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Lidia Cucurull and Richard A. Anthes

system that already is tuned to many different observational systems ( English et al. 2013 ). Since the expected end of the lifetime of Suomi-NPP is 2016, and the launch of the first JPSS satellite has been delayed from 2016 to at least early 2017, a gap or significant reduction in the U.S. microwave satellite data stream is possible. However, because there are other MW observations besides the ones on the NOAA satellites, as well as a number of infrared (IR) sensors on various satellites and radio

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Christopher S. Velden

DECEMBER 1992 V E L D E N 669Satellite-based Microwave Observations of Tropopause-Level Thermal Anomalies: Qualitative Applications in Extratropical Cyclone Events CHRISTOPHER S. VELDENCooperative Institute for Meteorological Satellite Studies, Madison, Wisconsin(Manuscript received 17 April 1992, in final form 4 August 1992)ABSTRACT The evolution of upper

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Fatima Karbou, Elisabeth Gérard, and Florence Rabier

1. Introduction Microwave observations from the Advanced Microwave Sounding Unit-A and -B [AMSU-A and -B; or Microwave Humidity Sounder (MHS)] instruments have been widely used in numerical weather prediction (NWP) to improve the initial conditions for short-range forecasts. AMSU instruments are on board low-orbiting satellites such as the different generations of the National Oceanic and Atmospheric Administration (NOAA) satellites, the National Aeronautics and Space Administration’s (NASA

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Fatima Karbou, Florence Rabier, Jean-Philippe Lafore, Jean-Luc Redelsperger, and Olivier Bock

observations have been found very useful for measuring the precipitable water vapor (PWV) at different scales over Africa ( Bock et al. 2007 ). The humidity measurement network has been temporally extended as part of AMMA, but more effort is still necessary in order to provide additional estimates of the humidity over all surfaces with good temporal and spatial sampling. Observations from satellite sensors such as the Advanced Microwave Sounding Unit-A and -B (AMSU-A and -B), which have considerable

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Elizabeth E. Ebert, Michael Turk, Sheldon J. Kusselson, Jianbin Yang, Matthew Seybold, Peter R. Keehn, and Robert J. Kuligowski

accumulations. In principle, an ensemble TRaP, henceforth called an eTRaP, can be made up of forecasts using observations from several microwave sensors, initialized at several observation times, using several different track forecasts. The diversity among the ensemble members helps to reduce the large (unknown) errors associated with a single-sensor, single-track TRaP. The large number of perturbations leads to ensembles with many members, allowing probability forecasts to be issued. The next section

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Banghua Yan and Fuzhong Weng

-affected data is most important. In the current GFS, the cloud liquid water path (CLW) algorithm in Weng and Grody (1994) is used to estimate a CLW value for the SSMI observations over the oceans. The threshold used to detect the cloud-affected data is 0.2 kg m −2 , which is similar to that used for other microwave observations in GFS. Note that this threshold can only remove those data that are highly affected by nearly precipitating clouds. A smaller threshold should be used if all data affected by

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Christopher M. Rozoff, Christopher S. Velden, John Kaplan, James P. Kossin, and Anthony J. Wimmers

. , Lee T. F. , and Richardson K. , 2008 : Observations of tropical cyclones with the SSMIS . IEEE Trans. Geosci. Remote Sens. , 46 , 901 – 912 , doi: 10.1109/TGRS.2008.915753 . Hendricks, E. A. , Peng M. S. , Fu B. , and Li T. , 2010 : Quantifying environmental control on tropical cyclone intensity change . Mon. Wea. Rev. , 138 , 3243 – 3271 , doi: 10.1175/2010MWR3185.1 . Hong, L. , 2008 : Inter-satellite microwave radiometer calibration. Ph.D. dissertation, University of

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N. L. Uhlenbrock, K. M. Bedka, W. F. Feltz, and S. A. Ackerman

. 1992 ; Salomonson et al. 2002 ) instruments aboard the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Aqua and Terra satellites. MODIS observations include measurements in the water vapor absorption channel at a 1-km nadir spatial resolution. These measurements provide a new opportunity to examine the spatial characteristics of mountain wave phenomena, particularly in clear-air conditions. The paper investigates mountain wave signatures in water vapor imagery

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Robert M. Rabin, Lynn A. McMurdie, Christopher M. Hayden, and Gary S. Wade

analyses from an operational model. Surface wind speeds are also obtained from the microwave data andare compared to the surface observations. Analyses from satellite data appear to add considerable informationto the moisture and wind analysis over the Gulf of Mexico and should help in forecasting moisture changes,particularly moisture return near the surrounding coastal areas.1. Introduction The Gulf of Mexico is the primary source of watervapor for most storms in the central United States andsouth

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John A. Knaff, Raymond M. Zehr, Mitchell D. Goldberg, and Stanley Q. Kidder

to climate applications. It utilizes a global linear operator to derive temperature retrievals from a climatological global mean and the input values of the AMSU-A channels ( Goldberg 1999 ). This is in contrast to operational centers that use location-and/or time-specific initial conditions for deriving their final retrieval. This method’s results are therefore independent of operational data assimilation and model physics. Unlike infrared observations, the microwave sounder observations have a

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