NASA's Advancements in Space-Based Spectrometry Lead to Improvements in Weather Prediction and Understanding of Climate Processes

AIRS (Atmospheric Infra-Red Sounder), was launched, in conjunction with AMSU-A (Advanced Microwave Sounding Unit-A) on the NASA polar orbiting research satellite EOS (Earth Observing System) Aqua satellite in May 2002 as a next generation atmospheric sounding system. Atmospheric sounders provide information primarily about the vertical distribution of atmospheric temperature and water vapor distribution. This is achieved by measuring outgoing radiation in discrete channels (spectral intervals) which are sensitive primarily to variations of these geophysical parameters. The primary objectives of AIRS/AMSU were to utilize such information in order to improve the skill of numerical weather prediction as well as to measure climate variability and trends. AIRS is a multi-detector array grating spectrometer with 2378 channels covering the spectral range 650/cm (15 microns) to 2660/cm (3.6 microns) with a resolving power (i/a i) of roughly 1200 where a i is the spectral channel bandpass. Atmospheric temperature profile can be determined from channel observations taken within the 15 micron (the long-wave CO2 absorption band) and within the 4.2 micron (the short-wave CO2 absorption band). Radiances in these (and all other) spectral intervals in the infrared are also sensitive to the presence of clouds in the instrument?s field of view (FOV), which are present about 95% of the time. AIRS was designed so as to allow for the ability to produce accurate Quality Controlled atmospheric soundings under most cloud conditions. This was achieved by having 1) extremely low channel noise values in the shortwave portion of the spectrum and 2) a very flat spatial response function within a channel?s FOV. IASI, the high spectral resolution IR interferometer flying on the European METOP satellite, does not contain either of these important characteristics. The AIRS instrument was also designed to be extremely stabile with regard to its spectral radiometric characteristics, which is critical with regard to the ability to measure accurate long term trends

AIRS Water Vapor and Cloud Products Validate and Explain Recent Short Term Decreases in Global and Tropical OLR as Observed by CERES

A strong equatorial SST cooling occurred from 160E westward to 120W during the period of September 2002 through August 2010, surrounded by a weaker warming ring to the west. This is the result of a transition from a strong El Nino in late 2002 to a strong La Nina in 2008. Late 2009 is characterized by the beginning of another El Nino. Average rates of change (ARC's) in 500mb specific humidity and cloud cover are in phase with those in the Sea surface temperature (SST). In the El Nino and surrounding region causing outgoing longwave radiation (OLR), to decrease significantly near the dateline and increase in the vicinity of Indonesia. Tropical OLR ARC's in these two areas cancel each other to first order. The negative zonal mean tropical OLR ARC from a drop in equatorial OLR in region 1 from 140W to 40E. This results from increasing water vapor and cloud cover in this area during La Nina with the reverse holding during El Nino

AIRS Products Explain the Close Relationship Between CERES OLR Anomalies and the El Nino Index

Nine years of AIRS products depict the interrelationship between El Nino, Water Vapor, Cloud Cover, and OLR Anomalies

Recent Spatial and Temporal Anomalies and Trends of OLR as Observed by CERES and Computed Based on AIRS Retrievals

We show that a recent CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe, for the time period of September 2002 through February 2010 used in this study, is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 Degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. We see this correspondence even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics. This essentially perfect agreement of OLR anomalies and even local trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate; and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by El-Nino-La Nina cycles . We use the anomalies and trends of AIRS derived products to explain why the global OLR has a large negative trend over this time period; Global and tropical OLR began to decrease significantly at the onset of a strong La Nina in mid-2007. AIRS products show that cloudiness and mid-tropospheric water vapor began to increase in the tropics at roughly the same time, especially in the region 5degN - 20degS latitude extending eastward from 150degW to 30degE longitude, with a corresponding very large drop in OLR in this region. Late 2009 is characterized by a strong El-Nino, with a corresponding change in sign of observed tropical water vapor, cloud cover, and OLR anomalies. If one excludes the area 5degN - 20degS, 150degW - 30degE from the statistics, area mean OLR trends over the rest of the globe are substantially reduced over the time period under study

Improved Surface and Tropospheric Temperatures Determined Using Only Shortwave Channels: The AIRS Science Team Version-6 Retrieval Algorithm

The Goddard DISC has 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 will be finalized in September 2011. This paper describes some of the significant improvements contained in the Version-6 retrieval algorithm, compared to that used in Version-5, with an emphasis on the improvement of atmospheric temperature profiles, ocean and land surface skin temperatures, and ocean and land surface spectral emissivities. AIRS contains 2378 spectral channels covering portions of the spectral region 650 cm(sup -1) (15.38 micrometers) - 2665 cm(sup -1) (3.752 micrometers). These spectral regions contain significant absorption features from two CO2 absorption bands, the 15 micrometers (longwave) CO2 band, and the 4.3 micrometers (shortwave) CO2 absorption band. There are also two atmospheric window regions, the 12 micrometer - 8 micrometer (longwave) window, and the 4.17 micrometer - 3.75 micrometer (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. 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 observations in longwave channels to determine coefficients which generate cloud cleared radiances R(sup ^)(sub i) for all channels, and uses R(sup ^)(sub i) only from shortwave channels in the determination of surface and atmospheric temperatures. This procedure is now being used in the AIRS Version-6 Retrieval Algorithm. Results are presented for both daytime and nighttime conditions showing improved Version-6 surface and atmospheric soundings under partial cloud cover

AIRS-Observed Interrelationships of Anomaly Time-Series of Moist Process-Related Parameters and Inferred Feedback Values on Various Spatial Scales

In the beginning, a good measure of a GMCs performance was their ability to simulate the observed mean seasonal cycle. That is, a reasonable simulation of the means (i.e., small biases) and standard deviations of TODAY?S climate would suffice. Here, we argue that coupled GCM (CG CM for short) simulations of FUTURE climates should be evaluated in much more detail, both spatially and temporally. Arguably, it is not the bias, but rather the reliability of the model-generated anomaly time-series, even down to the [C]GCM grid-scale, which really matter. This statement is underlined by the social need to address potential REGIONAL climate variability, and climate drifts/changes in a manner suitable for policy decisions

Results from CrIS/ATMS Obtained Using an AIRS "Version-6 Like" Retrieval Algorithm

We have tested and evaluated Version-6.22 AIRS and Version-6.22 CrIS products on a single day, December 4, 2013, and compared results to those derived using AIRS Version-6. AIRS and CrIS Version-6.22 O3(p) and q(p) products are both superior to those of AIRS Version-6All AIRS and CrIS products agree reasonably well with each other CrIS Version-6.22 T(p) and q(p) results are slightly poorer than AIRS under very cloudy conditions. Both AIRS and CrIS Version-6.22 run now at JPL. Our short term plans are to analyze many common months at JPL in the near future using Version-6.22 or a further improved algorithm to assess the compatibility of AIRS and CrIS monthly mean products and their interannual differencesUpdates to the calibration of both CrIS and ATMS are still being finalized. JPL plans, in collaboration with the Goddard DISC, to reprocess all AIRS data using a still to be finalized Version-7 retrieval algorithm, and to reprocess all recalibrated CrISATMS data using Version-7 as well

Usefulness of AIRS-Derived OLR, Temperature, Water Vapor and Cloudiness Anomaly Time-series for GCM Validation

The ROBUST nature (biases are not as important as previous GCM-evaluations suggest) of the AIRS-observations-generated ARC-maps and ATs as well as their interrelations suggest that they could be a useful tool to select CGCMs which may be considered the reliable, i.e., to be trusted even for longer-term climate drift/change predictions (even on the regional scale). Get monthly gridded CGCM time-series of atmospheric variables coinciding with the timeframe of the AIRS analyses for at least 5-6 years and do the actual evaluations of ARC-maps and ATs for the coinciding time periods

Results from CrIS/ATMS Obtained Using an "AIRS Version-6 Like" Retrieval Algorithm

A main objective of AIRS/AMSU on EOS is to provide accurate sounding products that are used to generate climate data sets. Suomi NPP carries CrIS/ATMS that were designed as follow-ons to AIRS/AMSU. Our objective is to generate a long term climate data set of products derived from CrIS/ATMS to serve as a continuation of the AIRS/AMSU products. We have modified an improved version of the operational AIRS Version-6 retrieval algorithm for use with CrIS/ATMS. CrIS/ATMS products are of very good quality, and are comparable to, and consistent with, those of AIRS

Improved Methodology for Surface and Atmospheric Soundings, Error Estimates, and Quality Control Procedures: the AIRS Science Team Version-6 Retrieval Algorithm

The AIRS Science Team Version-6 AIRS/AMSU retrieval algorithm is now operational at the Goddard DISC. AIRS Version-6 level-2 products are generated near real-time at the Goddard DISC and all level-2 and level-3 products are available starting from September 2002. This paper describes some of the significant improvements in retrieval methodology contained in the Version-6 retrieval algorithm compared to that previously used in Version-5. In particular, the AIRS Science Team made major improvements 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) derive error estimates and use them for Quality Control. Significant improvements have also been made in the generation of cloud parameters. In addition to the basic AIRS/AMSU mode, Version-6 also operates in an AIRS Only (AO) mode which produces results almost as good as those of the full AIRS/AMSU mode. This paper also demonstrates the improvements of some AIRS Version-6 and Version-6 AO products compared to those obtained using Version-5