Short-term Prediction Research
and Transition Center

Retrieval of Geophysical Parameters from MODIS

Real-time MODIS 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.

A wealth of geophysical parameters describing the Earth's surface (land and ocean) and atmosphere (including clouds) are derived from MODIS imagery by the EOS science team in support of NASA's global climate research projects. These products (MODIS product link) are derived at the various NASA DAACs with science team algorithms developed over the last 15 years. Only a few of these algorithms have been adapted for real-time applications as part of the IMAPP software (http://cimss.ssec.wisc.edu/~gumley/IMAPP/) available to Terra/Aqua direct broadcast ground stations. Some of these real-time EOS products used by SPoRT are from

MODIS:
Total Precipitable Water (TPW) and Stability (LI) - MOD07,
Cloud Phase and Cloud Top Pressure (CTP) - MOD06,
Cloud Mask - MOD35,
Sea Surface Temperature (SST) - MOD28,
Chlorophyll_a Pigment Concentration - MOD21, and
Suspended-Solids Concentration - MOD23 .

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.

NSSTC/GHCC Level 2 MODIS Products
Cloud Mask - The NSSTC/GHCC cloud mask algorithm (Haines et al 2004) uses only two MODIS channels with five spectral/spatial tests to determine the sky conditions at 1 km resolution for day and night scenes. The unique aspect of the algorithm incorporates the use of composite imagery to define dynamic threshold values used in the spectral tests. The NSSTC/GHCC cloud mask has been validated with manually determined sky conditions and performs better than the EOS science team product for night time data over the eastern two-thirds of the continental U. S.

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) - 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) or a CO2 approach as in McCarty et al. (2006). The infrared method is used where the CO2 approach fails (mainly with low clouds) and assumes an opaque cloud. The CO2 approach also retrieves an effective cloud fraction product which describes partial footprint coverage of the clouds and cloud emissivity. Both techniques use forecast model information as a first guess or reference profile.

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
SST Composite - Clouds often obscure a complete view of the land or ocean surface over a particular geographic region. Compositing images or data products over time often provides a more complete view of the surface and a more (spatially) continuous product. This approach has been taken with the real time sea surface temperature (SST) data obtained from USF and provided to the NWS at 1 km resolution. The compositing procedure averages the warmest three pixels over the last 20 days on a pixel by pixel basis to create the image. The compositing procedure eliminates clouds not detected by the cloud mask and maintains the accuracy of the real SSTs. An example of the composite images for the Gulf of Mexico and Florida coastal waters is presented in this SST animation. Comparisons and animations of composite sea surface temperature products are created by SPoRT several times daily.

NSSTC/GHCC MODIS Products
MODIS data to NWS Forecast Offices
Other MODIS Products
MODIS SST Composites
MODIS Ocean Color Composites