GOES-R Vertical Temperature Profile
Frequently Asked Questions about the GOES-R Legacy Vertical Temperature Profile Product
1) What is this product?
The GOES-R Vertical Temperature Profile product produces a vertical profile of moisture amounts that can be viewed as a skew-t diagram in AWIPS. The horizontal resolution of this product is 10km. The range of values are from 0 to 100%, with a product accuracy of 18% from the surface to 300 mb, and 20% from 300 mb - 100 mb. Vertical moisture profiles are created both day and night, and are considered quantitative out to at least 67 degrees local zenith angle. They are only created for pixels that are considered "clear" by the GOES-R Clear Sky Mask algorithm.
Vertical moisture profiles are useful when assessing the vertical temperature distribution of the atmosphere, especially in terms of atmospheric stability.
2) How often do I receive this data?
The cadence of the GOES-R Vertical Temperature Profile product is dependent upon which image from the satellite one is looking at. For Full Disk imagery, a profile is produced every 60 minutes. For CONUS imagery, a profile is produced every 30 minutes. For a Mesoscale scan, a profile is produced every 5 minutes.
3) How do I display this product in AWIPS-II?
To display this product in AWIPS-II, go to the "GOES-R" tab of the CAVE menu, then select "Legacy Temp/Moisture Profile." From there, select the region of interest (GOES-E, GOES-W, or GOES Test) and region (Full Disk, CONUS, and Mesoscale). A grid of "dots" will appear on the AWIPS map image. Clicking on any point will display a skew-t in NSHARP of both vertical temperature and moisture profiles.
The Vertical Temperature Profile cannot be viewed through the AWIPS Product Browser.
4) How do I interpret the color maps associated with this product?
As with all skew-t displays in NSHARP, the red vertical line represents temperature and the green vertical line represents dew point temperature.
5) What other imagery/products might I use in conjunction with this product?
The vertical temperature profile is viewed only in NSHARP on a skew-t diagram. It can be useful to also overlay vertical soundings derived by models, and also observed soundings from local weather balloons. Comparing the satellite-derived sounding to model soundings can give an idea of which model is best capturing the vertical profile of the atmosphere, and therefore which model should be chosen as "truth" for the coming hours. Comparing the satellite sounding to a balloon sounding can also lend confidence (or take away confidence) in how the algorithm is performing.
6) How is this product created?
The GOES-R Legacy Vertical Temperature Profile product is only calculated on pixels that are determined to be either "clear" or "probably clear" by the GOES-R Cloud Mask algorithm. It utilizes GOES-R bands 6.2 um, 7.0 um, 7.4 um, 8.5 um, 9.6 um, 10.33 um, 11.2 um, 12.3 um, and 13.3 um channels. The product relies on the infrared observations to avoid discontinuities associated with the transition from day to night. The algorithm performance is sensitive to imagery artifacts or instrument noise.1
The ancillary data used to calculate the vertical temperature profile include1:
- Surface pressure from 6–18 hour forecast from NWP model.
- Surface pressure level index from 6–18 hour forecast from NWP model.
- Near surface wind speed vectors (zonal and meridional) from 6–18 hour
forecast from NWP model.
- Surface skin temperature from 6–18 hour forecast from NWP model.
- Temperature profile from 6–18 hour forecast from NWP model.
- Moisture profile from 6–18 hour forecast from NWP model.
- Forecast error covariance matrix from comparisons between forecast and radiosondes. Assume there is no correlation between temperature and moisture in the error covariance matrix.
- Land Mask
- Surface Elevation
- Temperature profile
- Water vapor profile
- IR SEs for ABI bands from UW-Madison baseline fit database. A global database of monthly IR land SE derived from the MODIS operational land surface emissivity product (MOD11). Emissivity is available globally at ten wavelengths (3.6, 4.3, 5.0, 5.8, 7.6, 8.3, 9.3, 10.8, 12.1, and 14.3 µm) with 0.05 degree spatial resolution. Monthly SEs have been integrated into the ABI spectral response functions to match the ABI bands.
- LUT for ABI IR SEs over ocean as a function of LZA and wind speed above
ocean surface. (http://ams.confex.com/ams/pdfpapers/104810.pdf).
- Regression coefficient file. This coefficient file contains 81 regression coefficient datasets. Each coefficient dataset corresponds to one LZA ranging from 0 to 80 degrees. The regression coefficient file is an array of 81*110 * (3*L+1+9), where L(=101) is the atmospheric pressure levels used in RTM.
Only clear ABI IR BTs within each Field-of-Regard (FOR) are processed for derived products. Usually there are multiple clear sky FOVs in each FOR. Two methods are available in the algorithm to select the representing value for the specific FOR: one is the simple average of all clear sky FOVs for each channel; another method is to determine the warmest FOV with largest value of the IR 10.8 channel and use the values of all IR channels at this FOV as representatives of this FOR. A subroutine named Find_Good_BT is presented for the BT manipulation in the main sounding retrieval module and called right after the determination of clear pixels within the FOR. The simple average method is better to reduce the instrumental noise. However, since there are always some cloudy pixels misidentified as clear pixels, which in general have lower value at IR 10.8 channel, the second method is better than the simple average in mitigating cloud impact. According to several cases with SEVIRI as used as proxy, it is found that the cold bias is much stronger than the instrumental noise; therefore the warmest FOV method is set as the default method in the algorithm.1
Both the current GOES Sounder and ABI have three water vapor absorption channels although the spectral coverage is different. Studies have shown that the ABI, with numerical model forecast information used as the background, will be slightly inferior to the GOES-13/O/P sounder performance, yet both are substantially less capable than a high-spectral-resolution sounder with respect to information content and retrieval accuracy. The ABI will provide some continuity of the current sounder products to bridge the gap until the advent of the GOES advanced infrared sounder. Both theoretical analysis and retrieval simulations show that data from the ABI can be combined with temperature and moisture information from forecast models to produce derived products that will be adequate substitutes for the legacy products from the current GOES sounders.
The algorithm retrievals retrieval is a process of iteratively adjusting a first guess profile based on the BT residuals between observed and calculatedABI 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.
The 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.
1Li, Jun, Timothy J. Schmit, Xin Jin, and Graeme Martin. NOAA NESDIS Center for Satellite Applications and Research GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document: Legacy Atmospheric Moisture Profile, Legacy Atmospheric Temperature Profile, Total Precipitable Water, and Derived Atmospheric Stability Indices v.2.0. September 2010. http://www.goes-r.gov/products/ATBDs/baseline/Sounding_LAP_v2.0_no_color.pdf