A 0.01° Resolving TRMM PR Precipitation Climatology

Abstract

In this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.

Recording Rainfall Intensity: Has an Optimum Method Been Found?

“…Point-based rainfall data are desirable for some applications, such as data on soil loss from a small plot or field, while areal data are desirable at larger scales, such as when attempting to validate models of streamflow from catchment areas, especially those with short response times (post-fire landscapes, urban areas). Remotely sensed rainfall data have the capacity to provide spatial rainfall data, such as the 0.01 • spatially gridded data explored by Hirose & Okada [4], and multiple point-gauges can be deployed to achieve good spatial resolution of rainfall fields. Perhaps the best-known example is the network of 88 gauges in the 149 km 2 Walnut Gulch catchment in SE Arizona, USA (https://data.nal.…”

“…The same issue applies to sources of remotely sensed rainfall data, such as that from TRMM and its successor, the GRMP. For instance, Hirose & Okada [4] developed gridded rainfall TRMM data at 0.01 • resolution. Their data showed considerable divergence among data gridded at 0.1 • and 0.01 • , including in the distribution of rainfall across rugged topography.…”

“…At a single point on the ground, the intensity is actually zero in the moments between the arrival of successive drops and only becomes measurable as a (virtually) continuous variable when there are multiple drop arrivals across the substantial collecting area of a sensing device, such as the ~700 cm 2 area of a typical rain gauge collecting funnel. At the landscape scale, marked spatial variability in rainfall intensities and amounts arises over km scales (Fiener & Auerswald [2]) in part because wind and rain are steered by topography (Sharon [3], Hirose & Okada [4]), with small drops being more easily deflected by wind than larger drops. Though well-known (Blumen [5]), local-scale wind effects on rainfall have not been widely explored insofar as they affect intensity or storm pattern (intensity profile through the duration of a rainfall event) because rain gauge networks are rarely sufficiently dense to resolve these influences, especially in rugged or complex terrain where wind effects on rainfall might be most important.…”