111 citations as recorded by crossref.

1. Assessment of WRF Model Parameter Sensitivity for High‐Intensity Precipitation Events During the Indian Summer Monsoon S. Chinta et al. https://doi.org/10.1029/2020EA001471
2. Irrigation‐Induced Crop Growth Enhances Irrigation Cooling Effect Over the North China Plain by Increasing Transpiration G. Liu & W. Wang https://doi.org/10.1029/2022WR034142
3. Impacts of land use changes from the Hanoi Master Plan 2030 on urban heat islands: Part 1. Cooling effects of proposed green strategies T. Kubota et al. https://doi.org/10.1016/j.scs.2017.04.001
4. Determining the sensitive parameters of the Weather Research and Forecasting (WRF) model for the simulation of tropical cyclones in the Bay of Bengal using global sensitivity analysis and machine learning H. Baki et al. https://doi.org/10.5194/gmd-15-2133-2022
5. Irrigation Impact on Water and Energy Cycle During Dry Years Over the United States Using Convection‐Permitting WRF and a Dynamical Recycling Model Z. Yang et al. https://doi.org/10.1029/2019JD030524
6. Statistical properties of atmospheric greenhouse gas measurements: Looking down from space and looking up from the ground B. Zhang et al. https://doi.org/10.1016/j.chemolab.2016.11.014
7. Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America T. Portele et al. https://doi.org/10.3389/feart.2021.669427
8. Visualization of Time-Varying Weather Ensembles across Multiple Resolutions A. Biswas et al. https://doi.org/10.1109/TVCG.2016.2598869
9. Parametric and Structural Sensitivities of Turbine‐Height Wind Speeds in the Boundary Layer Parameterizations in the Weather Research and Forecasting Model B. Yang et al. https://doi.org/10.1029/2018JD029691
10. Parameter Tuning and Calibration of RegCM3 with MIT–Emanuel Cumulus Parameterization Scheme over CORDEX East Asia Domain L. Zou et al. https://doi.org/10.1175/JCLI-D-14-00229.1
11. Bayesian Analysis of Multifidelity Computer Models With Local Features and Nonnested Experimental Designs: Application to the WRF Model B. Konomi & G. Karagiannis https://doi.org/10.1080/00401706.2020.1855253
12. AI-empowered next-generation multiscale climate modelling for mitigation and adaptation V. Eyring et al. https://doi.org/10.1038/s41561-024-01527-w
13. A novel approach for determining source–receptor relationships in model simulations: a case study of black carbon transport in northern hemisphere winter P. Ma et al. https://doi.org/10.1088/1748-9326/8/2/024042
14. Assessing the applicability of WRF optimal parameters under the different precipitation simulations in the Greater Beijing Area Z. Di et al. https://doi.org/10.1007/s00382-017-3729-3
15. Recent Progress in Numerical Atmospheric Modeling in China R. Yu et al. https://doi.org/10.1007/s00376-019-8203-1
16. Knee Point‐Based Multiobjective Optimization for the Numerical Weather Prediction Model in the Greater Beijing Area H. Wang et al. https://doi.org/10.1029/2023GL104330
17. Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates K. Dagon et al. https://doi.org/10.1029/2022JD037038
18. On the Bayesian calibration of computer model mixtures through experimental data, and the design of predictive models G. Karagiannis & G. Lin https://doi.org/10.1016/j.jcp.2017.04.003
19. Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area Z. Di et al. https://doi.org/10.1002/2014GL061623
20. QuadTune version 1: a regional tuner for global atmospheric models V. Larson et al. https://doi.org/10.5194/gmd-18-9767-2025
21. Neglecting irrigation contributes to the simulated summertime warm-and-dry bias in the central United States Y. Qian et al. https://doi.org/10.1038/s41612-020-00135-w
22. More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia P. Nguyen et al. https://doi.org/10.1002/joc.7619
23. Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model Y. Qian et al. https://doi.org/10.1007/s00382-023-06977-3
24. Parametric sensitivity and calibration for the Kain‑Fritsch convective parameterization scheme in the WRF model H. Yan et al. https://doi.org/10.3354/cr01213
25. Assessing Parameter Importance of the Weather Research and Forecasting Model Based On Global Sensitivity Analysis Methods D. Ji et al. https://doi.org/10.1002/2017JD027348
26. Parameter Calibration to Improve the Prediction of Tropical Cyclones over the Bay of Bengal Using Machine Learning–Based Multiobjective Optimization H. Baki et al. https://doi.org/10.1175/JAMC-D-21-0184.1
27. Evaluation of iterative Kalman smoother schemes for multi-decadal past climate analysis with comprehensive Earth system models J. García-Pintado & A. Paul https://doi.org/10.5194/gmd-11-5051-2018
28. Uncertainty in humanities network visualization M. Conroy et al. https://doi.org/10.3389/fcomm.2023.1305137
29. Impacts of deforestation on drought patterns across multiple time scales in Europe: insights from LUCAS regional climate models Y. Li et al. https://doi.org/10.1016/j.jhydrol.2025.133781
30. Optimization of parameters in the surface-layer scheme of the Weather Research and Forecasting (WRF) model for near-surface wind and temperature J. Lee et al. https://doi.org/10.1016/j.atmosres.2026.109034
31. Assessing Climate Change Projections through High-Resolution Modelling: A Comparative Study of Three European Cities A. Ascenso et al. https://doi.org/10.3390/su16177276
32. Vegetation greening decrease water yield during the growing season over the Qinling-Daba Mountains in Central China Z. Li et al. https://doi.org/10.1016/j.ejrh.2025.102852
33. The Role of Nonconvective Condensation Processes in Response of Surface Shortwave Cloud Radiative Forcing to El Niño Warming L. Li et al. https://doi.org/10.1175/JCLI-D-13-00632.1
34. Quantitative Sensitivity Analysis of Physical Parameterizations for Cases of Deep Convection in the NASA GEOS-5 D. Posselt et al. https://doi.org/10.1175/JCLI-D-15-0250.1
35. Updates on Model Hierarchies for Understanding and Simulating the Climate System: A Focus on Data‐Informed Methods and Climate Change Impacts L. Mansfield et al. https://doi.org/10.1029/2023MS003715
36. Investigating Land Surface Effects on the Moisture Transport over South America with a Moisture Tagging Model Z. Yang & F. Dominguez https://doi.org/10.1175/JCLI-D-18-0700.1
37. Studying the dynamics of cloudburst events over Indian Himalayan region using model simulation P. Samantray & K. Gouda https://doi.org/10.1007/s40808-025-02680-w
38. Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble Z. Xu et al. https://doi.org/10.1007/s00376-020-9171-1
39. Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China X. Wen et al. https://doi.org/10.1016/j.pce.2015.09.016
40. Dynamic and thermodynamic mechanisms of precipitation efficiency variations in China under global warming Y. Gao et al. https://doi.org/10.1016/j.ejrh.2025.103089
41. Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots J. Wang et al. https://doi.org/10.1109/TVCG.2016.2598830
42. Calibration of Kain–Fritsch cumulus scheme in Weather Research and Forecasting (WRF) model over Western Luzon, Philippines J. Tolentino & G. Bagtasa https://doi.org/10.1007/s00703-021-00779-0
43. Intercomparison of atmospheric forcing datasets and twoPBLschemes for precipitation modelling over a coastal valley in northern British Columbia, Canada C. Onwukwe et al. https://doi.org/10.1002/met.2043
44. Machine learning based parameter sensitivity of regional climate models—a case study of the WRF model for heat extremes over Southeast Australia P. Reddy et al. https://doi.org/10.1088/1748-9326/ad0eb0
45. Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-Based Automatic Parameter Optimization Method Z. Di et al. https://doi.org/10.3390/atmos11010089
46. Simulated precipitation diurnal cycles over East Asia using different CAPE-based convective closure schemes in WRF model B. Yang et al. https://doi.org/10.1007/s00382-017-3712-z
47. Effect of convection schemes on the simulation of monsoon climates: a sensitivity study using RegCM4 J. Chen et al. https://doi.org/10.3354/cr01229
48. Bayesian network model for flood forecasting based on atmospheric ensemble forecasts L. Goodarzi et al. https://doi.org/10.5194/nhess-19-2513-2019
49. Impacts of regional climate change on air quality projections and associated uncertainties G. Lacressonnière et al. https://doi.org/10.1007/s10584-016-1619-z
50. Assessment of the Forecast Skill of Multiphysics and Multistochastic Methods within the GRAPES Regional Ensemble Prediction System in the East Asian Monsoon Region Z. Xu et al. https://doi.org/10.1175/WAF-D-19-0021.1
51. A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability Y. Xue et al. https://doi.org/10.1016/j.atmosres.2014.05.001
52. Uncertainty Quantification and Bayesian Inference of Cloud Parameterization in the NCAR Single Column Community Atmosphere Model (SCAM6) R. Pathak et al. https://doi.org/10.3389/fclim.2021.670740
53. A high-resolution dataset for lower atmospheric process studies over the Tibetan Plateau from 1981 to 2020 F. Li et al. https://doi.org/10.1080/20964471.2023.2277551
54. CH4 parameter estimation in CLM4.5bgc using surrogate global optimization J. Müller et al. https://doi.org/10.5194/gmd-8-3285-2015
55. Parametric behaviors of CLUBB in simulations of low clouds in the Community Atmosphere Model (CAM) Z. Guo et al. https://doi.org/10.1002/2014MS000405
56. Modelling uncertainty in WRF-based extreme rainfall simulations over India: A review toward integrated atmosphere-chemistry-hydrology frameworks S. Maurya & P. Soni https://doi.org/10.1016/j.atmosres.2026.109152
57. The Sensitivity of Extreme Rainfall Simulations to WRF Parameters During Two Intense Southwest Monsoon Events in the Philippines K. Henson et al. https://doi.org/10.1007/s13143-024-00380-6
58. On the Bayesian calibration of expensive computer models with input dependent parameters G. Karagiannis et al. https://doi.org/10.1016/j.spasta.2017.08.002
59. A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5 C. Zhao et al. https://doi.org/10.5194/acp-13-10969-2013
60. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models H. Wan et al. https://doi.org/10.5194/gmd-7-1961-2014
61. Crop physiology calibration in the CLM I. Bilionis et al. https://doi.org/10.5194/gmd-8-1071-2015
62. Impact of remotely sensed albedo and vegetation fraction on simulation of urban climate in WRF‐urban canopy model: A case study of the urban heat island in Los Angeles P. Vahmani & G. Ban‐Weiss https://doi.org/10.1002/2015JD023718
63. Quantifying Parametric Uncertainty Effects on Tropical Cloud Fraction in an AGCM F. Xie et al. https://doi.org/10.1029/2022MS003221
64. Impact of Irrigation over the California Central Valley on Regional Climate Z. Yang et al. https://doi.org/10.1175/JHM-D-16-0158.1
65. Upper-Troposphere MM5 and WRF Temperature Error and Vertical Velocity Coupling K. Soich & B. Rappenglueck https://doi.org/10.1175/JAMC-D-12-092.1
66. The roles of bias-correction and resolution in regional climate simulations of summer extremes D. PaiMazumder & J. Done https://doi.org/10.1007/s00382-014-2413-0
67. Urban Climate Risk Mitigation via Optimal Spatial Resource Allocation I. Nevat & M. Mughal https://doi.org/10.3390/atmos13030439
68. Parameter estimation of an atmospheric model using geostationary satellite observation to improve prediction of tropical cyclones: an idealized experiment Y. Hirose et al. https://doi.org/10.1007/s44394-026-00021-8
69. The Response of Cloud Precipitation Efficiency to Warming in a Rainfall Corridor Simulated by WRF Q. Guo et al. https://doi.org/10.3390/atmos15111381
70. A note on surface layer parameterizations in the weather research and forecast model P. Srivastava et al. https://doi.org/10.1016/j.dynatmoce.2021.101259
71. Uncertainty reduction in quantitative precipitation prediction by tuning of Kain–Fritch scheme input parameters in the WRF model using the simulated annealing optimization method M. Afshar et al. https://doi.org/10.1002/met.1919
72. An overview of mineral dust modeling over East Asia S. Chen et al. https://doi.org/10.1007/s13351-017-6142-2
73. Climatic effects of irrigation over the Huang‐Huai‐Hai Plain in China simulated by the weather research and forecasting model B. Yang et al. https://doi.org/10.1002/2015JD023736
74. Improvements in WRF simulation skills of southeastern United States summer rainfall: physical parameterization and horizontal resolution L. Li et al. https://doi.org/10.1007/s00382-013-2031-2
75. Bayesian Calibration of the Community Land Model Using Surrogates J. Ray et al. https://doi.org/10.1137/140957998
76. A decision-making model for flood warning system based on ensemble forecasts L. Goodarzi et al. https://doi.org/10.1016/j.jhydrol.2019.03.040
77. Combined impacts of convection and microphysics parameterizations on the simulations of precipitation and cloud properties over Asia D. Liu et al. https://doi.org/10.1016/j.atmosres.2018.05.017
78. Cloud ability to produce precipitation over arid and semiarid regions of Central and East Asia Y. Liu et al. https://doi.org/10.1002/joc.6304
79. Calibration of a convective parameterization scheme in the WRF model and its impact on the simulation of East Asian summer monsoon precipitation B. Yang et al. https://doi.org/10.1007/s00382-014-2118-4
80. An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model J. Quan et al. https://doi.org/10.1002/qj.2885
81. Lagrangian Investigation of the Precipitation Efficiency of Convective Clouds W. Langhans et al. https://doi.org/10.1175/JAS-D-14-0159.1
82. Parametric Sensitivity and Uncertainty Quantification in the Version 1 of E3SM Atmosphere Model Based on Short Perturbed Parameter Ensemble Simulations Y. Qian et al. https://doi.org/10.1029/2018JD028927
83. Parametric sensitivity analysis of precipitation at global and local scales in the Community Atmosphere Model CAM5 Y. Qian et al. https://doi.org/10.1002/2014MS000354
84. Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model B. Yang et al. https://doi.org/10.1007/s10546-016-0185-2
85. Investigating the linkage between simulated precipitation climatology and ENSO-related precipitation anomaly based on multi-model and perturbed parameter ensembles B. Yang et al. https://doi.org/10.1007/s00704-019-02990-x
86. Effect of the Near-Future Climate Change under RCP8.5 on the Heat Stress and Associated Work Performance in Thailand T. Amnuaylojaroen et al. https://doi.org/10.3390/atmos13020325
87. Effects of cumulus parameterization closures on simulations of summer precipitation over the United States coastal oceans F. Qiao & X. Liang https://doi.org/10.1002/2015MS000621
88. Assessment of WRF-CO2 simulated vertical profiles of CO2 over Delhi region using aircraft and global model data S. Ballav et al. https://doi.org/10.1007/s44273-024-00030-3
89. Thermodynamic and dynamic contributions to future changes in summer precipitation over Northeast Asia and Korea: a multi-RCM study D. Lee et al. https://doi.org/10.1007/s00382-017-3566-4
90. Can nudging be used to quantify model sensitivities in precipitation and cloud forcing? G. Lin et al. https://doi.org/10.1002/2016MS000659
91. Effects of Mosaic Land Use on Dynamically Downscaled WRF Simulations of the Contiguous United States M. Mallard & T. Spero https://doi.org/10.1029/2018JD029755
92. Evaluation of precipitation over an oceanic region of Japan in convection-permitting regional climate model simulations A. Murata et al. https://doi.org/10.1007/s00382-016-3172-x
93. Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble D. PaiMazumder & J. Done https://doi.org/10.1002/2016JD025322
94. Calibration of cloud and aerosol related parameters for solar irradiance forecasts in WRF-solar Y. Liu et al. https://doi.org/10.1016/j.solener.2022.05.064
95. High resolution WRF climatic simulations for the Iberian Peninsula: Model validation M. Marta-Almeida et al. https://doi.org/10.1016/j.pce.2016.03.010
96. Evaluation of Heavy Precipitation Simulated by the WRF Model Using 4D-Var Data Assimilation with TRMM 3B42 and GPM IMERG over the Huaihe River Basin, China L. Yi et al. https://doi.org/10.3390/rs10040646
97. Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 2, projections of future change M. Wehner https://doi.org/10.1016/j.wace.2020.100284
98. Improving WRF model turbine-height wind-speed forecasting using a surrogate- based automatic optimization method Z. Di et al. https://doi.org/10.1016/j.atmosres.2019.04.011
99. The Response of Cloud-Precipitation Recycling in China to Global Warming Q. Guo et al. https://doi.org/10.3390/rs13081601
100. Uncertainty quantification and optimization of precipitating hydrometeor parameters for winter precipitation in a cloud microphysics scheme K. Kim et al. https://doi.org/10.1016/j.atmosres.2025.108554
101. A novel method to improve temperature forecast in data-scarce urban environments with application to the Urban Heat Island in Beirut M. Ghadban et al. https://doi.org/10.1016/j.uclim.2020.100648
102. Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model Z. Di et al. https://doi.org/10.1007/s11430-016-9021-6
103. Parametric Sensitivity Analysis for the Asian Summer Monsoon Precipitation Simulation in the Beijing Climate Center AGCM, Version 2.1 B. Yang et al. https://doi.org/10.1175/JCLI-D-14-00655.1
104. Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon C. Wang et al. https://doi.org/10.1016/j.atmosres.2021.105761
105. CECAV-DNN: Collective Ensemble Comparison and Visualization using Deep Neural Networks W. He et al. https://doi.org/10.1016/j.visinf.2020.04.004
106. Fast and Slow Responses of Atmospheric Energy Budgets to Perturbed Cloud and Convection Processes in an Atmospheric Global Climate Model B. Yang et al. https://doi.org/10.1029/2023GL104305
107. Sensitivities of the interannual variability of the East Asian winter monsoon for the physical parameters in CAS-FGOALS-g3 Q. Wang et al. https://doi.org/10.1007/s00382-025-07675-y
108. Sensitivity of Dynamical Downscaling Seasonal Precipitation Forecasts to Convection and Land Surface Parameterization in a High-Resolution Regional Climate Model Y. Li et al. https://doi.org/10.1155/2019/6010674
109. Integrating remote sensing data with WRF model for improved 2-m temperature and humidity simulations in China D. Yan et al. https://doi.org/10.1016/j.dynatmoce.2019.101127
110. An integrated assessment of extreme hydrometeorological events in Bangladesh S. Moghim & A. Takallou https://doi.org/10.1007/s00477-023-02404-5
111. A Review of High Impact Weather for Aviation Meteorology I. Gultepe et al. https://doi.org/10.1007/s00024-019-02168-6