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A Simple Method to Retrieve 3-Hourly Estimates of Global Tropical and Subtropical Precipitation from International Satellite Cloud Climatology Program (ISCCP) D1 Data

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  • 1 School of Geography, University of Oxford, Oxford, United Kingdom
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Abstract

Algorithms to estimate rainfall from passive microwave or optical data from polar-orbiting satellites are limited by poor temporal sampling and are best suited to produce estimates integrated over periods of one month or more. There are numerous applications in the atmospheric sciences in which rainfall estimates are required at a much greater frequency. These can be derived from geostationary satellite infrared data, but currently no global archive of such products exists. This paper presents a simple technique to reconstruct Geostationary Operational Environmental Satellite Precipitation Index (GPI) estimates of rainfall over the global Tropics and subtropics at 3-hourly, 2.5° resolution from cloud-top temperature statistics contained in the extensive International Satellite Cloud Climatology Project D1 dataset. It is shown that the Reconstructed GPI (RGPI) estimates correlate very strongly with the GPI and have minimal bias, irrespective of the integration period selected or the underlying surface type. Comparison with the independent NASA WetNet PIP-3 surface rainfall validation data shows that the RGPI estimates of rainfall composited over monthly periods match the validation data with accuracy very similar to that of the GPI and are comparable to many passive microwave algorithms. Both the RGPI and GPI estimates of rainfall match the validation data more closely over the tropical Pacific Ocean than over the tropical and subtropical land masses where a positive bias is apparent. With 3-hourly temporal resolution, the RGPI represents a useful new resource for climate studies.

Corresponding author address: Dr. Martin Todd, School of Geography, University of Oxford, Mansfield Road, Oxford OX1 3TB, United Kingdom.

Email: martin.todd@geog.ox.ac.uk

Abstract

Algorithms to estimate rainfall from passive microwave or optical data from polar-orbiting satellites are limited by poor temporal sampling and are best suited to produce estimates integrated over periods of one month or more. There are numerous applications in the atmospheric sciences in which rainfall estimates are required at a much greater frequency. These can be derived from geostationary satellite infrared data, but currently no global archive of such products exists. This paper presents a simple technique to reconstruct Geostationary Operational Environmental Satellite Precipitation Index (GPI) estimates of rainfall over the global Tropics and subtropics at 3-hourly, 2.5° resolution from cloud-top temperature statistics contained in the extensive International Satellite Cloud Climatology Project D1 dataset. It is shown that the Reconstructed GPI (RGPI) estimates correlate very strongly with the GPI and have minimal bias, irrespective of the integration period selected or the underlying surface type. Comparison with the independent NASA WetNet PIP-3 surface rainfall validation data shows that the RGPI estimates of rainfall composited over monthly periods match the validation data with accuracy very similar to that of the GPI and are comparable to many passive microwave algorithms. Both the RGPI and GPI estimates of rainfall match the validation data more closely over the tropical Pacific Ocean than over the tropical and subtropical land masses where a positive bias is apparent. With 3-hourly temporal resolution, the RGPI represents a useful new resource for climate studies.

Corresponding author address: Dr. Martin Todd, School of Geography, University of Oxford, Mansfield Road, Oxford OX1 3TB, United Kingdom.

Email: martin.todd@geog.ox.ac.uk

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