The special sensor microwave imager (SSMI) is a seven-channel microwave radiometer which has dual polarized channels at 19, 37, and 85 GHz and a vertically polarized channel at 22 GHz. A detailed evaluation of all SSMI channels is made in arriving at the optimum channel selection for the global identification of precipitation and snow cover without the use of any ancillary information. The resulting algorithm takes the form of a decision tree which uses the dual polarized channels at 19 GHz and the vertically polarized channels at 22 and 85 GHz. These four channels enable the identification of precipitation from all other atmospheric and surface features, including snow cover.

[1] Although the advanced microwave sounding unit (AMSU) on board the NOAA 15 and NOAA 16 satellites is primarily designed for profiling atmospheric temperature and moisture, the products associated with clouds and precipitation are also derived using its window channel measurements with a quality similar to those derived from microwave imagers such as the Special Sensor Microwave Imager. However, the AMSU asymmetry in radiance along the scan was found to be obvious at its window channels and could severely degrade the quality of cloud and precipitation products if not properly corrected. Thus a postlaunch calibration scheme is developed for these channels, and the causes of the asymmetry are analyzed from the AMSU instrument model. A preliminary study shows that the asymmetry may be caused by either the AMSU polarization misalignment or the antenna pointing angle error. A generic radiative transfer model is developed for a single-layered cloud using a two-stream approximation and can be utilized for the retrievals of cloud liquid water (L) and total precipitable water (V), cloud ice water path (IWP), and particle effective diameter (D e ). At the AMSU lower frequencies the scattering from cloud liquid is neglected, and therefore the retrieval of L and V is linearly derived using 23.8 and 31.4 GHz. However, for ice clouds the radiative transfer model is simplified by neglecting the thermal emission, and therefore the retrieval of IWP and D e is analytically derived using the AMSU millimeter wavelength channels at 89 and 150 GHz. These cloud algorithms are tested for the AMSU on board the NOAA 15 and NOAA 16 satellites, and the results are rather promising. It is also found that the AMSUderived cloud ice water path is highly correlated with the surface rain rates and is now directly used to monitor surface precipitation throughout the world.

[1] The measurements from microwave sounding unit (MSU) on board different NOAA polar-orbiting satellites have been extensively used for detecting atmospheric temperature trend during the last several decades. However, temperature trends derived from these measurements are under significant debate, mostly caused by calibration errors. This study recalibrates the MSU channel 2 observations at level 0 using the postlaunch simultaneous nadir overpass (SNO) matchups and then provides a well-merged new MSU 1b data set for climate studies. The calibration algorithm consists of a dominant linear response of the MSU raw counts to the Earth-view radiance plus a smaller quadratic term. Uncertainties are represented by a constant offset and errors in the coefficient for the nonlinear quadratic term. A SNO matchup data set for nadir pixels with criteria of simultaneity of less than 100 s and within a ground distance of 111 km is generated for all overlaps of NOAA satellites. The simultaneous nature of these matchups eliminates the impact of orbital drifts on the calibration. A radiance error model for the SNO pairs is developed and then used to determine the offsets and nonlinear coefficients through regressions of the SNO matchups. It is found that the SNO matchups can accurately determine the differences of the offsets as well as the nonlinear coefficients between satellite pairs, thus providing a strong constraint to link calibration coefficients of different satellites together. However, SNO matchups alone cannot determine the absolute values of the coefficients because there is a high degree of colinearity between satellite SNO observations. Absolute values of calibration coefficients are obtained through sensitivity experiments, in which the percentage of variance in the brightness temperature difference time series that can be explained by the warm target temperatures of overlapping satellites is a function of the calibration coefficient. By minimizing these percentages of variance for overlapping observations, a new set of calibration coefficients is obtained from the SNO regressions. These new coefficients are significantly different from the prelaunch calibration values, but they result in bias-free SNO matchups and near-zero contaminations by the warm target temperatures in terms of the calibrated brightness temperature. Applying the new calibration coefficients to the Level 0 MSU observations, a well-merged MSU pentad data set is generated for climate trend studies. To avoid errors caused by small SNO samplings between NOAA 10 and 9, observations only from and after NOAA 10 are used. In addition, only ocean averages are investigated so that diurnal cycle effect can be ignored. The global ocean-averaged intersatellite biases for the pentad data set are between 0.05 and 0.1 K, which is an order of magnitude smaller than that obtained when using the unadjusted calibration algorithm. The ocean-only anomaly trend for the combined MSU channel 2 brightness temperature is found to be 0.

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The special sensor microwave imager (SSM/I) is a microwave radiometer having dual‐polarized channels at 19.35, 37, and 85.5 GHz and a vertically polarized channel at 22.235 GHz. The measurements at these frequencies are used to retrieve the liquid water path in precipitating and nonprecipitating clouds over oceans. Three separate algorithms, each accurate for different ranges of liquid water, are combined to measure a large dynamic range of cloud liquid water path up to 3.0 mm. The major improvements of our present algorithm over many other previous studies are (1) the algorithm detects the liquid water in optically thin stratus and low‐level clouds very well; (2) the algorithm measures the liquid water in highly convective clouds; (3) the algorithm can be applied to any climate regime because some of the coefficients (a1 and a2) are derived using a comprehensive training SSM/I data set obtained from various clear sky conditions; and (4) the liquid water derived using the present algorithm agree with that derived using the ground‐based microwave radiometer measurements very well. Global distributions of the cloud liquid water over oceans for August 1993 and January 1994 are derived using the SSM/I data from DMSP F10 and F11 satellites. Our analyses show that the cloud liquid water exhibits a strong diurnal variation over many regions. In particular, the variation over the tropical western Pacific and northwestern Pacific is largest and is attributed to the diurnal variation of raining clouds. The variation over the west coasts of major continents is also very large and is associated with nonraining stratus clouds.