Global oceanic rainfall estimation from AMSR-E data based on a radiative transfer model [electronic resource] /
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.
School:Texas A&M International University
School Location:USA - Texas
Source Type:Master's Thesis
Keywords:major atmospheric sciences rainfall retrieval microwave remote sensing
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