Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7859-2022
© 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-15-7859-2022
© 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
| 
26 Oct 2022
Development and technical paper |
| 26 Oct 2022
Data assimilation for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 1.0.0): EnVar implementation and evaluation
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Chris Snyder
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Jonathan J. Guerrette
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Byoung-Joo Jung
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Junmei Ban
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
Steven Vahl
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
now at: Joint Center for Satellite Data Assimilation, Boulder, USA
National Center for Atmospheric Research, Boulder, Colorado 80301, USA
now at: Shenzhen Institute of Meteorological Innovation, Shenzhen, China
Yannick Trémolet
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Thomas Auligné
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Benjamin Ménétrier
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Anna Shlyaeva
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Stephen Herbener
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Emily Liu
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
now at: NOAA National Centers for Environmental Prediction, College Park, USA
Daniel Holdaway
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
now at: NASA Goddard Space Flight Center, Greenbelt, USA
Benjamin T. Johnson
Joint Center for Satellite Data Assimilation, Boulder, Colorado 80301, USA
Related authors
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-131, https://doi.org/10.5194/gmd-2023-131, 2023
Revised manuscript under review for GMD
Short summary
Short summary
We described the multivariate static background error covariance (B) for JEDI-MPAS 3DVar data assimilation system. With a tuned B parameters, the multivariate B gives a physically-balanced analysis increment fields in the single observation test framework. In the month-long cycling experiment with global 60 km mesh, the 3DVar with static B performs stable. Due to its simple workflow and minimal computational requirements, the JEDI-MPAS 3DVar can be useful for the research community.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernandez Banos, Yonggang G. Yu, Soyoung Ha, Yannick Tremolet, Thomas Auligne, Clementine Gas, Benjamin Menetrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-54, https://doi.org/10.5194/gmd-2023-54, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Xinghong Cheng, Zilong Hao, Zengliang Zang, Zhiquan Liu, Xiangde Xu, Shuisheng Wang, Yuelin Liu, Yiwen Hu, and Xiaodan Ma
Atmos. Chem. Phys., 21, 13747–13761, https://doi.org/10.5194/acp-21-13747-2021, https://doi.org/10.5194/acp-21-13747-2021, 2021
Short summary
Short summary
We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
Wei Sun, Zhiquan Liu, Dan Chen, Pusheng Zhao, and Min Chen
Atmos. Chem. Phys., 20, 9311–9329, https://doi.org/10.5194/acp-20-9311-2020, https://doi.org/10.5194/acp-20-9311-2020, 2020
Short summary
Short summary
A new aerosol and gas pollutant assimilation capability is developed within the WRFDA system with the 3D variational algorithm and MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme. By assimilating surface PM2.5, PM10, SO2, NO2, O3, and CO, it improves 24 h air quality forecasting. Based on this system, model deficiencies are explored. Parameterization in the newly added inorganic aerosol heterogeneous reactions should be adjusted and verified by data assimilation.
Soyoung Ha, Zhiquan Liu, Wei Sun, Yonghee Lee, and Limseok Chang
Atmos. Chem. Phys., 20, 6015–6036, https://doi.org/10.5194/acp-20-6015-2020, https://doi.org/10.5194/acp-20-6015-2020, 2020
Short summary
Short summary
This study examines the effect of aerosol optical depth (AOD) retrieved from the Korean Geostationary Ocean Color Imager (GOCI) sensors on surface PM2.5 forecasts using the online coupled WRF-Chem forecasting model and the GSI 3D-Var analysis system. During the KORUS-AQ campaign period, the assimilation of GOCI AOD retrieved at the 550 nm wavelength greatly improved air quality forecasting up to 24 h when assimilated with surface PM2.5 observations, particularly for heavy pollution events.
Bin Yao, Chao Liu, Yan Yin, Zhiquan Liu, Chunxiang Shi, Hironobu Iwabuchi, and Fuzhong Weng
Atmos. Meas. Tech., 13, 1033–1049, https://doi.org/10.5194/amt-13-1033-2020, https://doi.org/10.5194/amt-13-1033-2020, 2020
Short summary
Short summary
Due to the complex spatiotemporal and physical properties of clouds, their quantitative depictions in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is developed to evaluate the quality of cloud properties by directly comparing them with satellite radiance observations. ERA5 and CRA are found to have great capability in representing the cloudy atmosphere over East Asia, and MERRA-2 tends to slightly overestimate clouds over the region.
Shizhang Wang and Zhiquan Liu
Geosci. Model Dev., 12, 4031–4051, https://doi.org/10.5194/gmd-12-4031-2019, https://doi.org/10.5194/gmd-12-4031-2019, 2019
Short summary
Short summary
A reflectivity operator was developed for directly assimilating radar reflectivity involving contributions from ice species with the variational data assimilation method. Its current version was implemented in WRFDA 3.9.1. This operator allows for not only the dry snow/graupel but also the wet species so that it can effectively obtain the rainwater, snow, and graupel analysis which improved the short-term precipitation forecasts compared to those of the experiment without DA.
Dan Chen, Zhiquan Liu, Junmei Ban, and Min Chen
Atmos. Chem. Phys., 19, 8619–8650, https://doi.org/10.5194/acp-19-8619-2019, https://doi.org/10.5194/acp-19-8619-2019, 2019
Short summary
Short summary
We updated the WRF/Chem-EnKF DA system to quantitatively estimate SO2 emissions using hourly surface observations as constraints. The 2010 MEIC prior emissions were used to generate January 2015 and 2016 analyzed emissions, which revealed inhomogeneous SO2 emission changes for northern, western, and southern China. These changes were related to facts in reality, indicating that the updated DA system was capable of detecting emission deficiencies and optimizing emissions.
Dan Chen, Zhiquan Liu, Junmei Ban, Pusheng Zhao, and Min Chen
Atmos. Chem. Phys., 19, 7409–7427, https://doi.org/10.5194/acp-19-7409-2019, https://doi.org/10.5194/acp-19-7409-2019, 2019
Short summary
Short summary
To better characterize the anthropogenic emission-relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from GOCART to MOSAIC-4BIN scheme. Wintertime 2015–2017 (January) surface PM2.5 observations from more than 1600 sites were assimilated hourly. The observations and reanalysis data from the assimilation experiment were used to investigate year-to-year changes. Roles of emission and meteorology in driving the changes were also distinguished and quantitatively assessed.
Zhen Peng, Lili Lei, Zhiquan Liu, Jianning Sun, Aijun Ding, Junmei Ban, Dan Chen, Xingxia Kou, and Kekuan Chu
Atmos. Chem. Phys., 18, 17387–17404, https://doi.org/10.5194/acp-18-17387-2018, https://doi.org/10.5194/acp-18-17387-2018, 2018
Short summary
Short summary
An EnKF system was developed to simultaneously assimilate multiple surface measurements, including PM10, PM2.5, SO2, NO2, O3, and CO, via the joint adjustment of ICs and source emissions. Large improvements were achieved in the first 24 h forecast for PM2.5, PM10, SO2, and CO during an extreme haze episode that occurred in early October 2014 over the North China Plain, but no improvements were achieved for NO2 and O3.
Dan Chen, Zhiquan Liu, Chris Davis, and Yu Gu
Atmos. Chem. Phys., 17, 7917–7939, https://doi.org/10.5194/acp-17-7917-2017, https://doi.org/10.5194/acp-17-7917-2017, 2017
Short summary
Short summary
Saharan dust influences Atlantic TC genesis, but the relationship and mechanisms are not fully understood. This study investigated the dust radiative effects on atmospheric thermodynamics and tropical cyclogenesis over the Atlantic Ocean using WRF-Chem coupled with an aerosol data assimilation system. Both statistics and a case study revealed that low-altitude (high-altitude) dust inhibits (favors) convection owing to changes in convective inhibition. Semi-direct effects were also noted.
Zhen Peng, Zhiquan Liu, Dan Chen, and Junmei Ban
Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, https://doi.org/10.5194/acp-17-4837-2017, 2017
Short summary
Short summary
In order to improve the forecasting of atmospheric aerosols over China, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions and primary and precursor emissions. This system was applied to assimilate hourly surface PM2.5 measurements. The forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment without data assimilation.
Dan Chen, Zhiquan Liu, Jerome Fast, and Junmei Ban
Atmos. Chem. Phys., 16, 10707–10724, https://doi.org/10.5194/acp-16-10707-2016, https://doi.org/10.5194/acp-16-10707-2016, 2016
Short summary
Short summary
Extreme haze events occurred frequently over China recently, and adequately predicting peak PM2.5 concentrations is still challenging. In this study, the sulfate–nitrate–ammonium relevant heterogeneous reactions were parameterized for the first time in the WRF-Chem model. We evaluated the performance of WRF-Chem and used the model to investigate the sensitivity of heterogeneous reactions on simulated peak sulfate, nitrate, and ammonium concentrations in the vicinity of Beijing during October 2014.
D. Chen, Z. Liu, C. S. Schwartz, H.-C. Lin, J. D. Cetola, Y. Gu, and L. Xue
Geosci. Model Dev., 7, 2709–2715, https://doi.org/10.5194/gmd-7-2709-2014, https://doi.org/10.5194/gmd-7-2709-2014, 2014
M. Pagowski, Z. Liu, G. A. Grell, M. Hu, H.-C. Lin, and C. S. Schwartz
Geosci. Model Dev., 7, 1621–1627, https://doi.org/10.5194/gmd-7-1621-2014, https://doi.org/10.5194/gmd-7-1621-2014, 2014
P. E. Saide, G. R. Carmichael, Z. Liu, C. S. Schwartz, H. C. Lin, A. M. da Silva, and E. Hyer
Atmos. Chem. Phys., 13, 10425–10444, https://doi.org/10.5194/acp-13-10425-2013, https://doi.org/10.5194/acp-13-10425-2013, 2013
Soyoung Ha, Jonathan J. Guerrette, Ivette Hernandez Banos, William C. Skamarock, and Michael G. Duda
EGUsphere, https://doi.org/10.5194/egusphere-2023-2299, https://doi.org/10.5194/egusphere-2023-2299, 2023
Short summary
Short summary
To mitigate the imbalances in the initial conditions, this study introduces our recent implementation of the the incremental analysis update (IAU) in the Model for Prediction Across Scales for the Atmospheric component (MPAS-A), coupled with the Joint Effort for Data assimilation Integration (JEDI), through the cycling system. A month-long cycling run demonstrates the successful implementation of the IAU capability in the MPAS-JEDI cycling system.
Byoung-Joo Jung, Benjamin Ménétrier, Chris Snyder, Zhiquan Liu, Jonathan J. Guerrette, Junmei Ban, Ivette Hernández Baños, Yonggang G. Yu, and William C. Skamarock
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-131, https://doi.org/10.5194/gmd-2023-131, 2023
Revised manuscript under review for GMD
Short summary
Short summary
We described the multivariate static background error covariance (B) for JEDI-MPAS 3DVar data assimilation system. With a tuned B parameters, the multivariate B gives a physically-balanced analysis increment fields in the single observation test framework. In the month-long cycling experiment with global 60 km mesh, the 3DVar with static B performs stable. Due to its simple workflow and minimal computational requirements, the JEDI-MPAS 3DVar can be useful for the research community.
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernandez Banos, Yonggang G. Yu, Soyoung Ha, Yannick Tremolet, Thomas Auligne, Clementine Gas, Benjamin Menetrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, and Benjamin T. Johnson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-54, https://doi.org/10.5194/gmd-2023-54, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
We demonstrate an ensemble of variational data assimilations (EDA) with the Model for Prediction Across Scales and the Joint Effort for Data assimilation Integration (JEDI) software framework. When compared to 20-member ensemble forecasts from operational initial conditions, those from 80-member EDA-generated initial conditions improve flow-dependent error covariances and subsequent 10 d forecasts. These experiments are repeatable for any atmospheric model with a JEDI interface.
Cheng-Hsuan Lu, Quanhua Liu, Shih-Wei Wei, Benjamin T. Johnson, Cheng Dang, Patrick G. Stegmann, Dustin Grogan, Guoqing Ge, Ming Hu, and Michael Lueken
Geosci. Model Dev., 15, 1317–1329, https://doi.org/10.5194/gmd-15-1317-2022, https://doi.org/10.5194/gmd-15-1317-2022, 2022
Short summary
Short summary
This article is a technical note on the aerosol absorption and scattering calculations of the Community Radiative Transfer Model (CRTM) v2.2 and v2.3. It also provides guidance for prospective users of the CRTM aerosol option and Gridpoint Statistical Interpolation (GSI) aerosol-aware radiance assimilation. Scientific aspects of aerosol-affected BT in atmospheric data assimilation are also briefly discussed.
Xinghong Cheng, Zilong Hao, Zengliang Zang, Zhiquan Liu, Xiangde Xu, Shuisheng Wang, Yuelin Liu, Yiwen Hu, and Xiaodan Ma
Atmos. Chem. Phys., 21, 13747–13761, https://doi.org/10.5194/acp-21-13747-2021, https://doi.org/10.5194/acp-21-13747-2021, 2021
Short summary
Short summary
We develop a new inversion method of emission sources based on sensitivity analysis and the three-dimension variational technique. The novel explicit observation operator matrix between emission sources and the receptor’s concentrations is established. Then this method is applied to a typical heavy haze episode in North China, and spatiotemporal variations of SO2, NO2, and O3 concentrations simulated using a posterior emission sources are compared with results using an a priori inventory.
Wei Sun, Zhiquan Liu, Dan Chen, Pusheng Zhao, and Min Chen
Atmos. Chem. Phys., 20, 9311–9329, https://doi.org/10.5194/acp-20-9311-2020, https://doi.org/10.5194/acp-20-9311-2020, 2020
Short summary
Short summary
A new aerosol and gas pollutant assimilation capability is developed within the WRFDA system with the 3D variational algorithm and MOSAIC (Model for Simulating Aerosol Interactions and Chemistry) aerosol scheme. By assimilating surface PM2.5, PM10, SO2, NO2, O3, and CO, it improves 24 h air quality forecasting. Based on this system, model deficiencies are explored. Parameterization in the newly added inorganic aerosol heterogeneous reactions should be adjusted and verified by data assimilation.
Soyoung Ha, Zhiquan Liu, Wei Sun, Yonghee Lee, and Limseok Chang
Atmos. Chem. Phys., 20, 6015–6036, https://doi.org/10.5194/acp-20-6015-2020, https://doi.org/10.5194/acp-20-6015-2020, 2020
Short summary
Short summary
This study examines the effect of aerosol optical depth (AOD) retrieved from the Korean Geostationary Ocean Color Imager (GOCI) sensors on surface PM2.5 forecasts using the online coupled WRF-Chem forecasting model and the GSI 3D-Var analysis system. During the KORUS-AQ campaign period, the assimilation of GOCI AOD retrieved at the 550 nm wavelength greatly improved air quality forecasting up to 24 h when assimilated with surface PM2.5 observations, particularly for heavy pollution events.
Bin Yao, Chao Liu, Yan Yin, Zhiquan Liu, Chunxiang Shi, Hironobu Iwabuchi, and Fuzhong Weng
Atmos. Meas. Tech., 13, 1033–1049, https://doi.org/10.5194/amt-13-1033-2020, https://doi.org/10.5194/amt-13-1033-2020, 2020
Short summary
Short summary
Due to the complex spatiotemporal and physical properties of clouds, their quantitative depictions in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is developed to evaluate the quality of cloud properties by directly comparing them with satellite radiance observations. ERA5 and CRA are found to have great capability in representing the cloudy atmosphere over East Asia, and MERRA-2 tends to slightly overestimate clouds over the region.
Shizhang Wang and Zhiquan Liu
Geosci. Model Dev., 12, 4031–4051, https://doi.org/10.5194/gmd-12-4031-2019, https://doi.org/10.5194/gmd-12-4031-2019, 2019
Short summary
Short summary
A reflectivity operator was developed for directly assimilating radar reflectivity involving contributions from ice species with the variational data assimilation method. Its current version was implemented in WRFDA 3.9.1. This operator allows for not only the dry snow/graupel but also the wet species so that it can effectively obtain the rainwater, snow, and graupel analysis which improved the short-term precipitation forecasts compared to those of the experiment without DA.
Dan Chen, Zhiquan Liu, Junmei Ban, and Min Chen
Atmos. Chem. Phys., 19, 8619–8650, https://doi.org/10.5194/acp-19-8619-2019, https://doi.org/10.5194/acp-19-8619-2019, 2019
Short summary
Short summary
We updated the WRF/Chem-EnKF DA system to quantitatively estimate SO2 emissions using hourly surface observations as constraints. The 2010 MEIC prior emissions were used to generate January 2015 and 2016 analyzed emissions, which revealed inhomogeneous SO2 emission changes for northern, western, and southern China. These changes were related to facts in reality, indicating that the updated DA system was capable of detecting emission deficiencies and optimizing emissions.
Dan Chen, Zhiquan Liu, Junmei Ban, Pusheng Zhao, and Min Chen
Atmos. Chem. Phys., 19, 7409–7427, https://doi.org/10.5194/acp-19-7409-2019, https://doi.org/10.5194/acp-19-7409-2019, 2019
Short summary
Short summary
To better characterize the anthropogenic emission-relevant aerosol species, the GSI-WRF/Chem data assimilation system was updated from GOCART to MOSAIC-4BIN scheme. Wintertime 2015–2017 (January) surface PM2.5 observations from more than 1600 sites were assimilated hourly. The observations and reanalysis data from the assimilation experiment were used to investigate year-to-year changes. Roles of emission and meteorology in driving the changes were also distinguished and quantitatively assessed.
Zhen Peng, Lili Lei, Zhiquan Liu, Jianning Sun, Aijun Ding, Junmei Ban, Dan Chen, Xingxia Kou, and Kekuan Chu
Atmos. Chem. Phys., 18, 17387–17404, https://doi.org/10.5194/acp-18-17387-2018, https://doi.org/10.5194/acp-18-17387-2018, 2018
Short summary
Short summary
An EnKF system was developed to simultaneously assimilate multiple surface measurements, including PM10, PM2.5, SO2, NO2, O3, and CO, via the joint adjustment of ICs and source emissions. Large improvements were achieved in the first 24 h forecast for PM2.5, PM10, SO2, and CO during an extreme haze episode that occurred in early October 2014 over the North China Plain, but no improvements were achieved for NO2 and O3.
Dan Chen, Zhiquan Liu, Chris Davis, and Yu Gu
Atmos. Chem. Phys., 17, 7917–7939, https://doi.org/10.5194/acp-17-7917-2017, https://doi.org/10.5194/acp-17-7917-2017, 2017
Short summary
Short summary
Saharan dust influences Atlantic TC genesis, but the relationship and mechanisms are not fully understood. This study investigated the dust radiative effects on atmospheric thermodynamics and tropical cyclogenesis over the Atlantic Ocean using WRF-Chem coupled with an aerosol data assimilation system. Both statistics and a case study revealed that low-altitude (high-altitude) dust inhibits (favors) convection owing to changes in convective inhibition. Semi-direct effects were also noted.
Zhen Peng, Zhiquan Liu, Dan Chen, and Junmei Ban
Atmos. Chem. Phys., 17, 4837–4855, https://doi.org/10.5194/acp-17-4837-2017, https://doi.org/10.5194/acp-17-4837-2017, 2017
Short summary
Short summary
In order to improve the forecasting of atmospheric aerosols over China, the ensemble square root filter algorithm was extended to simultaneously optimize the chemical initial conditions and primary and precursor emissions. This system was applied to assimilate hourly surface PM2.5 measurements. The forecasts with the optimized initial conditions and emissions typically outperformed those from the control experiment without data assimilation.
Dongmei Xu, Thomas Auligné, Gaël Descombes, and Chris Snyder
Geosci. Model Dev., 9, 3919–3932, https://doi.org/10.5194/gmd-9-3919-2016, https://doi.org/10.5194/gmd-9-3919-2016, 2016
Short summary
Short summary
This study proposed a new cloud retrieval method based on the particle filter (PF). The PF cloud retrieval method is compared with the Multivariate and Minimum Residual (MMR) method that was previously established and verified. Cloud retrieval experiments involving a variety of cloudy types are conducted with the PF and MMR methods with measurements of Infrared radiances on multi-sensors onboard both GOES and MODIS, respectively.
Dan Chen, Zhiquan Liu, Jerome Fast, and Junmei Ban
Atmos. Chem. Phys., 16, 10707–10724, https://doi.org/10.5194/acp-16-10707-2016, https://doi.org/10.5194/acp-16-10707-2016, 2016
Short summary
Short summary
Extreme haze events occurred frequently over China recently, and adequately predicting peak PM2.5 concentrations is still challenging. In this study, the sulfate–nitrate–ammonium relevant heterogeneous reactions were parameterized for the first time in the WRF-Chem model. We evaluated the performance of WRF-Chem and used the model to investigate the sensitivity of heterogeneous reactions on simulated peak sulfate, nitrate, and ammonium concentrations in the vicinity of Beijing during October 2014.
G. Descombes, T. Auligné, F. Vandenberghe, D. M. Barker, and J. Barré
Geosci. Model Dev., 8, 669–696, https://doi.org/10.5194/gmd-8-669-2015, https://doi.org/10.5194/gmd-8-669-2015, 2015
D. Chen, Z. Liu, C. S. Schwartz, H.-C. Lin, J. D. Cetola, Y. Gu, and L. Xue
Geosci. Model Dev., 7, 2709–2715, https://doi.org/10.5194/gmd-7-2709-2014, https://doi.org/10.5194/gmd-7-2709-2014, 2014
S. Metref, E. Cosme, C. Snyder, and P. Brasseur
Nonlin. Processes Geophys., 21, 869–885, https://doi.org/10.5194/npg-21-869-2014, https://doi.org/10.5194/npg-21-869-2014, 2014
M. Pagowski, Z. Liu, G. A. Grell, M. Hu, H.-C. Lin, and C. S. Schwartz
Geosci. Model Dev., 7, 1621–1627, https://doi.org/10.5194/gmd-7-1621-2014, https://doi.org/10.5194/gmd-7-1621-2014, 2014
S. Kim, B.-J. Jung, and Y. Jo
Geosci. Model Dev., 7, 1175–1182, https://doi.org/10.5194/gmd-7-1175-2014, https://doi.org/10.5194/gmd-7-1175-2014, 2014
P. E. Saide, G. R. Carmichael, Z. Liu, C. S. Schwartz, H. C. Lin, A. M. da Silva, and E. Hyer
Atmos. Chem. Phys., 13, 10425–10444, https://doi.org/10.5194/acp-13-10425-2013, https://doi.org/10.5194/acp-13-10425-2013, 2013
Related subject area
Atmospheric sciences
Comprehensive evaluation of typical planetary boundary layer (PBL) parameterization schemes in China – Part 1: Understanding expressiveness of schemes for different regions from the mechanism perspective
Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model
Implementation of a satellite-based tool for the quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: forward modelling evaluation against near-surface and satellite data
The capabilities of the adjoint of GEOS-Chem model to support HEMCO emission inventories and MERRA-2 meteorological data
Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020
Description and evaluation of the new UM–UKCA (vn11.0) Double Extended Stratospheric–Tropospheric (DEST vn1.0) scheme for comprehensive modelling of halogen chemistry in the stratosphere
A robust error correction method for numerical weather prediction wind speed based on Bayesian optimization, variational mode decomposition, principal component analysis, and random forest: VMD-PCA-RF (version 1.0.0)
Description and performance of a sectional aerosol microphysical model in the Community Earth System Model (CESM2)
A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx
The Hydro-ABC model (Version 2.0): a simplified convective-scale model with moist dynamics
Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0
A standardized methodology for the validation of air quality forecast applications (F-MQO): lessons learnt from its application across Europe
Application of the Multi-Scale Infrastructure for Chemistry and Aerosols version 0 (MUSICAv0) for air quality research in Africa
A Regional multi-Air Pollutant Assimilation System (RAPAS v1.0) for emission estimates: system development and application
Evaluation of vertically resolved longwave radiation in SPARTACUS-Urban 0.7.3 and the sensitivity to urban surface temperatures
Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning
QES-Plume v1.0: a Lagrangian dispersion model
A two-way coupled regional urban–street network air quality model system for Beijing, China
Simulations of idealised 3D atmospheric flows on terrestrial planets using LFRic-Atmosphere
Emulating lateral gravity wave propagation in a global chemistry–climate model (EMAC v2.55.2) through horizontal flux redistribution
Evaluating WRF-GC v2.0 predictions of boundary layer height and vertical ozone profile during the 2021 TRACER-AQ campaign in Houston, Texas
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Improved representation of volcanic sulfur dioxide depletion in Lagrangian transport simulations: a case study with MPTRAC v2.4
Use of threshold parameter variation for tropical cyclone tracking
Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes
The High-resolution Intermediate Complexity Atmospheric Research (HICAR v1.1) model enables fast dynamic downscaling to the hectometer scale
A gridded air quality forecast through fusing site-available machine learning predictions from RFSML v1.0 and chemical transport model results from GEOS-Chem v13.1.0 using the ensemble Kalman filter
Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
Plume detection and emission estimate for biomass burning plumes from TROPOMI carbon monoxide observations using APE v1.1
CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
A method to derive Fourier–wavelet spectra for the characterization of global-scale waves in the mesosphere and lower thermosphere and its MATLAB and Python software (fourierwavelet v1.1)
Dynamic Meteorology-induced Emissions Coupler (MetEmis) development in the Community Multiscale Air Quality (CMAQ): CMAQ-MetEmis
Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar1)
A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes
Simulating heat and CO2 fluxes in Beijing using SUEWS V2020b: sensitivity to vegetation phenology and maximum conductance
A Python library for computing individual and merged non-CO2 algorithmic climate change functions: CLIMaCCF V1.0
The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models
The development and validation of the Inhomogeneous Wind Scheme for Urban Street (IWSUS-v1)
GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)
Variability and combination as an ensemble of mineral dust forecasts during the 2021 CADDIWA experiment using the WRF 3.7.1 and CHIMERE v2020r3 models
Breakups are complicated: an efficient representation of collisional breakup in the superdroplet method
An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters
Sensitivity of tropospheric ozone to halogen chemistry in the chemistry–climate model LMDZ-INCA vNMHC
Segmentation of XCO2 images with deep learning: application to synthetic plumes from cities and power plants
Evaluating precipitation distributions at regional scales: a benchmarking framework and application to CMIP5 and 6 models
The Fire Inventory from NCAR version 2.5: an updated global fire emissions model for climate and chemistry applications
Wenxing Jia, Xiaoye Zhang, Hong Wang, Yaqiang Wang, Deying Wang, Junting Zhong, Wenjie Zhang, Lei Zhang, Lifeng Guo, Yadong Lei, Jizhi Wang, Yuanqin Yang, and Yi Lin
Geosci. Model Dev., 16, 6635–6670, https://doi.org/10.5194/gmd-16-6635-2023, https://doi.org/10.5194/gmd-16-6635-2023, 2023
Short summary
Short summary
Most current studies on planetary boundary layer (PBL) parameterization schemes are relatively fragmented and lack systematic in-depth analysis and discussion. In this study, we comprehensively evaluate the performance capability of the PBL scheme in five typical regions of China in different seasons from the mechanism of the scheme and the effects of PBL schemes on the near-surface meteorological parameters, vertical structures of the PBL, PBL height, and turbulent diffusion.
William Rudisill, Alejandro Flores, and Rosemary Carroll
Geosci. Model Dev., 16, 6531–6552, https://doi.org/10.5194/gmd-16-6531-2023, https://doi.org/10.5194/gmd-16-6531-2023, 2023
Short summary
Short summary
It is important to know how well atmospheric models do in mountains, but there are not very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado River basin against the available data. The model works rather well, but there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we could not do before.
Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina P. Nascimento
Geosci. Model Dev., 16, 6413–6431, https://doi.org/10.5194/gmd-16-6413-2023, https://doi.org/10.5194/gmd-16-6413-2023, 2023
Short summary
Short summary
A 1-year simulation of atmospheric CH4 over Europe is performed and evaluated against observations based on the TROPOspheric Monitoring Instrument (TROPOMI). A good general model–observation agreement is found, with discrepancies reaching their minimum and maximum values during the summer peak season and winter months, respectively. A huge and under-explored potential for CH4 inverse modeling using improved TROPOMI XCH4 data sets in large-scale applications is identified.
Zhaojun Tang, Zhe Jiang, Jiaqi Chen, Panpan Yang, and Yanan Shen
Geosci. Model Dev., 16, 6377–6392, https://doi.org/10.5194/gmd-16-6377-2023, https://doi.org/10.5194/gmd-16-6377-2023, 2023
Short summary
Short summary
We designed a new framework to facilitate emission inventory updates in the adjoint of GEOS-Chem model. It allows us to support Harmonized Emissions Component (HEMCO) emission inventories conveniently and to easily add more emission inventories following future updates in GEOS-Chem forward simulations. Furthermore, we developed new modules to support MERRA-2 meteorological data; this allows us to perform long-term analysis with consistent meteorological data.
Rui Zhu, Zhaojun Tang, Xiaokang Chen, Xiong Liu, and Zhe Jiang
Geosci. Model Dev., 16, 6337–6354, https://doi.org/10.5194/gmd-16-6337-2023, https://doi.org/10.5194/gmd-16-6337-2023, 2023
Short summary
Short summary
A single ozone (O3) tracer mode was developed in this work to build the capability of the GEOS-Chem model for rapid O3 simulation. It is combined with OMI and surface O3 observations to investigate the changes in tropospheric O3 in China in 2015–2020. The assimilations indicate rapid surface O3 increases that are underestimated by the a priori simulations. We find stronger increases in tropospheric O3 columns over polluted areas and a large discrepancy by assimilating different observations.
Ewa M. Bednarz, Ryan Hossaini, N. Luke Abraham, and Martyn P. Chipperfield
Geosci. Model Dev., 16, 6187–6209, https://doi.org/10.5194/gmd-16-6187-2023, https://doi.org/10.5194/gmd-16-6187-2023, 2023
Short summary
Short summary
Development and performance of the new DEST chemistry scheme of UM–UKCA is described. The scheme extends the standard StratTrop scheme by including important updates to the halogen chemistry, thus allowing process-oriented studies of stratospheric ozone depletion and recovery, including impacts from both controlled long-lived ozone-depleting substances and emerging issues around uncontrolled, very short-lived substances. It will thus aid studies in support of future ozone assessment reports.
Shaohui Zhou, Chloe Yuchao Gao, Zexia Duan, Xingya Xi, and Yubin Li
Geosci. Model Dev., 16, 6247–6266, https://doi.org/10.5194/gmd-16-6247-2023, https://doi.org/10.5194/gmd-16-6247-2023, 2023
Short summary
Short summary
The proposed wind speed correction model (VMD-PCA-RF) demonstrates the highest prediction accuracy and stability in the five southern provinces in nearly a year and at different heights. VMD-PCA-RF evaluation indices for 13 months remain relatively stable: the forecasting accuracy rate FA is above 85 %. In future research, the proposed VMD-PCA-RF algorithm can be extrapolated to the 3 km grid points of the five southern provinces to generate a 3 km grid-corrected wind speed product.
Simone Tilmes, Michael J. Mills, Yunqian Zhu, Charles G. Bardeen, Francis Vitt, Pengfei Yu, David Fillmore, Xiaohong Liu, Brian Toon, and Terry Deshler
Geosci. Model Dev., 16, 6087–6125, https://doi.org/10.5194/gmd-16-6087-2023, https://doi.org/10.5194/gmd-16-6087-2023, 2023
Short summary
Short summary
We implemented an alternative aerosol scheme in the high- and low-top model versions of the Community Earth System Model Version 2 (CESM2) with a more detailed description of tropospheric and stratospheric aerosol size distributions than the existing aerosol model. This development enables the comparison of different aerosol schemes with different complexity in the same model framework. It identifies improvements compared to a range of observations in both the troposphere and stratosphere.
Dien Wu, Joshua L. Laughner, Junjie Liu, Paul I. Palmer, John C. Lin, and Paul O. Wennberg
Geosci. Model Dev., 16, 6161–6185, https://doi.org/10.5194/gmd-16-6161-2023, https://doi.org/10.5194/gmd-16-6161-2023, 2023
Short summary
Short summary
To balance computational expenses and chemical complexity in extracting emission signals from tropospheric NO2 columns, we propose a simplified non-linear Lagrangian chemistry transport model and assess its performance against TROPOMI v2 over power plants and cities. Using this model, we then discuss how NOx chemistry affects the relationship between NOx and CO2 emissions and how studying NO2 columns helps quantify modeled biases in wind directions and prior emissions.
Jiangshan Zhu and Ross Noel Bannister
Geosci. Model Dev., 16, 6067–6085, https://doi.org/10.5194/gmd-16-6067-2023, https://doi.org/10.5194/gmd-16-6067-2023, 2023
Short summary
Short summary
We describe how condensation and evaporation are included in the existing (otherwise dry) simplified ABC model. The new model (Hydro-ABC) includes transport of vapour and condensate within a dynamical core, and it transitions between these two phases via a micro-physics scheme. The model shows the development of an anvil cloud and excitation of atmospheric waves over many frequencies. The covariances that develop between variables are also studied together with indicators of convective motion.
Jiangyong Li, Chunlin Zhang, Wenlong Zhao, Shijie Han, Yu Wang, Hao Wang, and Boguang Wang
Geosci. Model Dev., 16, 6049–6066, https://doi.org/10.5194/gmd-16-6049-2023, https://doi.org/10.5194/gmd-16-6049-2023, 2023
Short summary
Short summary
Photochemical box models, crucial for understanding tropospheric chemistry, face challenges due to slow computational efficiency with large chemical equations. The model introduced in this study, ROMAC, boosts efficiency by up to 96 % using an advanced atmospheric solver and an adaptive optimization algorithm. Moreover, ROMAC exceeds traditional box models in evaluating the impact of physical processes on pollutant concentrations.
Lina Vitali, Kees Cuvelier, Antonio Piersanti, Alexandra Monteiro, Mario Adani, Roberta Amorati, Agnieszka Bartocha, Alessandro D'Ausilio, Paweł Durka, Carla Gama, Giulia Giovannini, Stijn Janssen, Tomasz Przybyła, Michele Stortini, Stijn Vranckx, and Philippe Thunis
Geosci. Model Dev., 16, 6029–6047, https://doi.org/10.5194/gmd-16-6029-2023, https://doi.org/10.5194/gmd-16-6029-2023, 2023
Short summary
Short summary
Air quality forecasting models play a key role in fostering short-term measures aimed at reducing human exposure to air pollution. Together with this role comes the need for a thorough assessment of the model performances to build confidence in models’ capabilities, in particular when model applications support policymaking. In this paper, we propose an evaluation methodology and test it on several domains across Europe, highlighting its strengths and room for improvement.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
Short summary
Short summary
The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
Short summary
Short summary
We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Megan A. Stretton, William Morrison, Robin J. Hogan, and Sue Grimmond
Geosci. Model Dev., 16, 5931–5947, https://doi.org/10.5194/gmd-16-5931-2023, https://doi.org/10.5194/gmd-16-5931-2023, 2023
Short summary
Short summary
Cities' materials and forms impact radiative fluxes. We evaluate the SPARTACUS-Urban multi-layer approach to modelling longwave radiation, describing realistic 3D geometry statistically using the explicit DART (Discrete Anisotropic Radiative Transfer) model. The temperature configurations used are derived from thermal camera observations. SPARTACUS-Urban accurately predicts longwave fluxes, with a low computational time (cf. DART), but has larger errors with sunlit/shaded surface temperatures.
Daehyeon Han, Jungho Im, Yeji Shin, and Juhyun Lee
Geosci. Model Dev., 16, 5895–5914, https://doi.org/10.5194/gmd-16-5895-2023, https://doi.org/10.5194/gmd-16-5895-2023, 2023
Short summary
Short summary
To identify the key factors affecting quantitative precipitation nowcasting (QPN) using deep learning (DL), we carried out a comprehensive evaluation and analysis. We compared four key factors: DL model, length of the input sequence, loss function, and ensemble approach. Generally, U-Net outperformed ConvLSTM. Loss function and ensemble showed potential for improving performance when they synergized well. The length of the input sequence did not significantly affect the results.
Fabien Margairaz, Balwinder Singh, Jeremy A. Gibbs, Loren Atwood, Eric R. Pardyjak, and Rob Stoll
Geosci. Model Dev., 16, 5729–5754, https://doi.org/10.5194/gmd-16-5729-2023, https://doi.org/10.5194/gmd-16-5729-2023, 2023
Short summary
Short summary
The Quick Environmental Simulation (QES) tool is a low-computational-cost fast-response framework. It provides high-resolution wind and concentration information to study complex problems, such as spore or smoke transport, urban pollution, and air quality. This paper presents the particle dispersion model and its validation against analytical solutions and wind-tunnel data for a mock-urban setting. In all cases, the model provides accurate results with competitive computational performance.
Tao Wang, Hang Liu, Jie Li, Shuai Wang, Youngseob Kim, Yele Sun, Wenyi Yang, Huiyun Du, Zhe Wang, and Zifa Wang
Geosci. Model Dev., 16, 5585–5599, https://doi.org/10.5194/gmd-16-5585-2023, https://doi.org/10.5194/gmd-16-5585-2023, 2023
Short summary
Short summary
This paper developed a two-way coupled module in a new version of a regional urban–street network model, IAQMS-street v2.0, in which the mass flux from streets to background is considered. Test cases are defined to evaluate the performance of IAQMS-street v2.0 in Beijing by comparing it with that simulated by IAQMS-street v1.0 and a regional model. The contribution of local emissions and the influence of on-road vehicle control measures on air quality are evaluated by using IAQMS-street v2.0.
Denis E. Sergeev, Nathan J. Mayne, Thomas Bendall, Ian A. Boutle, Alex Brown, Iva Kavčič, James Kent, Krisztian Kohary, James Manners, Thomas Melvin, Enrico Olivier, Lokesh K. Ragta, Ben Shipway, Jon Wakelin, Nigel Wood, and Mohamed Zerroukat
Geosci. Model Dev., 16, 5601–5626, https://doi.org/10.5194/gmd-16-5601-2023, https://doi.org/10.5194/gmd-16-5601-2023, 2023
Short summary
Short summary
Three-dimensional climate models are one of the best tools we have to study planetary atmospheres. Here, we apply LFRic-Atmosphere, a new model developed by the Met Office, to seven different scenarios for terrestrial planetary climates, including four for the exoplanet TRAPPIST-1e, a primary target for future observations. LFRic-Atmosphere reproduces these scenarios within the spread of the existing models across a range of key climatic variables, justifying its use in future exoplanet studies.
Roland Eichinger, Sebastian Rhode, Hella Garny, Peter Preusse, Petr Pisoft, Aleš Kuchař, Patrick Jöckel, Astrid Kerkweg, and Bastian Kern
Geosci. Model Dev., 16, 5561–5583, https://doi.org/10.5194/gmd-16-5561-2023, https://doi.org/10.5194/gmd-16-5561-2023, 2023
Short summary
Short summary
The columnar approach of gravity wave (GW) schemes results in dynamical model biases, but parallel decomposition makes horizontal GW propagation computationally unfeasible. In the global model EMAC, we approximate it by GW redistribution at one altitude using tailor-made redistribution maps generated with a ray tracer. More spread-out GW drag helps reconcile the model with observations and close the 60°S GW gap. Polar vortex dynamics are improved, enhancing climate model credibility.
Xueying Liu, Yuxuan Wang, Shailaja Wasti, Wei Li, Ehsan Soleimanian, James Flynn, Travis Griggs, Sergio Alvarez, John T. Sullivan, Maurice Roots, Laurence Twigg, Guillaume Gronoff, Timothy Berkoff, Paul Walter, Mark Estes, Johnathan W. Hair, Taylor Shingler, Amy Jo Scarino, Marta Fenn, and Laura Judd
Geosci. Model Dev., 16, 5493–5514, https://doi.org/10.5194/gmd-16-5493-2023, https://doi.org/10.5194/gmd-16-5493-2023, 2023
Short summary
Short summary
With a comprehensive suite of ground-based and airborne remote sensing measurements during the 2021 TRacking Aerosol Convection ExpeRiment – Air Quality (TRACER-AQ) campaign in Houston, this study evaluates the simulation of the planetary boundary layer (PBL) height and the ozone vertical profile by a high-resolution (1.33 km) 3-D photochemical model Weather Research and Forecasting-driven GEOS-Chem (WRF-GC).
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023, https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Short summary
Precipitation collects airborne particles and deposits these on the ground. This process is called wet deposition and greatly determines how airborne radioactive particles (released routinely or accidentally) contaminate the surface. In this work we present a new method to improve the calculation of wet deposition in computer models. We apply this method to the existing model FLEXPART by simulating the Fukushima nuclear accident (2011) and show that it improves the simulation of wet deposition.
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023, https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Short summary
A new version of the Model of Urban Network of Intersecting Canyons and Highways (MUNICH) is developed to represent heterogeneities of concentrations in streets. The street volume is discretized vertically and horizontally to limit the artificial dilution of emissions and concentrations. This new version is applied to street networks in Copenhagen and Paris. The comparisons to observations are improved, with higher concentrations of pollutants emitted by traffic at the bottom of the street.
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023, https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
Short summary
In this study, the framework for calculating reactive nitrogen species using a deep neural network (RND) was developed. It works through simple Python codes and provides high-accuracy reactive nitrogen oxide data. In the first version (RNDv1.0), the model calculates the nitrous acid (HONO) in urban areas, which has an important role in producing O3 and fine aerosol.
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023, https://doi.org/10.5194/gmd-16-5237-2023, 2023
Short summary
Short summary
We present flexible tools to implement aerosol formation rate predictions in climate and chemical transport models. New-particle formation is a significant but uncertain factor affecting aerosol numbers and an active field within molecular modeling which provides data for assessing formation rates for different chemical species. We introduce tools to generate and interpolate formation rate lookup tables for user-defined data, thus enabling the easy inclusion and testing of formation schemes.
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023, https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary
Short summary
Measuring the performance of inversions in linear Bayesian problems is crucial in real-life applications. In this work, we provide analytical forms of the local and global sensitivities of the estimated fluxes with respect to various inputs. We provide methods to uniquely map the observational signal to spatiotemporal domains. Utilizing this, we also show techniques to assess correlations between the Jacobians that naturally translate to nonstationary covariance matrix components.
Mingzhao Liu, Lars Hoffmann, Sabine Griessbach, Zhongyin Cai, Yi Heng, and Xue Wu
Geosci. Model Dev., 16, 5197–5217, https://doi.org/10.5194/gmd-16-5197-2023, https://doi.org/10.5194/gmd-16-5197-2023, 2023
Short summary
Short summary
We introduce new and revised chemistry and physics modules in the Massive-Parallel Trajectory Calculations (MPTRAC) Lagrangian transport model aiming to improve the representation of volcanic SO2 transport and depletion. We test these modules in a case study of the Ambae eruption in July 2018 in which the SO2 plume underwent wet removal and convection. The lifetime of SO2 shows highly variable and complex dependencies on the atmospheric conditions at different release heights.
Bernhard M. Enz, Jan P. Engelmann, and Ulrike Lohmann
Geosci. Model Dev., 16, 5093–5112, https://doi.org/10.5194/gmd-16-5093-2023, https://doi.org/10.5194/gmd-16-5093-2023, 2023
Short summary
Short summary
An algorithm to track tropical cyclones in model simulation data has been developed. The algorithm uses many combinations of varying parameter thresholds to detect weaker phases of tropical cyclones while still being resilient to false positives. It is shown that the algorithm performs well and adequately represents the tropical cyclone activity of the underlying simulation data. The impact of false positives on overall tropical cyclone activity is shown to be insignificant.
Sepehr Fathi, Mark Gordon, and Yongsheng Chen
Geosci. Model Dev., 16, 5069–5091, https://doi.org/10.5194/gmd-16-5069-2023, https://doi.org/10.5194/gmd-16-5069-2023, 2023
Short summary
Short summary
We have combined various capabilities within a WRF model to generate simulations of atmospheric pollutant dispersion at 50 m resolution. The study objective was to resolve transport processes at the scale of measurements to assess and optimize aircraft-based emission rate retrievals. Model performance evaluation resulted in agreement within 5 % of observed meteorological and within 1–2 standard deviations of observed wind fields. Mass was conserved in the model within 5 % of input emissions.
Dylan Reynolds, Ethan Gutmann, Bert Kruyt, Michael Haugeneder, Tobias Jonas, Franziska Gerber, Michael Lehning, and Rebecca Mott
Geosci. Model Dev., 16, 5049–5068, https://doi.org/10.5194/gmd-16-5049-2023, https://doi.org/10.5194/gmd-16-5049-2023, 2023
Short summary
Short summary
The challenge of running geophysical models is often compounded by the question of where to obtain appropriate data to give as input to a model. Here we present the HICAR model, a simplified atmospheric model capable of running at spatial resolutions of hectometers for long time series or over large domains. This makes physically consistent atmospheric data available at the spatial and temporal scales needed for some terrestrial modeling applications, for example seasonal snow forecasting.
Li Fang, Jianbing Jin, Arjo Segers, Hong Liao, Ke Li, Bufan Xu, Wei Han, Mijie Pang, and Hai Xiang Lin
Geosci. Model Dev., 16, 4867–4882, https://doi.org/10.5194/gmd-16-4867-2023, https://doi.org/10.5194/gmd-16-4867-2023, 2023
Short summary
Short summary
Machine learning models have gained great popularity in air quality prediction. However, they are only available at air quality monitoring stations. In contrast, chemical transport models (CTM) provide predictions that are continuous in the 3D field. Owing to complex error sources, they are typically biased. In this study, we proposed a gridded prediction with high accuracy by fusing predictions from our regional feature selection machine learning prediction (RFSML v1.0) and a CTM prediction.
Willem Elias van Caspel, David Simpson, Jan Eiof Jonson, Anna Maria Katarina Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah Walker, and Mathew Heal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-147, https://doi.org/10.5194/gmd-2023-147, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the break-up, or photo-dissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of these photo-dissociation effects is therefore essential in atmospheric chemistry modeling. One such models is the EMEP MSC-W model, for which in this paper a new way of calculating the photo-dissociation rates is tested and evaluated.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
Short summary
Short summary
A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Drew C. Pendergrass, Daniel J. Jacob, Hannah Nesser, Daniel J. Varon, Melissa Sulprizio, Kazuyuki Miyazaki, and Kevin W. Bowman
Geosci. Model Dev., 16, 4793–4810, https://doi.org/10.5194/gmd-16-4793-2023, https://doi.org/10.5194/gmd-16-4793-2023, 2023
Short summary
Short summary
We have built a tool called CHEEREIO that allows scientists to use observations of pollutants or gases in the atmosphere, such as from satellites or surface stations, to update supercomputer models that simulate the Earth. CHEEREIO uses the difference between the model simulations of the atmosphere and real-world observations to come up with a good guess for the actual composition of our atmosphere, the true emissions of various pollutants, and whatever else they may want to study.
Yosuke Yamazaki
Geosci. Model Dev., 16, 4749–4766, https://doi.org/10.5194/gmd-16-4749-2023, https://doi.org/10.5194/gmd-16-4749-2023, 2023
Short summary
Short summary
The Earth's atmosphere can support various types of global-scale waves. Some waves propagate eastward and others westward, and they can have different zonal wavenumbers. The Fourier–wavelet analysis is a useful technique for identifying different components of global-scale waves and their temporal variability. This paper introduces an easy-to-implement method to derive Fourier–wavelet spectra from 2-D space–time data. Application examples are presented using atmospheric models.
Bok H. Baek, Carlie Coats, Siqi Ma, Chi-Tsan Wang, Yunyao Li, Jia Xing, Daniel Tong, Soontae Kim, and Jung-Hun Woo
Geosci. Model Dev., 16, 4659–4676, https://doi.org/10.5194/gmd-16-4659-2023, https://doi.org/10.5194/gmd-16-4659-2023, 2023
Short summary
Short summary
To enable the direct feedback effects of aerosols and local meteorology in an air quality modeling system without any computational bottleneck, we have developed an inline meteorology-induced emissions coupler module within the U.S. Environmental Protection Agency’s Community Multiscale Air Quality modeling system to dynamically model the complex MOtor Vehicle Emission Simulator (MOVES) on-road mobile emissions inline without a separate dedicated emissions processing model like SMOKE.
Christoph Neuhauser, Maicon Hieronymus, Michael Kern, Marc Rautenhaus, Annika Oertel, and Rüdiger Westermann
Geosci. Model Dev., 16, 4617–4638, https://doi.org/10.5194/gmd-16-4617-2023, https://doi.org/10.5194/gmd-16-4617-2023, 2023
Short summary
Short summary
Numerical weather prediction models rely on parameterizations for sub-grid-scale processes, which are a source of uncertainty. We present novel visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along trajectories regarding similarities in temporal development and spatiotemporal relationships. The proposed workflow is applied to cloud microphysical sensitivities along coherent strongly ascending trajectories.
Liangke Huang, Shengwei Lan, Ge Zhu, Fade Chen, Junyu Li, and Lilong Liu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-139, https://doi.org/10.5194/gmd-2023-139, 2023
Revised manuscript accepted for GMD
Short summary
Short summary
The existing ZTD models have limitations such as using a single fitting function, neglecting daily cycle variations, and relying on only one resolution grid data for modeling. This model considers the daily-cycle variation and latitude factor of ZTD, using the sliding window algorithm based on ERA5 atmospheric reanalysis data. The ZTD data from 545 radiosonde stations and MERRA-2 atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model.
Yingqi Zheng, Minttu Havu, Huizhi Liu, Xueling Cheng, Yifan Wen, Hei Shing Lee, Joyson Ahongshangbam, and Leena Järvi
Geosci. Model Dev., 16, 4551–4579, https://doi.org/10.5194/gmd-16-4551-2023, https://doi.org/10.5194/gmd-16-4551-2023, 2023
Short summary
Short summary
The performance of the Surface Urban Energy and Water Balance Scheme (SUEWS) is evaluated against the observed surface exchanges (fluxes) of heat and carbon dioxide in a densely built neighborhood in Beijing. The heat flux modeling is noticeably improved by using the observed maximum conductance and by optimizing the vegetation phenology modeling. SUEWS also performs well in simulating carbon dioxide flux.
Simone Dietmüller, Sigrun Matthes, Katrin Dahlmann, Hiroshi Yamashita, Abolfazl Simorgh, Manuel Soler, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Christian Weder, Volker Grewe, Feijia Yin, and Federica Castino
Geosci. Model Dev., 16, 4405–4425, https://doi.org/10.5194/gmd-16-4405-2023, https://doi.org/10.5194/gmd-16-4405-2023, 2023
Short summary
Short summary
Climate-optimized aircraft trajectories avoid atmospheric regions with a large climate impact due to aviation emissions. This requires spatially and temporally resolved information on aviation's climate impact. We propose using algorithmic climate change functions (aCCFs) for CO2 and non-CO2 effects (ozone, methane, water vapor, contrail cirrus). Merged aCCFs combine individual aCCFs by assuming aircraft-specific parameters and climate metrics. Technically this is done with a Python library.
Andreas A. Beckert, Lea Eisenstein, Annika Oertel, Tim Hewson, George C. Craig, and Marc Rautenhaus
Geosci. Model Dev., 16, 4427–4450, https://doi.org/10.5194/gmd-16-4427-2023, https://doi.org/10.5194/gmd-16-4427-2023, 2023
Short summary
Short summary
We investigate the benefit of objective 3-D front detection with modern interactive visual analysis techniques for case studies of extra-tropical cyclones and comparisons of frontal structures between different numerical weather prediction models. The 3-D frontal structures show agreement with 2-D fronts from surface analysis charts and augment them in the vertical dimension. We see great potential for more complex studies of atmospheric dynamics and for operational weather forecasting.
Zhenxin Liu, Yuanhao Chen, Yuhang Wang, Cheng Liu, Shuhua Liu, and Hong Liao
Geosci. Model Dev., 16, 4385–4403, https://doi.org/10.5194/gmd-16-4385-2023, https://doi.org/10.5194/gmd-16-4385-2023, 2023
Short summary
Short summary
The heterogeneous layout of urban buildings leads to the complex wind field in and over the urban canopy. Large discrepancies between the observations and the current simulations result from misunderstanding the character of the wind field. The Inhomogeneous Wind Scheme in Urban Street (IWSUS) was developed to simulate the heterogeneity of the wind speed in a typical street and then improve the simulated energy budget in the lower atmospheric layer over the urban canopy.
Kai Cao, Qizhong Wu, Lingling Wang, Nan Wang, Huaqiong Cheng, Xiao Tang, Dongqing Li, and Lanning Wang
Geosci. Model Dev., 16, 4367–4383, https://doi.org/10.5194/gmd-16-4367-2023, https://doi.org/10.5194/gmd-16-4367-2023, 2023
Short summary
Short summary
Offline performance experiment results show that the GPU-HADVPPM on a V100 GPU can achieve up to 1113.6 × speedups to its original version on an E5-2682 v4 CPU. A series of optimization measures are taken, and the CAMx-CUDA model improves the computing efficiency by 128.4 × on a single V100 GPU card. A parallel architecture with an MPI plus CUDA hybrid paradigm is presented, and it can achieve up to 4.5 × speedup when launching eight CPU cores and eight GPU cards.
Laurent Menut
Geosci. Model Dev., 16, 4265–4281, https://doi.org/10.5194/gmd-16-4265-2023, https://doi.org/10.5194/gmd-16-4265-2023, 2023
Short summary
Short summary
This study analyzes forecasts that were made in 2021 to help trigger measurements during the CADDIWA experiment. The WRF and CHIMERE models were run each day, and the first goal is to quantify the variability of the forecast as a function of forecast leads and forecast location. The possibility of using the different leads as an ensemble is also tested. For some locations, the correlation scores are better with this approach. This could be tested on operational forecast chains in the future.
Emily de Jong, John Ben Mackay, Oleksii Bulenok, Anna Jaruga, and Sylwester Arabas
Geosci. Model Dev., 16, 4193–4211, https://doi.org/10.5194/gmd-16-4193-2023, https://doi.org/10.5194/gmd-16-4193-2023, 2023
Short summary
Short summary
In clouds, collisional breakup occurs when two colliding droplets splinter into new, smaller fragments. Particle-based modeling approaches often do not represent breakup because of the computational demands of creating new droplets. We present a particle-based breakup method that preserves the computational efficiency of these methods. In a series of simple demonstrations, we show that this representation alters cloud processes in reasonable and expected ways.
Caiyi Jin, Qiangqiang Yuan, Tongwen Li, Yuan Wang, and Liangpei Zhang
Geosci. Model Dev., 16, 4137–4154, https://doi.org/10.5194/gmd-16-4137-2023, https://doi.org/10.5194/gmd-16-4137-2023, 2023
Short summary
Short summary
The semi-empirical physical approach derives PM2.5 with strong physical significance. However, due to the complex optical characteristic, the physical parameters are difficult to express accurately. Thus, combining the atmospheric physical mechanism and machine learning, we propose an optimized model. It creatively embeds the random forest model into the physical PM2.5 remote sensing approach to simulate a physical parameter. Our method shows great optimized performance in the validations.
Cyril Caram, Sophie Szopa, Anne Cozic, Slimane Bekki, Carlos A. Cuevas, and Alfonso Saiz-Lopez
Geosci. Model Dev., 16, 4041–4062, https://doi.org/10.5194/gmd-16-4041-2023, https://doi.org/10.5194/gmd-16-4041-2023, 2023
Short summary
Short summary
We studied the role of halogenated compounds (containing chlorine, bromine and iodine), emitted by natural processes (mainly above the oceans), in the chemistry of the lower layers of the atmosphere. We introduced this relatively new chemistry in a three-dimensional climate–chemistry model and looked at how this chemistry will disrupt the ozone. We showed that the concentration of ozone decreases by 22 % worldwide and that of the atmospheric detergent, OH, by 8 %.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Marc Bocquet, Jinghui Lian, Grégoire Broquet, Gerrit Kuhlmann, Alexandre Danjou, and Thomas Lauvaux
Geosci. Model Dev., 16, 3997–4016, https://doi.org/10.5194/gmd-16-3997-2023, https://doi.org/10.5194/gmd-16-3997-2023, 2023
Short summary
Short summary
Monitoring of CO2 emissions is key to the development of reduction policies. Local emissions, from cities or power plants, may be estimated from CO2 plumes detected in satellite images. CO2 plumes generally have a weak signal and are partially concealed by highly variable background concentrations and instrument errors, which hampers their detection. To address this problem, we propose and apply deep learning methods to detect the contour of a plume in simulated CO2 satellite images.
Min-Seop Ahn, Paul A. Ullrich, Peter J. Gleckler, Jiwoo Lee, Ana C. Ordonez, and Angeline G. Pendergrass
Geosci. Model Dev., 16, 3927–3951, https://doi.org/10.5194/gmd-16-3927-2023, https://doi.org/10.5194/gmd-16-3927-2023, 2023
Short summary
Short summary
We introduce a framework for regional-scale evaluation of simulated precipitation distributions with 62 climate reference regions and 10 metrics and apply it to evaluate CMIP5 and CMIP6 models against multiple satellite-based precipitation products. The common model biases identified in this study are mainly associated with the overestimated light precipitation and underestimated heavy precipitation. These biases persist from earlier-generation models and have been slightly improved in CMIP6.
Christine Wiedinmyer, Yosuke Kimura, Elena C. McDonald-Buller, Louisa K. Emmons, Rebecca R. Buchholz, Wenfu Tang, Keenan Seto, Maxwell B. Joseph, Kelley C. Barsanti, Annmarie G. Carlton, and Robert Yokelson
Geosci. Model Dev., 16, 3873–3891, https://doi.org/10.5194/gmd-16-3873-2023, https://doi.org/10.5194/gmd-16-3873-2023, 2023
Short summary
Short summary
The Fire INventory from NCAR (FINN) provides daily global estimates of emissions from open fires based on satellite detections of hot spots. This version has been updated to apply MODIS and VIIRS satellite fire detection and better represents both large and small fires. FINNv2.5 generates more emissions than FINNv1 and is in general agreement with other fire emissions inventories. The new estimates are consistent with satellite observations, but uncertainties remain regionally and by pollutant.
Cited articles
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and
Avellano, A.: The Data Assimilation Research Testbed: A Community Facility,
B. Am. Meteorol. Soc., 90, 1283–1296,
https://doi.org/10.1175/2009bams2618.1, 2009. a
Auligné, T., McNally, A. P., and Dee, D. P.: Adaptive bias correction for
satellite data in a numerical weather prediction system, Q. J.
Roy. Meteor. Soc., 133, 631–642, https://doi.org/10.1002/qj.56, 2007. a
Barker, D., Huang, X.-Y., Liu, Z., Auligné, T., Zhang, X., Rugg, S.,
Ajjaji, R., Bourgeois, A., Bray, J., Chen, Y., Demirtas, M., Guo, Y.-R.,
Henderson, T., Huang, W., Lin, H.-C., Michalakes, J., Rizvi, S., and Zhang,
X.: The Weather Research and Forecasting Model's Community
Variational/Ensemble Data Assimilation System: WRFDA, B. Am. Meteorol. Soc., 93, 831–843,
https://doi.org/10.1175/bams-d-11-00167.1, 2012. a
Brown, B., Jensen, T., Gotway, J. H., Bullock, R., Gilleland, E., Fowler, T.,
Newman, K., Adriaansen, D., Blank, L., Burek, T., Harrold, M., Hertneky, T.,
Kalb, C., Kucera, P., Nance, L., Opatz, J., Vigh, J., and Wolff, J.: The
Model Evaluation Tools (MET): More than a Decade of Community-Supported
Forecast Verification, B. Am. Meteorol. Soc., 102,
E782–E807, https://doi.org/10.1175/bams-d-19-0093.1, 2021. a
Chen, F. and Dudhia, J.: Coupling an Advanced Land
Surface–Hydrology Model with the Penn State–NCAR
MM5 Modeling System. Part II: Preliminary Model Validation, Mon. Weather Rev., 129, 587–604,
https://doi.org/10.1175/1520-0493(2001)129<0587:caalsh>2.0.co;2, 2001. a
Choi, H.-J. and Hong, S.-Y.: An updated subgrid orographic parameterization for
global atmospheric forecast models, J. Geophys. Res.-Atmos., 120, 12445–12457, https://doi.org/10.1002/2015jd024230, 2015. a
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. a
Dee, D. P. and Uppala, S.: Variational bias correction of satellite radiance
data in the ERA-Interim reanalysis, Q. J. Roy. Meteor. Soc., 135, 1830–1841, https://doi.org/10.1002/qj.493, 2009. a
Derber, J. and Rosati, A.: A Global Oceanic Data Assimilation System, J. Phys. Oceanogr., 19, 1333–1347,
https://doi.org/10.1175/1520-0485(1989)019<1333:agodas>2.0.co;2, 1989. a
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. a
Geer, A. J. and Bauer, P.: Observation errors in all-sky data assimilation,
Q. J. Roy. Meteor. Soc., 137, 2024–2037,
https://doi.org/10.1002/qj.830, 2011. a, b
Geer, A. J., Baordo, F., Bormann, N., Chambon, P., English, S. J., Kazumori,
M., Lawrence, H., Lean, P., Lonitz, K., and Lupu, C.: The growing impact of
satellite observations sensitive to humidity, cloud and precipitation,
Q. J. Roy. Meteor. Soc., 143, 3189–3206,
https://doi.org/10.1002/qj.3172, 2017. a
Geer, A. J., Lonitz, K., Weston, P., Kazumori, M., Okamoto, K., Zhu, Y., Liu,
E. H., Collard, A., Bell, W., Migliorini, S., Chambon, P., Fourrié, N.,
Kim, M.-J., Köpken-Watts, C., and Schraff, C.: All-sky satellite data
assimilation at operational weather forecasting centres, Q. J. Roy. Meteor. Soc., 144, 1191–1217, https://doi.org/10.1002/qj.3202,
2018. a
Golub, G. H. and Ye, Q.: Inexact Preconditioned Conjugate Gradient Method with
Inner-Outer Iteration, SIAM J. Sci. Comput., 21,
1305–1320, https://doi.org/10.1137/s1064827597323415, 1999. a
Ha, S., Snyder, C., Skamarock, W. C., Anderson, J., and Collins, N.: Ensemble
Kalman Filter Data Assimilation for the Model for Prediction Across Scales
(MPAS), Mon. Weather Rev., 145, 4673–4692,
https://doi.org/10.1175/mwr-d-17-0145.1, 2017. a
Hamill, T. M.: Hypothesis Tests for Evaluating Numerical Precipitation
Forecasts, Weather Forecast., 14, 155–167,
https://doi.org/10.1175/1520-0434(1999)014<0155:htfenp>2.0.co;2, 1999. a
Hamill, T. M. and Snyder, C.: A Hybrid Ensemble Kalman Filter – 3D
Variational Analysis Scheme, Mon. Weather Rev., 128, 2905–2919,
https://doi.org/10.1175/1520-0493(2000)128<2905:ahekfv>2.0.co;2, 2000. a
Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-Dependent Filtering of
Background Error Covariance Estimates in an Ensemble Kalman Filter, Mon. Weather Rev., 129, 2776–2790,
https://doi.org/10.1175/1520-0493(2001)129<2776:ddfobe>2.0.co;2, 2001. a
Hogan, R. J., Ferro, C. A. T., Jolliffe, I. T., and Stephenson, D. B.:
Equitability Revisited: Why the “Equitable Threat
Score” Is Not Equitable, Weather Forecast., 25,
710–726, https://doi.org/10.1175/2009waf2222350.1, 2010. a
Holdaway, D., Vernieres, G., Wlasak, M., and King, S.: Status of Model
Interfacing in the Joint Effort for Data Assimilation Integration (JEDI),
JCSDA Quarterly Newsletter, 66, 15–24, https://doi.org/10.25923/RB19-0Q26, 2020. a
Honeyager, R., Herbener, S., Zhang, X., Shlyaeva, A., and Trémolet, Y.:
Observations in the Joint Effort for Data Assimilation Integration (JEDI) –
Unified Forward Operator (UFO) and Interface for Observation Data Access
(IODA), JCSDA Quarterly Newsletter, 66, 24–33, https://doi.org/10.25923/RB19-0Q26,
2020. a
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. a
Houtekamer, P. L. and Mitchell, H. L.: A Sequential Ensemble Kalman Filter for
Atmospheric Data Assimilation, Mon. Weather Rev., 129, 123–137,
https://doi.org/10.1175/1520-0493(2001)129<0123:asekff>2.0.co;2, 2001. a
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., 113, D13103, https://doi.org/10.1029/2008jd009944, 2008. a
Ingleby, B.: Global assimilation of air temperature, humidity, wind and
pressure from surface stations, Q. J. Roy. Meteor.
Soc., 141, 504–517, https://doi.org/10.1002/qj.2372, 2014. a
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. a
Joint Center for Satellite Data Assimilation and National Center for
Atmospheric Research: JEDI-MPAS Data Assimilation System v1.0.0, Zenodo [code],
https://doi.org/10.5281/ZENODO.6784274, 2021. a
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R. and Joseph, D.: The NCEP/NCAR 40-year
reanalysis project, B. Am. Meteorol. Soc., 77,
437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996. a
Kleist, D. T. and Ide, K.: An OSSE-Based Evaluation of Hybrid
Variational – Ensemble Data Assimilation for the NCEP GFS. Part
II: 4DEnVar and Hybrid Variants, Mon. Weather Rev., 143, 452–470,
https://doi.org/10.1175/mwr-d-13-00350.1, 2015. a, b, c
Klemp, J. B.: A Terrain-Following Coordinate with Smoothed Coordinate Surfaces,
Mon. Weather Rev., 139, 2163–2169, https://doi.org/10.1175/mwr-d-10-05046.1,
2011. a
Klemp, J. B., Skamarock, W. C., and Dudhia, J.: Conservative Split-Explicit
Time Integration Methods for the Compressible Nonhydrostatic Equations,
Mon. Weather Rev., 135, 2897–2913, https://doi.org/10.1175/mwr3440.1, 2007. a
Liu, Q. and Weng, F.: Advanced Doubling – Adding Method for Radiative
Transfer in Planetary Atmospheres, J. Atmos. Sci., 63,
3459–3465, https://doi.org/10.1175/jas3808.1, 2006. a
Liu, Z., Ban, J., Hong, J.-S., and Kuo, Y.-H.: Multi-resolution incremental
4D-Var for WRF: Implementation and application at convective scale,
Q. J. Roy. Meteor. Soc., 146, 3661–3674,
https://doi.org/10.1002/qj.3865, 2020. a
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. a
Lorenc, A. C.: The potential of the ensemble Kalman filter for
NWP – a comparison with 4D-Var, Q. J. Roy. Meteor. Soc., 129, 3183–3203, https://doi.org/10.1256/qj.02.132, 2003. a
McCormack, J. P., Eckermann, S. D., Siskind, D. E., and McGee, T. J.: CHEM2D-OPP: A new linearized gas-phase ozone photochemistry parameterization for high-altitude NWP and climate models, Atmos. Chem. Phys., 6, 4943–4972, https://doi.org/10.5194/acp-6-4943-2006, 2006. a, b
Migliorini, S. and Candy, B.: All-sky satellite data assimilation of microwave
temperature sounding channels at the Met Office, Q. J. Roy. Meteor. Soc., 145, 867–883, https://doi.org/10.1002/qj.3470, 2019. a, b, c, d
National Centers For Environmental Prediction/National Weather
Service/NOAA/U.S. Department Of Commerce: NCEP ADP Global Upper Air and
Surface Weather Observations (PREPBUFR format), https://doi.org/10.5065/Z83F-N512,
2008. a
National Centers For Environmental Prediction/National Weather
Service/NOAA/U.S. Department Of Commerce: NCEP GDAS Satellite Data
2004-continuing, https://doi.org/10.5065/DWYZ-Q852, 2009. a
National Centers For Environmental Prediction/National Weather
Service/NOAA/U.S. Department Of Commerce: NCEP GFS 0.25 Degree Global
Forecast Grids Historical Archive, https://doi.org/10.5065/D65D8PWK, 2015. a
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, 2007. a
Schwartz, C. S., Liu, Z., and Huang, X.-Y.: Sensitivity of Limited-Area Hybrid
Variational-Ensemble Analyses and Forecasts to Ensemble Perturbation
Resolution, Mon. Weather Rev., 143, 3454–3477,
https://doi.org/10.1175/mwr-d-14-00259.1, 2015. a
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. a, b
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S.-H., and
Ringler, T. D.: A Multiscale Nonhydrostatic Atmospheric Model Using
Centroidal Voronoi Tesselations and C-Grid Staggering, Mon. Weather Rev., 140, 3090–3105, https://doi.org/10.1175/mwr-d-11-00215.1, 2012. a, b
Skamarock, W. C., Duda, M. G., Ha, S., and Park, S.-H.: Limited-Area
Atmospheric Modeling Using an Unstructured Mesh, Mon. Weather Rev., 146,
3445–3460, https://doi.org/10.1175/mwr-d-18-0155.1, 2018. a
Tarantola, A.: Inverse Problem Theory and Methods for Model Parameter
Estimation, Society for Industrial and Applied Mathematics,
https://doi.org/10.1137/1.9780898717921, 2005. a
Tiedtke, M.: A Comprehensive Mass Flux Scheme for Cumulus Parameterization in
Large-Scale Models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:acmfsf>2.0.co;2, 1989. a
Trémolet, Y.: Joint Effort for Data Assimilation Integration (JEDI)
Design and Structure, JCSDA Quarterly Newsletter, 66, 6–14,
https://doi.org/10.25923/RB19-0Q26, 2020. a
Trémolet, Y. and Auligné, T.: The Joint Effort for Data
Assimilation Integration (JEDI), JCSDA Quarterly Newsletter, 66, 1–5,
https://doi.org/10.25923/RB19-0Q26, 2020.
a
Wang, X.: Incorporating Ensemble Covariance in the Gridpoint Statistical
Interpolation Variational Minimization: A Mathematical Framework, Mon. Weather Rev., 138, 2990–2995, https://doi.org/10.1175/2010mwr3245.1, 2010. a
Wang, X., Parrish, D., Kleist, D., and Whitaker, J.: GSI 3DVar-Based
Ensemble – Variational Hybrid Data Assimilation for NCEP Global
Forecast System: Single-Resolution Experiments, Mon. Weather Rev., 141,
4098–4117, https://doi.org/10.1175/mwr-d-12-00141.1, 2013. a, b
Wu, Y., Liu, Z., and Li, D.: Improving forecasts of a record-breaking rainstorm
in Guangzhou by assimilating every 10-min AHI radiances with WRF 4DVAR,
Atmos. Res., 239, 104912, https://doi.org/10.1016/j.atmosres.2020.104912,
2020. a
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., and Lin, R.:
Reprocessed, Bias-Corrected CMORPH Global High-Resolution Precipitation
Estimates from 1998, J. Hydrometeorol., 18, 1617–1641,
https://doi.org/10.1175/jhm-d-16-0168.1, 2017. a
Xie, P., Joyce, R., Wu, S., Yoo, S.-H., Yarosh, Y., Sun, F., Lin, R., and NOAA
CDR Program: NOAA Climate Data Record (CDR) of CPC Morphing Technique
(CMORPH) High Resolution Global Precipitation Estimates, Version 1,
https://doi.org/10.25921/W9VA-Q159, 2019. a
Xu, K.-M. and Randall, D. A.: A Semiempirical Cloudiness Parameterization for
Use in Climate Models, J. Atmos. Sci., 53, 3084–3102,
https://doi.org/10.1175/1520-0469(1996)053<3084:ascpfu>2.0.co;2, 1996. a
Zhang, C. and Wang, Y.: Projected Future Changes of Tropical Cyclone Activity
over the Western North and South Pacific in a 20-km-Mesh Regional Climate
Model, J. Climate, 30, 5923–5941, https://doi.org/10.1175/jcli-d-16-0597.1,
2017. a
Zhou, X., Zhu, Y., Hou, D., Luo, Y., Peng, J., and Wobus, R.: Performance of
the new NCEP Global Ensemble Forecast System in a parallel experiment,
Weather Forecast., 32, 1989–2004, https://doi.org/10.1175/WAF-D-17-0023.1, 2017. a
Zhu, Y., Derber, J., Collard, A., Dee, D., Treadon, R., Gayno, G., and Jung,
J. A.: Enhanced radiance bias correction in the National Centers for
Environmental Prediction's Gridpoint Statistical
Interpolation data assimilation system, Q. J. Roy. Meteor. Soc., 140, 1479–1492, https://doi.org/10.1002/qj.2233, 2013. a
Zhu, Y., Gayno, G., Purser, R. J., Su, X., and Yang, R.: Expansion of the
All-Sky Radiance Assimilation to ATMS at NCEP, Mon. Weather Rev.,
147, 2603–2620, https://doi.org/10.1175/mwr-d-18-0228.1, 2019. a
Short summary
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released for community use. This article describes JEDI-MPAS's implementation of the ensemble–variational DA technique and demonstrates its robustness and credible performance by incrementally adding three types of microwave radiances (clear-sky AMSU-A, all-sky AMSU-A, clear-sky MHS) to a non-radiance DA experiment. We intend to periodically release new and improved versions of JEDI-MPAS in upcoming years.
JEDI-MPAS 1.0.0, a new data assimilation (DA) system for the MPAS model, was publicly released...
