Evaluating the Performance of Land Surface Models and Microphysics Schemes on Simulation of an Extreme Rainfall Event in Tanzania Using the Weather Research and Forecasting Model
Precise and accurate rainfall simulation is essential for Tanzania, where complex topography and diverse climatic influences result in variable precipitation patterns. In this study, the 31st October 2023 to 02nd November 2023 daily observation rainfall was used to assess the performance of 5 land surface models (LSMs) and 7 microphysics schemes (MPs) using the Weather Research and Forecasting (WRF) model. The 35 different simulations were then evaluated using the observation data from the ground stations (OBS) and the gridded satellite (CHIRPS) dataset. It was found that the WSM6 scheme performed better than other MPs even though the performance of the LSMs was dependent on the observation data used. The CLM4 performed better than others when the simulations were compared with OBS whereas the 5 Layer Slab produced the lowest mean absolute error (MAE) and root mean square error (RMSE) values while the Noah-MP and RUC schemes produced the lowest average values of RMSE and MAE respectively when the CHIRPS dataset was used. The difference in performance of land surface models when compared to different sets of observation data was attributed to the fact that each observation dataset had a different number of points over the same area, influencing their performances. Furthermore, it was revealed that the CLM4-WSM6 combination performed better than others in the simulation of this event when it was compared against OBS while the 5 Layer Slab-WSM6 combination performed well when the CHIRPS dataset was used for comparison. This research highlights the critical role of the selection of land surface models and microphysics schemes in forecasting extreme rainfall events and underscores the importance of integrating different observational data for model validation. These findings contribute to improving predictive capabilities for extreme rainfall events in similar climatic regions.
References
[1]
Borhara, K., Pokharel, B., Bean, B., Deng, L. and Wang, S.S. (2020) On Tanzania’s Precipitation Climatology, Variability, and Future Projection. Climate, 8, Article No. 34. https://doi.org/10.3390/cli8020034
[2]
Francis, J. (2013) Analysis of Rainfall Variations and Trends in Coastal Tanzania. Western Indian Ocean Journal of Marine Science, 11, 121-133.
[3]
Kijazi, A.L. and Reason, C.J.C. (2005) Relationships between Intraseasonal Rainfall Variability of Coastal Tanzania and Enso. Theoretical and Applied Climatology, 82, 153-176. https://doi.org/10.1007/s00704-005-0129-0
[4]
Yonah, I.B., Oteng’I, S.B.B. and Lukorito, C.B. (2006) Assessment of the Growing Season over the Unimodal Rainfall Regime Region of Tanzania Isack Yonah Tanzania Meteor-Ogical Authority (TMA) Assessment of the Growing Season Regime Region of Tanzania over the Unimodal Rainfall. Tanzania Journal of Agricultural Sciences, 7, 16-26.
[5]
Kijazi, A. and Reason, C. (2009) Analysis of the 1998 to 2005 Drought over the Northeastern Highlands of Tanzania. Climate Research, 38, 209-223. https://doi.org/10.3354/cr00784
[6]
Reddy, B.R., Srinivas, C.V., Shekhar, S.S.R., Baskaran, R. and Venkatraman, B. (2020) Impact of Land Surface Physics in WRF on the Simulation of Sea Breeze Circulation over Southeast Coast of India. Meteorology and Atmospheric Physics, 132, 925-943. https://doi.org/10.1007/s00703-020-00726-5
[7]
Deng, C., Chi, Y., Huang, Y., Jiang, C., Su, L., Lin, H., et al. (2023) Sensitivity of WRF Multiple Parameterization Schemes to Extreme Precipitation Event over the Poyang Lake Basin of China. Frontiers in Environmental Science, 10, Article 1102864. https://doi.org/10.3389/fenvs.2022.1102864
[8]
Di, Z., Zhang, S., Quan, J., Ma, Q., Qin, P. and Li, J. (2023) Performance of Seven Land Surface Schemes in the WRFv4.3 Model for Simulating Precipitation in the Record-Breaking Meiyu Season over the Yangtze-Huaihe River Valley in China. GeoHealth, 7, e2022GH000757. https://doi.org/10.1029/2022gh000757
[9]
Teklay, A., Dile, Y.T., Asfaw, D.H., Bayabil, H.K. and Sisay, K. (2019) Impacts of Land Surface Model and Land Use Data on WRF Model Simulations of Rainfall and Temperature over Lake Tana Basin, Ethiopia. Heliyon, 5, e02469. https://doi.org/10.1016/j.heliyon.2019.e02469
[10]
Zhou, Z., Du, M., Hu, Y., Kang, Z., Yu, R. and Guo, Y. (2024) An Evaluation and Improvement of Microphysical Parameterization for a Heavy Rainfall Process during the Meiyu Season. Remote Sensing, 16, Article No. 1636. https://doi.org/10.3390/rs16091636
[11]
Bakketun, Å., Blyverket, J. and Müller, M. (2023) Using a Reanalysis-Driven Land Surface Model for Initialization of a Numerical Weather Prediction System. Weather and Forecasting, 38, 2155-2168. https://doi.org/10.1175/waf-d-22-0184.1
[12]
Henderson, D.S., Otkin, J.A. and Mecikalski, J.R. (2022) Examining the Role of the Land Surface on Convection Using High-Resolution Model Forecasts over the Southeastern United States. Journal of Geophysical Research: Atmospheres, 127, e2022JD036563. https://doi.org/10.1029/2022jd036563
[13]
Chao, L., Zhang, K., Wang, S., Gu, Z., Xu, J. and Bao, H. (2022) Assimilation of Surface Soil Moisture Jointly Retrieved by Multiple Microwave Satellites into the WRF-Hydro Model in Ungauged Regions: Towards a Robust Flood Simulation and Forecasting. Environmental Modelling & Software, 154, Article ID: 105421. https://doi.org/10.1016/j.envsoft.2022.105421
[14]
Peters-Lidard, C.D., Mocko, D.M., Su, L., Lettenmaier, D.P., Gentine, P. and Barlage, M. (2021) Advances in Land Surface Models and Indicators for Drought Monitoring and Prediction. Bulletin of the American Meteorological Society, 102, E1099-E1122. https://doi.org/10.1175/bams-d-20-0087.1
[15]
Nemec, D., Brožková, R. and Van Ginderachter, M. (2024) Developments of Single-Moment ALARO Microphysics Scheme with Three Prognostic Ice Categories. Tellus A: Dynamic Meteorology and Oceanography, 76, 130-147. https://doi.org/10.16993/tellusa.3464
[16]
Xu, H., Li, X., Yin, J., Zhou, L., Song, Y. and Hu, T. (2023) Microphysics Affect the Sensitivities of Rainfall to Different Horizontal-Resolution Simulations: Evidence from a Case Study of the Weather Research and Forecasting Model Runs. Atmospheric Research, 296, Article ID: 107022. https://doi.org/10.1016/j.atmosres.2023.107022
[17]
Glotfelty, T., Ramírez-Mejía, D., Bowden, J., Ghilardi, A. and West, J.J. (2020) Limitations of WRF Land Surface Models for Simulating Land Use and Land Cover Change in Sub-Saharan Africa and Development of an Improved Model (CLM-AF v. 1.0). Geoscientific Model Development, 14, 3215-3249. https://doi.org/10.5194/gmd-2020-193
[18]
Lungo, A., Kim, S., Jiang, M., Cho, G. and Kim, Y. (2020) Sensitivity Study of WRF Simulations over Tanzania for Extreme Events during Wet and Dry Seasons. Atmosphere, 11, Article No. 459. https://doi.org/10.3390/atmos11050459
[19]
Pohl, B., Crétat, J. and Camberlin, P. (2011) Testing WRF Capability in Simulating the Atmospheric Water Cycle over Equatorial East Africa. Climate Dynamics, 37, 1357-1379. https://doi.org/10.1007/s00382-011-1024-2
[20]
Baki, H., Chinta, S., Balaji, C. and Srinivasan, B. (2021) A Sensitivity Study of WRF Model Microphysics and Cumulus Parameterization Schemes for the Simulation of Tropical Cyclones Using GPM Radar Data. Journal of Earth System Science, 130, Article No. 190. https://doi.org/10.1007/s12040-021-01682-3
[21]
Chen, F. and Dudhia, J. (2001) Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Monthly Weather Review, 129, 569-585. https://doi.org/10.1175/1520-0493(2001)129<0569:caalsh>2.0.co;2
[22]
Smirnova, T.G., Brown, J.M. and Benjamin, S.G. (1997) Performance of Different Soil Model Configurations in Simulating Ground Surface Temperature and Surface Fluxes. Monthly Weather Review, 125, 1870-1884. https://doi.org/10.1175/1520-0493(1997)125<1870:podsmc>2.0.co;2
[23]
Smirnova, T.G., Brown, J.M., Benjamin, S.G. and Kim, D. (2000) Parameterization of Cold-Season Processes in the MAPS Land-Surface Scheme. Journal of Geophysical Research: Atmospheres, 105, 4077-4086. https://doi.org/10.1029/1999jd901047
[24]
Niu, G., Yang, Z., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., et al. (2011) The Community Noah Land Surface Model with Multiparameterization Options (Noah-MP): 1. Model Description and Evaluation with Local-Scale Measurements. Journal of Geophysical Research, 116, D12109. https://doi.org/10.1029/2010jd015139
[25]
Yang, Z., Niu, G., Mitchell, K.E., Chen, F., Ek, M.B., Barlage, M., et al. (2011) The Community Noah Land Surface Model with Multiparameterization Options (Noah-MP): 2. Evaluation over Global River Basins. Journal of Geophysical Research, 116, D12110. https://doi.org/10.1029/2010jd015140
[26]
Oleson, K.W., Lawrence, D.M., Bonan, G.B., Flanner, M.G., Kluzek, E., Lawrence, P.J., et al. (2010) Technical Description of version 4.0 of the Community Land Model (CLM). https://doi.org/10.5065/D6FB50WZ
[27]
Kessler, E. (1995) On the Continuity and Distribution of Water Substance in Atmospheric Circulations. Atmospheric Research, 38, 109-145. https://doi.org/10.1016/0169-8095(94)00090-z
[28]
Lin, Y., Farley, R.D. and Orville, H.D. (1983) Bulk Parameterization of the Snow Field in a Cloud Model. Journal of Climate and Applied Meteorology, 22, 1065-1092. https://doi.org/10.1175/1520-0450(1983)022<1065:bpotsf>2.0.co;2
[29]
Hong, S.Y. and Lim, J.O.J. (2006) The WRF Single-Moment 6-Class Microphysics Scheme (WSM6). Journal of the Korean Meteorological Society, 42, 129-151.
[30]
Thompson, G., Field, P.R., Rasmussen, R.M. and Hall, W.D. (2008) Explicit Forecasts of Winter Precipitation Using an Improved Bulk Microphysics Scheme. Part II: Implementation of a New Snow Parameterization. Monthly Weather Review, 136, 5095-5115. https://doi.org/10.1175/2008mwr2387.1
[31]
Milbrandt, J.A. and Yau, M.K. (2005) A Multimoment Bulk Microphysics Parameterization. Part I: Analysis of the Role of the Spectral Shape Parameter. Journal of the Atmospheric Sciences, 62, 3051-3064. https://doi.org/10.1175/jas3534.1
[32]
Lim, K.S. and Hong, S. (2010) Development of an Effective Double-Moment Cloud Microphysics Scheme with Prognostic Cloud Condensation Nuclei (CCN) for Weather and Climate Models. Monthly Weather Review, 138, 1587-1612. https://doi.org/10.1175/2009mwr2968.1
[33]
Mlawer, E.J., Taubman, S.J., Brown, P.D., Iacono, M.J. and Clough, S.A. (1997) Radiative Transfer for Inhomogeneous Atmospheres: RRTM, a Validated Correlated-k Model for the Longwave. Journal of Geophysical Research: Atmospheres, 102, 16663-16682. https://doi.org/10.1029/97jd00237
[34]
Dudhia, J. (1989) Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. Journal of the Atmospheric Sciences, 46, 3077-3107. https://doi.org/10.1175/1520-0469(1989)046<3077:nsocod>2.0.co;2
[35]
Pleim, J.E. (2007) A Combined Local and Nonlocal Closure Model for the Atmospheric Boundary Layer. Part I: Model Description and Testing. Journal of Applied Meteorology and Climatology, 46, 1383-1395. https://doi.org/10.1175/jam2539.1
[36]
Grell, G.A. and Freitas, S.R. (2014) A Scale and Aerosol Aware Stochastic Convective Parameterization for Weather and Air Quality Modeling. Atmospheric Chemistry and Physics, 14, 5233-5250. https://doi.org/10.5194/acp-14-5233-2014
[37]
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., et al. (2015) The Climate Hazards Infrared Precipitation with Stations—A New Environmental Record for Monitoring Extremes. Scientific Data, 2, Article No. 150066. https://doi.org/10.1038/sdata.2015.66
[38]
Willmott, C. and Matsuura, K. (2005) Advantages of the Mean Absolute Error (MAE) over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Climate Research, 30, 79-82. https://doi.org/10.3354/cr030079
[39]
Chai, T. and Draxler, R.R. (2014) Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?—Arguments against Avoiding RMSE in the Literature. Geoscientific Model Development, 7, 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
[40]
Pavia, E.G. (2004) The Uncertainty of Climatological Values. Geophysical Research Letters, 31, L14206. https://doi.org/10.1029/2004gl020526
[41]
Siegert, S., Bellprat, O., Ménégoz, M., Stephenson, D.B. and Doblas-Reyes, F.J. (2017) Detecting Improvements in Forecast Correlation Skill: Statistical Testing and Power Analysis. Monthly Weather Review, 145, 437-450. https://doi.org/10.1175/mwr-d-16-0037.1
[42]
Biswasharma, R., Umakanth, N., Pongener, I., Longkumer, I., Rao, K.M.M., Pawar, S.D., et al. (2024) Sensitivity Analysis of Cumulus and Microphysics Schemes in the WRF Model in Simulating Extreme Rainfall Events over the Hilly Terrain of Nagaland. Atmospheric Research, 304, Article ID: 107393. https://doi.org/10.1016/j.atmosres.2024.107393
[43]
Taylor, K.E. (2001) Summarizing Multiple Aspects of Model Performance in a Single Diagram. Journal of Geophysical Research: Atmospheres, 106, 7183-7192. https://doi.org/10.1029/2000jd900719
[44]
Liu, Y. and Daum, P.H. (2003) Toward a Unified Formulation of the Kessler-Type Autoconversion Parameterizations. Thirteenth ARM Science Team Meeting Proceedings, Broomfield, 31 March-4 April 2003, 1-5.
[45]
Ghosh, S. and Jonas, P.R. (1998) On the Application of the Classic Kessler and Berry Schemes in Large Eddy Simulation Models with a Particular Emphasis on Cloud Autoconversion, the Onset Time of Precipitation and Droplet Evaporation. Annales Geophysicae, 16, 628-637. https://doi.org/10.1007/s00585-998-0628-2
[46]
Liu, Y. and Daum, P.H. (2004) Parameterization of the Autoconversion Process. Part I: Analytical Formulation of the Kessler-Type Parameterizations. Journal of the Atmospheric Sciences, 61, 1539-1548. https://doi.org/10.1175/1520-0469(2004)061<1539:potapi>2.0.co;2
[47]
Chakraborty, T., Pattnaik, S., Jenamani, R.K. and Baisya, H. (2021) Evaluating the Performances of Cloud Microphysical Parameterizations in WRF for the Heavy Rainfall Event of Kerala (2018). Meteorology and Atmospheric Physics, 133, 707-737. https://doi.org/10.1007/s00703-021-00776-3
[48]
Sharma, A., Sharma, D., Panda, S.K. and Kumar, A. (2024) Sensitivity Analysis of Different Parameterization Schemes of the Weather Research and Forecasting (WRF) Model to Simulate Heavy Rainfall Events over the Mahi River Basin, India. Agricultural and Forest Meteorology, 346, Article ID: 109885. https://doi.org/10.1016/j.agrformet.2023.109885
[49]
Lu, S., Guo, W., Xue, Y., Huang, F. and Ge, J. (2021) Simulation of Summer Climate over Central Asia Shows High Sensitivity to Different Land Surface Schemes in WRF. Climate Dynamics, 57, 2249-2268. https://doi.org/10.1007/s00382-021-05876-9
[50]
Köcher, G., Zinner, T. and Knote, C. (2022) Influence of Cloud Microphysics Schemes on Weather Model Predictions of Heavy Precipitation. Atmospheric Chemistry and Physics, 23, 6255-6269. https://doi.org/10.5194/acp-2022-835
