Articles | Volume 13, issue 6
https://doi.org/10.5194/gmd-13-2851-2020
© Author(s) 2020. 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-13-2851-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of scale-aware deep convection on the cloud liquid and ice water paths and precipitation using the Model for Prediction Across Scales (MPAS-v5.2)
Laura D. Fowler
CORRESPONDING AUTHOR
National Center for Atmospheric Research, Boulder, Colorado 80307-3000, USA
Mary C. Barth
National Center for Atmospheric Research, Boulder, Colorado 80307-3000, USA
Kiran Alapaty
Center for Environmental Measurements and Modeling, U.S. Environmental Protection Agency Research Triangle Park, North Carolina 27711, USA
Related authors
No articles found.
Chandrakala Bharali, Mary Barth, Rajesh Kumar, Sachin D. Ghude, Vinayak Sinha, and Baerbel Sinha
Atmos. Chem. Phys., 24, 6635–6662, https://doi.org/10.5194/acp-24-6635-2024, https://doi.org/10.5194/acp-24-6635-2024, 2024
Short summary
Short summary
This study examines the role of atmospheric aerosols in winter fog over the Indo-Gangetic Plains of India using WRF-Chem. The increase in RH with aerosol–radiation feedback (ARF) is found to be important for fog formation as it promotes the growth of aerosols in the polluted environment. Aqueous-phase chemistry in the fog increases PM2.5 concentration, further affecting ARF. ARF and aqueous-phase chemistry affect the fog intensity and the timing of fog formation by ~1–2 h.
Christopher Lawrence, Mary Barth, John Orlando, Paul Casson, Richard Brandt, Daniel Kelting, Elizabeth Yerger, and Sara Lance
EGUsphere, https://doi.org/10.5194/egusphere-2024-715, https://doi.org/10.5194/egusphere-2024-715, 2024
Short summary
Short summary
This work uses WRF-Chem and chemical box modeling to study the gas and aqueous phase production of organic acid concentrations measured in cloud water the summit of Whiteface Mountain on July 1st, 2018. Isoprene was the major source of formic, acetic, and oxalic acid. Gas phase chemistry greatly underestimated formic and acetic acid, indicating missing sources, while cloud chemistry was a key source of oxalic acid. More studies of organic acids are required to better constrain their sources.
Mengying Li, Shaocai Yu, Xue Chen, Zhen Li, Yibo Zhang, Zhe Song, Weiping Liu, Pengfei Li, Xiaoye Zhang, Meigen Zhang, Yele Sun, Zirui Liu, Caiping Sun, Jingkun Jiang, Shuxiao Wang, Benjamin N. Murphy, Kiran Alapaty, Rohit Mathur, Daniel Rosenfeld, and John H. Seinfeld
Atmos. Chem. Phys., 22, 11845–11866, https://doi.org/10.5194/acp-22-11845-2022, https://doi.org/10.5194/acp-22-11845-2022, 2022
Short summary
Short summary
This study constructed an emission inventory of condensable particulate matter (CPM) in China with a focus on organic aerosols (OAs), based on collected CPM emission information. The results show that OA emissions are enhanced twofold for the years 2014 and 2017 after the inclusion of CPM in the new inventory. Sensitivity cases demonstrated the significant contributions of CPM emissions from stationary combustion and mobile sources to primary, secondary, and total OA concentrations.
Mauro Morichetti, Sasha Madronich, Giorgio Passerini, Umberto Rizza, Enrico Mancinelli, Simone Virgili, and Mary Barth
Geosci. Model Dev., 15, 6311–6339, https://doi.org/10.5194/gmd-15-6311-2022, https://doi.org/10.5194/gmd-15-6311-2022, 2022
Short summary
Short summary
In the present study, we explore the effect of making simple changes to the existing WRF-Chem MEGAN v2.04 emissions to provide MEGAN updates that can be used independently of the land surface model chosen. The changes made to the MEGAN algorithm implemented in WRF-Chem were the following: (i) update of the emission activity factors, (ii) update of emission factor values for each plant functional type (PFT), and (iii) the assignment of the emission factor by PFT to isoprene.
Liji M. David, Mary Barth, Lena Höglund-Isaksson, Pallav Purohit, Guus J. M. Velders, Sam Glaser, and A. R. Ravishankara
Atmos. Chem. Phys., 21, 14833–14849, https://doi.org/10.5194/acp-21-14833-2021, https://doi.org/10.5194/acp-21-14833-2021, 2021
Short summary
Short summary
We calculated the expected concentrations of trifluoroacetic acid (TFA) from the atmospheric breakdown of HFO-1234yf (CF3CF=CH2), a substitute for global warming hydrofluorocarbons, emitted now and in the future by India, China, and the Middle East. We used two chemical transport models. We conclude that the projected emissions through 2040 would not be detrimental, given the current knowledge of the effects of TFA on humans and ecosystems.
Andreas Tilgner, Thomas Schaefer, Becky Alexander, Mary Barth, Jeffrey L. Collett Jr., Kathleen M. Fahey, Athanasios Nenes, Havala O. T. Pye, Hartmut Herrmann, and V. Faye McNeill
Atmos. Chem. Phys., 21, 13483–13536, https://doi.org/10.5194/acp-21-13483-2021, https://doi.org/10.5194/acp-21-13483-2021, 2021
Short summary
Short summary
Feedbacks of acidity and atmospheric multiphase chemistry in deliquesced particles and clouds are crucial for the tropospheric composition, depositions, climate, and human health. This review synthesizes the current scientific knowledge on these feedbacks using both inorganic and organic aqueous-phase chemistry. Finally, this review outlines atmospheric implications and highlights the need for future investigations with respect to reducing emissions of key acid precursors in a changing world.
Yuting Wang, Yong-Feng Ma, Domingo Muñoz-Esparza, Cathy W. Y. Li, Mary Barth, Tao Wang, and Guy P. Brasseur
Atmos. Chem. Phys., 21, 3531–3553, https://doi.org/10.5194/acp-21-3531-2021, https://doi.org/10.5194/acp-21-3531-2021, 2021
Short summary
Short summary
Large-eddy simulations (LESs) were performed in the mountainous region of the island of Hong Kong to investigate the degree to which the rates of chemical reactions between two reactive species are reduced due to the segregation of species within the convective boundary layer. We show that the inhomogeneity in emissions plays an important role in the segregation effect. Topography also has a significant influence on the segregation locally.
Havala O. T. Pye, Athanasios Nenes, Becky Alexander, Andrew P. Ault, Mary C. Barth, Simon L. Clegg, Jeffrey L. Collett Jr., Kathleen M. Fahey, Christopher J. Hennigan, Hartmut Herrmann, Maria Kanakidou, James T. Kelly, I-Ting Ku, V. Faye McNeill, Nicole Riemer, Thomas Schaefer, Guoliang Shi, Andreas Tilgner, John T. Walker, Tao Wang, Rodney Weber, Jia Xing, Rahul A. Zaveri, and Andreas Zuend
Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, https://doi.org/10.5194/acp-20-4809-2020, 2020
Short summary
Short summary
Acid rain is recognized for its impacts on human health and ecosystems, and programs to mitigate these effects have had implications for atmospheric acidity. Historical measurements indicate that cloud and fog droplet acidity has changed in recent decades in response to controls on emissions from human activity, while the limited trend data for suspended particles indicate acidity may be relatively constant. This review synthesizes knowledge on the acidity of atmospheric particles and clouds.
Rebecca H. Schwantes, Louisa K. Emmons, John J. Orlando, Mary C. Barth, Geoffrey S. Tyndall, Samuel R. Hall, Kirk Ullmann, Jason M. St. Clair, Donald R. Blake, Armin Wisthaler, and Thao Paul V. Bui
Atmos. Chem. Phys., 20, 3739–3776, https://doi.org/10.5194/acp-20-3739-2020, https://doi.org/10.5194/acp-20-3739-2020, 2020
Short summary
Short summary
Ozone is a greenhouse gas and air pollutant that is harmful to human health and plants. During the summer in the southeastern US, many regional and global models are biased high for surface ozone compared to observations. Here adding more complex and updated chemistry for isoprene and terpenes, which are biogenic hydrocarbons emitted from trees and vegetation, into an earth system model greatly reduces the simulated surface ozone bias compared to aircraft and monitoring station data.
R. Kumar, M. C. Barth, V. S. Nair, G. G. Pfister, S. Suresh Babu, S. K. Satheesh, K. Krishna Moorthy, G. R. Carmichael, Z. Lu, and D. G. Streets
Atmos. Chem. Phys., 15, 5415–5428, https://doi.org/10.5194/acp-15-5415-2015, https://doi.org/10.5194/acp-15-5415-2015, 2015
Short summary
Short summary
We examine differences in the surface BC between the Bay of Bengal (BoB) and the Arabian Sea (AS) and identify dominant sources of BC in South Asia during ICARB. Anthropogenic emissions were the main source of BC during ICARB and had about 5 times stronger influence on the BoB compared to the AS. Regional-scale transport contributes up to 25% of BC mass concentrations in western and eastern India, suggesting that surface BC mass concentrations cannot be linked directly to the local emissions.
M. S. Mallard, C. G. Nolte, T. L. Spero, O. R. Bullock, K. Alapaty, J. A. Herwehe, J. Gula, and J. H. Bowden
Geosci. Model Dev., 8, 1085–1096, https://doi.org/10.5194/gmd-8-1085-2015, https://doi.org/10.5194/gmd-8-1085-2015, 2015
Short summary
Short summary
Because global climate models (GCMs) are typically run at coarse spatial resolution, lakes are often poorly resolved in their global fields. When downscaling such GCMs using the Weather Research & Forecasting (WRF) model, use of WRF’s default interpolation methods can result in unrealistic lake temperatures and ice cover, which can impact simulated air temperatures and precipitation. Here, alternative methods for setting lake variables in WRF downscaling applications are presented and compared.
T. Amnuaylojaroen, M. C. Barth, L. K. Emmons, G. R. Carmichael, J. Kreasuwun, S. Prasitwattanaseree, and S. Chantara
Atmos. Chem. Phys., 14, 12983–13012, https://doi.org/10.5194/acp-14-12983-2014, https://doi.org/10.5194/acp-14-12983-2014, 2014
S. Yu, R. Mathur, J. Pleim, D. Wong, R. Gilliam, K. Alapaty, C. Zhao, and X. Liu
Atmos. Chem. Phys., 14, 11247–11285, https://doi.org/10.5194/acp-14-11247-2014, https://doi.org/10.5194/acp-14-11247-2014, 2014
R. Kumar, M. C. Barth, S. Madronich, M. Naja, G. R. Carmichael, G. G. Pfister, C. Knote, G. P. Brasseur, N. Ojha, and T. Sarangi
Atmos. Chem. Phys., 14, 6813–6834, https://doi.org/10.5194/acp-14-6813-2014, https://doi.org/10.5194/acp-14-6813-2014, 2014
J. Ortega, A. Turnipseed, A. B. Guenther, T. G. Karl, D. A. Day, D. Gochis, J. A. Huffman, A. J. Prenni, E. J. T. Levin, S. M. Kreidenweis, P. J. DeMott, Y. Tobo, E. G. Patton, A. Hodzic, Y. Y. Cui, P. C. Harley, R. S. Hornbrook, E. C. Apel, R. K. Monson, A. S. D. Eller, J. P. Greenberg, M. C. Barth, P. Campuzano-Jost, B. B. Palm, J. L. Jimenez, A. C. Aiken, M. K. Dubey, C. Geron, J. Offenberg, M. G. Ryan, P. J. Fornwalt, S. C. Pryor, F. N. Keutsch, J. P. DiGangi, A. W. H. Chan, A. H. Goldstein, G. M. Wolfe, S. Kim, L. Kaser, R. Schnitzhofer, A. Hansel, C. A. Cantrell, R. L. Mauldin, and J. N. Smith
Atmos. Chem. Phys., 14, 6345–6367, https://doi.org/10.5194/acp-14-6345-2014, https://doi.org/10.5194/acp-14-6345-2014, 2014
R. Kumar, M. C. Barth, G. G. Pfister, M. Naja, and G. P. Brasseur
Atmos. Chem. Phys., 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, https://doi.org/10.5194/acp-14-2431-2014, 2014
X. Jiang, M. C. Barth, C. Wiedinmyer, and S. T. Massie
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-21383-2013, https://doi.org/10.5194/acpd-13-21383-2013, 2013
Revised manuscript not accepted
J. Wong, M. C. Barth, and D. Noone
Geosci. Model Dev., 6, 429–443, https://doi.org/10.5194/gmd-6-429-2013, https://doi.org/10.5194/gmd-6-429-2013, 2013
K. A. Cummings, T. L. Huntemann, K. E. Pickering, M. C. Barth, W. C. Skamarock, H. Höller, H.-D. Betz, A. Volz-Thomas, and H. Schlager
Atmos. Chem. Phys., 13, 2757–2777, https://doi.org/10.5194/acp-13-2757-2013, https://doi.org/10.5194/acp-13-2757-2013, 2013
Related subject area
Atmospheric sciences
Calibrating and validating the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) urban cooling model: case studies in France and the United States
The ddeq Python library for point source quantification from remote sensing images (version 1.0)
Incorporating Oxygen Isotopes of Oxidized Reactive Nitrogen in the Regional Atmospheric Chemistry Mechanism, version 2 (ICOIN-RACM2)
A general comprehensive evaluation method for cross-scale precipitation forecasts
Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation
WRF-PDAF v1.0: implementation and application of an online localized ensemble data assimilation framework
Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6
An improved and extended parameterization of the CO2 15 µm cooling in the middle and upper atmosphere (CO2_cool_fort-1.0)
Development of a multiphase chemical mechanism to improve secondary organic aerosol formation in CAABA/MECCA (version 4.7.0)
Application of regional meteorology and air quality models based on the microprocessor without interlocked piped stages (MIPS) and LoongArch CPU platforms
Investigating ground-level ozone pollution in semi-arid and arid regions of Arizona using WRF-Chem v4.4 modeling
An objective identification technique for potential vorticity structures associated with African easterly waves
Importance of microphysical settings for climate forcing by stratospheric SO2 injections as modeled by SOCOL-AERv2
Assessment of surface ozone products from downscaled CAMS reanalysis and CAMS daily forecast using urban air quality monitoring stations in Iran
Open boundary conditions for atmospheric large-eddy simulations and their implementation in DALES4.4
Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)
Three-dimensional variational assimilation with a multivariate background error covariance for the Model for Prediction Across Scales – Atmosphere with the Joint Effort for Data assimilation Integration (JEDI-MPAS 2.0.0-beta)
FUME 2.0 – Flexible Universal processor for Modeling Emissions
DEUCE v1.0: a neural network for probabilistic precipitation nowcasting with aleatoric and epistemic uncertainties
Evaluation of multi-season convection-permitting atmosphere – mixed-layer ocean simulations of the Maritime Continent
Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands
Balloon drift estimation and improved position estimates for radiosondes
Emission ensemble approach to improve the development of multi-scale emission inventories
What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?
Diagnosing drivers of PM2.5 simulation biases in China from meteorology, chemical composition, and emission sources using an efficient machine learning method
Validation and analysis of the Polair3D v1.11 chemical transport model over Quebec
Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1
Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1
Assessing acetone for the GISS ModelE2.1 Earth system model
Bergen metrics: composite error metrics for assessing performance of climate models using EURO-CORDEX simulations
A dynamic approach to three-dimensional radiative transfer in subkilometer-scale numerical weather prediction models: the dynamic TenStream solver v1.0
Evaluation and development of surface layer scheme representation of temperature inversions over boreal forests in Arctic wintertime conditions
Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation
Analytical and adaptable initial conditions for dry and moist baroclinic waves in the global hydrostatic model OpenIFS (CY43R3)
Challenges of constructing and selecting the “perfect” boundary conditions for the large-eddy simulation model PALM
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy
A grid model for vertical correction of precipitable water vapor over the Chinese mainland and surrounding areas using random forest
MEXPLORER 1.0.0 – a mechanism explorer for analysis and visualization of chemical reaction pathways based on graph theory
Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote sensing observations
Advances and prospects of deep learning for medium-range extreme weather forecasting
An overview of the Western United States Dynamically Downscaled Dataset (WUS-D3)
cloudbandPy 1.0: an automated algorithm for the detection of tropical–extratropical cloud bands
PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations
TAMS: A Tracking, Classifying, and Variable-Assigning Algorithm for Mesoscale Convective Systems in Simulated and Satellite-Derived Datasets
Deep learning applied to CO2 power plant emissions quantification using simulated satellite images
Sensitivity of the WRF-Chem v4.4 simulations of ozone and formaldehyde and their precursors to multiple bottom-up emission inventories over East Asia during the KORUS-AQ 2016 field campaign
Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL
Assessment of climate biases in OpenIFS version 43r3 across model horizontal resolutions and time steps
Perrine Hamel, Martí Bosch, Léa Tardieu, Aude Lemonsu, Cécile de Munck, Chris Nootenboom, Vincent Viguié, Eric Lonsdorf, James A. Douglass, and Richard P. Sharp
Geosci. Model Dev., 17, 4755–4771, https://doi.org/10.5194/gmd-17-4755-2024, https://doi.org/10.5194/gmd-17-4755-2024, 2024
Short summary
Short summary
The InVEST Urban Cooling model estimates the cooling effect of vegetation in cities. We further developed an algorithm to facilitate model calibration and evaluation. Applying the algorithm to case studies in France and in the United States, we found that nighttime air temperature estimates compare well with reference datasets. Estimated change in temperature from a land cover scenario compares well with an alternative model estimate, supporting the use of the model for urban planning decisions.
Gerrit Kuhlmann, Erik Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner
Geosci. Model Dev., 17, 4773–4789, https://doi.org/10.5194/gmd-17-4773-2024, https://doi.org/10.5194/gmd-17-4773-2024, 2024
Short summary
Short summary
We present a Python software library for data-driven emission quantification (ddeq). It can be used to determine the emissions of hot spots (cities, power plants and industry) from remote sensing images using different methods. ddeq can be extended for new datasets and methods, providing a powerful community tool for users and developers. The application of the methods is shown using Jupyter notebooks included in the library.
Wendell W. Walters, Masayuki Takeuchi, Nga L. Ng, and Meredith G. Hastings
Geosci. Model Dev., 17, 4673–4687, https://doi.org/10.5194/gmd-17-4673-2024, https://doi.org/10.5194/gmd-17-4673-2024, 2024
Short summary
Short summary
The study introduces a novel chemical mechanism for explicitly tracking oxygen isotope transfer in oxidized reactive nitrogen and odd oxygen using the Regional Atmospheric Chemistry Mechanism, version 2. This model enhances our ability to simulate and compare oxygen isotope compositions of reactive nitrogen, revealing insights into oxidation chemistry. The approach shows promise for improving atmospheric chemistry models and tropospheric oxidation capacity predictions.
Bing Zhang, Mingjian Zeng, Anning Huang, Zhengkun Qin, Couhua Liu, Wenru Shi, Xin Li, Kefeng Zhu, Chunlei Gu, and Jialing Zhou
Geosci. Model Dev., 17, 4579–4601, https://doi.org/10.5194/gmd-17-4579-2024, https://doi.org/10.5194/gmd-17-4579-2024, 2024
Short summary
Short summary
By directly analyzing the proximity of precipitation forecasts and observations, a precipitation accuracy score (PAS) method was constructed. This method does not utilize a traditional contingency-table-based classification verification; however, it can replace the threat score (TS), equitable threat score (ETS), and other skill score methods, and it can be used to calculate the accuracy of numerical models or quantitative precipitation forecasts.
Hai Bui, Mostafa Bakhoday-Paskyabi, and Mohammadreza Mohammadpour-Penchah
Geosci. Model Dev., 17, 4447–4465, https://doi.org/10.5194/gmd-17-4447-2024, https://doi.org/10.5194/gmd-17-4447-2024, 2024
Short summary
Short summary
We developed a new wind turbine wake model, the Simple Actuator Disc for Large Eddy Simulation (SADLES), integrated with the widely used Weather Research and Forecasting (WRF) model. WRF-SADLES accurately simulates wind turbine wakes at resolutions of a few dozen meters, aligning well with idealized simulations and observational measurements. This makes WRF-SADLES a promising tool for wind energy research, offering a balance between accuracy, computational efficiency, and ease of implementation.
Changliang Shao and Lars Nerger
Geosci. Model Dev., 17, 4433–4445, https://doi.org/10.5194/gmd-17-4433-2024, https://doi.org/10.5194/gmd-17-4433-2024, 2024
Short summary
Short summary
This paper introduces and evaluates WRF-PDAF, a fully online-coupled ensemble data assimilation (DA) system. A key advantage of the WRF-PDAF configuration is its ability to concurrently integrate all ensemble states, eliminating the need for time-consuming distribution and collection of ensembles during the coupling communication. The extra time required for DA amounts to only 20.6 % per cycle. Twin experiment results underscore the effectiveness of the WRF-PDAF system.
Jan Clemens, Lars Hoffmann, Bärbel Vogel, Sabine Grießbach, and Nicole Thomas
Geosci. Model Dev., 17, 4467–4493, https://doi.org/10.5194/gmd-17-4467-2024, https://doi.org/10.5194/gmd-17-4467-2024, 2024
Short summary
Short summary
Lagrangian transport models simulate the transport of air masses in the atmosphere. For example, one model (CLaMS) is well suited to calculating transport as it uses a special coordinate system and special vertical wind. However, it only runs inefficiently on modern supercomputers. Hence, we have implemented the benefits of CLaMS into a new model (MPTRAC), which is already highly efficient on modern supercomputers. Finally, in extensive tests, we showed that CLaMS and MPTRAC agree very well.
Manuel López-Puertas, Federico Fabiano, Victor Fomichev, Bernd Funke, and Daniel R. Marsh
Geosci. Model Dev., 17, 4401–4432, https://doi.org/10.5194/gmd-17-4401-2024, https://doi.org/10.5194/gmd-17-4401-2024, 2024
Short summary
Short summary
The radiative infrared cooling of CO2 in the middle atmosphere is crucial for computing its thermal structure. It requires one however to include non-local thermodynamic equilibrium processes which are computationally very expensive, which cannot be afforded by climate models. In this work, we present an updated, efficient, accurate and very fast (~50 µs) parameterization of that cooling able to cope with CO2 abundances from half the pre-industrial values to 10 times the current abundance.
Felix Wieser, Rolf Sander, Changmin Cho, Hendrik Fuchs, Thorsten Hohaus, Anna Novelli, Ralf Tillmann, and Domenico Taraborrelli
Geosci. Model Dev., 17, 4311–4330, https://doi.org/10.5194/gmd-17-4311-2024, https://doi.org/10.5194/gmd-17-4311-2024, 2024
Short summary
Short summary
The chemistry scheme of the atmospheric box model CAABA/MECCA is expanded to achieve an improved aerosol formation from emitted organic compounds. In addition to newly added reactions, temperature-dependent partitioning of all new species between the gas and aqueous phases is estimated and included in the pre-existing scheme. Sensitivity runs show an overestimation of key compounds from isoprene, which can be explained by a lack of aqueous-phase degradation reactions and box model limitations.
Zehua Bai, Qizhong Wu, Kai Cao, Yiming Sun, and Huaqiong Cheng
Geosci. Model Dev., 17, 4383–4399, https://doi.org/10.5194/gmd-17-4383-2024, https://doi.org/10.5194/gmd-17-4383-2024, 2024
Short summary
Short summary
There is relatively limited research on the application of scientific computing on RISC CPU platforms. The MIPS architecture CPUs, a type of RISC CPUs, have distinct advantages in energy efficiency and scalability. The air quality modeling system can run stably on the MIPS and LoongArch platforms, and the experiment results verify the stability of scientific computing on the platforms. The work provides a technical foundation for the scientific application based on MIPS and LoongArch.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
Short summary
Short summary
This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Christoph Fischer, Andreas H. Fink, Elmar Schömer, Marc Rautenhaus, and Michael Riemer
Geosci. Model Dev., 17, 4213–4228, https://doi.org/10.5194/gmd-17-4213-2024, https://doi.org/10.5194/gmd-17-4213-2024, 2024
Short summary
Short summary
This study presents a method for identifying and tracking 3-D potential vorticity structures within African easterly waves (AEWs). Each identified structure is characterized by descriptors, including its 3-D position and orientation, which have been validated through composite comparisons. A trough-centric perspective on the descriptors reveals the evolution and distinct characteristics of AEWs. These descriptors serve as valuable statistical inputs for the study of AEW-related phenomena.
Sandro Vattioni, Andrea Stenke, Beiping Luo, Gabriel Chiodo, Timofei Sukhodolov, Elia Wunderlin, and Thomas Peter
Geosci. Model Dev., 17, 4181–4197, https://doi.org/10.5194/gmd-17-4181-2024, https://doi.org/10.5194/gmd-17-4181-2024, 2024
Short summary
Short summary
We investigate the sensitivity of aerosol size distributions in the presence of strong SO2 injections for climate interventions or after volcanic eruptions to the call sequence and frequency of the routines for nucleation and condensation in sectional aerosol models with operator splitting. Using the aerosol–chemistry–climate model SOCOL-AERv2, we show that the radiative and chemical outputs are sensitive to these settings at high H2SO4 supersaturations and how to obtain reliable results.
Najmeh Kaffashzadeh and Abbas-Ali Aliakbari Bidokhti
Geosci. Model Dev., 17, 4155–4179, https://doi.org/10.5194/gmd-17-4155-2024, https://doi.org/10.5194/gmd-17-4155-2024, 2024
Short summary
Short summary
This paper assesses the capability of two state-of-the-art global datasets in simulating surface ozone over Iran using a new methodology. It is found that the global model data need to be downscaled for regulatory purposes or policy applications at local scales. The method can be useful not only for the evaluation but also for the prediction of other chemical species, such as aerosols.
Franciscus Liqui Lung, Christian Jakob, A. Pier Siebesma, and Fredrik Jansson
Geosci. Model Dev., 17, 4053–4076, https://doi.org/10.5194/gmd-17-4053-2024, https://doi.org/10.5194/gmd-17-4053-2024, 2024
Short summary
Short summary
Traditionally, high-resolution atmospheric models employ periodic boundary conditions, which limit simulations to domains without horizontal variations. In this research open boundary conditions are developed to replace the periodic boundary conditions. The implementation is tested in a controlled setup, and the results show minimal disturbances. Using these boundary conditions, high-resolution models can be forced by a coarser model to study atmospheric phenomena in realistic background states.
Caroline Arnold, Shivani Sharma, Tobias Weigel, and David S. Greenberg
Geosci. Model Dev., 17, 4017–4029, https://doi.org/10.5194/gmd-17-4017-2024, https://doi.org/10.5194/gmd-17-4017-2024, 2024
Short summary
Short summary
In atmospheric models, rain formation is simplified to be computationally efficient. We trained a machine learning model, SuperdropNet, to emulate warm-rain formation based on super-droplet simulations. Here, we couple SuperdropNet with an atmospheric model in a warm-bubble experiment and find that the coupled simulation runs stable and produces reasonable results, making SuperdropNet a viable ML proxy for droplet simulations. We also present a comprehensive benchmark for coupling architectures.
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., 17, 3879–3895, https://doi.org/10.5194/gmd-17-3879-2024, https://doi.org/10.5194/gmd-17-3879-2024, 2024
Short summary
Short summary
We describe the multivariate static background error covariance (B) for the JEDI-MPAS 3D-Var data assimilation system. With tuned B parameters, the multivariate B gives physically balanced analysis increment fields in the single-observation test framework. In the month-long cycling experiment with a global 60 km mesh, 3D-Var with static B performs stably. Due to its simple workflow and minimal computational requirements, JEDI-MPAS 3D-Var can be useful for the research community.
Michal Belda, Nina Benešová, Jaroslav Resler, Peter Huszár, Ondřej Vlček, Pavel Krč, Jan Karlický, Pavel Juruš, and Kryštof Eben
Geosci. Model Dev., 17, 3867–3878, https://doi.org/10.5194/gmd-17-3867-2024, https://doi.org/10.5194/gmd-17-3867-2024, 2024
Short summary
Short summary
For modeling atmospheric chemistry, it is necessary to provide data on emissions of pollutants. These can come from various sources and in various forms, and preprocessing of the data to be ingestible by chemistry models can be quite challenging. We developed the FUME processor to use a database layer that internally transforms all input data into a rigid structure, facilitating further processing to allow for emission processing from the continental to the street scale.
Bent Harnist, Seppo Pulkkinen, and Terhi Mäkinen
Geosci. Model Dev., 17, 3839–3866, https://doi.org/10.5194/gmd-17-3839-2024, https://doi.org/10.5194/gmd-17-3839-2024, 2024
Short summary
Short summary
Probabilistic precipitation nowcasting (local forecasting for 0–6 h) is crucial for reducing damage from events like flash floods. For this goal, we propose the DEUCE neural-network-based model which uses data and model uncertainties to generate an ensemble of potential precipitation development scenarios for the next hour. Trained and evaluated with Finnish precipitation composites, DEUCE was found to produce more skillful and reliable nowcasts than established models.
Emma Howard, Steven Woolnough, Nicholas Klingaman, Daniel Shipley, Claudio Sanchez, Simon C. Peatman, Cathryn E. Birch, and Adrian J. Matthews
Geosci. Model Dev., 17, 3815–3837, https://doi.org/10.5194/gmd-17-3815-2024, https://doi.org/10.5194/gmd-17-3815-2024, 2024
Short summary
Short summary
This paper describes a coupled atmosphere–mixed-layer ocean simulation setup that will be used to study weather processes in Southeast Asia. The set-up has been used to compare high-resolution simulations, which are able to partially resolve storms, to coarser simulations, which cannot. We compare the model performance at representing variability of rainfall and sea surface temperatures across length scales between the coarse and fine models.
Andrés Yarce Botero, Michiel van Weele, Arjo Segers, Pier Siebesma, and Henk Eskes
Geosci. Model Dev., 17, 3765–3781, https://doi.org/10.5194/gmd-17-3765-2024, https://doi.org/10.5194/gmd-17-3765-2024, 2024
Short summary
Short summary
HARMONIE WINS50 reanalysis data with 0.025° × 0.025° resolution from 2019 to 2021 were coupled with the LOTOS-EUROS Chemical Transport Model. HARMONIE and ECMWF meteorology configurations against Cabauw observations (52.0° N, 4.9° W) were evaluated as simulated NO2 concentrations with ground-level sensors. Differences in crucial meteorological input parameters (boundary layer height, vertical diffusion coefficient) between the hydrostatic and non-hydrostatic models were analysed.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
Short summary
Short summary
This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Philippe Thunis, Jeroen Kuenen, Enrico Pisoni, Bertrand Bessagnet, Manjola Banja, Lech Gawuc, Karol Szymankiewicz, Diego Guizardi, Monica Crippa, Susana Lopez-Aparicio, Marc Guevara, Alexander De Meij, Sabine Schindlbacher, and Alain Clappier
Geosci. Model Dev., 17, 3631–3643, https://doi.org/10.5194/gmd-17-3631-2024, https://doi.org/10.5194/gmd-17-3631-2024, 2024
Short summary
Short summary
An ensemble emission inventory is created with the aim of monitoring the status and progress made with the development of EU-wide inventories. This emission ensemble serves as a common benchmark for the screening and allows for the comparison of more than two inventories at a time. Because the emission “truth” is unknown, the approach does not tell which inventory is the closest to reality, but it identifies inconsistencies that require special attention.
Laurent Menut, Bertrand Bessagnet, Arineh Cholakian, Guillaume Siour, Sylvain Mailler, and Romain Pennel
Geosci. Model Dev., 17, 3645–3665, https://doi.org/10.5194/gmd-17-3645-2024, https://doi.org/10.5194/gmd-17-3645-2024, 2024
Short summary
Short summary
This study is about the modelling of the atmospheric composition in Europe during the summer of 2022, when massive wildfires were observed. It is a sensitivity study dedicated to the relative impacts of two modelling processes that are able to modify the meteorology used for the calculation of the atmospheric chemistry and transport of pollutants.
Shuai Wang, Mengyuan Zhang, Yueqi Gao, Peng Wang, Qingyan Fu, and Hongliang Zhang
Geosci. Model Dev., 17, 3617–3629, https://doi.org/10.5194/gmd-17-3617-2024, https://doi.org/10.5194/gmd-17-3617-2024, 2024
Short summary
Short summary
Numerical models are widely used in air pollution modeling but suffer from significant biases. The machine learning model designed in this study shows high efficiency in identifying such biases. Meteorology (relative humidity and cloud cover), chemical composition (secondary organic components and dust aerosols), and emission sources (residential activities) are diagnosed as the main drivers of bias in modeling PM2.5, a typical air pollutant. The results will help to improve numerical models.
Shoma Yamanouchi, Shayamilla Mahagammulla Gamage, Sara Torbatian, Jad Zalzal, Laura Minet, Audrey Smargiassi, Ying Liu, Ling Liu, Forood Azargoshasbi, Jinwoong Kim, Youngseob Kim, Daniel Yazgi, and Marianne Hatzopoulou
Geosci. Model Dev., 17, 3579–3597, https://doi.org/10.5194/gmd-17-3579-2024, https://doi.org/10.5194/gmd-17-3579-2024, 2024
Short summary
Short summary
Air pollution is a major health hazard, and chemical transport models (CTMs) are valuable tools that aid in our understanding of the risks of air pollution at both local and regional scales. In this study, the Polair3D CTM of the Polyphemus air quality modeling platform was set up over Quebec, Canada, to assess the model’s capability in predicting key air pollutant species over the region, at seasonal temporal scales and at regional spatial scales.
Rohith Thundathil, Florian Zus, Galina Dick, and Jens Wickert
Geosci. Model Dev., 17, 3599–3616, https://doi.org/10.5194/gmd-17-3599-2024, https://doi.org/10.5194/gmd-17-3599-2024, 2024
Short summary
Short summary
Global Navigation Satellite Systems (GNSS) provides moisture observations through its densely distributed ground station network. In this research, we assimilate a new type of observation called tropospheric gradient observations, which has never been incorporated into a weather model. We develop a forward operator for gradient-based observations and conduct an assimilation impact study. The study shows significant improvements in the model's humidity fields.
Ankur Mahesh, Travis A. O'Brien, Burlen Loring, Abdelrahman Elbashandy, William Boos, and William D. Collins
Geosci. Model Dev., 17, 3533–3557, https://doi.org/10.5194/gmd-17-3533-2024, https://doi.org/10.5194/gmd-17-3533-2024, 2024
Short summary
Short summary
Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. We train convolutional neural networks (CNNs) to detect ARs with an estimate of the uncertainty. We present a framework to generalize these CNNs to a variety of datasets of past, present, and future climate. Using a simplified simulation of the Earth's atmosphere, we validate the CNNs. We explore the role of ARs in maintaining energy balance in the Earth system.
Alexandra Rivera, Kostas Tsigaridis, Gregory Faluvegi, and Drew Shindell
Geosci. Model Dev., 17, 3487–3505, https://doi.org/10.5194/gmd-17-3487-2024, https://doi.org/10.5194/gmd-17-3487-2024, 2024
Short summary
Short summary
This paper describes and evaluates an improvement to the representation of acetone in the GISS ModelE2.1 Earth system model. We simulate acetone's concentration and transport across the atmosphere as well as its dependence on chemistry, the ocean, and various global emissions. Comparisons of our model’s estimates to past modeling studies and field measurements have shown encouraging results. Ultimately, this paper contributes to a broader understanding of acetone's role in the atmosphere.
Alok K. Samantaray, Priscilla A. Mooney, and Carla A. Vivacqua
Geosci. Model Dev., 17, 3321–3339, https://doi.org/10.5194/gmd-17-3321-2024, https://doi.org/10.5194/gmd-17-3321-2024, 2024
Short summary
Short summary
Any interpretation of climate model data requires a comprehensive evaluation of the model performance. Numerous error metrics exist for this purpose, and each focuses on a specific aspect of the relationship between reference and model data. Thus, a comprehensive evaluation demands the use of multiple error metrics. However, this can lead to confusion. We propose a clustering technique to reduce the number of error metrics needed and a composite error metric to simplify the interpretation.
Richard Maier, Fabian Jakub, Claudia Emde, Mihail Manev, Aiko Voigt, and Bernhard Mayer
Geosci. Model Dev., 17, 3357–3383, https://doi.org/10.5194/gmd-17-3357-2024, https://doi.org/10.5194/gmd-17-3357-2024, 2024
Short summary
Short summary
Based on the TenStream solver, we present a new method to accelerate 3D radiative transfer towards the speed of currently used 1D solvers. Using a shallow-cumulus-cloud time series, we evaluate the performance of this new solver in terms of both speed and accuracy. Compared to a 3D benchmark simulation, we show that our new solver is able to determine much more accurate irradiances and heating rates than a 1D δ-Eddington solver, even when operated with a similar computational demand.
Julia Maillard, Jean-Christophe Raut, and François Ravetta
Geosci. Model Dev., 17, 3303–3320, https://doi.org/10.5194/gmd-17-3303-2024, https://doi.org/10.5194/gmd-17-3303-2024, 2024
Short summary
Short summary
Atmospheric models struggle to reproduce the strong temperature inversions in the vicinity of the surface over forested areas in the Arctic winter. In this paper, we develop modified simplified versions of surface layer schemes widely used by the community. Our modifications are used to correct the fact that original schemes place strong limits on the turbulent collapse, leading to a lower surface temperature gradient at low wind speeds. Modified versions show a better performance.
Jana Fischereit, Henrik Vedel, Xiaoli Guo Larsén, Natalie E. Theeuwes, Gregor Giebel, and Eigil Kaas
Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, https://doi.org/10.5194/gmd-17-2855-2024, 2024
Short summary
Short summary
Wind farms impact local wind and turbulence. To incorporate these effects in weather forecasting, the explicit wake parameterization (EWP) is added to the forecasting model HARMONIE–AROME. We evaluate EWP using flight data above and downstream of wind farms, comparing it with an alternative wind farm parameterization and another weather model. Results affirm the correct implementation of EWP, emphasizing the necessity of accounting for wind farm effects in accurate weather forecasting.
Clément Bouvier, Daan van den Broek, Madeleine Ekblom, and Victoria A. Sinclair
Geosci. Model Dev., 17, 2961–2986, https://doi.org/10.5194/gmd-17-2961-2024, https://doi.org/10.5194/gmd-17-2961-2024, 2024
Short summary
Short summary
An analytical initial background state has been developed for moist baroclinic wave simulation on an aquaplanet and implemented into OpenIFS. Seven parameters can be controlled, which are used to generate the background states and the development of baroclinic waves. The meteorological and numerical stability has been assessed. Resulting baroclinic waves have proven to be realistic and sensitive to the jet's width.
Jelena Radović, Michal Belda, Jaroslav Resler, Kryštof Eben, Martin Bureš, Jan Geletič, Pavel Krč, Hynek Řezníček, and Vladimír Fuka
Geosci. Model Dev., 17, 2901–2927, https://doi.org/10.5194/gmd-17-2901-2024, https://doi.org/10.5194/gmd-17-2901-2024, 2024
Short summary
Short summary
Boundary conditions are of crucial importance for numerical model (e.g., PALM) validation studies and have a large influence on the model results, especially when studying the atmosphere of real, complex, and densely built urban environments. Our experiments with different driving conditions for the large-eddy simulation model PALM show its strong dependency on boundary conditions, which is important for the proper separation of errors coming from the boundary conditions and the model itself.
Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado
Geosci. Model Dev., 17, 2641–2662, https://doi.org/10.5194/gmd-17-2641-2024, https://doi.org/10.5194/gmd-17-2641-2024, 2024
Short summary
Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
Short summary
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Simon Rosanka, Holger Tost, Rolf Sander, Patrick Jöckel, Astrid Kerkweg, and Domenico Taraborrelli
Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, https://doi.org/10.5194/gmd-17-2597-2024, 2024
Short summary
Short summary
The capabilities of the Modular Earth Submodel System (MESSy) are extended to account for non-equilibrium aqueous-phase chemistry in the representation of deliquescent aerosols. When applying the new development in a global simulation, we find that MESSy's bias in modelling routinely observed reduced inorganic aerosol mass concentrations, especially in the United States. Furthermore, the representation of fine-aerosol pH is particularly improved in the marine boundary layer.
Junyu Li, Yuxin Wang, Lilong Liu, Yibin Yao, Liangke Huang, and Feijuan Li
Geosci. Model Dev., 17, 2569–2581, https://doi.org/10.5194/gmd-17-2569-2024, https://doi.org/10.5194/gmd-17-2569-2024, 2024
Short summary
Short summary
In this study, we have developed a model (RF-PWV) to characterize precipitable water vapor (PWV) variation with altitude in the study area. RF-PWV can significantly reduce errors in vertical correction, enhance PWV fusion product accuracy, and provide insights into PWV vertical distribution, thereby contributing to climate research.
Rolf Sander
Geosci. Model Dev., 17, 2419–2425, https://doi.org/10.5194/gmd-17-2419-2024, https://doi.org/10.5194/gmd-17-2419-2024, 2024
Short summary
Short summary
The open-source software MEXPLORER 1.0.0 is presented here. The program can be used to analyze, reduce, and visualize complex chemical reaction mechanisms. The mathematics behind the tool is based on graph theory: chemical species are represented as vertices, and reactions as edges. MEXPLORER is a community model published under the GNU General Public License.
Hossain Mohammed Syedul Hoque, Kengo Sudo, Hitoshi Irie, Yanfeng He, and Md Firoz Khan
EGUsphere, https://doi.org/10.22541/essoar.169903618.82717612/v2, https://doi.org/10.22541/essoar.169903618.82717612/v2, 2024
Short summary
Short summary
Using multi-platform observations, we validated global formaldehyde (HCHO) simulations from a chemistry transport model. HCHO is a crucial intermediate of the chemical catalytic cycle that governs the ozone formation in the troposphere. The model was capable of replicating the observed spatiotemporal variability in HCHO. In a few cases, the model capability was limited. This is attributed to the uncertainties in the observations and the model parameters.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 2347–2358, https://doi.org/10.5194/gmd-17-2347-2024, https://doi.org/10.5194/gmd-17-2347-2024, 2024
Short summary
Short summary
In the last decades, weather forecasting up to 15 d into the future has been dominated by physics-based numerical models. Recently, deep learning models have challenged this paradigm. However, the latter models may struggle when forecasting weather extremes. In this article, we argue for deep learning models specifically designed to handle extreme events, and we propose a foundational framework to develop such models.
Stefan Rahimi, Lei Huang, Jesse Norris, Alex Hall, Naomi Goldenson, Will Krantz, Benjamin Bass, Chad Thackeray, Henry Lin, Di Chen, Eli Dennis, Ethan Collins, Zachary J. Lebo, Emily Slinskey, Sara Graves, Surabhi Biyani, Bowen Wang, Stephen Cropper, and the UCLA Center for Climate Science Team
Geosci. Model Dev., 17, 2265–2286, https://doi.org/10.5194/gmd-17-2265-2024, https://doi.org/10.5194/gmd-17-2265-2024, 2024
Short summary
Short summary
Here, we project future climate across the western United States through the end of the 21st century using a regional climate model, embedded within 16 latest-generation global climate models, to provide the community with a high-resolution physically based ensemble of climate data for use at local scales. Strengths and weaknesses of the data are frankly discussed as we overview the downscaled dataset.
Romain Pilon and Daniela I. V. Domeisen
Geosci. Model Dev., 17, 2247–2264, https://doi.org/10.5194/gmd-17-2247-2024, https://doi.org/10.5194/gmd-17-2247-2024, 2024
Short summary
Short summary
This paper introduces a new method for detecting atmospheric cloud bands to identify long convective cloud bands that extend from the tropics to the midlatitudes. The algorithm allows for easy use and enables researchers to study the life cycle and climatology of cloud bands and associated rainfall. This method provides insights into the large-scale processes involved in cloud band formation and their connections between different regions, as well as differences across ocean basins.
Salvatore Larosa, Domenico Cimini, Donatello Gallucci, Saverio Teodosio Nilo, and Filomena Romano
Geosci. Model Dev., 17, 2053–2076, https://doi.org/10.5194/gmd-17-2053-2024, https://doi.org/10.5194/gmd-17-2053-2024, 2024
Short summary
Short summary
PyRTlib is an attractive educational tool because it provides a flexible and user-friendly way to broadly simulate how electromagnetic radiation travels through the atmosphere as it interacts with atmospheric constituents (such as gases, aerosols, and hydrometeors). PyRTlib is a so-called radiative transfer model; these are commonly used to simulate and understand remote sensing observations from ground-based, airborne, or satellite instruments.
Kelly M. Núñez Ocasio and Zachary L. Moon
EGUsphere, https://doi.org/10.5194/egusphere-2024-259, https://doi.org/10.5194/egusphere-2024-259, 2024
Short summary
Short summary
TAMS is an open-source mesoscale convective system tracking and classifying Python-based package that can be used to study observed and simulated systems. Each step of the algorithm is described in this paper with examples showing how to make use of visualization and post-processing tools within the package. A unique and valuable feature of this tracker is its support for unstructured grids in the identification stage and grid-independent tracking.
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 17, 1995–2014, https://doi.org/10.5194/gmd-17-1995-2024, https://doi.org/10.5194/gmd-17-1995-2024, 2024
Short summary
Short summary
Our research presents an innovative approach to estimating power plant CO2 emissions from satellite images of the corresponding plumes such as those from the forthcoming CO2M satellite constellation. The exploitation of these images is challenging due to noise and meteorological uncertainties. To overcome these obstacles, we use a deep learning neural network trained on simulated CO2 images. Our method outperforms alternatives, providing a positive perspective for the analysis of CO2M images.
Kyoung-Min Kim, Si-Wan Kim, Seunghwan Seo, Donald R. Blake, Seogju Cho, James H. Crawford, Louisa K. Emmons, Alan Fried, Jay R. Herman, Jinkyu Hong, Jinsang Jung, Gabriele G. Pfister, Andrew J. Weinheimer, Jung-Hun Woo, and Qiang Zhang
Geosci. Model Dev., 17, 1931–1955, https://doi.org/10.5194/gmd-17-1931-2024, https://doi.org/10.5194/gmd-17-1931-2024, 2024
Short summary
Short summary
Three emission inventories were evaluated for East Asia using data acquired during a field campaign in 2016. The inventories successfully reproduced the daily variations of ozone and nitrogen dioxide. However, the spatial distributions of model ozone did not fully agree with the observations. Additionally, all simulations underestimated carbon monoxide and volatile organic compound (VOC) levels. Increasing VOC emissions over South Korea resulted in improved ozone simulations.
Sanam Noreen Vardag and Robert Maiwald
Geosci. Model Dev., 17, 1885–1902, https://doi.org/10.5194/gmd-17-1885-2024, https://doi.org/10.5194/gmd-17-1885-2024, 2024
Short summary
Short summary
We use the atmospheric transport model GRAMM/GRAL in a Bayesian inversion to estimate urban CO2 emissions on a neighbourhood scale. We analyse the effect of varying number, precision and location of CO2 sensors for CO2 flux estimation. We further test the inclusion of co-emitted species and correlation in the inversion. The study showcases the general usefulness of GRAMM/GRAL in measurement network design.
Abhishek Savita, Joakim Kjellsson, Robin Pilch Kedzierski, Mojib Latif, Tabea Rahm, Sebastian Wahl, and Wonsun Park
Geosci. Model Dev., 17, 1813–1829, https://doi.org/10.5194/gmd-17-1813-2024, https://doi.org/10.5194/gmd-17-1813-2024, 2024
Short summary
Short summary
The OpenIFS model is used to examine the impact of horizontal resolutions (HR) and model time steps. We find that the surface wind biases over the oceans, in particular the Southern Ocean, are sensitive to the model time step and HR, with the HR having the smallest biases. When using a coarse-resolution model with a shorter time step, a similar improvement is also found. Climate biases can be reduced in the OpenIFS model at a cheaper cost by reducing the time step rather than increasing the HR.
Cited articles
Alapaty, K., Herwehe, J. A., Otte, T. L., Nolte, C. G., Bullock, O. R., Ballard,
M. S., Kain, J. S., and Dudhia, J.: Introducing subgrid-scale cloud feedbacks
to radiation for regional meteorological and climate modeling, Geophys. Res.
Lett., 39, L24809, https://doi.org/10.1029/2012GL054031, 2012.
Alishouse, J. C., Snider, J. B., Westwater, E. R., Swift, C. T., Ruf, C. S.,
Vongsathron, S. A., and Ferraro, R. R.: Determination of cloud liquid water
content using the SSM/I, IEEE T. Geosci. Remote, 28, 817–822, https://doi.org/10.1109/36.58968, 1990.
Arakawa, A. and Schubert, W. H.: Interaction of a cumulus cloud ensemble
with the large-scale environment, Part I, J. Atmos. Sci., 31, 674–701,
https://doi.org/10.1175/1520-0469(1974)031<0674:IOACCE>2.0.CO;2, 1974.
Arakawa, A. and Wu, C.-M.: A unified representation of deep moist
convection in numerical modeling of the atmosphere. Part I, J. Atmos. Sci.,
70, 1977–1992, https://doi.org/10.1175/JAS-D-12-0330.1, 2013.
Bechtold, P., Bazile, E., Guichard, F., Mascart, P., and Richard, E.: A
mass-flux convection scheme for regional and global models, Q. J. Roy.
Meteor. Soc., 130, 3139–3172, https://doi.org/10.1002/qj.49712757309, 2001.
Bechtold, P., Köhler, M., Jung, T., Doblas-Reyes, F., Leutbecher, M.,
Rodwell, M. J., Vitart, F., and Balsamo, G.: Advances in simulating
atmospheric variability with the ECMWF model: From synoptic to decadal
time-scales, Q. J. Roy. Meteor. Soc., 134, 1337–1351, https://doi.org/10.1002/qj.289, 2008.
Brown, J. M.: Mesoscale unsaturated downdrafts driven by rainfall
evaporation: A numerical study, J. Atmos. Sci., 36, 313–338, https://doi.org/10.1175/1520-0469(1979)036<0313:MUDDBR>2.0.CO;2, 1979.
Chen, F. and Dudhia, J.: Coupling an advanced land surface-hydrology model
with the Penn State-NCAR MM5 modeling system. Part I: Model implementation
and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, I., Biblot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Greer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Holm, E. V., Isaksen, L., Kallberg, P., Kohler, M.,
Matricardi, M., McNally, A. P., Mong-Sanz, B. M., Morcette, J.-J., Park,
B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thepaut, J. N., and Vitart,
F.: The ERA-Interim reanalysis: Configuration and performance of the data
assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011.
Dolinar, E. K., Dong, X., Xi, B., Jiang, J. H., and Su, H.: Evaluation of
CMIP5 simulated clouds and TOA radiation budgets using NASA satellite
observations, Clim. Dynam., 44, 2229-2247, https://doi.org/10.1007/s00382-014-2158-9, 2015.
Fowler, L. D.: experimentsMPAS-v5.2, Zenodo, https://doi.org/10.5281/zenodo.3515440, 2019.
Fowler, L. D., Skamarock, W. C., Grell, G. A., Freitas, S. R., and Duda, M. G.:
Analyzing the Grell-Freitas convection scheme from hydrostatic to
nonhydrostatic scales within a global model, Mon. Weather Rev., 144,
2285–2306, https://doi.org/10.1175/MWR-D-15-0311.1, 2016.
Frank, W. M., and Cohen, C.: Simulation of tropical convective systems. Part
I: A cumulus parameterization, J. Atmos. Sci., 44, 3787–3799, https://doi.org/10.1175/1520-0469(1987)044<3787:SOTCSP>2.0.CO;2, 1987.
Fritsch, J. M. and Chappell, C. F.: Numerical prediction of convectively
driven mesoscale pressure systems. Part I: Convective parameterization, J.
Atmos. Sci., 37, 1722–1733, https://doi.org/10.1175/1520-0469(1980)037<1722:NPOCDM>2.0.CO;2, 1980.
Geier, E. B., Green, R. N., Kratz, D. P., Minnis, P., Miller, W. F., Nolan,
S. K., and Franklin, C. B.: Clouds and the Earth's Radiant Energy System
(CERES) data management system, Single Satellite Footprint TOA/Surface
Fluxes and Clouds (SSF) collection document, Release 2, Version 1, 243 pp.,
2003.
Giorgetta, M. A., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert,
J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen,
T., Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt,
H., Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G., and
Stevens, B: ICON-A, the atmosphere component of the ICON Earth System Model:
I. Model description, J. Adv. Model. Earth Sy., 10, 1613–1637, https://doi.org/10.1029/2017MS001242, 2018.
Glotfelty, T., Alapaty, K., He, J., Hawbecker, P., Song, X., and Zhang, G.:
The Weather Research and Forecasting Model with aerosol cloud-interactions
(WRF-ACI): Development, evaluation, and initial applications, Mon. Wea.
Rev., 147, 1491–1511, https://doi.org/10.1175/MWR-D-18-0267.1,
2019.
Greenwald, T. J., Stephens, G. L., Vonder Haar, T. H., and Jackson, D. L.: A
physical retrieval of cloud liquid water over global oceans using special
sensor microwave/imager (SSM/I) observations, J. Geophys. Res., 98,
18471–18488, https://doi.org/10.1029/93JD00339, 1993.
Grell, G. A.: Prognostic evaluation of assumptions uses by cumulus
parameterizations, Mon. Weather Rev., 121, 764–787, https://doi.org/10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2, 1993.
Grell, G. A. and Dévényi, D.: A generalized approach to
parameterizing convection combining ensemble and data assimilation
techniques, Geophys. Res. Lett., 29, 38-1–38-4, https://doi.org/10.1029/2002GL015311, 2002.
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.
Guo, H., Golaz, J.-C., Donner, L., Wyman, B., Zhao, M., and Ginoux, P.:
CLUBB as a unified cloud parameterization: opportunities and challenges,
Geophys. Res. Lett., 42, 4540–4547, https://doi.org/10.1002/2015GL063672, 2015.
Hagos, S., Leung, L. R., Rauscher, S. A., and Ringler, T.: Error
characteristics of two grid refinement approaches in aquaplanet simulations:
MPAS-A and WRF, Mon. Wea. Rev., 141, 3022–3036, https://doi.org/10.1175/MWR-D-12-00338.1, 2013.
He, J. and Alapaty, K.: Precipitation partitioning in multiscale
atmospheric simulations: Impacts of stability restoration methods, J.
Geophys. Res., 123, 10185–10201, https://doi.org/10.1029/2018JD028710, 2018.
Herwehe, J. A., Alapaty, K., and Bullock Jr., O. R: Evaluation of developments
toward a multi-scale Kain-Fritsch parameterization in WRF. 2014 Community
Modeling and Analysis System Conference, Chapel Hill, NC, EPA, 2014.
Hong, S.-Y. and Lim, J.-O: The WRF single moment 6-class microphysics
scheme (WSM6), J. Korean Meteor. Soc., 42, 129–151, 2006.
Hong, S.-Y., Choi, J., Chang, E.-C., Park, H., and Kim Y.-J.:
Lower-tropospheric enhancement of gravity wave drag in a global spectral
atmospheric forecast model, Weather Forecast., 23, 523–531, https://doi.org/10.1175/2007WAF2007030.1, 2008.
Huffman, G. J., Balvin, D. T., Nelkin, E. J., and Wolff, D. B.: The TRMM
Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear,
combined-sensor precipitation at fine scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Iacono, M. J., Mlawer, E. J., Clough, S. A., and Morcrette, J.-J.: Impact of
an improved longwave radiation model, RRTM, on the energy budget and
thermodynamic properties of the NCAR Community Climate Model, CCM3, J.
Geophys. Res., 105, 14873–14890, https://doi.org/10.1029/2000JD900091, 2000.
Jiang, J. H., Su, H., Zhai, C., Perun, V. S, Del Genio, A., Nazarenko, L. S.,
Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A., Ringer,
M. A., Rotstayn, L., Jeffrey, S., Wu, T., Brient, F., Dufresne, J.-L., Kawai,
H., Koshiro, T., Watanabe, M., L'Ecuyer, T. S., Volodin, E. M., Iversen, T.,
Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W., Tian, B., Teixeira,
J., and Stephens, G. L.: Evaluation of cloud and water vapor simulations in
CMIP5 climate models using NASA “A-Train” satellite observations, J.
Geophys. Res., 117, D14105, https://doi.org/10.1029/2011JD017237, 2012.
Ju, L., Ringler, T., and Gunzburger, M.: Voronoi tessellations and their
applications to climate and global modeling, in: Numerical Techniques for Global
Atmospheric Models, edited by: Lauritzen, P., Jablonowski, C., Taylor, M., and Nair R., Springer, 313–342, 2011.
Judt, F.: Atmospheric predictability of the tropics, middle latitudes, and
polar regions explored through global storm-resolving simulations, J. Atmos.
Sci., 77, 257–276, https://doi.org/10.1175/JAS-D-19-0116.1, 2020.
Kain, J. S.: The Kain-Fritsch parameterization: An update, J. Appl.
Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kain, J. S and Fritsch, J. M.: A one-dimensional entraining/detraining plume
model and its application in convective parameterization, J. Atmos. Sci.,
47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
Kain, J. S. and Fritsch, J. M.: The role of convective “trigger function”
in numerical forecasts of mesoscale convective systems, Meteorol. Atmos.
Phys., 49, 93–106, https://doi.org/10.1007/BF01025402, 1992.
Kain, J. S. and Fritsch, J. M.: Convective parameterization for mesoscale
models: The Kain-Fritsch scheme, The Representation of Cumulus Convection in
Numerical Models, Meteor. Mon., No. 24, American Meteorological Society,
Boston, MA, 165–170, https://doi.org/10.1007/978-1-935704-13-3_16, 1993.
Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannary, C., Strand, G.,
Arblaster, J. M., Bates, S. C., Danabasoglu, G., Edwards, J., Holland, M.,
Kushner, P., Lamarque, J.-F., Lawrence, D., Lindsay, K., Middleton, A.,
Munoz, E., Neale, R., Oleson, K., Polvani, L., and Vertenstein, M.: The
Community Earth System Model (CESM) large ensemble project, B. Am. Meteorol.
Soc., 96, 1333–1349, https://doi.org/10.1175/BAMS-D-13-00255.1,
2015.
Kessler, E.: On the distribution and continuity of water substances in
atmospheric circulation, Meteor. Mon., No. 10, American Meteorological
Society, Boston, MA, 1–84, https://doi.org/10.1007/978-1-935704-36-2_1, 1969.
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
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.
Krishnamurti, T. N., Low-Nam, S., and Pasch, R.: Cumulus parameterization and
rainfall rates II, Mon. Weather Rev., 111, 815–828, https://doi.org/10.1175/1520-0493(1983)111<0815:CPARRI>2.0.CO;2, 1983.
Li, J.-L., Waliser, D., Woods, C., Teixeira, J., Bacmeister, J., Chern, J.,
Shen, B.-W., Tompkins, A., Tao, W.-K., and Köhler, M.: Comparisons of
satellites liquid water estimates to ECMWF and GMAO analyses, 20th
century IPCC AR4 climate simulations and GCM simulations, Geophys. Res.
Lett., 35, L9710, https://doi.org/10.1029/2008GL035427, 2008.
Li, J.-L., Waliser, D.E., Chen, W.-T., Guan, B., Kubar, T., Stephens, G.,
Ma, H.-Y., Deng, M., Donner, L., Seman, C., and Horowitz, L.: An
observational based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs
and contemporary reanalyses using contemporary satellite data, J. Geophys.
Res., 117, D16105, https://doi.org/10.1029/2012JD017640, 2012.
Li, J.-L., Lee, S., Ma, H.-Y, Stephens, G., and Guan, B.: Assessment of the
cloud liquid water from climate models and reanalysis using satellite
observations, Terr. Atmos. Ocean. Sci., 29, 653–678, https://doi.org/10.3319/TAO.2018.07.04.01, 2018.
Mahoney, K. M.: The representation of cumulus convection in high-resolution
simulations of the 2013 Colorado front range flood, Mon. Weather Rev., 144,
4265–4278, https://doi.org/10.1175/MWR-D-16-0211.1, 2016.
Meehl, G. A., Delworth, T. L., Latiff, M., McAveney, B., Mitchell, J. F. B.,
Stouffer, R. J., and Taylor, K. E.: The WCRP CMIP3 multimodel dataset: A new
era in climate change research, B. Am. Meteorol. Soc., 88, 1383–1394,
https://doi.org/10.1175/BAMS-88-9-1383, 2007.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen, Y., Spangenberg, D. A., Arduini,
R. F., Trepte, Q. Z., Smith, W. L., Ayers, J. K., Gibson, S. C., Miller, W. F., Hong, G., Chakrapani, V.,
Takano, Y., Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 cloud property retrievals using
TRMM VIRS and Terra and Aqua MODIS data-Part I: Algorithms, IEEE T. Geosci.
Remote., 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601, 2011.
Minnis, P., Kratz, D. P, Coakley, J. J. A., King, M. D., Garber, D., Heck, P.,
Mayor, S., Young, D. F., and Arduini, R.: Cloud optical property retrieval
(Subsystem 4.3), in: Clouds and the Earth's Radiant Energy System (CERES)
Algorithm Theoretical Basis Document, Vol. III, Clouds and Radiance
Inversions (Subsystem 4), NASA RP 1376, edited by: Science Team CERES,
NASA, Washington DC, 135–176, 1995.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res., 102, 16663–16682,
https://doi.org/10.1029/97JD00237, 1997.
Molod, A., Takacs, L., Suarez, M., Bacmeister, J., Song, I.-S., and Eichman,
A.: The GEOS-5 atmospheric general circulation model: Mean climate from
MERRA to Fortuna, Technical Report Series on Global Modeling and
Assimilations, Vol. 28, 124 pp., 2012.
Moorthi, S. and Suarez, M. J.: Relaxed Arakawa-Schubert: a parameterization
of moist convection for general circulation models, Mon. Weather Rev., 210,
978–1002, https://doi.org/10.1175/1520-0493(1992)120<0978:RASAPO>2.0.CO;2, 1992.
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteor. Soc. Jpn., 87,
895–912, https://doi.org/10.2151/jmsj.87.895, 2009.
NCAR: Command Language, Version 6.3.2, software, UCAR/NCAR/CISL/TDD, Boulder, CO, https://doi.org/10.5065/D6WD3XH5, 2019.
Ogura, Y. and Cho, H.-R.: Diagnostic determination of cumulus cloud
populations from observed large-scale variables, J. Atmos. Sci., 30,
1276–1286, https://doi.org/10.1175/1520-0469(1973)030<1276:DDOCCP>2.0.CO;2, 1973.
Olson, J. B., Kenyon, J. S., Angevine, W. M., Brown, J. M., Pagowski, M., and
Suselj, K.: A description of the MYNN-EDMF scheme and the coupling to other
components in WRF-ARW, NOAA Technical Memorandum OAR GSD, 61, 37 pp., 2019.
Platnick, S., King, M. D., Ackerman, S. A., Wenzel, W. P., Baum, B. A., Riedl,
J. C., and Frey, R. A.: The MODIS cloud products: Algorithms and examples from
Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Qiao, F. and Liang, X.-Z: Effects of cumulus parameterization closures on
the simulations of summer precipitation over the United States coastal
oceans, J. Adv. Model. Earth Sy., 8, 764–785, https://doi.org/10.1002/2015MS000621, 2015.
Raymond, D. J.: Regulation of moist convection over the west Pacific warm
pool, J. Atmos. Sci., 52, 3945–3959, https://doi.org/10.1175/1520-0469(1995)052<3945:ROMCOT>2.0.CO;2, 1995.
Sakaguchi, K., Leung, L.R., Zhao, C., Yang, Q., Lu, J., and Hagos, S.:
Exploring a multiresolution approach using AMIP simulations, J. Clim., 28,
5549–5574, https://doi.org/10.1175/JCLI-D-14-00729.1, 2015.
Schwarz, C. S.: Medium-range convection-allowing ensemble forecasts with a
variable-resolution global model, Mon. Weather Rev., 147, 2997–3023,
https://doi.org/10.1175/MWR-D-18-0452.1, 2019.
Simpson, J. and Wiggert, V.: Models of precipitating cumulus towers, Mon.
Weather Rev., 97, 471–489, https://doi.org/10.1175/1520-0493(1969)097<0471:MOPCT>2.3.CO;2, 1969.
Skamarock, W. C. and Gassmann, A.: Conservative transport schemes for
spherical geodesic grids: High-order flux operators for ODE-based time
integration, Mon. Weather Rev., 139, 2962–2975, https://doi.org/10.1175/MWR-D-10-05056.1, 2011.
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 tessellations and C-grid staggering, Mon. Weather Rev.,
140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X.-Y., Wang,
W., and Powers, J. G.: A description of the Advanced Research WRF
version 3, NCAR Tech. Note NCAR/TN-475+STR, 113 pp., 2008.
Smagorinsky, J.: General circulation experiments with the primitive
equations. I. The basic experiment, Mon. Weather Rev., 91, 99–164,
https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963.
Stanfield, R.E., Dong, X., Xi, B., Del Genio, A.D., Minnis, P., Doelling,
D., and Loeb, N.: Assessment of NASA GISS CMIP5 and Post-CMIP5 simulated
clouds and TOA radiation budgets using satellite observations. Part II: TOA
radiation budget and CREs, J. Climate, 28, 1842–1863, https://doi.org/10.1175/JCLI-D-14-00249.1, 2015.
Stephens, G. L. and Kummerow, C. D.: The remote sensing of clouds and
precipitation from space: A review, J. Atmos. Sci., 64, 3742–3765,
https://doi.org/10.1175/2006JAS2375.1, 2007.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z.,
Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D.,
Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team:
The CloudSat mission and the A-Train: A new dimension and space-based
observations of clouds and precipitation, B. Am. Meteorol. Soc., 83,
1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Storer, R. L., Griffin, B. M., Höft, J., Weber, J. K., Raut, E., Larson, V. E., Wang, M., and Rasch, P. J.: Parameterizing deep convection using the assumed probability density function method, Geosci. Model Dev., 8, 1–19, https://doi.org/10.5194/gmd-8-1-2015, 2015.
Strauss, D. and Paolino, D.: Intermediate time error growth and
predictability: tropics versus mid-latitudes, Tellus A, 61, 579–586, https://doi.org/10.1111/j.1600-0870.2009.00411.x, 2008.
Suhas, E. and Zhang, Q. J.: Evaluation of trigger functions for convective
parameterization schemes using observations, J. Climate, 27, 7647–7666,
https://doi.org/10.1175/JCLI-D-13-00718.1, 2014.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the
experiment design, B. Am. Meteorol. Soc., 93, 485–4398, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Thayer-Calder, K., Gettelman, A., Craig, C., Goldhaber, S., Bogenschutz, P. A., Chen, C.-C., Morrison, H., Höft, J., Raut, E., Griffin, B. M., Weber, J. K., Larson, V. E., Wyant, M. C., Wang, M., Guo, Z., and Ghan, S. J.: A unified parameterization of clouds and turbulence using CLUBB and subcolumns in the Community Atmosphere Model, Geosci. Model Dev., 8, 3801–3821, https://doi.org/10.5194/gmd-8-3801-2015, 2015.
Thompson, G., Field, P. R., Rasmussen, R. M., and Hall, W. D.: Explicit
forecasts of winter precipitation using an improved bulk cloud microphysics
scheme. Part II: Implementation of a new snow parameterization, Mon. Weather
Rev., 136, 5095–5115, https://doi.org/10.1175/2008MWR2387.1,
2008.
Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit forecasts of winter
precipitation using an improved bulk cloud microphysics scheme. Part I:
Description and sensitivity analysis, Mon. Weather Rev., 132, 519–542,
https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2004.
Thompson, G., Tewari, M., Ikeda, K., Tessendorf, S., Weeks, C., Otkin, J.,
and Kong, F.: Explicitly-coupled cloud physics and radiation
parameterizations and subsequent evaluation in WRF high-resolution
convective forecasts, Atmos. Res., 168, 92–104, https://doi.org/10.1016/j.atmosres.2015.09.005, 2016.
Tokioka, T., Yamazaki, K., Kotoh, A., and Ose, T.: The equatorial 30-60 day
oscillation and the Arakawa–Schubert penetrative cumulus parameterization,
J. Meteor. Soc. Jpn., 66, 883–900, https://doi.org/10.2151/jmsj1965.66.6_883, 1988.
Waliser, D. E., Li, J.-L., Woods, C. P., Austin, R. T., Bacmeister, J., Chern,
J., Del Genio, A., Jiang, J. H., Juang, Z., Meng, H., Minnis, P., Platnick,
S., Rossow, W. B., Stephens, G. L., Sun-Mack, S., Tao, W.-K., Tompkins, A. M.,
Vane, D. G., Walker, C., and Wu, D.: Cloud ice: A climate model challenge
with signs and expectations of progress, J. Geophys. Res., 114, D00A21,
https://doi.org/10.1029/2008JD010015, 2009.
Wicker, L. J. and Skamarock, W. C.: Time-splitting methods for elastic models
using forward time schemes, Mon. Weather Rev., 130, 2088–2097, https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Smith, G. L.,
and Cooper, J. E.: Clouds and the Earth's Radiation Energy System (CERES): An
Earth Observing System experiment, B. Am. Meteorol. Soc., 77, 853–868,
https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996.
Williamson, D.: The effect of time step and time-scales on parameterization
suites, Q. J. Roy. Meteor. Soc., 139, 548–560, https://doi.org/10.1002/qj.1992, 2013.
Wong, M. and Skamarock, W. C.: Spectral characteristics of convective-scale
precipitation observations and forecasts, Mon. Weather Rev., 144, 4183–4195,
https://doi.org/10.1175/MWR-D-16-0183.1, 2016.
Xu, K.-M. and Krueger, S. K.: Evaluation of cloudiness parameterizations
using a cumulus ensemble model, Mon. Weather Rev., 119, 342–367, https://doi.org/10.1175/1520-0493(1991)119<0342:EOCPUA>2.0.CO;2, 1991.
Xu, K.-M. and Randall, D. A.: A semi-empirical 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.
Zheng, Y., Alapaty, K., Herwehe, J. A., Del Genio, A. D., and Niyogi, D.:
Improving high-resolution weather forecasts using the Weather Research and
Forecasting (WRF) model with an updated Kain-Fritsch scheme, Mon. Weather
Rev., 144, 833–860, https://doi.org/10.1175/MWR-D-15-0005.1,
2016.
Short summary
The cloud liquid and ice water path and precipitation simulated with the Model for Prediction Across Scales are compared against satellite data over the tropical Pacific Ocean. Uniform and variable-resolution experiments using scale-aware convection schemes produce strong biases between simulated and observed diagnostics. Results underscore the importance of evaluating clouds, their optical properties, and radiation budget in addition to precipitation in mesh refinement global simulations.
The cloud liquid and ice water path and precipitation simulated with the Model for Prediction...