Beamfilling error is one of the main error sources for microwave oceanic rainfall retrieval. An accurate beamfilling correction can improve the rainfall retrieval accuracy significantly. Quantitative understanding of the uncertainty of the Beamfilling Correction Factor (BCF) is very important for the understanding of the accuracy of microwave passive rainfall retrieval. Refinement of the calculation of the BCF and the estimation of BCF uncertainty are the main purposes of this thesis. The characteristic of rainfall distribution is investigated. Quantitative understanding of the statistical characteristics of rainfall distribution provides an indication of the beamfilling error and the uncertainty of BCF in many ways. Some refinements to the traditional BCF calculation algorithm are provided in this thesis. Scattering is included in the new algorithm. Also the BCF calculation only considers the cases within the useful dynamic range. These refinements make the BCF calculation closer to how it is used in the retrieval algorithm. The BCF based on the new algorithm should be more accurate. The global BCF uncertainty and the local BCF uncertainty are estimated using the available A/C radar data. The results show that the uncertainty of BCF is much smaller than expected, and also show that the BCF derived from a specific set of data can be used globally.

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.

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.