The Effect of Storm Life Cycle on Satellite Rainfall Estimation Error

Abstract

The study uses storm tracking information to evaluate error statistics of satellite rain estimation at different maturity stages of storm life cycles. Two satellite rain retrieval products are used for this purpose: (i) NASA's Multisatellite Precipitation Analysis-Real Time product available at 25-km/hourly resolution (3B41-RT) and (ii) the University of California (Irvine) Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product available at 4-km-hourly resolution. Both algorithms use geostationary satellite infrared (IR) observations calibrated to an array of passive microwave (PM) earthorbiting satellite sensor rain retrievals. The techniques differ in terms of algorithmic structure and in the way they use the PM rainfall to calibrate the IR rain algorithms. The satellite retrievals are evaluated against rain gauge-calibrated radar rainfall estimates over the continental United States. Error statistics of hourly rain volumes are determined separately for thunderstorm and shower-type convective systems and for different storm life durations and stages of maturity. The authors show distinct differences between the two satellite retrieval error characteristics. The most notable difference is the strong storm life cycle dependence of 3B41-RT relative to the nearly independent PERSIANN behavior. Another is in the algorithm performance between thunderstorms and showers; 3B41-RT exhibits significant bias increase at longer storm life durations. PERSIANN exhibits consistently improved correlations relative to the 3B41-RT for all storm life durations and maturity stages. The findings of this study support the hypothesis that incorporating cloud type information into the retrieval (done by the PERSIANN algorithm) can help improve the satellite retrieval accuracy.

Event‐Based Evaluation of the GPM Multisatellite Merged Precipitation Product From 2014 to 2018 Over China: Methods and Results

“…Some of these studies assessed the event properties of satellite products for their hydrological utilities but mostly evaluated the basin‐averaged properties with limited event cases and insufficient indicators (Mei et al., 2016; Nikolopoulos et al., 2013; Stampoulis et al., 2013). The other part of these studies tried to reveal the relationship between TRMM precipitation product uncertainties with the life cycle stage of precipitation systems (Imaoka & Nakamura, 2012; Rajendran & Nakazawa, 2005; Tadesse & Anagnostou, 2009). Without high temporal resolution “ground truth” data, most of these studies could only use PR data as references (Imaoka & Nakamura, 2012; Rajendran & Nakazawa, 2005), which had their own bias, and only Tadesse and Anagnostou (2009) used ground radar data for comparison.…”

“…However, hardly any study has conducted a systematic event‐based evaluation of satellite products, even the studies that partly concerned the event‐related evaluations were limited (Mei et al., 2014, 2016; Nikolopoulos et al., 2013; Rajendran & Nakazawa, 2005; Stampoulis et al., 2013; Tadesse & Anagnostou, 2009). Some of these studies assessed the event properties of satellite products for their hydrological utilities but mostly evaluated the basin‐averaged properties with limited event cases and insufficient indicators (Mei et al., 2016; Nikolopoulos et al., 2013; Stampoulis et al., 2013).…”

“…The other part of these studies tried to reveal the relationship between TRMM precipitation product uncertainties with the life cycle stage of precipitation systems (Imaoka & Nakamura, 2012; Rajendran & Nakazawa, 2005; Tadesse & Anagnostou, 2009). Without high temporal resolution “ground truth” data, most of these studies could only use PR data as references (Imaoka & Nakamura, 2012; Rajendran & Nakazawa, 2005), which had their own bias, and only Tadesse and Anagnostou (2009) used ground radar data for comparison. However, the definitions of the life cycle of precipitation in this part of studies were all based on the spaceborne IR images or even TRMM sensors, and these definitions did not have truly independent validation data, which might lead to unreliable conclusions.…”

A Lagrangian analysis of cold cloud clusters and their life cycles with satellite observations

“…In morphing techniques, the shape and intensity of rain clusters is held constant between overpasses [ Joyce et al , ], while in Figures and we show that both horizontal size and temperature growth rates are not constant during cloud cluster evolution. Biases in hourly rain volume estimates vary across life cycle stages, lifetimes, and precipitation algorithm [ Tadesse and Anagnostou , ]. Knowing the age of the cloud could be useful in devising the next‐generation multisensor algorithms.…”

“…These approaches have been proven effective and are being incorporated into Integrated Multi‐satellitE Retrievals for GPM (IMERG) [ Huffman et al , ], the next‐generation, Global Precipitation Measurement (GPM) [ Hou et al , ] era product suite. However, the accuracy of the PMW‐based estimates is also influenced by the life cycle stage [ Tadesse and Anagnostou , ]. Developing a more detailed understanding of evolution can provide additional context.…”