As a part of the Joint Polar Satellite System (JPSS, formerly the NPOESS afternoon orbit), the instruments Cross-track
Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) make up the Cross-track Infrared and
Microwave Sounder Suite (CrIMSS). CrIMSS will primarily provide global temperature, moisture, and pressure
profiles and calibrated radiances [1]. In preparation for the NPP launch in 2011, we have ported and tested the
operational CrIMSS Environmental Data Record (EDR) algorithms using both synthetic and proxy data generated from
the IASI, AMSU, MHS data from Metop-A satellite.
An evaluation of the temperature, water vapor, and ozone profile retrievals from the AIRS data is performed with more
than three years of collocated radiosondes (RAOBs) and ozonesonde (O3SND) measurements. The Aqua-AIRS version
4.0 retrievals, global RAOB and O3SND measurements, forecast data from the NCEP_GFS, ECMWF, and the NOAA-
16 ATOVS retrievals are used in this validation and relative performance assessment. The results of the inter-comparison
of AIRS temperature, water vapor and ozone retrievals reveal very good agreement with the measurements
from RAOBs and O3SND s. The temperature RMS difference is close to the expected product goal accuracies, viz. 1oK
in 1 km layers for the temperature and close to 15% in 2-km layers for the water vapor in the troposphere. The AIRS
temperature retrieval bias is a little larger than the biases shown by the ATOVS, NCEP_GFS, and ECMWF forecasts.
With respect to the ozone profile retrieval, the retrieval bias and RMS difference with O3SNDs is less than 5% and 20%
respectively for the stratosphere. The total ozone from the AIRS retrievals matches very well with the Dobson/Brewer
station measurements with a bias less than 2%. Overall, the analysis performed in this paper show a remarkable degree
of confidence in the AIRS retrievals.
Observations from the high spectral resolution Atmospheric InfraRed Sounder (AIRS) on the NASA EOS AQUA platform are providing improved information on the temporal and spatial distribution of key atmospheric parameters, such as temperature, moisture and clouds. These parameters are important for improving real-time weather forecasting, climate monitoring, and climate prediction. Trace gas products such as ozone, carbon dioxide, carbon monoxide, and methane are also derived. High spectral resolution infrared radiances from AIRS are assimilated into numerical weather prediction models. The soundings and radiances are provided in near real-time by NOAA/NESDIS to the NWP community.
A significant component of the NOAA/NESDIS AIRS processing is to apply Principal Component Analysis (PCA) to the original AIRS 2000+ channel radiances. PCA is used for monitoring of the AIRS detectors, dynamic noise estimation and filtering, errant channel recovery, radiance reconstruction, and deriving an initial guess for profiles of temperature, moisture, ozone and other geophysical parameters. Since PCA has the ability to reduce the dimensionality of a dataset while retaining the significant information content, investigations are being done on its applications to AIRS data compression and archiving. Data compression is one of the key issues for the new generation of high spectral resolution satellite sensors.
Our current AIRS research will allow us to provide valuable information and real-time experience to the generation of products for future sensors, such as the EUMETSAT IASI and NPOESS CrIS advanced infrared sounders. Examples of each application, along with details on the generation and application of eigenvectors are presented in this paper.
Since October, 2002, NESDIS has provided specially tailored radiance and retrieval products derived from Aqua AIRS and AMSU-A observations operationally (24 hours x 7 days) to a number of Numerical Weather Prediction (NWP) centers, including NCEP, ECMWF and the UK Met. Office. Two types of products are available -- thinned radiance data and full resolution retrieval products consisting of atmospheric temperature, moisture and ozone as well as surface parameters of temperature and emissivity. The radiances are thinned because of current limitations in communication bandwidth and computational resources at NWP centers. There are two types of thinning: a) spatial and spectral thinning, and b) data compression using principal component analysis (PCA). PCA is used for a) reconstructing radiances with the properties of reduced noise, b) independent instrument noise estimation, c) quality control, and d)
deriving the retrieval first guess used in the AIRS processing software. The radiance products also include cloud cleared radiances. The cloud clearing procedure remove the effect of cloud contamination in partial overcast conditions and have been demonstrated to increase the amount of data that can be treated as clear from 5% to 50%. The AIRS temperature and moisture retrieval are significantly more accurate than AMSU-only retrievals in clear, cloud contaminated and cloud-cleared conditions. Most NWP centers are currently assimilating clear radiances, which we believe severely limits the impact of AIRS data. Fortunately, results presented in this paper have stimulated new upcoming experiments to test the impact of cloud-cleared radiances.
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