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Affiliation: University of Oklahoma, Norman, OK, USA Project Mentees: Odinaka Echeta

Overview: Understanding the relationship between aerosols and the fuel that govern deep convective storm events can significantly improve meteorologists’ and scientific researchers’ capacity to predict and monitor these severe weather events. For this project, I am analyzing shallow, deep convective and stratiform events over Central Kalimantan using GPM DPR data and comparing it to the amount of atmospheric aerosols using AOD retrievals from ground based measurements provided by AERONET. Mentors: Aaron Funk, Courtney Schumacher

Overview: Numerous isolated precipitation features (PFs) can have similar rain intensities as organized ones and thus have potential to cause catastrophic flooding, landslides, and infrastructure damage. Understanding more about the properties of these storm systems can help improve forecast analysis, and improve mitigation and disaster response. For this project, I am doing a global analysis to sort out when and where high intensity isolated PFs were formed using GPM DPR data and indirect measurements of latent heating from GPM. Mentor: Chuntao Liu

Overview: The Canadian Shield Region (CSR) is the Precambrian rock-dominated, deglaciated region of Northern America. There are more lakes than anywhere else in the world. As projected climate change intensifies the water and energy cycles, understanding fundamental hydrologic changes in these environments will be critical for predicting and managing water resources. In this study, I am using IMERG to find the potential sensitivity of Canadian shield depressions to inundation depth. Mentor: Chuntao Liu

Overview: Extreme precipitation events have caused catastrophic flooding throughout West Africa and will continue to wreak havoc over these parts as the climate continues to change. For this project, I am analyzing monthly convective shallow, deep convective and stratiform events over the West Africa region and comparing rainfall totals using GPM DPR data. Mentors: Aaron Funk, Courtney Schumacher

Overview: Argentina has a low-density rain gauge network. As a result, there are often challenges in representing accurate estimates of precipitation. Satellite quantitative precipitation estimates (SQPEs) derived from satellite measurements can help fill these gaps and serve a critical role in hydrological and weather forecasting applications. This project explores different gauge bias correction methods of SQPE products using the IMERG Early Run in Argentina to improve SQPE data products. Mentor: Joe Turk

Overview: This project evaluates the GPM IMERG Late product against ground data to develop applications for a hydrological modelling online platform. Mentor: Mircea Grecu

Overview: This project works to validate the GPM dual-frequency precipitation radar (DPR) merged scan product against a quality-controlled ground reference data (GV-MRMS) in the continental US. Knowledge from this concept study will be applied to validating/ evaluating and selecting remotely-sensed satellite rainfall products that could be useful for rainfall estimation in data-scarce regions such as West Africa. Mentors: Yagmur Derin, Pierre Kirstetter

Affiliation: Texas A&M University – Corpus Christi, Corpus Christi, Texas, USA Project Mentees: Eva Yamamoto, June Choi; Nimisha Wagle

Affiliation: Goddard Earth Sciences Technology and Research, Morgan State University, and NASA Goddard Space Flight Center, USA Project Mentees: Charlene Gaba, Idelbert Behanzin