A High‐Resolution Tropical Mesoscale Convective System Reanalysis (TMeCSR). (29th August 2022)

• Record Type: Journal Article
• Authors: Chan, Man‐Yau; Chen, Xingchao; Leung, L. Ruby
• Is Part Of: Journal of advances in modeling earth systems. Volume 14:Number 9(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-29
• Subjects: mesoscale convective system -- reanalysis -- data assimilation; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2021MS002948
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Modern global reanalysis products have greatly accelerated meteorological research in synoptic‐to‐planetary‐scale phenomena. However, their use in studying tropical mesoscale convective systems (MCSs) and their regional‐to‐global impact has mostly been limited to supplying initial and boundary conditions for MCS‐resolving simulations and providing information about the large‐scale environments of MCSs. These limitations are due to difficulties in resolving tropical MCS dynamics in the relatively low‐resolution global models and that tropical MCSs often occur over poorly observed regions. In this work, a Tropical MCS‐resolving Reanalysis product (TMeCSR) was created over a region with frequent tropical MCSs. This region spans the tropical Indian Ocean, tropical continental Asia, Maritime Continent, and Western Pacific. TMeCSR is produced by assimilating all‐sky infrared radiances from geostationary satellites and other conventional observations into an MCS‐resolving regional model using the Ensemble Kalman Filter. The resulting observation‐constrained high‐resolution (9‐km grid spacing) data set is available hourly during the boreal summer (June‐August) of 2017, during which widespread severe flooding occurred. Comparisons of TMeCSR and European Center for Medium Range Weather Forecast Reanalysis version 5 (ERA5) against independent satellite retrievals indicate that TMeCSR's cloud and multiscale rain fields are better than those of ERA5. Furthermore, TMeCSR better captured the diurnal variability of rainfall and the statistical characteristics of MCSs. Forecasts initialized from TMeCSR also have more accurate rain and clouds than those initialized from ERA5. The TMeCSR and ERA5 forecasts have similar performances with respect to sounding and surface observations. These results indicate that TMeCSR is a promising MCS‐resolving data set for tropical MCS studies. Plain Language Summary: Thunderstorms provide much of the rainfall over the Tropics and have important impacts on global weather and climate. However, these important systems often occur over regions with sparse in‐situ observations. Hence, it is difficult to use in‐situ observations to study the detailed dynamics and thermodynamics of these thunderstorm systems. While combining observations with computer simulation data can produce three‐dimensional data sets over the Tropics, the currently available combination data sets have difficulty resolving these thunderstorm systems. In this study, we combined high‐resolution satellite measurements with high‐resolution weather simulations to produce a high‐resolution four‐dimensional data set. This new data set can capture tropical thunderstorm systems over an area spanning the tropical Indian Ocean to the western edge of Pacific Ocean. We compared the accuracy of our new data set against a gold standard global data set. Using independent satellite‐derived radiation and rainfall data, we found that our new data set has more accurate storm characteristics compared to the gold standard. These characteristics include clouds and rainfall. Furthermore, simulations initialized from our new data set had a similar advantage over simulations initialized from the gold standard. These promising results suggest that our new data set might be better at capturing tropical thunderstorm systems than the gold standard. Key Points: Tropical mesoscale convective systems (MCSs) research can benefit from an observation‐constrained MCS‐resolving reanalysis data set We produced such a data set using all‐sky satellite infrared radiances, MCS‐resolving regional simulations, and ensemble data assimilation Compared to European Center for Medium Range Weather Forecast Reanalysis version 5, the new data set better captured cloud, rainfall, and frequency of tropical MCSs and produced better short‐term forecasts