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Chris Kidd, Toshihisa Matsui, Jiundar Chern, Karen Mohr, Chris Kummerow, and Dave Randel

Abstract

The estimation of precipitation across the globe from satellite sensors provides a key resource in the observation and understanding of our climate system. Estimates from all pertinent satellite observations are critical in providing the necessary temporal sampling. However, consistency in these estimates from instruments with different frequencies and resolutions is critical. This paper details the physically based retrieval scheme to estimate precipitation from cross-track (XT) passive microwave (PM) sensors on board the constellation satellites of the Global Precipitation Measurement (GPM) mission. Here the Goddard profiling algorithm (GPROF), a physically based Bayesian scheme developed for conically scanning (CS) sensors, is adapted for use with XT PM sensors. The present XT GPROF scheme utilizes a model-generated database to overcome issues encountered with an observational database as used by the CS scheme. The model database ensures greater consistency across meteorological regimes and surface types by providing a more comprehensive set of precipitation profiles. The database is corrected for bias against the CS database to ensure consistency in the final product. Statistical comparisons over western Europe and the United States show that the XT GPROF estimates are comparable with those from the CS scheme. Indeed, the XT estimates have higher correlations against surface radar data, while maintaining similar root-mean-square errors. Latitudinal profiles of precipitation show the XT estimates are generally comparable with the CS estimates, although in the southern midlatitudes the peak precipitation is shifted equatorward while over the Arctic large differences are seen between the XT and the CS retrievals.

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William Randel, Petra Udelhofen, Eric Fleming, Marvin Geller, Mel Gelman, Kevin Hamilton, David Karoly, Dave Ortland, Steve Pawson, Richard Swinbank, Fei Wu, Mark Baldwin, Marie-Lise Chanin, Philippe Keckhut, Karin Labitzke, Ellis Remsberg, Adrian Simmons, and Dong Wu

Abstract

An updated assessment of uncertainties in “observed” climatological winds and temperatures in the middle atmosphere (over altitudes ∼10–80 km) is provided by detailed intercomparisons of contemporary and historic datasets. These datasets include global meteorological analyses and assimilations, climatologies derived from research satellite measurements, historical reference atmosphere circulation statistics, rocketsonde wind and temperature data, and lidar temperature measurements. The comparisons focus on a few basic circulation statistics (temperatures and zonal winds), with special attention given to tropical variability. Notable differences are found between analyses for temperatures near the tropical tropopause and polar lower stratosphere, temperatures near the global stratopause, and zonal winds throughout the Tropics. Comparisons of historical reference atmosphere and rocketsonde temperatures with more recent global analyses show the influence of decadal-scale cooling of the stratosphere and mesosphere. Detailed comparisons of the tropical semiannual oscillation (SAO) and quasi- biennial oscillation (QBO) show large differences in amplitude between analyses; recent data assimilation schemes show the best agreement with equatorial radiosonde, rocket, and satellite data.

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