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Gail Bayler
and
Howard Lewit

Abstract

At the Fleet Numerical Oceanography Center, two computer models, the Navy Operational Global Atmospheric Prediction System, NOGAPS, and the Navy Operational Regional Atmospheric Prediction System, NORAPS, generate a twice-daily suite of atmospheric analyses and forecasts. NOGAPS is the driving force behind many of the center's products and has become a complex, highly structured system designed to run automatically. The execution of NOGAPS and NORAPS within the operational schedule is described. The systems consist of 1) automated data processing and quality control, 2) a multivariate optimum interpolation analysis, 3) initialization and forecast, and 4) output. The data-processing step is shared between the two systems.

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Gail M. Bayler
,
R. M. Aune
, and
W. H. Raymond

Abstract

Cloud-top pressures and effective cloud amounts, derived from Geostationary Operational Environmental Satellite (GOES) sounders, are used to specify a three-dimensional mesoscale cloud field for numerical weather prediction. A bulk cloud parameterization scheme based on relative humidity is introduced into the forecast model to efficiently use the satellite sounder observations. The cloud initialization and bulk cloud parameterization are tested in the Cooperative Institute for Meteorological Satellite Studies Regional Assimilation System for summer and winter periods. Comparisons between cloud forecasts and GOES cloud observations for four model configurations are presented. It is shown that the use of the initialization improved the areal coverage of the nonprecipitating clouds early in the forecast period, which translated to an improvement of the precipitation equitable threat score for the 24-h forecast (when the model initial time was 1200 UTC) and the 36-h forecast (when the model initial time was 0000 UTC).

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Youri Plokhenko
,
W. Paul Menzel
,
Gail Bayler
, and
Timothy J. Schmit

Abstract

The spatial and temporal continuity of the infrared measurements from the Geostationary Operational Environmental Satellite (GOES)-8 sounder data are investigated, and an experimental processing approach is presented. Spatial filtering and cloud detection are performed in a joint algorithm: the preparation of the data for sounding analysis starts with spatial smoothing, followed by cloud detection, followed by averaging the clear-sky (cloud free) subsamples. Analysis of the sounder images reveals the presence of coherent noise on large spatial scales in some of the spectral bands. Analysis of a temporal sequence of spatially smoothed sounder images reveals regions of unphysical hourly change likely induced by instrument noise. A nonlinear temporal–spatial filtering algorithm is presented and tested that improves the noise filtering for the sounder spectral measurements and the thermodynamical spatial and temporal consistency of the sounding retrievals in the troposphere.

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