IMAPP - International MODIS and AIRS Processing Package H.-L. Allen Huang, Liam Gumley, Thomas Rink, Jun Li, Elisabeth Weisz, Kevin Baggett, Xiangqian Wu, Kathy Strabala, Chris Moeller Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison and William L. Smith Langley Research Center, NASA 3.AIRS Algorithm Development 2. IMAPP Software SSEC is funded by NASA to develop the International MODIS/AIRS Processing Package (IMAPP). The goals of the IMAPP project include: i. To release a freely available package for processing MODIS and AIRS/AMSU/HSB data, ii. To promote and support the worldwide use of EOS data, and to involve the international community in EOS validation efforts. For this purpose, SSEC has adapted the operational Level-0 to Level-1 MODIS geolocation and calibration software developed by NASA. The main differences are that IMAPP: Has been ported to major UNIX platforms, Requires only the NCSA HDF 4.1r3 toolkit, Runs in a much simpler processing environment, Can use downlink or definitive ephemeris/attitude, Can process satellite overpasses of arbitrary size. IMAPP may be downloaded at no cost, and is licensed under the terms of the GNU General Public License (GPL). IMAPP is now used in USA, UK, Germany, Russia, Japan, China, Korea, Singapore, and Australia to process Terra MODIS data. The latest release (v 1.3) was in December 2001, Science algorithms currently under development for release as part of IMAPP include: MODIS Cloud Mask MODIS Temperature and Moisture Profiles MODIS Cloud Top height, temperature, phase Fire Detection (courtesy of MODIS Land Team) AIRS/AMSU/HSB Level 0 to level 1 S/W AIRS Temperature and moisture Profiles 4.Processing Architecture MODIS direct broadcast data are processed at SSEC using an automated data-driven architecture. The SeaSpace TeraScan system is responsible for acquiring Level-0 (reconstructed time-ordered instrument packets) for every overpass. When a new Level-0 pass file is created, it is sent to a separate computer for IMAPP Level-1 processing, which involves unpacking, geolocation, and calibration of the MODIS image data at all spatial resolutions. When a new Level-1B file is created, browse images are created for a variety of predetermined scenes in the continental US, Canada, and Northern Mexico. The browse images and pass information are then sent to a third computer that maintains a database-backed website for all passes and images. Level-0 Ingestor (SeaSpace TeraScan) Online Level-1B (Anonymous FTP) Level-1 Processor (IBM Netfinity, Solaris) Tape archive Exabyte 8mm Web Server (IBM Netfinity, Solaris) Database (MySQL, PHP) Level-0 Data Level-1B Data Level-0 Data Browse Images Web Pages Browse Images 5.2Water Vapor Product: June 2,  m reflectance Total Precipitable Water Vapor This pass over the US east coast and Gulf of Mexico ranges from a dry atmosphere north of the Great Lakes to a moist atmosphere over the Yucatan peninsula. Note the high density of TPW retrievals in clear skies. A retrieval is made for every 5 x 5 box of 1000 meter pixels if 5 or more pixels are clear. 5.3Sea Surface Temperature Product: May 2, 2001 FrontDramatic structure in the Gulf Stream is revealed in this daytime sea surface temperature retrieval from the combination of the MODIS 11  m and 12  m channels. Note the temperature front where a gradient of almost 20C is observed over a few kilometers. 5.1Cloud Mask Product: February 12, 2002 (Real time Product) 3 color-composite Image (Band 1- R; Band 6 - G; Band 7 -B) Red-Snow; White-Clouds; Aqua-Clear Ground 12 Feb., :23 UTC Barents Sea Mar. 1, 2001: MODIS scene acquired by ScanEx Moscow and processed by IMAPP. Eastern Europe Oct. 3, 2001: MODIS scene acquired at the University of Dundee and processed by IMAPP. SSEC is adapting the following MODIS science algorithms for IMAPP release by the end of 2001: Cloud Mask: For every 1000 m MODIS pixel, determines whether the field of view is obstructed (usually by cloud). Uses a combination of fuzzy spectral tests using visible and infrared bands. Temperature and Moisture Profiles: Retrieves profiles of atmospheric temperature and water vapor for every 5 x 5 box of 1000 m MODIS pixels. Uses a statistical regression for efficiency. Cloud Top Properties: Retrieves cloud top properties (including height, effective emissivity, phase) for every 5 x 5 box of 1000 m MODIS pixels. Uses longwave infrared CO 2 slicing and tri-spectral thresholds. Initial Retrieval: a. PCC (EOF) applied to training data b. Compute linear regression RTV coefficients using noisy data c. PCR applied to noisy independent testing data Physical Retrieval: a. PCR-profiles (1c) used as initial (‘a priori’) data b. Channel selection using average Jacobian K c. PCC applied to PCR (1c) retrieved spectra (20 T, 15 Q, 5 O 3, 1 T s ) d. Optimal estimation retrieval: X = X a + SK T S e -1 (y-y a ) S = (K T S e -1 K + S a -1 ) -1 x, x a rtv profile, initial (‘a priori’) profile y, y a measurements, measurements associated with x a K Jacobian (or weighting function matrix) S e, S a, and S measurement, ‘a priori’ and rtv error covariance Cloud Detection: Ice and Water Cloudy Signature Approach Simultaneous Cloud Height and Cloud Emissivity: Minimum Local Emissivity Variance Approach Simulated AIRS Data (From JPL & NOAA) Simulated AIRS Spectrum & Noise Simulated AIRS Retrieval & Residual MLEV Cloud Height Retrieval Sensitivity 1. IMAPP Revision History May 12 th, 2000 (v1.0) Initial release for Terra MODIS November 1 st, 2000 (v1.1) Calibration algorithm updated to version April 13 th, 2001 (v1.2) Calibration algorithm updated to version December 3 rd, 2001 (v1.3) Level-1A: fixed problems arising from bad Level-0 input Geolocation: fixed interpolation problems at pass boundaries Calibration: Algorithm updated to ScanEX (Moscow) has ported IMAPP to Windows 5.4 Striping & Out of Band Influence Correction: Dec. 8, 2000 MODIS 1.38  m band image before and after correction 0215 UTC Dec. 8, 2000 SSEC is also developing MODIS in-house science algorithms for future IMAPP release: Sea Surface Temperature: GOES like retrieval. Striping & Out of Band Correction: Detector dependent ratio technique (determination of correction coefficients is under investigation) Blue - Clear (probably) Green - clear (confident) Red - Uncertain White - clouds MODIS N*/MODIS Filter 11µm N*/MODIS Filter11 µm Only Clear Column Radiance Results With and without MODIS 2x2 ~ 4 Km Resolution 3x3 ~ 6 Km Resolution 4x4 ~ 8 Km Resolution 6x6 ~ 12 Km Resolution Average CCR Yields: 2x2 = x3 = 4.5 4x4 = 2.4 6x6 = 0.9 Average CCR Yields: 2x2 = x3 = 5.5 4x4 = 3.0 6x6 = 1.2 Average CCR Yields: 2x2 = x3 = 5.6 4x4 = 3.1 6x6 = 1.2 MODIS & AIRS Cloud Clearing Demonstration Lower spectral resolution MODIS data can provide high spatial information to AIRS to improve cloud clearing performance: 1. Using N* windows to produce a spectrum of N* 2. Using MODIS radiance to filter N* results 3. Single 11 micron channel N* (No MODIS data)