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  • Author or Editor: Peter R. Keehn x
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George J. Huffman
,
Robert F. Adler
,
Bruno Rudolf
,
Udo Schneider
, and
Peter R. Keehn

Abstract

The “satellite-gauge-model” (SGM) technique is described for combining precipitation estimates from microwave satellite data, infrared satellite data, rain gauge analyses, and numerical weather prediction models into improved estimates of global precipitation. Throughout, monthly estimates on a 2.5° × 2.5° lat-long grid are employed. First, a multisatellite product is developed using a combination of low-orbit microwave and geosynchronous-orbit infrared data in the latitude range 40°N–40–S (the adjusted geosynchronous precipitation index) and low-orbit microwave data alone at higher latitudes. Then the rain gauge analysis is brought in, weighting each field by its inverse relative error variance to produce a nearly global, observationally based precipitation estimate. To produce a complete global estimate, the numerical model results are used to fill data voids in the combined satellite-gauge estimate. Our sequential approach to combining estimates allows a user to select the multisatellite estimate, the satellite-gauge estimate, or the full SGM estimate (observationally based estimates plus the model information). The primary limitation in the method is imperfections in the estimation of relative error for the individual fields.

The SGM results for one year of data (July 1987 to June 1988) show important differences from the individual estimates, including model estimates as well as climatological estimates. In general, the SGM results are drier in the subtropics than the model and climatological results, reflecting the relatively dry microwave estimates that dominate the SGM in oceanic regions

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Andrew J. Negri
,
Robert F. Adler
,
Robert A. Maddox
,
Kenneth W. Howard
, and
Peter R. Keehn

Abstract

A three-year climatology of satellite-estimated rainfall for the warm season for the southwest United States and Mexico has been derived from data from the Special Sensor Microwave Imager (SSM/1). The microwave data have been stratified by month (June, July, August), yew (1988, 1989, 1990), and time of day (morning and evening orbits). A rain algorithm was employed that relates 86-GHz brightness temperatures to rain rate using a coupled cloud-radiative transfer model.

Results identify an early evening maximum in rainfall along the western slope of the Sierra Madre Occidental during all three months. A prominent morning rainfall maximum was found off the western Mexican coast near Mazatlan in July and August. Substantial differences between morning and evening estimates were noted. To the extent that three years constitute a climatology, results of interannual variability are presented. Results are compared and contrasted to high-resolution (8 km, hourly) infrared cloud climatologies, which consist of the frequency of occurrence of cloud colder than −38°C and −58°C. This comparison has broad implications for the estimation of rainfall by simple (cloud threshold) techniques.

By sampling the infrared data to approximate the time and space resolution of the microwave, we produce ratios (or adjustment factors) by which we can adjust the infrared rain estimation schemes. This produces a combined micro wave/infrared rain algorithm for monthly rainfall. Using a limited set of raingage data as ground truth, an improvement (lower bias and root-mean-square error) was demonstrated by this combined technique when compared to either method alone. The diurnal variability of convection during July 1990 was examined using hourly rain estimates from the GOES precipitation index and the convective stratiform technique, revealing a maximum in estimated rainfall from 1800 to 2100 local time. It is in this time period when the SSM/1 evening orbit occurs. A high-resolution topographic database was available to aid in interpreting the influence of topography on the rainfall patterns.

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Robert F. Adler
,
Andrew J. Negri
,
Peter R. Keehn
, and
Ida M. Hakkarinen

Abstract

This paper describes a method to combine geosynchronous IR and low-orbit microwave data to estimate mean monthly rainfall useful for climate studies. The IR data have the advantage of high time resolution (important for rapidly changing precipitation patterns and for the detection of diurnal signals) but lack a strong physical connection between the remotely sensed signal and the surface rainfall. The microwave data provide a stronger relation between the radiance and the rainfall but provide poor time sampling of the rainfall signal.

The microwave technique uses the brightness temperature at 37 and 86 GHz from the Special Sensor Microwave/Imager instrument on board the Defense Meteorological Satellite Program (DMSP) satellite to define raining areas over water and land and uses the 86-GHz scattering signal to assign rain rate based on cloud model-microwave calculations. The microwave results are generally good for both individual swaths and monthly totals, except for a glaring underestimation of shallow, orographic rain systems over the southern coast of Japan. The IR techniques used are the GOES precipitation index of Arkin and Meisner and the convective-stratiform technique of Adler and Negri.

Initially the IR estimates are computed separately using hourly data from the Japanese Geostationary Meteorological Satellite. Calibration or adjustment factors are derived by dividing the microwave monthly estimate by a second IR estimate (made with the microwave sampling that simulates the observations from an IR radiometer on board the DMSP satellite). The spatial array of coefficients are then multiplied by the original IR monthly estimates (produced from all the hourly data) to produce the merged IR-Microwave monthly estimates. The results show that in areas where the base (microwave) technique performs well, that is, has a relatively small bias, the combined microwave-IR monthly total estimates have better error statistics than either the microwave or IR techniques individually.

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Elizabeth E. Ebert
,
Michael Turk
,
Sheldon J. Kusselson
,
Jianbin Yang
,
Matthew Seybold
,
Peter R. Keehn
, and
Robert J. Kuligowski

Abstract

Ensemble tropical rainfall potential (eTRaP) has been developed to improve short-range forecasts of heavy rainfall in tropical cyclones. Evolving from the tropical rainfall potential (TRaP), a 24-h rain forecast based on estimated rain rates from microwave sensors aboard polar-orbiting satellites, eTRaP combines all single-pass TRaPs generated within ±3 h of 0000, 0600, 1200, and 1800 UTC to form a simple ensemble. This approach addresses uncertainties in satellite-derived rain rates and spatial rain structures by using estimates from different sensors observing the cyclone at different times. Quantitative precipitation forecasts (QPFs) are produced from the ensemble mean field using a probability matching approach to recalibrate the rain-rate distribution against the ensemble members (e.g., input TRaP forecasts) themselves. ETRaPs also provide probabilistic forecasts of heavy rain, which are potentially of enormous benefit to decision makers. Verification of eTRaP forecasts for 16 Atlantic hurricanes making landfall in the United States between 2004 and 2008 shows that the eTRaP rain amounts are more accurate than single-sensor TRaPs. The probabilistic forecasts have useful skill, but the probabilities should be interpreted within a spatial context. A novel concept of a “radius of uncertainty” compensates for the influence of location error in the probability forecasts. The eTRaPs are produced in near–real time for all named tropical storms and cyclones around the globe. They can be viewed online (http://www.ssd.noaa.gov/PS/TROP/etrap.html) and are available in digital form to users.

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