For the next-generation of GOES-R instruments to meet stated performance requirements, state-of-the-art algorithms will
be needed to convert raw instrument data to calibrated radiances and derived geophysical parameters (atmosphere, land,
ocean, and space weather). The GOES-R Program Office (GPO) assigned the NOAA/NESDIS Center for Satellite
Research and Applications (STAR) the responsibility for technical leadership and management of GOES-R algorithm
development and calibration/validation. STAR responded with the creation of the GOES-R Algorithm Working Group
(AWG) to manage and coordinate development and calibration/validation activities for GOES-R proxy data and
geophysical product algorithms. The AWG consists of 15 application teams that bring expertise in product algorithms
that span atmospheric, land, oceanic, and space weather disciplines. Each AWG teams will develop new scientific Level-
2 algorithms for GOES-R and will also leverage science developments from other communities (other government
agencies, universities and industry), and heritage approaches from current operational GOES and POES product systems.
All algorithms will be demonstrated and validated in a scalable operational demonstration environment. All software
developed by the AWG will adhere to new standards established within NOAA/NESDIS. The AWG Algorithm
Integration Team (AIT) has the responsibility for establishing the system framework, integrating the product software
from each team into this framework, enforcing the established software development standards, and preparing system
deliveries. The AWG will deliver an Algorithm Theoretical Basis Document (ATBD) for each GOES-R geophysical
product as well as Delivered Algorithm Packages (DAPs) to the GPO.
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.
Errors due to wireless transmission can have an arbitrarily large impact on a compressed file. A single bit error appearing in the compressed file can propagate during a decompression procedure and destroy the entire granule. Such a loss is unacceptable since this data is critical for a range of applications, including weather prediction and emergency response planning. The impact of a bit error in the compressed granule is very sensitive to the error's location in the file. There is a natural hierarchy of compressed data in terms of impact on the final retrieval products. For the considered compression scheme, errors in some parts of the data yield no noticeable degradation in the final products. We formulate a priority scheme for the compressed data and present an error correction approach based on minimizing impact on the retrieval products. Forward error correction codes (e.g., turbo, LDPC) allow the tradeoff between error correction strength and file inflation (bandwidth expansion). We propose segmenting the compressed data based on its priority and applying different-strength FEC codes to different segments. In this paper we demonstrate that this approach can achieve negligible product degradation while maintaining an overall 3-to-1 compression ratio on the final file. We apply this to AIRS sounder data to demonstrate viability for the sounder on the next-generation GOES-R platform.
Traditional cloud clearing methods utilize a clear estimate of the atmosphere inferred from a microwave sounder to extrapolate cloud cleared radiances (CCR's) from a spatial interpolation of multiple cloudy infrared footprints. Unfortunately, sounders have low information content in the lower atmosphere due to broad weighting functions, interference from surface radiance and the microwave radiances can also suffer from uncorrected side-lobe contamination. Therefore, scenes with low altitude clouds can produce errant CCR's that, in-turn, produce errant sounding products. Radiances computed from the corrupted products can agree with the measurements within the error budget making detection and removal of the errant scenes impractical; typically, a large volume of high quality retrievals are rejected in order to remove a few errant scenes. In this paper we compare and contrast the yield and accuracy of the traditional approach with alternative methods of obtaining CCR's. The goal of this research is three-fold: (1) to have a viable approach if the microwave instruments fail on the EOS-AQUA platform; (2) to improve the accuracy and reliability of infrared products derived from CCR's; and (3) to investigate infrared approaches for geosynchronous platforms where microwave sounding is difficult. The methods discussed are (a) use of assimilation products, (b) use of a statistical regression trained on cloudy radiances, (c) an infrared multi-spectral approach exploiting the non-linearity of the Planck function, and (d) use of clear MODIS measurements in the AIRS sub-pixel space. These approaches can be used independently of the microwave measurements; however, they also enhance the traditional approach in the context of quality control, increased spatial resolution, and increased information content.
A near real-time AIRS processing and distribution system is fully operational at NOAA/NESDIS/ORA. The AIRS system went though three separate production phases: design and development, implementation, and operations. The design and development phase consisted of two years of preparation for the near real-time AIRS data. The approach was to fully emulate the AIRS measurement stream. This was accomplished by using a forecast model to represent the geophysical state and computation of simulated AIRS measurements using the characteristics of the AIRS channels. The preparation included file format development and the creation of a program to subset the radiance and product data. The implementation phase lasted over a year and involved utilizing AIRS/AMSU/HSB simulated data quasi-operationally. This simulated data was placed into deliverable files and distributed to the customers for their pre-launch preparations. The operational phase consisted of switching the simulation system to real data and is the current system status. Details of what went right and wrong at each production phase will be presented. This methodology eased the transition to operations and will be applied to other advanced sounders such as IASI and CrIS.
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.
Development and testing of the IASI processing and distribution system is currently ongoing at NOAA/NESDIS/ORA. Level 1C data for 8461 channels will be available to NESDIS/NOAA from EUMETSAT shortly after MetOp 1 launch (currently scheduled for October 2005). Prior to launch, a simulation system will provide pseudo near-real time data for system testing and refinement. This will allow for a smooth and immediate system transition to the actual data processing when it becomes available. The ingested EUMETSAT level1C data will be subset both spectrally and spatially and then placed into BUFR format for a number of products including: (1) Level 1C (calibrated, apodized, and navigated) brightness temperatures, (2) cloud-cleared radiances, and (3) PCA reconstructed radiances. The subset level 1C data will be delivered within three hours of observation. System validation will consist of comparing the products to collocated radiosonde observations and model forecasts.