Evaluation of Multivariate Downscaling of GEOS S2S Meteorological Forecasts Using Climate Regionalization for the Amazon

Speaker:  Prakrut Kansara, Postdoctoral Fellow, Johns Hopkins University

With record-breaking heat and extreme precipitation events intensifying each year, accurate subseasonal to seasonal (S2S) forecasts are vital in preparing for and managing frequent extreme events. S2S forecasts typically rely on low-resolution global models for predictions, often insensitive to local variation of weather anomalies and their interactions with regional surface properties. This suggests the necessity of downscaling these forecasts to enhance their ability in capturing local and regional spatio-temporal patterns. Kansara will discuss an evaluation of downscaled Goddard Earth Observing System (GEOS) S2S meteorological hindcasts using the Generalized Analog Regression Downscaling (GARD) approach.

This study downscaled precipitation and 2m air temperature fields from 0.5° to 0.125°, utilizing the NASA’s GPM-IMERG and Global Data Assimilation System (GDAS) respectively as the observed data source. The approach employs multivariate downscaling using nine meteorological variables to effectively capture the local dynamics and global teleconnections. Additionally, climate regionalization is used to classify regions exhibiting localized discrepancies. The study assesses the downscaled hindcasts for improvements in skill and bias reduction, particularly in regions identified with low skill metrics through climate regionalization.

Livestream:  https://youtube.com/live/MqAIwJecKCU?feature=share


Feb 20 2024


1:00 pm - 2:00 pm


811 Atmospheric, Oceanic and Space Sciences
1225 W. Dayton St.