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Description:
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An improved physically -based rainfall algorithm was developed using AMSR -E data based on a radiative transfer model . In addition , error models were designed and embedded in the algorithm to assess retrieval errors quantitatively and to reduce net retrieval uncertainties . The algorithm uses six channels (dual polarizations at 36 .5 , 18 .7 and 10 .65GHz ) and retrieves rain rates on a pixel -by -pixel basis . Monthly rain totals are estimated by summing average rain rates computed by merging six rain rates based on proper weights that are estimated from error models . Error models were constructed based upon the principal error sources of rainfall retrieval such as beam filling error , drop size distribution uncertainty and instrument calibration errors . Several improved schemes that minimize uncertainties of the rainfall retrieval were developed in this study . In particular , improved offset correction that corrects the biases near zero rain plays a very important role for reducing uncertainties which are mainly driven by calibration uncertainty including the modeling errors . AMSR -E's larger calibration uncertainty was substantially absorbed by this offset correction as well as by the weighted average scheme to combine all six channels optimally . As a framework for inter -comparison with the experimental algorithm , the current operational algorithm (NASA , level 3 algorithm ) was also updated with respect to AMSR -E data . The experimental algorithm was compared with the operational algorithm for both AMSR -E and TMI data and rainfall retrieval uncertainties were analyzed using error models . When the experimental algorithm was used , many limitations of the operational algorithm were overcome and uncertainties of rainfall retrieval were considerably eliminated . |