Ronald Stenz

Date of Award

January 2014

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Atmospheric Sciences

First Advisor

Xiquan Dong

Abstract

As Deep Convective Systems (DCSs) are responsible for most severe weather events, increased understanding of these systems along with more accurate satellite precipitation estimates will improve NWS (National Weather Service) warnings and monitoring of hazardous weather conditions. A DCS can be classified into convective core (CC) regions (heavy rain), stratiform (SR) regions (moderate-light rain), and anvil (AC) regions (no rain). These regions share similar infrared (IR) brightness temperatures (BT), which can create large errors for many existing rain detection algorithms. This study assesses the performance of the National Mosaic and Multi-sensor Quantitative Precipitation Estimation System (NMQ) Q2, and a simplified version of the GOES-R Rainfall Rate algorithm (also known as the Self-Calibrating Multivariate Precipitation Retrieval, or SCaMPR), over the state of Oklahoma (OK) using OK MESONET observations as ground truth. While the average annual Q2 precipitation estimates were about 35% higher than MESONET observations , there were very strong correlations between these two data sets for multiple temporal and spatial scales. Additionally, the Q2 estimated precipitation distributions over the CC, SR, and AC regions of DCSs strongly resembled the MESONET observed ones, indicating that Q2 can accurately capture the precipitation characteristics of DCSs although it has a wet bias . SCaMPR retrievals were typically three to four times higher than the collocated MESONET observations, with relatively weak correlations during a year of comparisons in 2012. Overestimates from SCaMPR retrievals that produced a high false alarm rate were primarily caused by precipitation retrievals from the anvil regions of DCSs when collocated MESONET stations recorded no precipitation. A modified SCaMPR retrieval algorithm, employing both cloud optical depth and IR temperature, has the potential to make significant improvements to reduce the SCaMPR false alarm rate of retrieved precipitation especially over non-precipitating (anvil) regions of a DCS. Preliminary testing of this new algorithm to identify precipitating area has produced significant improvements over the current SCaMPR algorithm. This modified version of SCaMPR can be used to provide precipitation estimates in gaps of radar and rain gauge coverage to aid in hydrological and flood forecasting.

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