Integrating Satellite-Based Precipitation Analysis: A Case Study in Norfolk, Virginia

In many developing cities, the scarcity of adequate observed precipitation stations, due to constraints such as limited space, urban growth, and maintenance challenges, compromises data reliability. This study explores the use of satellite-based precipitation products (SbPPs) as a solution to supplement missing data over the long term, thereby enabling more accurate environmental analysis and decision-making. Specifically, the effectiveness of SbPPs in Norfolk, Virginia, is assessed by comparing them with observed precipitation data from Norfolk International Airport (NIA) using common bias adjustment methods. The study applies three different methods to correct biases caused by sensor limitations and calibration discrepancies and then identifies the most effective methods based on statistical indicators, detection capability indices, and graphical methods. Bias adjustment methods include additive bias correction (ABC), which subtracts systematic errors; multiplicative bias correction (MBC), which scales satellite data to match observed data; and distribution transformation normalization (DTN), which aligns the statistical distribution of satellite data with observations. Additionally, the study addresses the uncertainties in SbPPs for estimating precipitation, preparing practitioners for the challenges in practical applications. The additive bias correction (ABC) method overestimated mean monthly precipitation, while the PERSIANN-Cloud Classification System (CCS), adjusted with multiplicative bias correction (MBC), was found to be the most accurate bias-adjusted model. The MBC method resulted in slight PBias adjustments of 0.09% (CCS), 0.10% (CDR), and 0.15% (PERSIANN) in mean monthly precipitation estimates, while the DTN method produced larger adjustments of 21.36% (CCS), 31.74% (CDR), and 19.27% (PERSIANN), with CCS, when bias corrected using MBC, identified as the most accurate SbPP for Norfolk, Virginia. This case study not only provides insights into the technical processes but also serves as a guideline for integrating advanced hydrological modeling and urban resilience strategies, contributing to improved strategies for climate change adaptation and disaster preparedness.