WRF Sensitivity for Seasonal Climate Simulations of Precipitation Fields on the CORDEX South America Domain

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https://www.mdpi.com/2073-4433/13/1/107

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https://doi.org/10.3390/atmos13010107
 http://hdl.handle.net/11603/24112

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UMBC Physics Department
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https://orcid.org/0000-0002-4027-1855

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2022-01-10

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journal articles
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Citation of Original Publication

Gomes, Helber B., Maria C. Lemos da Silva, Henrique d.M.J. Barbosa, Tércio Ambrizzi, Hakki Baltaci, Heliofábio B. Gomes, Fabrício D.d.S. Silva, Rafaela L. Costa, Silvio N. Figueroa, Dirceu L. Herdies, and Theotonio M. Pauliquevis Júnior. 2022. "WRF Sensitivity for Seasonal Climate Simulations of Precipitation Fields on the CORDEX South America Domain" Atmosphere 13, no. 1: 107. https://doi.org/10.3390/atmos13010107

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UMBC High Performance Computing Facility (HPCF)

Abstract

Dynamic numerical models of the atmosphere are the main tools used for weather and climate forecasting as well as climate projections. Thus, this work evaluated the systematic errors and areas with large uncertainties in precipitation over the South American continent (SAC) based on regional climate simulations with the weather research and forecasting (WRF) model. Ten simulations using different convective, radiation, and microphysical schemes, and an ensemble mean among them, were performed with a resolution of 50 km, covering the CORDEX-South America domain. First, the seasonal precipitation variability and its differences were discussed. Then, its annual cycle was investigated through nine sub-domains on the SAC (AMZN, AMZS, NEBN, NEBS, SE, SURU, CHAC, PEQU, and TOTL). The Taylor Diagrams were used to assess the sensitivity of the model to different parameterizations and its ability to reproduce the simulated precipitation patterns. The results showed that the WRF simulations were better than the ERA-interim (ERAI) reanalysis when compared to the TRMM, showing the added value of dynamic downscaling. For all sub-domains the best result was obtained with the ensemble compared to the satellite TRMM. The largest errors were observed in the SURU and CHAC regions, and with the greatest dispersion of members during the rainy season. On the other hand, the best results were found in the AMZS, NEBS, and TOTL regions.