Improving Global Satellite Precipitation Products Utilizing Machine Learning
type: CreativeWork
description: This dissertation investigates applications of machine learning and deep learning to improve global satellite precipitation products. This includes providing practical guidance and analysis to determine which sensor or algorithm can provide the most effective precipitation input to the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG; or other global precipitation products such as the Global Precipitation Climatology Project; GPCP) in high latitudes (i.e., poleward of 60°S/N). The possibility of mitigating the latency gap of satellite precipitation products through deep-learning-based nowcasting is also investigated in this dissertation.IMERG is the most popular product among GPM products and is used for various scientific analyses/applications. The latest version of the IMERG (V06) was extended to the poles but still has gaps over snow and ice surfaces poleward of 60°S/N, where it also shows significant underestimation. This is mainly due to the low-quality level-2 precipitation retrievals used in IMERG (currently only passive microwave [PMW] sensors are used in high latitudes). While infrared data from geostationary satellites are used to fill some of the gaps within 60°S/N, outside of this range no infrared estimates have been used in IMERG, mainly because high-quality geostationary observation and precipitation estimates from that are not available and using single polar-orbital infrared satellites (e.g., Atmospheric Infrared Sensor [AIRS] already used in GPCP) cannot provide temporal sampling required by IMERG. So far, no study has carefully assessed/ranked available infrared products (or alternative retrievals) in high latitudes to provide practical guidance and recommendation to the IMERG and GPCP teams for enhancing their products in high latitudes. In this dissertation, I performed a comprehensive analysis to collect and assess available precipitation estimates from the AIRS and the recently developed precipitation esti…
abstract: This dissertation investigates applications of machine learning and deep learning to improve global satellite precipitation products. This includes providing practical guidance and analysis to determine which sensor or algorithm can provide the most effective precipitation input to the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG; or other global precipitation products such as the Global Precipitation Climatology Project; GPCP) in high latitudes (i.e., poleward of 60°S/N). The possibility of mitigating the latency gap of satellite precipitation products through deep-learning-based nowcasting is also investigated in this dissertation.IMERG is the most popular product among GPM products and is used for various scientific analyses/applications. The latest version of the IMERG (V06) was extended to the poles but still has gaps over snow and ice surfaces poleward of 60°S/N, where it also shows significant underestimation. This is mainly due to the low-quality level-2 precipitation retrievals used in IMERG (currently only passive microwave [PMW] sensors are used in high latitudes). While infrared data from geostationary satellites are used to fill some of the gaps within 60°S/N, outside of this range no infrared estimates have been used in IMERG, mainly because high-quality geostationary observation and precipitation estimates from that are not available and using single polar-orbital infrared satellites (e.g., Atmospheric Infrared Sensor [AIRS] already used in GPCP) cannot provide temporal sampling required by IMERG. So far, no study has carefully assessed/ranked available infrared products (or alternative retrievals) in high latitudes to provide practical guidance and recommendation to the IMERG and GPCP teams for enhancing their products in high latitudes. In this dissertation, I performed a comprehensive analysis to collect and assess available precipitation estimates from the AIRS and the recently developed precipitation esti…
author: Ehsani, Mohammad Reza
language: en
keywords: Climate, Deep Learning, IMERG, Machine Learning, NASA GPM, Precipitation
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2023
date modified: 2026-07-24T00:57:45Z
provider: University of Arizona
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Arizona.

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10150/668077