scholarly journals An Infrared Atmospheric Sounding Interferometer – New Generation ( IASI‐NG ) channel selection for Numerical Weather Prediction

2021 ◽  
Author(s):  
F. Vittorioso ◽  
V. Guidard ◽  
N. Fourrié
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Channel Selection ◽  
Numerical Weather ◽  
Atmospheric Sounding ◽  
Selection For ◽  
New Generation

scholarly journals Update of Infrared Atmospheric Sounding Interferometer (IASI) channel selection with correlated observation errors for numerical weather prediction (NWP)

2020 ◽  
Vol 13 (5) ◽  
pp. 2659-2680
Author(s):  
Olivier Coopmann ◽  
Vincent Guidard ◽  
Nadia Fourrié ◽  
Béatrice Josse ◽  
Virginie Marécal
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Channel Selection ◽  
Observation Error ◽  
Numerical Weather ◽  
Atmospheric Sounding ◽  
Analysis Error ◽  
Concept Of Information ◽  
Selection Of

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is an essential instrument for numerical weather prediction (NWP). It measures radiances at the top of the atmosphere using 8461 channels. The huge amount of observations provided by IASI has led the community to develop techniques to reduce observations while conserving as much information as possible. Thus, a selection of the 300 most informative channels was made for NWP based on the concept of information theory. One of the main limitations of this method was to neglect the covariances between the observation errors of the different channels. However, many centres have shown a significant benefit for weather forecasting to use them. Currently, the observation-error covariances are only estimated on the current IASI channel selection, but no studies to make a new selection of IASI channels taking into account the observation-error covariances have yet been carried out. The objective of this paper was therefore to perform a new selection of IASI channels by taking into account the observation-error covariances. The results show that with an equivalent number of channels, accounting for the observation-error covariances, a new selection of IASI channels can reduce the analysis error on average in temperature by 3 %, humidity by 1.8 % and ozone by 0.9 % compared to the current selection. Finally, we go one step further by proposing a robust new selection of 400 IASI channels to further reduce the analysis error for NWP.

scholarly journals A channel selection method for hyperspectral atmospheric infrared sounders based on layering

2020 ◽  
Vol 13 (2) ◽  
pp. 629-644 ◽  
Author(s):  
Shujie Chang ◽  
Zheng Sheng ◽  
Huadong Du ◽  
Wei Ge ◽  
Wei Zhang
Keyword(s):  
Numerical Weather Prediction ◽  
Seasonal Variations ◽  
Weather Prediction ◽  
Channel Selection ◽  
Selection Method ◽  
Atmospheric Infrared Sounder ◽  
Weighting Functions ◽  
Numerical Weather ◽  
Effective Channel ◽  
Primary Channel

Abstract. This study introduces an effective channel selection method for hyperspectral infrared sounders. The method is illustrated for the Atmospheric InfraRed Sounder (AIRS) instrument. The results are as follows. (1) Using the improved channel selection (ICS), the atmospheric retrievable index is more stable, with the value reaching 0.54. The coverage of the weighting functions is more evenly distributed over height with this method. (2) Statistical inversion comparison experiments show that the accuracy of the retrieval temperature, using the improved channel selection method in this paper, is consistent with that of 1D-Var channel selection. In the stratosphere and mesosphere especially, from 10 to 0.02 hPa, the accuracy of the retrieval temperature of our improved channel selection method is improved by about 1 K. The accuracy of the retrieval temperature of ICS is also improved at lower heights. (3) Statistical inversion comparison experiments for four different regions illustrate latitudinal and seasonal variations and better performance of ICS compared to the numerical weather prediction (NWP) channel selection (NCS) and primary channel selection (PCS) methods. The ICS method shows potential for future applications.

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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