Abstract
Abstract A new satellite-based rainfall monitoring algorithm that integrates the strengths of both low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite information has been developed. The Lagrangian Model (LMODEL) algorithm combines a 2D cloud-advection tracking system and a GEO data–driven cloud development and rainfall generation model with procedures to update model parameters and state variables in near–real time. The details of the LMODEL algorithm were presented in Part I. This paper describes a comparative validation against ground radar rainfall measurements of 1- and 3-h LMODEL accumulated rainfall outputs. LMODEL rainfall estimates consistently outperform accumulated 3-h microwave (MW)-only rainfall estimates, even before the more restricted spatial coverage provided by the latter is taken into account. In addition, the performance of LMODEL products remains effective and consistent between MW overpasses. Case studies demonstrate that the LMODEL provides the potential to synergize available satellite data to generate useful precipitation measurements at an hourly scale.
Highlights
Floods caused by extreme precipitation events are among the most serious natural disasters worldwide
This paper presents a validation of Lagrangian Model (LMODEL) outputs and evaluates the performance of the new algorithm
LMODEL is an integrated rainfall estimation algorithm developed to combine the strengths of low Earth-orbiting (LEO) and geostationary Earth-orbiting (GEO) satellite data from current and future satellite missions (e.g., Global Precipitation Measurement (GPM)) by making optimal use of the complementary nature of different sensors and their respective sampling capabilities
Summary
Floods caused by extreme precipitation events are among the most serious natural disasters worldwide. LMODEL is an integrated rainfall estimation algorithm developed to combine the strengths of LEO and GEO satellite data from current and future satellite missions (e.g., GPM) by making optimal use of the complementary nature of different sensors and their respective sampling capabilities.
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