NASA's Global Precipitation Mission will rely on a constellation of cooperative satellites with a variety of microwave radiometers to make global rainfall measurements. Thus, it is crucial to achieve brightness temperature (T b ) measurement consistency among the constellation as well as to maintain sustained calibration accuracy over the lifetime of each satellite sensor. In-orbit inter-satellite radiometric calibration techniques provide a long term, group-wise solution; however, since radiometers operate at different frequencies and viewing angles,T b  normalizations are made before making intermediate comparisons of their near-simultaneous measurements. In this paper, we investigate two approaches to perform these normalizations from one satellite's measurements to another. One approach is uses Taylor's series expansion around source frequency to predict T b 's of destination frequency. The relationship between T b 's and frequencies are derived from simulations using an oceanic radiative transfer model (RTM) over a wide variety of environmental conditions. The other approach uses a multi-variable linear regression built from RTMT b  simulations among all frequency and V- and H-polarizations. These two approaches are applied in comparing external measurements between WindSat (on Coriolis),TMI (on TRMM) and AMSR (on ADEOS-II). Accuracy prediction and error source analysis are discussed along with calibration results.
['Liang Hong', 'W.L. Jones', 'Thomas T. Wilheit']
Inter-satellite radiometer calibrations between WindSat, TMI and AMSR