|
Short-term Prediction Research
|
|
|
|
|
Short-term Prediction Research
|
|
|
|
Mission Products Research Transition to Partners Library 
|
Retrieval of Geophysical Parameters from MODISReal-time MODIS and AMSR-E Level 1B imagery is available from a number of direct broadcast ground stations throughout the world (http://modis.gsfc.nasa.gov/data/directbrod/). The SPoRT program obtains this imagery from the University of Wisconsin (UW) and the University of South Florida's (USF) direct broadcast stations and provides data and selected products to NWS Forecast Offices in their AWIPS systems.
Additional real time products are generated as part of the SPoRT program activities with algorithms developed by NSSTC/GHCC scientists. A discussion of these in-house products is presented below. Total Precipitable Water (TPW) - This product differs from the UW/EOS real-time product (MOD07) because it uses the physical split window retrieval algorithm (Suggs et al 2004), with an Eta model forecast as a first guess and the NSSTC/GHCC cloud mask to isolate clear and cloudy regions. The latter procedure produces considerable product differences because the UW/EOS algorithm does not produce TPW retrievals in "uncertain clear" regions, which often over determines clouds over land at night. The quality of the TPW retrievals depends partially on the appropriateness of the first guess. The quality degrades under inversion conditions (either in the first guess or retrieval environment). Under optimal observing conditions, TPW retrieval errors will approach 2.0 mm, while Land Surface Temperature (LST) errors are as small as 0.2 K. Variations in surface thermal emissivity unaccounted for in the retrieval process will increase the magnitude of the errors. For details on the emissivity effect see Suggs et al (2004). Land Surface Temperature (LST) - LST is a by-product of the the TPW retrieval with the physical split window retrieval algorithm and produces single pixel (1km) retrievals day and night. The product compares favorably with that produced by the EOS science team as show by Suggs et al (2004) and is used to initialize regional forecast models and for minimum temperature estimation over North Alabama by the Huntsville NWS Forecast Office (Jones et al 2004). Cloud Top Pressure (CTP) - Each cloudy pixel in the NSSTC/GHCC cloud mask is assigned a cloud top pressure based on an infrared method which matches the observed window channel brightness temperature with an adjacent thermodynamic profile as described in Haines et al (2004). This method assumes an opaque cloud and will over-estimate cloud top pressure for thin or sub-pixel clouds. A more robust CO2 slicing method will be implemented in the near future. Natural Color Composite Image - The three-channel natural color composite image enhances ocean, land surface, cloud and other atmospheric features (such as smoke and dust). The natural composites are created by assigning colors at each pixel location with the red, green and blue intensities in proportion to the radiance values of MODIS channels 1 (.620 - .670 μm), 4 (.545 - .565 μm) and 3 (.459 - .479 μm) at that location, thus approximating the actual (natural) colors within a scene. Atmospheric and geometric corrections are applied to the MODIS channels 1, 4 and 3 to account for atmospheric radiative interactions and cross track variation of satellite field of view, respectively (Gumley et al. 2003). Data from channels 4 and 3 are resampled to match the 250 m resolution of channel 1. False Color Snow Image - While a natural color composite image enhances "visible" features, false color composites often combine one or two visible channels with an infrared channel to highlight features with infrared signatures. One such false color composite image has been developed to distinguish between snow and clouds, both of which appear white on a natural color composite image. While snow may look like clouds in the visible portion of the spectrum (what the eye sees), in other portions of the spectrum snow reflects radiation differently than clouds. Snow is spectrally different from clouds at wavelengths longer than 1.4 micrometers. MODIS channels at 1.63 and 2.13 micrometers therefore can be used to distinguish between snow and clouds. To make the distinction between clouds and snow obvious in the MODIS data, a visible channel is combined with the 1.63 and 2.13 micrometer channels to produce a “false color” snow image. Before compositing, the MODIS imagery is stretched to enhance contrast between the features assuring good color differentiation between the various features of interest. The MODIS data is combined such that features with large reflectance in the visible, 1.63, and 2.13 micrometer channels take on color characteristics corresponding to red, green, and blue information, respectively.The Great Falls, Montana WFO has used this product since Winter 2004 to map snow on the ground in order to improve flood forecasts from springtime snow melt. Details about the product can be found in the training module developed for the Great Falls office (False Color Product training module). Fog Product - The MODIS fog product takes advantage of the lower thermal emissivity of water clouds (3.9 μm) versus land surfaces (11 μm). This difference is characterized by the 3.9-11 μm difference image calculated during the pre-dawn hours over a given region. Currently a single subjectively determined threshold value (2.5 K) defines the cutoff region in the image: image values with greater differences are labeled as fog and values below the threshold are clear. In reality this threshold is not constant and can change spatially, temporally (time of night), and seasonally. Spatially/temporally varying fog thresholds are being explored analogous to that used in the NSSTC/GHCC cloud mask algorithm. NSSTC/GHCC Level 3 MODIS Products Retrieval of Geophysical Parameters from GOES
Cloud Mask - The GOES cloud mask algorithm (Jedlovec and Laws 2003) uses the 3.9 and 11 micrometer window channels in five spectral/spatial tests to determine the sky conditions at 4 km resolution for day and night scenes on an hourly basis. The unique aspect of the algorithm is that it uses hourly image composites to define dynamic threshold values used in the spectral tests. The GOES cloud mask has been validated with manually determined sky conditions. Insolation and Albedo - The amount of solar energy reaching the Earth's surface (insolation) is estimated from the broadband visible channel on GOES. It is desirable to estimate both direct and diffuse radiation (scattering from the atmosphere and clouds). The albedo of the surface is required to accurately compute these components. The surface albedo (for each hour) is calculated using a short term history of GOES visible channel reflectance measurements from cloud-free images (minimum visible value over the history), the solar constant, and an estimate of the water vapor content of the atmosphere. In cloudy regions a historical estimate of the albedo (from cloud-free data) is used. The procedure includes three processes: attenuation of downward flux of solar radiation in cloud-free regions by molecular scattering and absorption by atmospheric water vapor, absorption and scattering of solar radiation by clouds, and the attenuation of solar radiation by the atmosphere below the clouds. Atmospheric absorption is calculated with a parameterized radiative transfer model appropriate for shortwave radiation and is dependent on water vapor (total precipitable water in our case), and satellite and solar viewing geometry. Cloud absorption is parameterized solely on visible reflectance and Rayleigh scattering with a molecular path length. GOES Information More information regarding the Infrared Processes Group of the GHCC. |
Technical Contact: Dr. Gary Jedlovec (gary.jedlovec@nasa.gov)
Responsible Official: Dr. James L. Smoot (James.L.Smoot@nasa.gov)
Page Curator: Paul J. Meyer (paul.meyer@nasa.gov)
A wealth of geophysical parameters describing the Earth's surface (land and ocean) and atmosphere (including clouds and precipitation) are derived from MODIS and AMSR-E imagery by the EOS science team in support of NASA's global climate research projects. These products (
NSSTC/GHCC Level 2 MODIS Products
Land Surface Temperature and Precipitable Water - The retrieval of land surface temperature (LST) (also known as skin temperature) and total precipitable water (TPW) from GOES measurements is accomplished with an algorithm and the 11 and 12 micrometer channels of either the Imager or the Sounder. The algorithm, know as the Physical Split Window (PSW) technique, is derived from a perturbation form of the radiative transfer equation that is simplified through parameterization to retrieve bulk layer parameters rather than profile information. The physical approach requires a priori information, which includes estimates of temperature and mixing ratio profiles, TPW, and skin temperature. The guess information is used with forward radiative transfer code and GOES spectral response information to calculate channel transmittances and brightness temperatures, which are required for the solution equations. Variations in the retrieval methodology used for particular applications can be found in Haines et al (2004). Often GOES field of views (FOVs) are averaged together over a limited region before the retrieval process to reduce the affect of random noise on the resulting products. Proper screening of the individual FOVs for cloud contamination is crucial for successful retrievals. The quality of the TPW retrievals depends on the appropriateness of the first guess which is typically a short term forecast from the Eta model. LST retrievals are only weakly dependent on the guess profile information. The retrieval accuracy varies with application. The quality of both TPW and LST degrades under inversion conditions (either in the first guess or retrieval environment). Under optimal observing conditions, TPW retrieval errors will approach 2.0 mm, while LST errors are as small as 0.2 K. Variations in surface thermal emissivity unaccounted for in the retrieval process will increase the magnitude of the errors.