Possibilities and Limitations for Quantitative Precipitation Forecasts Using Nowcasting Methods with Infrared Geosynchronous Satellite Imagery

Andrew M. E. Grose Department of Meteorology, The Florida State University, Tallahassee, Florida

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Eric A. Smith NASA Goddard Space Flight Center, Greenbelt, Maryland

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Hyo-Sang Chung Meteorological Research Institute, Korean Meteorological Administration, Seoul, Korea

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Mi-Lim Ou Department of Meteorology, The Florida State University, Tallahassee, Florida

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Byung-Ju Sohn School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea

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F. Joseph Turk Marine Meteorology Division, Naval Research Laboratory, Monterey, California

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Abstract

A rainfall nowcasting system is developed that identifies locations of raining clouds on consecutive infrared geosynchronous satellite images while predicting the movement of the rain cells for up to 10 h using cloud-motion-based winds. As part of the analysis, the strengths and weaknesses of various kinds of cloud wind filtering schemes and both steady and nonsteady storm advection techniques as forecast operators for quantitative precipitation forecasting are evaluated. The first part of the study addresses the development of a probability matching method (PMM) between histograms of equivalent blackbody temperatures (EBBTs) and Special Sensor Microwave Imager (SSM/I)–derived rain rates (RRs), which enables estimating RRs from instantaneous infrared imagery and allows for RR forecasts from the predicted EBBT fields. The second part of the study addresses the development and testing of the nowcasting system built upon the PMM capability and analyzes its success according to various skill score metrics. Key processes involved in the nowcasting system include the retrieved cloud-motion wind field, the filtered cloud-motion wind field, and the forecasting of a future rain field by storm advection and EBBT tendencies. These processes allow for the short-term forecasting of cloud and rain locations and of rain intensity, using PMM-based RRs from different datasets of infrared Geostationary Meteorological Satellite (GMS) and Geostationary Operational Environmental Satellite (GOES) imagery. For this study, three convective rain sequences from the Caribbean basin, the Amazon basin, and the Korean peninsula are analyzed. The final part of the study addresses the decay of forecast accuracy with time (i.e., the point at which the asymptotic limit on forecast skill is reached). This analysis indicates that the nowcasting system can produce useful rainfall forecast information out to approximately 6 h.

Corresponding author address: Eric A. Smith, NASA Goddard Space Flight Center, Code 912.1, Greenbelt, MD 20771. easmith@pop900.gsfc.nasa.gov

Abstract

A rainfall nowcasting system is developed that identifies locations of raining clouds on consecutive infrared geosynchronous satellite images while predicting the movement of the rain cells for up to 10 h using cloud-motion-based winds. As part of the analysis, the strengths and weaknesses of various kinds of cloud wind filtering schemes and both steady and nonsteady storm advection techniques as forecast operators for quantitative precipitation forecasting are evaluated. The first part of the study addresses the development of a probability matching method (PMM) between histograms of equivalent blackbody temperatures (EBBTs) and Special Sensor Microwave Imager (SSM/I)–derived rain rates (RRs), which enables estimating RRs from instantaneous infrared imagery and allows for RR forecasts from the predicted EBBT fields. The second part of the study addresses the development and testing of the nowcasting system built upon the PMM capability and analyzes its success according to various skill score metrics. Key processes involved in the nowcasting system include the retrieved cloud-motion wind field, the filtered cloud-motion wind field, and the forecasting of a future rain field by storm advection and EBBT tendencies. These processes allow for the short-term forecasting of cloud and rain locations and of rain intensity, using PMM-based RRs from different datasets of infrared Geostationary Meteorological Satellite (GMS) and Geostationary Operational Environmental Satellite (GOES) imagery. For this study, three convective rain sequences from the Caribbean basin, the Amazon basin, and the Korean peninsula are analyzed. The final part of the study addresses the decay of forecast accuracy with time (i.e., the point at which the asymptotic limit on forecast skill is reached). This analysis indicates that the nowcasting system can produce useful rainfall forecast information out to approximately 6 h.

Corresponding author address: Eric A. Smith, NASA Goddard Space Flight Center, Code 912.1, Greenbelt, MD 20771. easmith@pop900.gsfc.nasa.gov

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