wang
Atmos. Chem. Phys., 18, 18203–18217, https://doi.org/10.5194/acp-18-18203-2018, https://doi.org/10.5194/acp-18-18203-2018, 2018
Short summary
Short summary
East Asia is characterized by severe anthropogenic pollution and dust storms due to fuel consumption and the downwind location of dust sources. We studied the mixing states of dust and pollutants using an optical particle counter equipped with a polarization detection module, providing a deeper understanding of possible mechanisms of aerosols’ morphological change. In Beijing, the heterogeneous processes in the mixture of dust and emitted pollutants have a great influence on smog formation.
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-128, https://doi.org/10.5194/gmd-2018-128, 2018
Revised manuscript not accepted
Syuichi Itahashi, Keiya Yumimoto, Itsushi Uno, Hiroshi Hayami, Shin-ichi Fujita, Yuepeng Pan, and Yuesi Wang
Atmos. Chem. Phys., 18, 2835–2852, https://doi.org/10.5194/acp-18-2835-2018, https://doi.org/10.5194/acp-18-2835-2018, 2018
Short summary
Short summary
Ground-based observations of precipitation chemistry over China, Korea, and Japan from 2001 to 2015 were compiled, and the ratio of nitrate to non-sea-salt sulfate concentration in precipitation was analyzed to identify the long-term record of acidifying species. The variations in the ratio in East Asia corresponded to the NOx / SO2 emission ratio and the NO2 / SO2 column ratio in China. The results indicated that the acidity of precipitation shifted from sulfur to nitrogen.
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Brent Holben, Thomas F. Eck, Zhengqiang Li, and Chul H. Song
Atmos. Meas. Tech., 11, 385–408, https://doi.org/10.5194/amt-11-385-2018, https://doi.org/10.5194/amt-11-385-2018, 2018
Short summary
Short summary
This study is a major version upgrade of the aerosol product from GOCI, the first and unique ocean color imager in geostationary earth orbit. It describes the improvement of version 2 of the GOCI Yonsei aerosol retrieval algorithm for near-real-time processing with improved accuracy from the modification of cloud masking, surface reflectance, etc. The product is validated against AERONET/SONET over East Asia with analyses of various errors features, and a pixel-level uncertainty is calculated.
Itsushi Uno, Kazuo Osada, Keiya Yumimoto, Zhe Wang, Syuichi Itahashi, Xiaole Pan, Yukari Hara, Yugo Kanaya, Shigekazu Yamamoto, and Thomas Duncan Fairlie
Atmos. Chem. Phys., 17, 14181–14197, https://doi.org/10.5194/acp-17-14181-2017, https://doi.org/10.5194/acp-17-14181-2017, 2017
Short summary
Short summary
We analyzed long-term fine- and coarse-mode nitrate and related aerosols synergetic observations at Fukuoka, Japan. GEOS Chem model including dust and sea-salt acid uptake processes was used to assess the observed seasonal variation, and the impact of long-range transport from the Asian continent. A numerical model reproduced the seasonal variations of fine aerosols. For coarse nitrate, large-scale dust-nitrate outflow from China was confirmed during all dust events between January and June.
Xiaole Pan, Yugo Kanaya, Fumikazu Taketani, Takuma Miyakawa, Satoshi Inomata, Yuichi Komazaki, Hiroshi Tanimoto, Zhe Wang, Itsushi Uno, and Zifa Wang
Atmos. Chem. Phys., 17, 13001–13016, https://doi.org/10.5194/acp-17-13001-2017, https://doi.org/10.5194/acp-17-13001-2017, 2017
Short summary
Short summary
Characteristics of refractory black carbon (rBC) from open biomass burning (OBB) have a great impact on regional pollution and climate, in particular in East Asia. However, experimental study on characteristics of rBC from agricultural residue burning in East China was limited. This study performed laboratory experiments: we found that emission of rBC is highly related to flaming burning, and non-rBC to smoldering burning. Rapid condensation of semi-volatile organics resulted in coated rBC.
Keiya Yumimoto, Taichu Y. Tanaka, Naga Oshima, and Takashi Maki
Geosci. Model Dev., 10, 3225–3253, https://doi.org/10.5194/gmd-10-3225-2017, https://doi.org/10.5194/gmd-10-3225-2017, 2017
Short summary
Short summary
A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1° × 1.1°).
Syuichi Itahashi, Itsushi Uno, Kazuo Osada, Yusuke Kamiguchi, Shigekazu Yamamoto, Kei Tamura, Zhe Wang, Yasunori Kurosaki, and Yugo Kanaya
Atmos. Chem. Phys., 17, 3823–3843, https://doi.org/10.5194/acp-17-3823-2017, https://doi.org/10.5194/acp-17-3823-2017, 2017
Short summary
Short summary
Over East Asia, the transboundary air pollution of SO42− has been recognized. The importance of the transboundary air pollution of NO3− in winter was demonstrated in this study through synergetic ground-based observations with state-of-the-art measurements of secondary inorganic aerosols (SO42−, NO3−, and NH4+) and a regional chemical transport model analysis. This study will help to refine the understanding of transboundary heavy PM2.5 pollution in winter.
Osamu Uchino, Tetsu Sakai, Toshiharu Izumi, Tomohiro Nagai, Isamu Morino, Akihiro Yamazaki, Makoto Deushi, Keiya Yumimoto, Takashi Maki, Taichu Y. Tanaka, Taiga Akaho, Hiroshi Okumura, Kohei Arai, Takahiro Nakatsuru, Tsuneo Matsunaga, and Tatsuya Yokota
Atmos. Chem. Phys., 17, 1865–1879, https://doi.org/10.5194/acp-17-1865-2017, https://doi.org/10.5194/acp-17-1865-2017, 2017
Short summary
Short summary
To validate products of GOSAT, we observed vertical profiles of aerosols, thin cirrus clouds, and tropospheric ozone with a mobile lidar system that consisted of a two-wavelength (532 and 1064 nm) polarization lidar and tropospheric ozone differential absorption lidar (DIAL). We used these lidars to make continuous measurements over Saga (33.24° N, 130.29° E) during 20–31 March 2015. High ozone and high aerosol concentrations were observed almost simultaneously and impacted surface air quality.
Yugo Kanaya, Xiaole Pan, Takuma Miyakawa, Yuichi Komazaki, Fumikazu Taketani, Itsushi Uno, and Yutaka Kondo
Atmos. Chem. Phys., 16, 10689–10705, https://doi.org/10.5194/acp-16-10689-2016, https://doi.org/10.5194/acp-16-10689-2016, 2016
Short summary
Short summary
Wet removal of atmospheric black carbon particles was quantitatively characterized in terms of accumulated precipitation along a backward trajectory (APT) using long-term observations at Fukue Island, western Japan, receiving Asian continental outflow with variable degrees of influence from precipitation. The emission inventory of BC over East Asia was assessed in terms of the observed BC/CO ratios. Model simulations should be diagnosed with these improved knowledge on the emission and removal.
Xiaole Pan, Itsushi Uno, Yukari Hara, Kazuo Osada, Shigekazu Yamamoto, Zhe Wang, Nobuo Sugimoto, Hiroshi Kobayashi, and Zifa Wang
Atmos. Chem. Phys., 16, 9863–9873, https://doi.org/10.5194/acp-16-9863-2016, https://doi.org/10.5194/acp-16-9863-2016, 2016
Short summary
Short summary
Polarization properties of aerosol particles at a suburban site in the western Japan was studied on the basis of long-term observation and trajectory analysis. This study provides the detailed information on the polarization characteristics of particles from different origins, and proposed a reliable criterion to classify spherical and non-spherical particles. This study introduced a new method to investigate the mixed state of dust particle with anthropogenic pollutant.
Myungje Choi, Jhoon Kim, Jaehwa Lee, Mijin Kim, Young-Je Park, Ukkyo Jeong, Woogyung Kim, Hyunkee Hong, Brent Holben, Thomas F. Eck, Chul H. Song, Jae-Hyun Lim, and Chang-Keun Song
Atmos. Meas. Tech., 9, 1377–1398, https://doi.org/10.5194/amt-9-1377-2016, https://doi.org/10.5194/amt-9-1377-2016, 2016
Short summary
Short summary
The Geostationary Ocean Color Imager (GOCI) is the first ocean color sensor in geostationary orbit. It enables hourly aerosol optical properties to be observed in high spatial resolution. This study presents improvements of the GOCI Yonsei Aerosol Retrieval (YAER) algorithm and its validation results using ground-based and other satellite-based observation products during DRAGON-NE Asia 2012 Campaign. Retrieval errors are also analyzed according to various factors through the validation studies.
Soon-Young Park, Dong-Hyeok Kim, Soon-Hwan Lee, and Hwa Woon Lee
Atmos. Chem. Phys., 16, 3631–3649, https://doi.org/10.5194/acp-16-3631-2016, https://doi.org/10.5194/acp-16-3631-2016, 2016
Short summary
Short summary
In order to improve the predictability of air quality, we optimize initial ozone state throughout the 4D-Var data assimilation. Previously developed code for the data assimilation has been modified to consider background error in matrix form, and various numerical tests are conducted. A surface observational assimilation is conducted and the statistical results for the 12 h assimilation periods show a 49.4 % decrease in RMSE and a 59.9 % increase in IOA.
S. Lee, C. H. Song, R. S. Park, M. E. Park, K. M. Han, J. Kim, M. Choi, Y. S. Ghim, and J.-H. Woo
Geosci. Model Dev., 9, 17–39, https://doi.org/10.5194/gmd-9-17-2016, https://doi.org/10.5194/gmd-9-17-2016, 2016
Short summary
Short summary
We developed an integrated air quality modeling system using AOD data retrieved from a geostationary satellite sensor, GOCI (Geostationary Ocean Color Imager), over Northeast Asia with an application of the spatiotemporal-kriging (STK) method and conducted short-term hindcast runs using the developed system. It appears that the STK approach can greatly reduce not only the errors and biases of AOD and PM10 predictions but also the computational burden of a chemical weather forecast (CWF).
X. Pan, Y. Kanaya, H. Tanimoto, S. Inomata, Z. Wang, S. Kudo, and I. Uno
Atmos. Chem. Phys., 15, 6101–6111, https://doi.org/10.5194/acp-15-6101-2015, https://doi.org/10.5194/acp-15-6101-2015, 2015
K. M. Han, S. Lee, L. S. Chang, and C. H. Song
Atmos. Chem. Phys., 15, 1913–1938, https://doi.org/10.5194/acp-15-1913-2015, https://doi.org/10.5194/acp-15-1913-2015, 2015
S. Seo, J. Kim, H. Lee, U. Jeong, W. Kim, B. N. Holben, S.-W. Kim, C. H. Song, and J. H. Lim
Atmos. Chem. Phys., 15, 319–334, https://doi.org/10.5194/acp-15-319-2015, https://doi.org/10.5194/acp-15-319-2015, 2015
Short summary
Short summary
The estimation of PM10 from optical measurement of AERONET and MODIS by various empirical models was evaluated for the DRAGON-Asia campaign. The results showed the importance of boundary layer height (BLH) and effective radius (Reff) in estimating PM10. The highest correlation between the estimated and measured values was found to be 0.81 in winter due to the stagnant air mass and low BLH, while the poorest values were 0.54 in spring due to the influence of long-range transport above BLH.
H.-K. Kim, J.-H. Woo, R. S. Park, C. H. Song, J.-H. Kim, S.-J. Ban, and J.-H. Park
Atmos. Chem. Phys., 14, 7461–7484, https://doi.org/10.5194/acp-14-7461-2014, https://doi.org/10.5194/acp-14-7461-2014, 2014
S. Itahashi, I. Uno, H. Irie, J.-I. Kurokawa, and T. Ohara
Atmos. Chem. Phys., 14, 3623–3635, https://doi.org/10.5194/acp-14-3623-2014, https://doi.org/10.5194/acp-14-3623-2014, 2014
R. S. Park, S. Lee, S.-K. Shin, and C. H. Song
Atmos. Chem. Phys., 14, 2185–2201, https://doi.org/10.5194/acp-14-2185-2014, https://doi.org/10.5194/acp-14-2185-2014, 2014
M. E. Park, C. H. Song, R. S. Park, J. Lee, J. Kim, S. Lee, J.-H. Woo, G. R. Carmichael, T. F. Eck, B. N. Holben, S.-S. Lee, C. K. Song, and Y. D. Hong
Atmos. Chem. Phys., 14, 659–674, https://doi.org/10.5194/acp-14-659-2014, https://doi.org/10.5194/acp-14-659-2014, 2014
K. Yumimoto and T. Takemura
Geosci. Model Dev., 6, 2005–2022, https://doi.org/10.5194/gmd-6-2005-2013, https://doi.org/10.5194/gmd-6-2005-2013, 2013
H. Irie, K. Yamaji, K. Ikeda, I. Uno, S. Itahashi, T. Ohara, and J. Kurokawa
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-14037-2013, https://doi.org/10.5194/acpd-13-14037-2013, 2013
Preprint withdrawn
Related subject area
Atmospheric sciences
Forecasting contrail climate forcing for flight planning and air traffic management applications: the CocipGrid model in pycontrails 0.51.0
Simulation of the heat mitigation potential of unsealing measures in cities by parameterizing grass grid pavers for urban microclimate modelling with ENVI-met (V5)
AI-NAOS: an AI-based nonspherical aerosol optical scheme for the chemical weather model GRAPES_Meso5.1/CUACE
Orbital-Radar v1.0.0: a tool to transform suborbital radar observations to synthetic EarthCARE cloud radar data
The Modular and Integrated Data Assimilation System at Environment and Climate Change Canada (MIDAS v3.9.1)
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
Observational operator for fair model evaluation with ground NO2 measurements
Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting
An updated parameterization of the unstable atmospheric surface layer in the Weather Research and Forecasting (WRF) modeling system
The impact of cloud microphysics and ice nucleation on Southern Ocean clouds assessed with single-column modeling and instrument simulators
An updated aerosol simulation in the Community Earth System Model (v2.1.3): dust and marine aerosol emissions and secondary organic aerosol formation
Exploring ship track spreading rates with a physics-informed Langevin particle parameterization
Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
Development of the MPAS-CMAQ coupled system (V1.0) for multiscale global air quality modeling
Assessment of object-based indices to identify convective organization
The Global Forest Fire Emissions Prediction System version 1.0
NEIVAv1.0: Next-generation Emissions InVentory expansion of Akagi et al. (2011) version 1.0
FLEXPART version 11: improved accuracy, efficiency, and flexibility
Challenges of high-fidelity air quality modeling in urban environments – PALM sensitivity study during stable conditions
Air quality modeling intercomparison and multiscale ensemble chain for Latin America
Recommended coupling to global meteorological fields for long-term tracer simulations with WRF-GHG
Selecting CMIP6 global climate models (GCMs) for Coordinated Regional Climate Downscaling Experiment (CORDEX) dynamical downscaling over Southeast Asia using a standardised benchmarking framework
Improved definition of prior uncertainties in CO2 and CO fossil fuel fluxes and its impact on multi-species inversion with GEOS-Chem (v12.5)
RASCAL v1.0: an open-source tool for climatological time series reconstruction and extension
Introducing graupel density prediction in Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) microphysics and evaluation of the modified scheme during the ICE-POP field campaign
Enabling high-performance cloud computing for the Community Multiscale Air Quality Model (CMAQ) version 5.3.3: performance evaluation and benefits for the user community
Atmospheric-river-induced precipitation in California as simulated by the regionally refined Simple Convective Resolving E3SM Atmosphere Model (SCREAM) Version 0
Sensitivity of predicted ultrafine particle size distributions in Europe to different nucleation rate parameterizations using PMCAMx-UF v2.2
Recent improvements and maximum covariance analysis of aerosol and cloud properties in the EC-Earth3-AerChem model
GPU-HADVPPM4HIP V1.0: using the heterogeneous-compute interface for portability (HIP) to speed up the piecewise parabolic method in the CAMx (v6.10) air quality model on China's domestic GPU-like accelerator
Preliminary evaluation of the effect of electro-coalescence with conducting sphere approximation on the formation of warm cumulus clouds using SCALE-SDM version 0.2.5–2.3.0
Similarity-Based Analysis of Atmospheric Organic Compounds for Machine Learning Applications
Cell tracking -based framework for assessing nowcasting model skill in reproducing growth and decay of convective rainfall
Exploring the footprint representation of microwave radiance observations in an Arctic limited-area data assimilation system
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Improving the EnSRF in the Community Inversion Framework: a case study with ICON-ART 2024.01
AeroMix v1.0.1: a Python package for modeling aerosol optical properties and mixing states
Impact of ITCZ width on global climate: ITCZ-MIP
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
A conservative immersed boundary method for the multi-physics urban large-eddy simulation model uDALES v2.0
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
RCEMIP-II: mock-Walker simulations as phase II of the radiative–convective equilibrium model intercomparison project
The MESSy DWARF (based on MESSy v2.55.2)
Objective identification of meteorological fronts and climatologies from ERA-Interim and ERA5
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
Short summary
Short summary
Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
Short summary
Short summary
Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
Short summary
Short summary
The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
Short summary
Short summary
The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
Short summary
Short summary
The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
Short summary
We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
Short summary
Short summary
We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
Short summary
A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Short summary
To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary
Short summary
Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
Short summary
Short summary
Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
Short summary
Short summary
The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
Short summary
Short summary
Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
Short summary
Short summary
Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
Short summary
Short summary
This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
Short summary
Short summary
Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
Short summary
Short summary
Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
Short summary
Short summary
This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
Short summary
Short summary
In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
Short summary
Short summary
The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
Short summary
Short summary
The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
Short summary
Short summary
Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
Short summary
Short summary
Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Jorge E. Pachón, Mariel A. Opazo, Pablo Lichtig, Nicolas Huneeus, Idir Bouarar, Guy Brasseur, Cathy W. Y. Li, Johannes Flemming, Laurent Menut, Camilo Menares, Laura Gallardo, Michael Gauss, Mikhail Sofiev, Rostislav Kouznetsov, Julia Palamarchuk, Andreas Uppstu, Laura Dawidowski, Nestor Y. Rojas, María de Fátima Andrade, Mario E. Gavidia-Calderón, Alejandro H. Delgado Peralta, and Daniel Schuch
Geosci. Model Dev., 17, 7467–7512, https://doi.org/10.5194/gmd-17-7467-2024, https://doi.org/10.5194/gmd-17-7467-2024, 2024
Short summary
Short summary
Latin America (LAC) has some of the most populated urban areas in the world, with high levels of air pollution. Air quality management in LAC has been traditionally focused on surveillance and building emission inventories. This study performed the first intercomparison and model evaluation in LAC, with interesting and insightful findings for the region. A multiscale modeling ensemble chain was assembled as a first step towards an air quality forecasting system.
David Ho, Michał Gałkowski, Friedemann Reum, Santiago Botía, Julia Marshall, Kai Uwe Totsche, and Christoph Gerbig
Geosci. Model Dev., 17, 7401–7422, https://doi.org/10.5194/gmd-17-7401-2024, https://doi.org/10.5194/gmd-17-7401-2024, 2024
Short summary
Short summary
Atmospheric model users often overlook the impact of the land–atmosphere interaction. This study accessed various setups of WRF-GHG simulations that ensure consistency between the model and driving reanalysis fields. We found that a combination of nudging and frequent re-initialization allows certain improvement by constraining the soil moisture fields and, through its impact on atmospheric mixing, improves atmospheric transport.
Phuong Loan Nguyen, Lisa V. Alexander, Marcus J. Thatcher, Son C. H. Truong, Rachael N. Isphording, and John L. McGregor
Geosci. Model Dev., 17, 7285–7315, https://doi.org/10.5194/gmd-17-7285-2024, https://doi.org/10.5194/gmd-17-7285-2024, 2024
Short summary
Short summary
We use a comprehensive approach to select a subset of CMIP6 models for dynamical downscaling over Southeast Asia, taking into account model performance, model independence, data availability and the range of future climate projections. The standardised benchmarking framework is applied to assess model performance through both statistical and process-based metrics. Ultimately, we identify two independent model groups that are suitable for dynamical downscaling in the Southeast Asian region.
Ingrid Super, Tia Scarpelli, Arjan Droste, and Paul I. Palmer
Geosci. Model Dev., 17, 7263–7284, https://doi.org/10.5194/gmd-17-7263-2024, https://doi.org/10.5194/gmd-17-7263-2024, 2024
Short summary
Short summary
Monitoring greenhouse gas emission reductions requires a combination of models and observations, as well as an initial emission estimate. Each component provides information with a certain level of certainty and is weighted to yield the most reliable estimate of actual emissions. We describe efforts for estimating the uncertainty in the initial emission estimate, which significantly impacts the outcome. Hence, a good uncertainty estimate is key for obtaining reliable information on emissions.
Álvaro González-Cervera and Luis Durán
Geosci. Model Dev., 17, 7245–7261, https://doi.org/10.5194/gmd-17-7245-2024, https://doi.org/10.5194/gmd-17-7245-2024, 2024
Short summary
Short summary
RASCAL is an open-source Python tool designed for reconstructing daily climate observations, especially in regions with complex local phenomena. It merges large-scale weather patterns with local weather using the analog method. Evaluations in central Spain show that RASCAL outperforms ERA20C reanalysis in reconstructing precipitation and temperature. RASCAL offers opportunities for broad scientific applications, from short-term forecasts to local-scale climate change scenarios.
Sun-Young Park, Kyo-Sun Sunny Lim, Kwonil Kim, Gyuwon Lee, and Jason A. Milbrandt
Geosci. Model Dev., 17, 7199–7218, https://doi.org/10.5194/gmd-17-7199-2024, https://doi.org/10.5194/gmd-17-7199-2024, 2024
Short summary
Short summary
We enhance the WDM6 scheme by incorporating predicted graupel density. The modification affects graupel characteristics, including fall velocity–diameter and mass–diameter relationships. Simulations highlight changes in graupel distribution and precipitation patterns, potentially influencing surface snow amounts. The study underscores the significance of integrating predicted graupel density for a more realistic portrayal of microphysical properties in weather models.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
Short summary
Short summary
We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Peter A. Bogenschutz, Jishi Zhang, Qi Tang, and Philip Cameron-Smith
Geosci. Model Dev., 17, 7029–7050, https://doi.org/10.5194/gmd-17-7029-2024, https://doi.org/10.5194/gmd-17-7029-2024, 2024
Short summary
Short summary
Using high-resolution and state-of-the-art modeling techniques we simulate five atmospheric river events for California to test the capability to represent precipitation for these events. We find that our model is able to capture the distribution of precipitation very well but suffers from overestimating the precipitation amounts over high elevation. Increasing the resolution further has no impact on reducing this bias, while increasing the domain size does have modest impacts.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-145, https://doi.org/10.5194/gmd-2024-145, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements in 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Manu Anna Thomas, Klaus Wyser, Shiyu Wang, Marios Chatziparaschos, Paraskevi Georgakaki, Montserrat Costa-Surós, Maria Gonçalves Ageitos, Maria Kanakidou, Carlos Pérez García-Pando, Athanasios Nenes, Twan van Noije, Philippe Le Sager, and Abhay Devasthale
Geosci. Model Dev., 17, 6903–6927, https://doi.org/10.5194/gmd-17-6903-2024, https://doi.org/10.5194/gmd-17-6903-2024, 2024
Short summary
Short summary
Aerosol–cloud interactions occur at a range of spatio-temporal scales. While evaluating recent developments in EC-Earth3-AerChem, this study aims to understand the extent to which the Twomey effect manifests itself at larger scales. We find a reduction in the warm bias over the Southern Ocean due to model improvements. While we see footprints of the Twomey effect at larger scales, the negative relationship between cloud droplet number and liquid water drives the shortwave radiative effect.
Kai Cao, Qizhong Wu, Lingling Wang, Hengliang Guo, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongxing Li, Lina Liu, Dongqing Li, Hao Wu, and Lanning Wang
Geosci. Model Dev., 17, 6887–6901, https://doi.org/10.5194/gmd-17-6887-2024, https://doi.org/10.5194/gmd-17-6887-2024, 2024
Short summary
Short summary
AMD’s heterogeneous-compute interface for portability was implemented to port the piecewise parabolic method solver from NVIDIA GPUs to China's GPU-like accelerators. The results show that the larger the model scale, the more acceleration effect on the GPU-like accelerator, up to 28.9 times. The multi-level parallelism achieves a speedup of 32.7 times on the heterogeneous cluster. By comparing the results, the GPU-like accelerators have more accuracy for the geoscience numerical models.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
Short summary
Short summary
Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Hilda Sandström and Patrick Rinke
EGUsphere, https://doi.org/10.48550/arXiv.2406.18171, https://doi.org/10.48550/arXiv.2406.18171, 2024
Short summary
Short summary
Machine learning has the potential to aid the identification organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning model in atmospheric sciences.
Jenna Ritvanen, Seppo Pulkkinen, Dmitri Moisseev, and Daniele Nerini
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-99, https://doi.org/10.5194/gmd-2024-99, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
Nowcasting models struggle with the rapid evolution of heavy rain, and common verification methods are unable to describe how accurately the models predict the growth and decay of heavy rainfall. We propose a framework to assess model performance. In the framework, convective cells are identified and tracked in the forecasts and observations, and then the model skill is evaluated by comparing differences between forecast and observed cells. We demonstrate the framework with 4 open-source models.
Máté Mile, Stephanie Guedj, and Roger Randriamampianina
Geosci. Model Dev., 17, 6571–6587, https://doi.org/10.5194/gmd-17-6571-2024, https://doi.org/10.5194/gmd-17-6571-2024, 2024
Short summary
Short summary
Satellite observations provide crucial information about atmospheric constituents in a global distribution that helps to better predict the weather over sparsely observed regions like the Arctic. However, the use of satellite data is usually conservative and imperfect. In this study, a better spatial representation of satellite observations is discussed and explored by a so-called footprint function or operator, highlighting its added value through a case study and diagnostics.
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, https://doi.org/10.5194/gmd-17-6489-2024, 2024
Short summary
Short summary
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, https://doi.org/10.5194/gmd-17-6465-2024, 2024
Short summary
Short summary
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
Joël Thanwerdas, Antoine Berchet, Lionel Constantin, Aki Tsuruta, Michael Steiner, Friedemann Reum, Stephan Henne, and Dominik Brunner
EGUsphere, https://doi.org/10.5194/egusphere-2024-2197, https://doi.org/10.5194/egusphere-2024-2197, 2024
Short summary
Short summary
The Community Inversion Framework (CIF) brings together methods for estimating greenhouse gas fluxes from atmospheric observations. The initial ensemble method implemented in CIF was found to be incomplete and could hardly be compared to other ensemble methods employed in the inversion community. In this paper, we present and evaluate a more efficient implementation of the serial and batch versions of the Ensemble Square Root Filter (EnSRF) algorithm in CIF.
Sam P. Raj, Puna Ram Sinha, Rohit Srivastava, Srinivas Bikkina, and Damu Bala Subrahamanyam
Geosci. Model Dev., 17, 6379–6399, https://doi.org/10.5194/gmd-17-6379-2024, https://doi.org/10.5194/gmd-17-6379-2024, 2024
Short summary
Short summary
A Python successor to the aerosol module of the OPAC model, named AeroMix, has been developed, with enhanced capabilities to better represent real atmospheric aerosol mixing scenarios. AeroMix’s performance in modeling aerosol mixing states has been evaluated against field measurements, substantiating its potential as a versatile aerosol optical model framework for next-generation algorithms to infer aerosol mixing states and chemical composition.
Angeline G. Pendergrass, Michael P. Byrne, Oliver Watt-Meyer, Penelope Maher, and Mark J. Webb
Geosci. Model Dev., 17, 6365–6378, https://doi.org/10.5194/gmd-17-6365-2024, https://doi.org/10.5194/gmd-17-6365-2024, 2024
Short summary
Short summary
The width of the tropical rain belt affects many aspects of our climate, yet we do not understand what controls it. To better understand it, we present a method to change it in numerical model experiments. We show that the method works well in four different models. The behavior of the width is unexpectedly simple in some ways, such as how strong the winds are as it changes, but in other ways, it is more complicated, especially how temperature increases with carbon dioxide.
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Short summary
Short summary
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, https://doi.org/10.5194/gmd-17-6301-2024, 2024
Short summary
Short summary
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
Sam O. Owens, Dipanjan Majumdar, Chris E. Wilson, Paul Bartholomew, and Maarten van Reeuwijk
Geosci. Model Dev., 17, 6277–6300, https://doi.org/10.5194/gmd-17-6277-2024, https://doi.org/10.5194/gmd-17-6277-2024, 2024
Short summary
Short summary
Designing cities that are resilient, sustainable, and beneficial to health requires an understanding of urban climate and air quality. This article presents an upgrade to the multi-physics numerical model uDALES, which can simulate microscale airflow, heat transfer, and pollutant dispersion in urban environments. This upgrade enables it to resolve realistic urban geometries more accurately and to take advantage of the resources available on current and future high-performance computing systems.
Felipe Cifuentes, Henk Eskes, Folkert Boersma, Enrico Dammers, and Charlotte Bryan
EGUsphere, https://doi.org/10.5194/egusphere-2024-2225, https://doi.org/10.5194/egusphere-2024-2225, 2024
Short summary
Short summary
We tested the capability of the flux divergence approach (FDA) to reproduce known NOX emissions using synthetic NO2 satellite column retrievals derived from high-resolution model simulations. The FDA accurately reproduced NOX emissions when column observations were limited to the boundary layer and when the variability of NO2 lifetime, NOX:NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces a strong model dependency, reducing the simplicity of the original FDA formulation.
Allison A. Wing, Levi G. Silvers, and Kevin A. Reed
Geosci. Model Dev., 17, 6195–6225, https://doi.org/10.5194/gmd-17-6195-2024, https://doi.org/10.5194/gmd-17-6195-2024, 2024
Short summary
Short summary
This paper presents the experimental design for a model intercomparison project to study tropical clouds and climate. It is a follow-up from a prior project that used a simplified framework for tropical climate. The new project adds one new component – a specified pattern of sea surface temperatures as the lower boundary condition. We provide example results from one cloud-resolving model and one global climate model and test the sensitivity to the experimental parameters.
Astrid Kerkweg, Timo Kirfel, Doung H. Do, Sabine Griessbach, Patrick Jöckel, and Domenico Taraborrelli
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-117, https://doi.org/10.5194/gmd-2024-117, 2024
Revised manuscript accepted for GMD
Short summary
Short summary
This article introduces the MESSy DWARF. Usually, the Modular Earth Submodel System (MESSy) is linked to full dynamical models to build chemistry climate models. However, due to the modular concept of MESSy, and the newly developed DWARF component, it is now possible to create simplified models containing just one or some process descriptions. This renders very useful for technical optimisation (e.g., GPU porting) and can be used to create less complex models, e.g., a chemical box model.
Philip G. Sansom and Jennifer L. Catto
Geosci. Model Dev., 17, 6137–6151, https://doi.org/10.5194/gmd-17-6137-2024, https://doi.org/10.5194/gmd-17-6137-2024, 2024
Short summary
Short summary
Weather fronts bring a lot of rain and strong winds to many regions of the mid-latitudes. We have developed an updated method of identifying these fronts in gridded data that can be used on new datasets with small grid spacing. The method can be easily applied to different datasets due to the use of open-source software for its development and shows improvements over similar previous methods. We present an updated estimate of the average frequency of fronts over the past 40 years.
Cited articles
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data
Assimilation, Mon. Weather Rev., 129, 2884–2903,
https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001.
Appel, K. W., Pouliot, G. A., Simon, H., Sarwar, G., Pye, H. O. T., Napelenok, S. L., Akhtar, F., and Roselle, S. J.: Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0, Geosci. Model Dev., 6, 883–899, https://doi.org/10.5194/gmd-6-883-2013, 2013.
Benedetti, A., Di Giuseppe, F., Jones, L., Peuch, V.-H., Rémy, S., and Zhang, X.: The value of satellite observations in the analysis and short-range prediction of Asian dust, Atmos. Chem. Phys., 19, 987–998, https://doi.org/10.5194/acp-19-987-2019, 2019.
Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325–5358, https://doi.org/10.5194/acp-15-5325-2015, 2015.
Byun, D. and Schere, K. L.: Review of the Governing Equations, Computational
Algorithms, and Other Components of the Models-3 Community Multiscale Air
Quality (CMAQ) Modeling System, Appl. Mech. Rev., 59, 51–77,
https://doi.org/10.1115/1.2128636, 2006.
Byun, D. W. and Ching, J. K. S.: Science algorithms of the EPA models-3
community multiscale air quality (CMAQ) modeling system, U.S. Environmental
Protection Agency, EPA/600/R- 99/030 (NTIS PB2000-100561), 1999.
Candiani, G., Carnevale, C., Finzi, G., Pisoni, E., and Volta, M.: A
comparison of reanalysis techniques: Applying optimal interpolation and
Ensemble Kalman Filtering to improve air quality monitoring at mesoscale,
Sci. Total Environ., 458–460, 7–14, https://doi.org/10.1016/j.scitotenv.2013.03.089, 2013.
Carmichael, G. R., Sakurai, T., Streets, D., Hozumi, Y., Ueda, H., Park, S.
U., Fung, C., Han, Z., Kajino, M., Engardt, M., Bennet, C., Hayami, H.,
Sartelet, K., Holloway, T., Wang, Z., Kannari, A., Fu, J., Matsuda, K.,
Thongboonchoo, N., and Amann, M.: MICS-Asia II: The model intercomparison
study for Asia Phase II methodology and overview of findings, Atmos.
Environ., 42, 3468–3490, https://doi.org/10.1016/j.atmosenv.2007.04.007, 2008.
Chai, T., Kim, H.-C., Pan, L., Lee, P., and Tong, D.: Impact of Moderate
Resolution Imaging Spectroradiometer Aerosol Optical Depth and AirNow
PM2.5 assimilation on Community Multi-scale Air Quality aerosol
predictions over the contiguous United States, J. Geophys.
Res.-Atmos., 122, 5399–5415, https://doi.org/10.1002/2016JD026295, 2017.
Chang, L.-S., Cho, A., Park, H., Nam, K., Kim, D., Hong, J.-H., and Song,
C.-K.: Human-model hybrid Korean air quality forecasting system, J. Air Waste Manage., 66, 896–911,
https://doi.org/10.1080/10962247.2016.1206995, 2016.
Chen, D., Liu, Z., Ban, J., Zhao, P., and Chen, M.: Retrospective analysis of 2015–2017 wintertime PM2.5 in China: response to emission regulations and the role of meteorology, Atmos. Chem. Phys., 19, 7409–7427, https://doi.org/10.5194/acp-19-7409-2019, 2019.
Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep,
K., Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V.,
Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y.,
Martin, R., Morawska, L., Pope, C. A., Shin, H., Straif, K., Shaddick, G.,
Thomas, M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J. L.,
and Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of
disease attributable to ambient air pollution: an analysis of data from the
Global Burden of Diseases Study 2015, The Lancet, 389, 1907–1918, https://doi.org/10.1016/S0140-6736(17)30505-6, 2017.
Colella, P. and Woodward, P. R.: The Piecewise Parabolic Method (PPM) for
gas-dynamical simulations, J. Comput. Phys., 54, 174–201,
https://doi.org/10.1016/0021-9991(84)90143-8, 1984.
Coman, A., Foret, G., Beekmann, M., Eremenko, M., Dufour, G., Gaubert, B., Ung, A., Schmechtig, C., Flaud, J.-M., and Bergametti, G.: Assimilation of IASI partial tropospheric columns with an Ensemble Kalman Filter over Europe, Atmos. Chem. Phys., 12, 2513–2532, https://doi.org/10.5194/acp-12-2513-2012, 2012.
Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.:
Assessment of ensemble-based chemical data assimilation in an idealized
setting, Atmos. Environ., 41, 18–36, https://doi.org/10.1016/j.atmosenv.2006.08.006, 2007a.
Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.:
Ensemble-based chemical data assimilation. II: Covariance localization,
Q. J. Roy. Meteor. Soc., 133, 1245–1256,
https://doi.org/10.1002/qj.77, 2007b.
Courtier, P., Thépaut, J. N., and Hollingsworth, A.: A strategy for
operational implementation of 4D-Var, using an incremental approach,
Q. J. Roy. Meteor. Soc., 120, 1367–1387,
https://doi.org/10.1002/qj.49712051912, 1994.
Dai, T., Schutgens, N. A. J., Goto, D., Shi, G., and Nakajima, T.:
Improvement of aerosol optical properties modeling over Eastern Asia with
MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol
transport model, Environ. Pollut., 195, 319–329, https://doi.org/10.1016/j.envpol.2014.06.021, 2014.
Dehghani, M., Keshtgar, L., Javaheri, M. R., Derakhshan, Z., Oliveri Conti,
G., Zuccarello, P., and Ferrante, M.: The effects of air pollutants on the
mortality rate of lung cancer and leukemia, Mol. Med. Rep., 15, 3390–3397,
https://doi.org/10.3892/mmr.2017.6387, 2017.
Eder, B., Kang, D., Mathur, R., Yu, S., and Schere, K.: An operational
evaluation of the Eta–CMAQ air quality forecast model, Atmos.
Environ., 40, 4894–4905, https://doi.org/10.1016/j.atmosenv.2005.12.062, 2006.
Elbern, H. and Schmidt, H.: Ozone episode analysis by four-dimensional
variational chemistry data assimilation, J. Geophys. Res.-Atmos., 106, 3569–3590, https://doi.org/10.1029/2000JD900448, 2001.
Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, https://doi.org/10.5194/acp-7-3749-2007, 2007.
Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic
model using Monte Carlo methods to forecast error statistics, J.
Geophys. Res.-Oceans, 99, 10143–10162, https://doi.org/10.1029/94JC00572, 1994.
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and
practical implementation, Ocean Dynam., 53, 343–367,
https://doi.org/10.1007/s10236-003-0036-9, 2003.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, Remote Sens.
Environ., 114, 168–182, https://doi.org/10.1016/j.rse.2009.08.016, 2010.
Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two
and three dimensions, Q. J. Roy. Meteor. Soc.,
125, 723–757, https://doi.org/10.1002/qj.49712555417, 1999.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
Guenther, A. B., Jiang, X., Heald, C. L., Sakulyanontvittaya, T., Duhl, T., Emmons, L. K., and Wang, X.: The Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1): an extended and updated framework for modeling biogenic emissions, Geosci. Model Dev., 5, 1471–1492, https://doi.org/10.5194/gmd-5-1471-2012, 2012.
Ha, S., Liu, Z., Sun, W., Lee, Y., and Chang, L.: Improving air quality forecasting with the assimilation of GOCI aerosol optical depth (AOD) retrievals during the KORUS-AQ period, Atmos. Chem. Phys., 20, 6015–6036, https://doi.org/10.5194/acp-20-6015-2020, 2020.
Hertel, O., Berkowicz, R., Christensen, J., and Hov, Ø.: Test of two
numerical schemes for use in atmospheric transport-chemistry models,
Atmos. Environ. A.-Gen., 27, 2591–2611, https://doi.org/10.1016/0960-1686(93)90032-T, 1993.
Hong, S.-Y.: A new stable boundary-layer mixing scheme and its impact on the
simulated East Asian summer monsoon, Q. J. Roy.
Meteor. Soc., 136, 1481–1496, https://doi.org/10.1002/qj.665, 2010.
Hong, S.-Y. and Lim, J.-O. J.: The WRF Single-Moment 6-Class Microphysics
Scheme (WSM6), Asia-Pac. J. Atmos. Sci., 42, 129–151,
2006.
Hong, S.-Y., Noh, Y., and Dudhia, J.: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Houtekamer, P. L. and Mitchell, H. L.: Data Assimilation Using an Ensemble Kalman Filter Technique, Mon. Weather Rev., 126, 3, 796–811, https://doi.org/10.1175/1520-0493(1998)126<0796:DAUAEK>2.0.CO;2, 1998.
Hunt, B. R., Kostelich, E. J., and Szunyogh, I.: Efficient data assimilation
for spatiotemporal chaos: A local ensemble transform Kalman filter, Physica
D, 230, 112–126, https://doi.org/10.1016/j.physd.2006.11.008, 2007.
Hutzell, W. T., Luecken, D. J., Appel, K. W., and Carter, W. P. L.:
Interpreting predictions from the SAPRC07 mechanism based on regional and
continental simulations, Atmos. Environ., 46, 417–429, https://doi.org/10.1016/j.atmosenv.2011.09.030, 2012.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys.
Res.-Atmos., 113, D13103, https://doi.org/10.1029/2008JD009944, 2008.
Jang, Y., Lee, Y., Kim, J., Kim, Y., and Woo, J.-H.: Improvement China Point
Source for Improving Bottom-Up Emission Inventory, Asia-Pac. J.
Atmos. Sci., 56, 107–118, https://doi.org/10.1007/s13143-019-00115-y, 2020.
Jiang, Z., Liu, Z., Wang, T., Schwartz, C. S., Lin, H.-C., and Jiang, F.:
Probing into the impact of 3DVAR assimilation of surface PM10 observations
over China using process analysis, J. Geophys. Res.-Atmos., 118, 6738–6749, https://doi.org/10.1002/jgrd.50495, 2013.
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J.,
Montávez, J. P., and García-Bustamante, E.: A Revised Scheme for
the WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918,
https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Jordan, C. E., Crawford, J. H., Beyersdorf, A. J., Eck, T. F., Halliday, H.
S., Nault, B. A., Chang, L.-S., Park, J., Park, R., Lee, G., Kim, H., Ahn,
J.-y., Cho, S., Shin, H. J., Lee, J. H., Jung, J., Kim, D.-S., Lee, M., Lee,
T., Whitehill, A., Szykman, J., Schueneman, M. K., Campuzano-Jost, P.,
Jimenez, J. L., DiGangi, J. P., Diskin, G. S., Anderson, B. E., Moore, R.
H., Ziemba, L. D., Fenn, M. A., Hair, J. W., Kuehn, R. E., Holz, R. E.,
Chen, G., Travis, K., Shook, M., Peterson, D. A., Lamb, K. D., and Schwarz,
J. P.: Investigation of factors controlling PM2.5 variability across
the South Korean Peninsula during KORUS-AQ, Elementa: Science of the
Anthropocene, 8, 28, https://doi.org/10.1525/elementa.424, 2020.
Kalman, R. E.: A New Approach to Linear Filtering and Prediction Problems,
Journal of Basic Engineering, 82, 35–45, https://doi.org/10.1115/1.3662552, 1960.
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability,
Cambridge University Press, Cambridge, https://doi.org/10.1017/CBO9780511802270, 2002.
Lee, E.-H., Ha, J.-C., Lee, S.-S., and Chun, Y.: PM10 data assimilation over
south Korea to Asian dust forecasting model with the optimal interpolation
method, Asia-Pac. J. Atmos. Sci., 49, 73–85,
https://doi.org/10.1007/s13143-013-0009-y, 2013.
Lee, K., Yu, J., Lee, S., Park, M., Hong, H., Park, S. Y., Choi, M., Kim, J., Kim, Y., Woo, J.-H., Kim, S.-W., and Song, C. H.: Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues, Geosci. Model Dev., 13, 1055–1073, https://doi.org/10.5194/gmd-13-1055-2020, 2020.
Lee, S., Song, C. H., Han, K. M., Henze, D. K., Lee, K., Yu, J., Woo, J. H.,
Jung, J., Choi, Y., Saide, P. E., and Carmichael, G. R.: Impacts of
uncertainties in emissions on aerosol data assimilation and short-term
PM2.5 predictions over Northeast Asia, Atmos. Environ., 271, 11921,
https://doi.org/10.1016/j.atmosenv.2021.118921, 2022.
Li, Z., Zang, Z., Li, Q. B., Chao, Y., Chen, D., Ye, Z., Liu, Y., and Liou, K. N.: A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction, Atmos. Chem. Phys., 13, 4265–4278, https://doi.org/10.5194/acp-13-4265-2013, 2013.
Lin, C., Wang, Z., and Zhu, J.: An Ensemble Kalman Filter for severe dust storm data assimilation over China, Atmos. Chem. Phys., 8, 2975–2983, https://doi.org/10.5194/acp-8-2975-2008, 2008.
Liu, Z., Liu, Q., Lin, H.-C., Schwartz, C. S., Lee, Y.-H., and Wang, T.:
Three-dimensional variational assimilation of MODIS aerosol optical depth:
Implementation and application to a dust storm over East Asia, J.
Geophys. Res.-Atmos., 116, D23206, https://doi.org/10.1029/2011JD016159, 2011.
Lopez-Restrepo, S., Yarce, A., Pinel, N., Quintero, O. L., Segers, A., and
Heemink, A. W.: Forecasting PM10 and PM2.5 in the Aburrá Valley
(Medellín, Colombia) via EnKF based data assimilation, Atmos.
Environ., 232, 117507, https://doi.org/10.1016/j.atmosenv.2020.117507, 2020.
Lorenc, A. C.: A Global Three-Dimensional Multivariate Statistical
Interpolation Scheme, Mon. Weather Rev., 109, 701–721,
https://doi.org/10.1175/1520-0493(1981)109<0701:AGTDMS>2.0.CO;2, 1981.
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q.
J. Roy. Meteor. Soc., 112, 1177–1194,
https://doi.org/10.1002/qj.49711247414, 1986.
Louis, J.-F.: A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, https://doi.org/10.1007/BF00117978, 1979.
Menut, L. and Bessagnet, B.: What Can We Expect from Data Assimilation for
Air Quality Forecast? Part I: Quantification with Academic Test Cases,
J. Atmos. Ocean. Tech., 36, 269–279,
https://doi.org/10.1175/JTECH-D-18-0002.1, 2019.
Morcrette, J. J., Boucher, O., Jones, L., Salmond, D., Bechtold, P.,
Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz,
M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A. M., and
Untch, A.: Aerosol analysis and forecast in the European Centre for
Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling,
J. Geophys. Res.-Atmos., 114, D06206, https://doi.org/10.1029/2008JD011235,
2009.
Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J.,
Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G. R., Lotsch, A., Friedl,
M., Morisette, J. T., Votava, P., Nemani, R. R., and Running, S. W.: Global
products of vegetation leaf area and fraction absorbed PAR from year one of
MODIS data, Remote Sens. Environ., 83, 214–231, https://doi.org/10.1016/S0034-4257(02)00074-3, 2002.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, D12109,
https://doi.org/10.1029/2010JD015139, 2011.
Otte, T. L. and Pleim, J. E.: The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci. Model Dev., 3, 243–256, https://doi.org/10.5194/gmd-3-243-2010, 2010.
Pagowski, M. and Grell, G. A.: Experiments with the assimilation of fine
aerosols using an ensemble Kalman filter, J. Geophys. Res.-Atmos., 117, D21302, https://doi.org/10.1029/2012JD018333, 2012.
Pagowski, M., Grell, G. A., McKeen, S. A., Peckham, S. E., and Devenyi, D.:
Three-dimensional variational data assimilation of ozone and fine
particulate matter observations: some results using the Weather Research and
Forecasting – Chemistry model and Grid-point Statistical Interpolation,
Q. J. Roy. Meteor. Soc., 136, 2013–2024,
https://doi.org/10.1002/qj.700, 2010.
Pang, J., Liu, Z., Wang, X., Bresch, J., Ban, J., Chen, D., and Kim, J.:
Assimilating AOD retrievals from GOCI and VIIRS to forecast surface
PM2.5 episodes over Eastern China, Atmos. Environ., 179,
288–304, https://doi.org/10.1016/j.atmosenv.2018.02.011, 2018.
Park, M. E., Song, C. H., Park, R. S., Lee, J., Kim, J., Lee, S., Woo, J.-H., Carmichael, G. R., Eck, T. F., Holben, B. N., Lee, S.-S., Song, C. K., and Hong, Y. D.: New approach to monitor transboundary particulate pollution over Northeast Asia, Atmos. Chem. Phys., 14, 659–674, https://doi.org/10.5194/acp-14-659-2014, 2014.
Park, R. S., Song, C. H., Han, K. M., Park, M. E., Lee, S.-S., Kim, S.-B., and Shimizu, A.: A study on the aerosol optical properties over East Asia using a combination of CMAQ-simulated aerosol optical properties and remote-sensing data via a data assimilation technique, Atmos. Chem. Phys., 11, 12275–12296, https://doi.org/10.5194/acp-11-12275-2011, 2011.
Park, S.-Y.: Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Data Assimilation Codes, Zenodo [code], https://doi.org/10.5281/zenodo.5376214, 2021a.
Park, S.-Y.: Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs, Zenodo [data set], https://doi.org/10.5281/zenodo.5566441, 2021b.
Park, S.-Y., Kim, D.-H., Lee, S.-H., and Lee, H. W.: Variational data assimilation for the optimized ozone initial state and the short-time forecasting, Atmos. Chem. Phys., 16, 3631–3649, https://doi.org/10.5194/acp-16-3631-2016, 2016.
Parrish, D. F. and Derber, J. C.: The National Meteorological Center's
Spectral Statistical-Interpolation Analysis System, Mon. Weather Rev.,
120, 1747–1763, https://doi.org/10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2, 1992.
Peng, Z., Liu, Z., Chen, D., and Ban, J.: Improving PM2.5 forecast over China by the joint adjustment of initial conditions and source emissions with an ensemble Kalman filter, Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, 2017.
Peng, Z., Lei, L., Liu, Z., Sun, J., Ding, A., Ban, J., Chen, D., Kou, X., and Chu, K.: The impact of multi-species surface chemical observation assimilation on air quality forecasts in China, Atmos. Chem. Phys., 18, 17387–17404, https://doi.org/10.5194/acp-18-17387-2018, 2018.
Peterson, D. A., Hyer, E. J., Han, S.-O., Crawford, J. H., Park, R. J.,
Holz, R., Kuehn, R. E., Eloranta, E., Knote, C., Jordan, C. E., and Lefer,
B. L.: Meteorology influencing springtime air quality, pollution transport,
and visibility in Korea, Elementa: Science of the Anthropocene, 7, 57,
https://doi.org/10.1525/elementa.395, 2019.
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the
Atmospheric Boundary Layer. Part I: Model Description and Testing, J. Appl. Meteorol. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1,
2007a.
Pleim, J. E.: A Combined Local and Nonlocal Closure Model for the
Atmospheric Boundary Layer. Part II: Application and Evaluation in a
Mesoscale Meteorological Model, J. Appl. Meteorol.
Clim., 46, 1396–1409, https://doi.org/10.1175/JAM2534.1, 2007b.
Pleim, J. E. and Xiu, A.: Development of a Land Surface Model. Part II: Data
Assimilation, J. Appl. Meteorol., 42, 1811–1822,
https://doi.org/10.1175/1520-0450(2003)042<1811:DOALSM>2.0.CO;2, 2003.
Pope, C. A. and Dockery, D. W.: Health Effects of Fine Particulate Air
Pollution: Lines that Connect, J. Air Waste Manage., 56, 709–742, https://doi.org/10.1080/10473289.2006.10464485, 2006.
Rabier, F., McNally, A., Andersson, E., Courtier, P., Undén, P., Eyre,
J., Hollingsworth, A., and Bouttier, F.: The ECMWF implementation of
three-dimensional variational assimilation (3D-Var). II: Structure
functions, Q. J. Roy. Meteor. Soc., 124,
1809–1829, https://doi.org/10.1002/qj.49712455003, 1998.
Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J. F., and Simmons, A.:
The ECMWF operational implementation of four-dimensional variational
assimilation. I: Experimental results with simplified physics, Q.
J. Roy. Meteor. Soc., 126, 1143–1170,
https://doi.org/10.1002/qj.49712656415, 2000.
Roustan, Y. and Bocquet, M.: Inverse modelling for mercury over Europe, Atmos. Chem. Phys., 6, 3085–3098, https://doi.org/10.5194/acp-6-3085-2006, 2006.
Rubin, J. I., Reid, J. S., Hansen, J. A., Anderson, J. L., Collins, N., Hoar, T. J., Hogan, T., Lynch, P., McLay, J., Reynolds, C. A., Sessions, W. R., Westphal, D. L., and Zhang, J.: Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS) and its application of the Data Assimilation Research Testbed (DART) in support of aerosol forecasting, Atmos. Chem. Phys., 16, 3927–3951, https://doi.org/10.5194/acp-16-3927-2016, 2016.
Saha, S., Moorthi, S., Pan, H.-L., Wu, X., Wang, J., Nadiga, S., Tripp, P.,
Kistler, R., Woollen, J., Behringer, D., Liu, H., Stokes, D., Grumbine, R.,
Gayno, G., Wang, J., Hou, Y.-T., Chuang, H.-Y., Juang, H.-M. H., Sela, J.,
Iredell, M., Treadon, R., Kleist, D., Van Delst, P., Keyser, D., Derber, J.,
Ek, M., Meng, J., Wei, H., Yang, R., Lord, S., van den Dool, H., Kumar, A.,
Wang, W., Long, C., Chelliah, M., Xue, Y., Huang, B., Schemm, J.-K.,
Ebisuzaki, W., Lin, R., Xie, P., Chen, M., Zhou, S., Higgins, W., Zou,
C.-Z., Liu, Q., Chen, Y., Han, Y., Cucurull, L., Reynolds, R. W., Rutledge,
G., and Goldberg, M.: NCEP Climate Forecast System Reanalysis (CFSR)
6-hourly Products, January 1979 to December 2010, Research Data Archive at
the National Center for Atmospheric Research, Computational and Information
Systems Laboratory [data set], https://doi.org/10.5065/D69K487J, 2010.
Saide, P. E., Carmichael, G. R., Liu, Z., Schwartz, C. S., Lin, H. C., da Silva, A. M., and Hyer, E.: Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts, Atmos. Chem. Phys., 13, 10425–10444, https://doi.org/10.5194/acp-13-10425-2013, 2013.
Sandu, A. and Chai, T.: Chemical Data Assimilation – An Overview,
Atmosphere, 2, 426–463, https://doi.org/10.3390/atmos2030426, 2011.
Schutgens, N. A. J., Miyoshi, T., Takemura, T., and Nakajima, T.: Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model, Atmos. Chem. Phys., 10, 2561–2576, https://doi.org/10.5194/acp-10-2561-2010, 2010.
Schwartz, C. S., Liu, Z., Lin, H.-C., and McKeen, S. A.: Simultaneous
three-dimensional variational assimilation of surface fine particulate
matter and MODIS aerosol optical depth, J. Geophys. Res.-Atmos., 117, D13202, https://doi.org/10.1029/2011JD017383, 2012.
Schwartz, C. S., Liu, Z., Lin, H.-C., and Cetola, J. D.: Assimilating
aerosol observations with a “hybrid” variational-ensemble data
assimilation system, J. Geophys. Res.-Atmos., 119,
4043–4069, https://doi.org/10.1002/2013JD020937, 2014.
Sekiyama, T. T., Tanaka, T. Y., Shimizu, A., and Miyoshi, T.: Data assimilation of CALIPSO aerosol observations, Atmos. Chem. Phys., 10, 39–49, https://doi.org/10.5194/acp-10-39-2010, 2010.
Shao, H., Derber, J., Huang, X.-Y., Hu, M., Newman, K., Stark, D., Lueken,
M., Zhou, C., Nance, L., Kuo, Y.-H., and Brown, B.: Bridging Research to
Operations Transitions: Status and Plans of Community GSI, B.
Am. Meteorol. Soc., 97, 1427–1440, https://doi.org/10.1175/BAMS-D-13-00245.1,
2016.
Skachko, S., Errera, Q., Ménard, R., Christophe, Y., and Chabrillat, S.: Comparison of the ensemble Kalman filter and 4D-Var assimilation methods using a stratospheric tracer transport model, Geosci. Model Dev., 7, 1451–1465, https://doi.org/10.5194/gmd-7-1451-2014, 2014.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D., Duda,
M. G., Huang, X., Wang, W., and Powers, J. G.: A Description of the Advanced
Research WRF Version 3, University Corporation for Atmospheric Research, no.
NCAR/TN-475+STR, https://doi.org/10.5065/D68S4MVH, 2008.
Solazzo, E., Bianconi, R., Pirovano, G., Matthias, V., Vautard, R., Moran,
M. D., Wyat Appel, K., Bessagnet, B., Brandt, J., Christensen, J. H.,
Chemel, C., Coll, I., Ferreira, J., Forkel, R., Francis, X. V., Grell, G.,
Grossi, P., Hansen, A. B., Miranda, A. I., Nopmongcol, U., Prank, M.,
Sartelet, K. N., Schaap, M., Silver, J. D., Sokhi, R. S., Vira, J., Werhahn,
J., Wolke, R., Yarwood, G., Zhang, J., Rao, S. T., and Galmarini, S.:
Operational model evaluation for particulate matter in Europe and North
America in the context of AQMEII, Atmos. Environ., 53, 75–92,
https://doi.org/10.1016/j.atmosenv.2012.02.045, 2012.
Stauffer, D. R. and Seaman, N. L.: Use of Four-Dimensional Data Assimilation
in a Limited-Area Mesoscale Model. Part I: Experiments with Synoptic-Scale
Data, Mon. Weather Rev., 118, 1250–1277,
https://doi.org/10.1175/1520-0493(1990)118<1250:UOFDDA>2.0.CO;2, 1990.
Talagrand, O. and Courtier, P.: Variational Assimilation of Meteorological
Observations With the Adjoint Vorticity Equation. I: Theory, Q.
J. Roy. Meteor. Soc., 113, 1311–1328,
https://doi.org/10.1002/qj.49711347812, 1987.
Tang, X., Zhu, J., Wang, Z. F., and Gbaguidi, A.: Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions, Atmos. Chem. Phys., 11, 12901–12916, https://doi.org/10.5194/acp-11-12901-2011, 2011.
Tang, Y., Chai, T., Pan, L., Lee, P., Tong, D., Kim, H.-C., and Chen, W.:
Using optimal interpolation to assimilate surface measurements and satellite
AOD for ozone and PM2.5: A case study for July 2011, J. Air
Waste Manage., 65, 1206–1216,
https://doi.org/10.1080/10962247.2015.1062439, 2015.
Tang, Y., Pagowski, M., Chai, T., Pan, L., Lee, P., Baker, B., Kumar, R., Delle Monache, L., Tong, D., and Kim, H.-C.: A case study of aerosol data assimilation with the Community Multi-scale Air Quality Model over the contiguous United States using 3D-Var and optimal interpolation methods, Geosci. Model Dev., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017, 2017.
US EPA Office of Research and Development: CMAQv5.1 (5.1), Zenodo [code], https://doi.org/10.5281/zenodo.1079909, 2015.
Whitaker, J. S. and Hamill, T. M.: Ensemble Data Assimilation without
Perturbed Observations, Mon. Weather Rev., 130, 1913–1924,
https://doi.org/10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2, 2002.
Whitaker, J. S. and Hamill, T. M.: Evaluating Methods to Account for System
Errors in Ensemble Data Assimilation, Mon. Weather Rev., 140,
3078–3089, https://doi.org/10.1175/MWR-D-11-00276.1, 2012.
Wiedinmyer, C., Quayle, B., Geron, C., Belote, A., McKenzie, D., Zhang, X.,
O'Neill, S., and Wynne, K. K.: Estimating emissions from fires in North
America for air quality modeling, Atmos. Environ., 40, 3419–3432,
https://doi.org/10.1016/j.atmosenv.2006.02.010, 2006.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
WRF Users Page: WRF Model Users' Page, WRF Users Page [code], https://doi.org/10.5065/D6MK6B4K, 2022.
Yamartino, R. J.: Nonnegative, Conserved Scalar Transport Using
Grid-Cell-centered, Spectrally Constrained Blackman Cubics for Applications
on a Variable-Thickness Mesh, Mon. Weather Rev., 121, 753–763,
https://doi.org/10.1175/1520-0493(1993)121<0753:NCSTUG>2.0.CO;2, 1993.
Yang, Z.-L., Niu, G.-Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 2. Evaluation over global river basins, J. Geophys.
Res.-Atmos., 116, D12110, https://doi.org/10.1029/2010JD015140, 2011.
Yin, X.-M., Dai, T., Xin, J.-Y., Gong, D.-Y., Yang, J., Teruyuki, N., and Shi, G.-Y.: Estimation of aerosol properties over the Chinese desert region with MODIS AOD assimilation in a global model, Adv. Clim. Chang. Res., 7, 90–98, https://doi.org/10.1016/j.accre.2016.04.001, 2016.
Yuan, H., Dai, Y., Xiao, Z., Ji, D., and Shangguan, W.: Reprocessing the
MODIS Leaf Area Index products for land surface and climate modelling,
Remote Sens. Environ., 115, 1171–1187, https://doi.org/10.1016/j.rse.2011.01.001, 2011.
Yumimoto, K. and Takemura, T.: Long-term inverse modeling of Asian dust:
Interannual variations of its emission, transport, deposition, and radiative
forcing, J. Geophys. Res.-Atmos., 120, 1582–1607,
https://doi.org/10.1002/2014JD022390, 2015.
Yumimoto, K., Nagao, T. M., Kikuchi, M., Sekiyama, T. T., Murakami, H.,
Tanaka, T. Y., Ogi, A., Irie, H., Khatri, P., Okumura, H., Arai, K., Morino,
I., Uchino, O., and Maki, T.: Aerosol data assimilation using data from
Himawari-8, a next-generation geostationary meteorological satellite,
Geophys. Res. Lett., 43, 5886–5894, https://doi.org/10.1002/2016GL069298, 2016.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.:
Real-time air quality forecasting, part II: State of the science, current
research needs, and future prospects, Atmos. Environ., 60, 656–676,
https://doi.org/10.1016/j.atmosenv.2012.02.041, 2012a.
Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.:
Real-time air quality forecasting, part I: History, techniques, and current
status, Atmos. Environ., 60, 632–655,
https://doi.org/10.1016/j.atmosenv.2012.06.031, 2012b.
Short summary
An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South Korea. The EnKF performed better than that without assimilation and even superior to 3D-Var. The reduced MBs in 24 h predictions were 48 % and 27 % by improving ICs and BCs, respectively.
An EnKF was applied to CMAQ for assimilating ground PM2.5 observations from China and South...
