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Martin C. Todd, Chris Kidd, Dominic Kniveton, and Tim J. Bellerby

Abstract

There are numerous applications in climatology and hydrology where accurate information at scales smaller than the existing monthly/2.5° products would be invaluable. Here, a new microwave/infrared rainfall algorithm is introduced that combines satellite passive microwave (PMW) and infrared (IR) data to account for limitations in both data types. Rainfall estimates are produced at the high spatial resolution and temporal frequency of the IR data using rainfall information from the PMW data. An IRTb–rain rate relationship, variable in space and time, is derived from coincident observations of IRTb and PMW rain rate (accumulated over a calibration domain) using the probability matching method. The IRTb–rain rate relationship is then applied to IR imagery at full temporal resolution.

MIRA estimates of rainfall are evaluated over a range of spatial and temporal scales. Over the global Tropics and subtropics, optimum IR thresholds and IRTb–rain rate relationships are highly variable, reflecting the complexity of dominant cloud microphysical processes. As a result, MIRA shows sensitivity to these variations, resulting in potentially useful improvements in estimate accuracy at small scales in comparison to the Geostationary Operational Environmental Satellite Precipitation Index (GPI) and the PMW-calibrated Universally Adjusted GPI (UAGPI). Unlike some existing PMW/IR techniques, MIRA can successfully capture variability in rain rates at the smallest possible scales. At larger scales MIRA and UAGPI produce very similar improvements over the GPI. The results demonstrate the potential for a new high-resolution rainfall climatology from 1987 onward, using International Satellite Cloud Climatology Project DX and Special Sensor Microwave Imager data. For real-time regional or quasi-global applications, a temporally “rolling” calibration window is suggested.

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