scholarly journals Evaluation of global numerical weather prediction analyses and forecasts using DMSP special sensor microwave imager retrievals: 2. Analyses/forecasts intercomparison with SSM/I retrievals

1997 ◽  
Vol 102 (D2) ◽  
pp. 1851-1866 ◽  
Author(s):  
Godelieve Deblonde ◽  
W. Yu ◽  
L. Garand ◽  
A. P. Dastoor
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Microwave Imager

scholarly journals Assessment of New Satellite Missions within the Framework of Numerical Weather Prediction

2020 ◽  
Vol 12 (10) ◽  
pp. 1580 ◽  
Author(s):  
Stuart Newman ◽  
Fabien Carminati ◽  
Heather Lawrence ◽  
Niels Bormann ◽  
Kirsti Salonen ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Gap Analysis ◽  
Weather Prediction ◽  
Data Sets ◽  
Next Generation ◽  
Numerical Weather ◽  
Surface Emission ◽  
Precipitation Measurement ◽  
Microwave Imager ◽  
Global Precipitation

Confidence in the use of Earth observations for monitoring essential climate variables (ECVs) relies on the validation of satellite calibration accuracy to within a well-defined uncertainty. The gap analysis for integrated atmospheric ECV climate monitoring (GAIA-CLIM) project investigated the calibration/validation of satellite data sets using non-satellite reference data. Here, we explore the role of numerical weather prediction (NWP) frameworks for the assessment of several meteorological satellite sensors: the advanced microwave scanning radiometer 2 (AMSR2), microwave humidity sounder-2 (MWHS-2), microwave radiation imager (MWRI), and global precipitation measurement (GPM) microwave imager (GMI). We find departures (observation-model differences) are sensitive to instrument calibration artefacts. Uncertainty in surface emission is identified as a key gap in our ability to validate microwave imagers quantitatively in NWP. The prospects for NWP-based validation of future instruments are considered, taking as examples the microwave sounder (MWS) and infrared atmospheric sounding interferometer-next generation (IASI-NG) on the next generation of European polar-orbiting satellites. Through comparisons with reference radiosondes, uncertainties in NWP fields can be estimated in terms of equivalent top-of-atmosphere brightness temperature. We find NWP-sonde differences are consistent with a total combined uncertainty of 0.15 K for selected temperature sounding channels, while uncertainties for humidity sounding channels typically exceed 1 K.

scholarly journals Assessments of F16 Special Sensor Microwave Imager and Sounder Antenna Temperatures at Lower Atmospheric Sounding Channels

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Banghua Yan ◽  
Fuzhong Weng
Keyword(s):  
Brightness Temperature ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Numerical Weather ◽  
Temperature Anomalies ◽  
Atmospheric Sounding ◽  
Microwave Imager

The main reflector of the Special Sensor Microwave Imager/Sounder (SSMIS) aboard the Defense Meteorological Satellite Program (DMSP) F-16 satellite emits variable radiation, and the SSMIS warm calibration load is intruded by direct and indirect solar radiation. These contamination sources produce antenna brightness temperature anomalies of around 2 K at SSMIS sounding channels which are obviously inappropriate for assimilation into numerical weather prediction models and remote sensing retrievals of atmospheric and surface parameters. In this study, antenna brightness temperature anomalies at several lower atmospheric sounding (LAS) channels are assessed, and the algorithm is developed for corrections of these antenna temperature anomalies. When compared against radiative transfer model simulations and simultaneous observations from AMSU-A aboard NOAA-16, the SSMIS antenna temperatures at 52.8, 53.6, 54.4, 55.5, 57.3, and 59.4 GHz after the anomaly correction exhibit small residual errors (<0.5 K). After such SSMIS antenna temperatures are applied to the National Center for Environmental Prediction Numerical Weather Prediction (NWP) model, more satellite data is used and the analysis field of the geopotential height is significantly improved throughout troposphere and lower stratosphere. Therefore, the SSMIS antenna temperatures after the anomaly correction have demonstrated their potentials in NWP models.

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