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Dudley B. Chelton and Frank J. Wentz

Obtaining global sea surface temperature (SST) fields for the ocean boundary condition in numerical weather prediction (NWP) models and for climate research has long been problematic. Historically, such fields have been constructed by a blending of in situ observations from ships and buoys and satellite infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) that has been operational on NOAA satellites since November 1981. The resolution of these global SST fields is limited by the sparse coverage of in situ observations in many areas of the World Ocean and cloud contamination of AVHRR observations, which can exceed 75% over the subpolar oceans. As clouds and aerosols are essentially transparent to microwave radiation, satellite microwave observations can greatly improve the sampling and resolution of global SST fields. The Advanced Microwave Scanning Radiometer on the NASA Earth Observing System (EOS) Aqua satellite (AMSR-E) is providing the first highly accurate and global satellite microwave observations of SST. The potential for AMSR-E observations to improve the sampling, resolution, and accuracy of SST fields for NWP and climate research is demonstrated from example SST fields and from an investigation of the sensitivity of NWP models to specification of the SST boundary condition.

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Arthur Y. Hou, Sara Q. Zhang, Arlindo M. da Silva, William S. Olson, Christian D. Kummerow, and Joanne Simpson

As a follow-on to the Tropical Rainfall Measuring Mission (TRMM), the National Aeronautics and Space Administration in the United States, the National Space Development Agency of Japan, and the European Space Agency are considering a satellite mission to measure the global rainfall. The plan envisions an improved TRMM-like satellite and a constellation of eight satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-h intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and two Special Sensor Microwave/Imager (SSM/I) instruments.

Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the Tropics. It is shown that assimilating the 6-h-averaged TMI and SSM/I surface rain rate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper-tropospheric moisture in the analysis produced by the Goddard Earth Observing System Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System instrument and brightness temperature observations by the Television Infrared Observational Satellite Operational Vertical Sounder instruments.

Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the Tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the Tropics.

These results were obtained using a variational procedure based on a 6-h time integration of a column model of moist physics with prescribed dynamical and other physical tendencies. The procedure estimates moisture tendency corrections at observation locations by minimizing the least square differences between the observed TPW and rain rates and those generated by the column model over a 6-h analysis window. These tendency corrections are then applied during the assimilation cycle to compensate for errors arising from both initial conditions and deficiencies in model physics. Our results point to the importance of addressing deficiencies in model physics in assimilating data types such as precipitation, for which the forward model based on convective parameterizations may have significant systematic errors.

This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of four-dimensional global datasets for climate analysis and weather forecasting applications.

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Maximilian Maahn, David D. Turner, Ulrich Löhnert, Derek J. Posselt, Kerstin Ebell, Gerald G. Mace, and Jennifer M. Comstock

advanced instrumentation and algorithms, it is important that the retrieval uncertainties are accurately quantified and made available with the retrieved information. To illustrate how OE works, we discuss two simplified retrieval applications: retrieving temperature and humidity profiles from a multichannel ground-based microwave radiometer and retrieving drop size distribution parameters from ground-based cloud radar observations. We use the recently published Python pyOptimalEstimation library 2

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Ross N. Hoffman and Robert Atlas

to develop and test an improved methodology for assimilating both passive and active microwave satellite surface wind data, which led to the first beneficial impact of scatterometer data on numerical weather prediction, as well as to the assimilation of Special Sensor Microwave Imager (SSM/I) wind speed data ( Atlas et al. 1996 ; Atlas and Hoffman 2000 ; Atlas et al. 2001 , 2011 ; Atlas 2004 ). The quantitative impact of observations from the Atmospheric Infrared Sounder (AIRS) and the

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M. P. Jensen, W. A. Petersen, A. Bansemer, N. Bharadwaj, L. D. Carey, D. J. Cecil, S. M. Collis, A. D. Del Genio, B. Dolan, J. Gerlach, S. E. Giangrande, A. Heymsfield, G. Heymsfield, P. Kollias, T. J. Lang, S. W. Nesbitt, A. Neumann, M. Poellot, S. A. Rutledge, M. Schwaller, A. Tokay, C. R. Williams, D. B. Wolff, S. Xie, and E. J. Zipser

region ( ). At the SGP CF there is a comprehensive instrumentation suite for cloud, precipitation, aerosols, and atmospheric-state observations ( ). Most important for the goals of MC3E are remote sensing observations from a Raman lidar, a two-channel microwave radiometer, and the Atmospheric Emitted Radiance Interferometer (AERI) that are used to retrieve atmospheric water vapor. A micropulse lidar, ceilometer, and total-sky imager (TSI; Long et al

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A. J. Illingworth, D. Cimini, C. Gaffard, M. Haeffelin, V. Lehmann, U. Löhnert, E. J. O’Connor, and D. Ruffieux

; research demonstrations are under way.” Finally, for three-dimensional winds, they conclude the following: “There is currently no present or planned capability. Research is required on indirect observations via sequences of geostationary infrared imagery, or through Doppler enabled microwave sensors” ( WMO 2014 , p. 9). Two recent documents ( NRC 2009 , 2010 ) concluded that the structure and variability of the lower troposphere is currently not well known because vertical profiles of water vapor

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Minghua Zheng, Luca Delle Monache, Xingren Wu, F. Martin Ralph, Bruce Cornuelle, Vijay Tallapragada, Jennifer S. Haase, Anna M. Wilson, Matthew Mazloff, Aneesh Subramanian, and Forest Cannon

summarized in Table 2 . NCEP GDAS applies the all-sky method (Zhu et al. 2016, 2019 ) to the radiances not affected by precipitating clouds from the Advanced Microwave Sounding Unit-A (AMSU-A) and the Advanced Technology Microwave Sounder (ATMS), therefore we label these two types of radiance as all-sky radiance and the rest of the sensors, which are primarily IRs, as clear-sky radiance. To make observations available for the 6-h assimilation window centered at 0000 UTC, as required in the NWSOP ( OFCM

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J. Boutin, Y. Chao, W. E. Asher, T. Delcroix, R. Drucker, K. Drushka, N. Kolodziejczyk, T. Lee, N. Reul, G. Reverdin, J. Schanze, A. Soloviev, L. Yu, J. Anderson, L. Brucker, E. Dinnat, A. Santos-Garcia, W. L. Jones, C. Maes, T. Meissner, W. Tang, N. Vinogradova, and B. Ward

penetration depth of microwave radiation into the ocean ( Swift 1980 ), microwave radiometers measure salinity in the top few centimeters of the ocean. In contrast, in situ measurements commonly used for calibration and validation (e.g., Argo floats, moorings, and ship observations) are made at depths of a few meters ( Fig. 1 ). Second, a satellite measures salinity as a spatial average over the satellite’s footprint, whereas in situ sensors provide data at a single point [SMOS synthetic antennas have

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Paul Poli, Dick P. Dee, Roger Saunders, Viju O. John, Peter Rayer, Jörg Schulz, Kenneth Holmlund, Dorothee Coppens, Dieter Klaes, James E. Johnson, Asghar E. Esfandiari, Irina V. Gerasimov, Emily B. Zamkoff, Atheer F. Al-Jazrawi, David Santek, Mirko Albani, Pascal Brunel, Karsten Fennig, Marc Schröder, Shinya Kobayashi, Dieter Oertel, Wolfgang Döhler, Dietrich Spänkuch, and Stephan Bojinski

) brightness temperature observations explained by ERA-20C, for scenes believed to be clear, by considering where observation minus ERA-20C differences for channel 8 (window) are between –1 and 2 K. Variances are computed within 5° × 5° latitude–longitude bin, for 17–31 Aug 1975 and 31 Jan–3 Mar 1976. Early microwave sensors for humidity. Continued measurements of humidity by microwave sensors have been available since SMMR on Nimbus-7 . Its record was reprocessed two decades ago as part of the leading

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L. W. de Vos, A. M. Droste, M. J. Zander, A. Overeem, H. Leijnse, B. G. Heusinkveld, G. J. Steeneveld, and R. Uijlenhoet

observations are available for the entire study period for the Amsterdam metropolitan region (larger domain in Fig. 1a ). Fig . 1. Maps of (a) Amsterdam metropolitan area and city center with locations of all sensor networks: personal weather stations (PWSs), commercial microwave links (CMLs), and WMO station 06240 (Amsterdam airport) and (b) smartphone battery temperature readings and Amsterdam Atmospheric Monitoring Supersite (AAMS) stations. The OpenSignal dataset includes self-reported accuracy scores

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