The legacy atmospheric profile (LAP) product provides temperature and moisture profiles, along with derived total precipitable water (TPW) and atmospheric instability indices from clear sky radiances within a ABI field-of-view (FOV) box area, here one FOV means one pixel. One field-of-regard (FOR) is defined as several FOVs. The derived instability indices include lifted index (LI), convective available potential energy (CAPE), total totals index (TT), Showalter index (SI), and K-index (KI). The ABI LAP product is a continuation of the current GOES Sounder product before it is presumably succeeded by an advanced hyperspectral IR sounding instrument in the post-GOES-R era.
The GOES-R ABI LAP algorithm is responsible for the retrieval of atmospheric temperature and moisture profiles for a FOR consisting of M x M ABI FOVs, in this document FOR specifically refers to the pixel group for one profile retrieval. At the time of this writing, M = 5 is assumed, although because current requirements call for 4 km mesoscale stability parameters, a smaller value for M may be necessary. From the temperature and moisture profiles, the associated TPW and atmospheric stability indices such as LI, TT, KI, SI and CAPE are also derived. The product generation needs IR BTs from all ABI channels along with NWP output. The LAP output includes temperature and moisture profiles at all 101-levels but only the 54 level temperatures from 100 hPa to 1050 hPa and 35 level moistures from 300 hPa to 1050 hPa are useful. The surface skin temperature, TPW, PW at three atmospheric layers in sigma ordinate (PW_low: 0.9 - SFC, PW_mid: 0.7 - 0.9, PW_high: 0.3 - 0.7), LI, CAPE, TT, KI and SI are also products included in the output.
Improvements and Benefits
The LAP product is a continuation of the current GOES Sounder product. As we prepare for the next generation of geostationary satellites, it is important to ensure the continuity and quality of products that users depend on from the current satellite series. The GOES Sounders have provided quality hourly radiances and derived products over the continental United States (CONUS) and adjacent oceans for over a decade (Menzel et al. 1998). The derived products include: clear-sky radiances; temperature and moisture profiles; TPW and layer PW; atmospheric stability indices such as CAPE and LI. These products are used for a number of numerical weather prediction (NWP) and forecasting applications.
The next generation GOES series will enable many improvements and new capabilities for imager-based products. Given that GOES-R will not host a sounding instrument, the question arises whether the ABI-based products will provide an adequate substitute for legacy sounder-based products.
How does it work? - Algorithm
LAP retrieval is a process of iteratively adjusting a first guess profile based on the BT residuals between observed and calculated ABI IR bands. The first guess is used in the initial calculation. ABI spectral and spatial radiance signatures are used in the retrieval process.
Assuming CO2 is a well-mixed gas, an IR band with CO2 absorption contains temperature profile information (assuming a non-isothermal atmosphere), while IR bands with varying gas absorption (e.g., H2O) contains both temperature and the gas concentration information. ABI has 10 IR bands within which three bands contain strong water vapor absorption, one has strong ozone absorption and one has CO2 absorption. The other ABI IR bands are atmospheric "window" bands that contain information of the surface skin temperature, emissivity and low level moisture.
Examples of a number of the atmospheric stability products as generated by the GOES-R Temperature, Moisture, and Atmospheric Stability algorithm using simulated GOES-R data on 04 June 2005 22:00 UTC. Stability indices shown include Lifted Index (upper left), CAPE (upper right), Totals Total (lower left) and K-Index (lower right).
The LAP algorithm infers a temperature and moisture profile from the satellite observed radiances in a given set of spectral bands. The air mass parameters are then derived from this profile. The method is an optimal estimation using an inversion technique. The method thus tries to find an atmospheric profile which best reproduces the observations. In general, this is a multi-solution problem, and therefore a "background profile" is here used as a constraint. This background profile is often from a short range forecast model, which is fed to the iteration scheme as an initial proposal for a solution. The original background is then slowly modified in a controlled manner until its radiative properties fit the satellite observations. In addition to the background, a first guess which is the starting point in the iteration procedure is used. The first guess is important, for example, if the first guess contains structure similar to the real atmosphere, the final solution will be good. A typical first guess field is a short-term forecast; however, we found a regression is usually better than the forecast since the regression uses combined forecast and ABI IR radiances as predictors, so the regression is used here as the first guess. Major limitations of this method are the high computational effort and the fact that the retrieved profiles tend to retain features of the first guess due to low spectral resolution and few spectral bands.
See the GOES-R ATBD page for all ATBDs.
How are the results compared to existing data? - Calibration and Validation
Validation of the current GOES sounding product compares GOES radiance measurements, forecast guess, and radiosonde observations. In addition, high temporal resolution ground-based microwave water vapor measurements at the DOE ARM Southern Great Plains Cloud and Radiation Testbed (CART) Site, temporally and spatially, co-located GPS-Met total precipitable water and GOES retrievals/products are also used. In addition, the comparisons with other satellite measurements, especially from high-spectral resolution instruments (AIRS, IASI, CrIS) can be made to assess product quality for some of the overlap times.
A more technical validation presentation, (PDF, 30.49 MB) is also available.