Search Results
Abstract
The 85-GHz polarization corrected temperature (PCT85) algorithm, using the V85 and H85 channels of the SSM/I sensor, is evaluated for estimation of midlatitude rainfall. The algorithm θ parameter and rain/no-rain thresholds are examined and found to be highly variable. Methods for automatic calibration, to amount for variable atmospheric and surface conditions, are presented. Derivation of θ and thresholds for each individual scene provides a marked improvement in rainfall identification accuracy over the equivalent monthly values. The algorithm is calibrated by comparison with radar data for the estimation of instantaneous rain rates. Detailed evaluation of a number of case studies suggest the relationship of PCT85 and rain rate is substantially different for frontal and mesoscale convective system rainfall. For most frontal conditions the PCT85 provides useful estimates of rain rates with sensitivity to rain intensities as low as 0.1 mm h−1. Overall, the PCT85 estimates of instantaneous rain rate at the footprint scale are to within ±75% of the radar quantity only 50% of the time. Systematic errors result from both the calibration process and from the inability of microwave scattering methods to identify warm rain processes, including orographically enhanced rainfall over land. The results show the need for improved empirical calibration of passive microwave algorithms to provide sensitivity to subsynoptic-scale surface and atmospheric conditions and rainfall processes.
Abstract
The 85-GHz polarization corrected temperature (PCT85) algorithm, using the V85 and H85 channels of the SSM/I sensor, is evaluated for estimation of midlatitude rainfall. The algorithm θ parameter and rain/no-rain thresholds are examined and found to be highly variable. Methods for automatic calibration, to amount for variable atmospheric and surface conditions, are presented. Derivation of θ and thresholds for each individual scene provides a marked improvement in rainfall identification accuracy over the equivalent monthly values. The algorithm is calibrated by comparison with radar data for the estimation of instantaneous rain rates. Detailed evaluation of a number of case studies suggest the relationship of PCT85 and rain rate is substantially different for frontal and mesoscale convective system rainfall. For most frontal conditions the PCT85 provides useful estimates of rain rates with sensitivity to rain intensities as low as 0.1 mm h−1. Overall, the PCT85 estimates of instantaneous rain rate at the footprint scale are to within ±75% of the radar quantity only 50% of the time. Systematic errors result from both the calibration process and from the inability of microwave scattering methods to identify warm rain processes, including orographically enhanced rainfall over land. The results show the need for improved empirical calibration of passive microwave algorithms to provide sensitivity to subsynoptic-scale surface and atmospheric conditions and rainfall processes.
Abstract
As part of the U.S. Agency for International Development/National Oceanic and Atmospheric Administration project to develop an improved monitoring, forecasting, and simulation system for the river Nile, the Remote Sensing Unit of the University of Bristol has been investigating and developing satellite infrared techniques for small-scale estimation of rainfall over the region of the upper Nile basin. In this paper, the need for variable IR rain/no-rain temperature thresholds as a basis for reliable satellite identification of rain areas over small scales is explained, and the spatially and temporally variable nature of optimum IR rain/no-rain threshold temperatures is examined.
Meteosat IR data covering a period of 17 months have been analyzed along with daily rain gauge reports for calibration and validation. Analyses have been carried out on a monthly basis. Optimum IR rain/no rain threshold temperatures over the study area in the east Africa region are shown to have exhibited a marked seasonal trend, with an annual variation approaching 40 K. Minimum threshold temperature values were found at the onset of the summer wet season, and maximum threshold temperature values during the driest winter months. Generally, summer threshold temperatures were low, around 230 K, and winter thresholds high, in the range of 240–260 K.
During the wet season, optimum IR rain/no-rain threshold temperatures exhibited a distinct pattern of spatial variation. This was modeled as a function of pixel latitude, longitude, and surface elevation. This threshold temperature model was then used to generate threshold temperature estimates at the pixel scale from an independent Meteosat dataset for 1992. Compared with the performance of spatially uniform threshold methods, marked improvements in rain-area classification accuracy were obtained. Optimum IR rain/no-rain threshold temperature variation is therefore seen to be a result of a complex interaction of climatology, meteorology, and topography, and as such the implications of this for the design and use of regional-scale rainfall monitoring techniques are discussed.
Abstract
As part of the U.S. Agency for International Development/National Oceanic and Atmospheric Administration project to develop an improved monitoring, forecasting, and simulation system for the river Nile, the Remote Sensing Unit of the University of Bristol has been investigating and developing satellite infrared techniques for small-scale estimation of rainfall over the region of the upper Nile basin. In this paper, the need for variable IR rain/no-rain temperature thresholds as a basis for reliable satellite identification of rain areas over small scales is explained, and the spatially and temporally variable nature of optimum IR rain/no-rain threshold temperatures is examined.
Meteosat IR data covering a period of 17 months have been analyzed along with daily rain gauge reports for calibration and validation. Analyses have been carried out on a monthly basis. Optimum IR rain/no rain threshold temperatures over the study area in the east Africa region are shown to have exhibited a marked seasonal trend, with an annual variation approaching 40 K. Minimum threshold temperature values were found at the onset of the summer wet season, and maximum threshold temperature values during the driest winter months. Generally, summer threshold temperatures were low, around 230 K, and winter thresholds high, in the range of 240–260 K.
During the wet season, optimum IR rain/no-rain threshold temperatures exhibited a distinct pattern of spatial variation. This was modeled as a function of pixel latitude, longitude, and surface elevation. This threshold temperature model was then used to generate threshold temperature estimates at the pixel scale from an independent Meteosat dataset for 1992. Compared with the performance of spatially uniform threshold methods, marked improvements in rain-area classification accuracy were obtained. Optimum IR rain/no-rain threshold temperature variation is therefore seen to be a result of a complex interaction of climatology, meteorology, and topography, and as such the implications of this for the design and use of regional-scale rainfall monitoring techniques are discussed.
