TY - JOUR
T1 - Enhancing Extreme Precipitation Predictions With Dynamical Downscaling
T2 - A Convection-Permitting Modeling Study in Texas and Oklahoma
AU - Chang, Hsin I.
AU - Chikamoto, Yoshimitsu
AU - Wang, Simon S.Y.
AU - Castro, Christopher L.
AU - LaPlante, Matthew D.
AU - Risanto, C. Bayu
AU - Huang, Xingying
AU - Bunn, Patrick
N1 - Publisher Copyright:
© 2024. American Geophysical Union. All Rights Reserved.
PY - 2024/4/28
Y1 - 2024/4/28
N2 - Precipitation in the Southern Plains of the United States is relatively well depicted by the Community Earth System Model (CESM). However, despite its ability to capture seasonal mean precipitation anomalies, CESM consistently underestimates extreme pluvial and drought events, rendering it an insufficient tool for extending simulation lead times for exceptional events, such as the abnormally dry May 2011, which helped drive Texas into its worst period of drought in more than a century, and the abnormally wet May 2015, which led to widespread flooding in that state. Ensemble-based regional climate experiments are completed for the two extreme years using Weather Research and Forecasting model (WRF) and downscaled from CESM. WRF simulations are at convection-permitting grid resolution for improved physical representation of simulated precipitation over the Southern Great Plains. By integrating convection-permitting models (CPMs) into each individual member of a CESM ocean data assimilation ensemble, this study demonstrates that high-resolution dynamical downscaling can improve model skillfulness at capturing these two events and is thus a potentially useful tool for forecasting extremely high and extremely low precipitation events at subseasonal or even seasonal lead times.
AB - Precipitation in the Southern Plains of the United States is relatively well depicted by the Community Earth System Model (CESM). However, despite its ability to capture seasonal mean precipitation anomalies, CESM consistently underestimates extreme pluvial and drought events, rendering it an insufficient tool for extending simulation lead times for exceptional events, such as the abnormally dry May 2011, which helped drive Texas into its worst period of drought in more than a century, and the abnormally wet May 2015, which led to widespread flooding in that state. Ensemble-based regional climate experiments are completed for the two extreme years using Weather Research and Forecasting model (WRF) and downscaled from CESM. WRF simulations are at convection-permitting grid resolution for improved physical representation of simulated precipitation over the Southern Great Plains. By integrating convection-permitting models (CPMs) into each individual member of a CESM ocean data assimilation ensemble, this study demonstrates that high-resolution dynamical downscaling can improve model skillfulness at capturing these two events and is thus a potentially useful tool for forecasting extremely high and extremely low precipitation events at subseasonal or even seasonal lead times.
KW - Texas flood
KW - climate prediction
KW - convection-permitting model
KW - dynamic downscaling
KW - ocean data assimilation
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U2 - 10.1029/2023JD038765
DO - 10.1029/2023JD038765
M3 - Article
AN - SCOPUS:85190879788
SN - 2169-897X
VL - 129
JO - Journal of Geophysical Research Atmospheres
JF - Journal of Geophysical Research Atmospheres
IS - 8
M1 - e2023JD038765
ER -