John Blaisdell

Retrieval of atmospheric and surface parameters from AIRS/AMSU/HSB data in the presence of clouds

New state-of-the-art methodology is described to analyze the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit/Humidity Sounder for Brazil (AIRS/AMSU/HSB) data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm, which will be used to analyze AIRS/AMSU/HSB data on the Earth Observing System Aqua platform. The cloud-clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first-guess field and the first-guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that clear column radiances can be reconstructed under partial cloud cover with an accuracy comparable to single spot channel noise in the temperature and water vapor sounding regions; temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 1 K in 1-km-thick layers from the surface to 700 mb, 1-km layers from 700-300 mb, 3-km layers from 300-30 mb, and 5-km layers from 30-1 mb; and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 1-km layers from the surface to nearly 200 mb.

AIRS: Improving Weather Forecasting and Providing New Data on Greenhouse Gases

Abstract The Atmospheric Infrared Sounder (AIRS) and its two companion microwave sounders, AMSU and HSB were launched into polar orbit onboard the NASA Aqua Satellite in May 2002. NASA required the sounding system to provide high-quality research data for climate studies and to meet NOAAs requirements for improving operational weather forecasting. The NOAA requirement translated into global retrieval of temperature and humidity profiles with accuracies approaching those of radiosondes. AIRS also provides new measurements of several greenhouse gases, such as CO2, CO, CH4, O3, SO2, and aerosols. The assimilation of AIRS data into operational weather forecasting has already demonstrated significant improvements in global forecast skill. At NOAA/NCEP, the improvement in the forecast skill achieved at 6 days is equivalent to gaining an extension of forecast capability of six hours. This improvement is quite significant when compared to other forecast improvements over the last decade. In addition to NCEP, ECM...

Improved Temperature Sounding and Quality Control Methodology Using AIRS/AMSU Data: The AIRS Science Team Version 5 Retrieval Algorithm

This paper describes the Atmospheric Infrared Sounder (AIRS) Science Team Version 5 retrieval algorithm in terms of its three most significant improvements over the methodology used in the AIRS Science Team Version 4 retrieval algorithm: the use of AIRS clear-column radiances in the entire 4.3-μm CO2 absorption band in the retrieval of temperature profiles T(p) during both day and night, with tropospheric sound ing of 15-μm CO2 observations now being used primarily in the generation of clear-column radiances R̂i for all channels; development of a new methodology to provide accurate case-by-case error estimates for retrieved geophysical parameters and for channel-by-channel clear column radiances and their use in a new approach for quality control; and an approach to provide AIRS soundings in partially cloudy conditions that does not require use of any microwave data. This new AIRS-only sounding methodology, referred to as AIRS Version 5 AO, was developed as a backup to AIRS Version 5 should the Advanced Microwave Sounding Unit (AMSU)-A instrument fail. Results are shown that compare the relative performance of the AIRS Version 4, Version 5, and Version 5 AO. Results using Version 5 retrievals in conjunction with different quality control thresholds are also shown for a recent period to demonstrate that empirical coefficients continue to be applicable in later time periods. The Goddard Data and Information Services Center (DISC) is now generating and distributing products derived using the AIRS Science Team Version 5 retrieval algorithm. This paper describes the quality control flags contained in the DISC AIRS/AMSU retrieval products and their intended use for scientific purposes.

Practical methods for rapid and accurate computation of interferometric spectra for remote sensing applications

The apodization of an interferogram corresponds to a linear transformation in spectral space between unapodized and apodized radiances. Many apodization functions have well-behaved numerical inverse transformations, and we show an analytic inverse for the Hamming apodization function. The inverse transformation has many practical uses for remote sensing applications and can also be used theoretically to show the equivalence between unapodized spectra and properly apodized spectra. The inverse transformation, which is a representation of the discrete convolution theorem, can be used to readily convert computed apodized spectra to spectra computed for other symmetric apodization functions (including unapodized), which may have poorer characteristics with regard to calculating channel-transmittance parameters or radiances. We also show a quantitative method for comparing apodization functions of different mathematical forms.

Determination of atmospheric and surface parameters from simulated AIRS/AMSU/HSB sounding data: Retrieval and cloud clearing methodology

Abstract New state of the art methodology is described to analyze AIRS/AMSU/HSB data in the presence of multiple cloud formations. The methodology forms the basis for the AIRS Science Team algorithm which will be used to analyze AIRS/AMSU/HSB data on EOS PM1. The cloud clearing methodology requires no knowledge of the spectral properties of the clouds. The basic retrieval methodology is general and extracts the maximum information from the radiances, consistent with the channel noise covariance matrix. The retrieval methodology minimizes the dependence of the solution on the first guess field and does not require modelling or knowledge of the first guess error characteristics. Results are shown for AIRS Science Team simulation studies with multiple cloud formations. These simulation studies imply that temperature soundings can be produced under partial cloud cover with RMS errors better than 1°K in 1 km thick layers from the surface to 700 mb, 1 km layers from 700 mb to 300 mb, 3 km layers from 300 mb to 30 mb, and 5 km layers from 30 mb to 1 mb, and moisture profiles can be obtained with an accuracy of about 10% absolute errors in 1 km layers from the surface to 200 mb.

Improved Surface Parameter Retrievals using AIRS/AMSU Data

The AIRS Science Team Version 5.0 retrieval algorithm became operational at the Goddard DAAC in July 2007 generating near real-time products from analysis of AIRS/AMSU sounding data. This algorithm contains many significant theoretical advances over the AIRS Science Team Version 4.0 retrieval algorithm used previously. Two very significant developments of Version 5 are: 1) the development and implementation of an improved Radiative Transfer Algorithm (RTA) which allows for accurate treatment of non-Local Thermodynamic Equilibrium (non-LTE) effects on shortwave sounding channels; and 2) the development of methodology to obtain very accurate case by case product error estimates which are in turn used for quality control. These theoretical improvements taken together enabled a new methodology to be developed which further improves soundings in partially cloudy conditions. In this methodology, longwave CO2 channel observations in the spectral region 700 cm-1 to 750 cm-1 are used exclusively for cloud clearing purposes, while shortwave CO2 channels in the spectral region 2195 cm-1 to 2395 cm-1 are used for temperature sounding purposes. This allows for accurate temperature soundings under more difficult cloud conditions. This paper further improves on the methodology used in Version 5 to derive surface skin temperature and surface spectral emissivity from AIRS/AMSU observations. Now, following the approach used to improve tropospheric temperature profiles, surface skin temperature is also derived using only shortwave window channels. This produces improved surface parameters, both day and night, compared to what was obtained in Version 5. These in turn result in improved boundary layer temperatures and retrieved total O3 burden.

Remote Sensing of Atmospheric Climate Parameters from the Atmospheric Infrared Sounder

This paper presents the standard and research products from Atmospheric Infrared Sounder (AIRS) and their current accuracies as demonstrated through validation efforts. It also summarizes ongoing research using AIRS data for weather prediction and improving climate models.

Significant advances in the AIRS Science Team Version-6 retrieval algorithm

The Goddard DISC generated products derived from AIRS/AMSU-A observations, starting from September 2002 when the AIRS instrument became stable, using the AIRS Science Team Version-5 retrieval algorithm. The AIRS Science Team Version-6 retrieval algorithm became operational at the Goddard DISC in late 2012. This paper describes some of the significant improvements in retrieval methodology contained in the Version-6 retrieval algorithm, compared to that used in Version-5. In particular, the Science Team made major changes with regard to the algorithms used to 1) derive surface skin temperature and surface spectral emissivity; 2) generate the initial state used to start the cloud clearing and retrieval procedures; and 3) determine Quality Control. This paper describes these advances found in the AIRS Version- 6 retrieval algorithm and demonstrates the improvements of some AIRS Version-6 products compared to those obtained using Version-5.

Version 5 product improvements from the atmospheric infrared sounder (AIRS)

The AIRS instrument was launched in May 2002 into a polar sun-synchronous orbit onboard the EOS Aqua Spacecraft. Since then we have released three versions of the AIRS data product to the scientific community. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts. The first version of software, Version 2.0 was available to scientists shortly after launch with Version 3.0 released to the public in June 2003. Like all AIRS product releases, all products are accessible to the public in order to have the best user feedback on issues that appear in the data. Fortunately the products have had exceptional accuracy and stability. This paper presents the improvement between AIRS Version 4.0 and Version 5.0 products and shows examples of the new products available in Version 5.0.

Improved determination of surface and atmospheric temperatures using only shortwave AIRS channels

AIRS was launched on EOS Aqua on May 4, 2002, together with AMSU-A and HSB, to form a next generation polar orbiting infrared and microwave atmospheric sounding system. AIRS is a grating spectrometer with a number of linear arrays of detectors with each detector sensitive to outgoing radiation in a characteristic frequency υi with a spectral band pass Δυi of roughly υi/1200 AIRS contains 2378 spectral channels covering portions of the spectral region 650 cm-1 (15.38 μm) - 2665 cm-1 (3.752 μm). These spectral regions contain significant absorption features from two CO2 absorption bands, the 15 μm (longwave) CO2 band, and the 4.3 μm (shortwave) CO2 absorption band. There are also two atmospheric window regions, the 12 μm - 8 μm (longwave) window, and the 4.17 μm - 3.75 μm (shortwave) window. Historically, determination of surface and atmospheric temperatures from satellite observations was performed using primarily observations in the longwave window and CO2 absorption regions. One reason for this was concerns about the effects, during the day, of reflected sunlight and non-Local Thermodynamic Equilibrium (non-LTE) on the observed radiances in the shortwave portion of the spectrum. According to cloud clearing theory, more accurate soundings of both surface skin and atmospheric temperatures can be obtained under partial cloud cover conditions if one uses the longwave channels to determine cloud cleared radiances Ri for all channels, and uses Ri only from shortwave channels in the determination of surface and atmospheric temperatures. This procedure is now being used by the AIRS Science Team in preparation for the AIRS Version 6 Retrieval Algorithm. This paper describes how the effects on the radiances of solar radiation reflected by clouds and the Earths surface, and also of non-LTE, are accounted for in the analysis of the data. Results are presented for both daytime and nighttime conditions showing improved surface and atmospheric soundings under partial cloud cover resulted from not using Ri in the retrieval process for any longwave channels sensitive to cloud effects. This improvement is made possible because AIRS NEDT in the shortwave portion of the spectrum is extremely low.