1. POSTER TEMPLATE BY: www.PosterPresentations.com VIIRS Active Fire algorithm integration in Suomi NPP Data Exploitation (NDE) environment: research to operations Marina Tsidulko 1, Walter Wolf 2, Ivan Csiszar 2, Louis Giglio 3, Wilfrid Schroeder 3 (1)IMSG at NOAA/NESDIS/STAR, College Park, MD, (2) NOAA/NESDIS/STAR, College Park, MD, (3)University of Maryland, College Park, MD Active Fire Algorithm Basics and History Active Fires Product on Operations Active Fire Algorithm Inputs and Outputs Data Flow Active Fire Algorithm at STAR AIT Summary NDE Environment The current IDPS version of the VIIRS Active Fire algorithm runs over land and produces a list of fire detections in a sparse array format. The University of Maryland (UMD) enhanced version of the algorithm: - provides additional outputs including the Fire Radiative Power (FRP) of each fire pixel and a new attribute to describe land for each pixel (Fire Mask). - has global coverage including water - planned to be implemented in the NDE development environment - initially will run on S-NPP data and is planned to create the J1 product in the future The final product is in NetCDF-4 format and will be available for users through the OSPO distribution system.  Current Operational Shortfalls:  Accuracy of Product  Explicit validation is not feasible due to lack of fire mask and sufficient independent ref erence data  Current IDPS implementation lacking: - User-required data layers (fire mask, fire radiative power) - Processing over water pixels (e.g., detection of gas flares) - Latest algorithm updates (MODIS Collection 6-equivalent)  New development addresses shortfalls:  Address operational satellite active fire data user requirements - Incorporate thematic classification (fire mask) and fire radiative power (FRP) retrieval - Provide data globally, including water  Satisfy the JPSS VIIRS Active Fire EDR product requirements  Implement latest algorithm updates (MODIS Collection 6)  Achieve greater compatibility between EOS/NASA-MODIS and JPSS-VIIRS active fire products  The primary mission of the NDE system is to provide near real time products derived from S-NPP observations to NOAA’s operational and climate communities as well as other end users.  The NDE receives data from the IDPS operational stream and provides them for algorithms employed in the system.  The interface between NDE and the AF driver scripts will consist of Production Control Files (PCF) and Production Status Files (PSF) files.  PCF contains: - All the required input files to process a granule, including paths if they’re located outside the working directory. This includes input instrument and ancillary data, static files such as templates and lookup tables. - Any run parameters or flags  PSF contains all successfully generated output files  The VIIRS Active Fire Algorithm (AF) has been developed at the University of Maryland (UMD) as heritage of MODIS Active Fires algorithm.  An earlier and simplified version of the VIIRS AF algorithm – based on MODIS collection 4 - is implemented in the operational Interface Data Processing System (IDPS).  The replacement version of the VIIRS AF algorithm is significantly enhanced. It is based on the EOS/NASA MODIS Fire and Thermal Anomalies algorithm (MOD14/MYD14) and equivalent to MODIS collection 6.  The algorithm uses hybrid fixed-threshold and contextual approach to detect sub-pixel fires: - Small (sub-pixel) fires produce contrasting radiometric response across different spectral channels as a result of high temperatures - Fixed thresholds used to detect unambiguous fires - Contextual tests compare target pixel with immediate surrounding using dynamic sampling window - Reflective channels are used to differentiate other bright pixels (e.g., clouds, sandy soils, sun glint) from biomass burning - Land/water mask used to distinguish between biomass burning and gas flares  The algorithm provides additional products in output.  The UMD replacement code:  Works with NASA LPEATE inputs in HDF4 format  Provides output in HDF4 format  Works on aggregated granules  STAR AIT Development:  Integrate the replacement code into STAR Linux machines  Modify the code to work with IDPS binary inputs (unpacked HDF5 files) on single granules  Provide granulation for Land/Water Mask (granulated Land/Water product is non-deliverable in IDPS)  Add conversion to NetCDF4 which is a standard NDE output format  Create wrapping Perl scripts in compliance with the NDE standards  STAR AIT Testing:  Generate set of runs for test granules for different geographical areas and chosen for different conditions such as day, night, missing SDR data, off-shore gas flares  Compare outputs with NASA LPEATE non-operational AF products  Compare outputs with operational IDPS products Fire pixels Example of Fire Mask March 1, 2015 Classes: 0 missing input data; 1 not processed (obsolete) ; 2 not processed (obsolete) ; 3 non-fire water ; 4 cloud ; 5 non-fire land ; 6 unknown; 7 fire (low confidence); 8 fire (nominal confidence); 9 fire (high confidence) Algorithm/TilesInput to AFBinary fileHDF5 file VIIRS-SDRlatitude longitude view zenith angle solar zenith angle view azimuth angle solar azimuth angle VIIRS-MOD-GEO- TC GMTCO_*.h5 VIIRS-SDRM13 brightness temperature M13 QF1 M13 radiance VIIRS-M13-SDRSVM13_*.h5 VIIRS-SDRM15 scaled brightness temperature, QF1 VIIRS-M15-SDRSVM15_*.h5 VIIRS-SDRM16 scaled brightness temperature, QF1 VIIRS-M16-SDRSVM16_*.h5 VIIRS-SDRM5 scaled reflectance, QF1 VIIRS-M5-SDRSVM05_*.h5 VIIRS-SDRM7 scaled reflectance, QF1 VIIRS-M7-SDRSVM07_*.h5 VIIRS-SDRM11 scaled reflectance, QF1 VIIRS-M11-SDRSVM11_*.h5 Quarterly Surface Type Land/Water Mask Tiles Granulated Land/Water Mask VIIRS-GridIP- VIIRS-Qst-Lwm- Mod-Gran N/A (non- deliverable product in IDPS) Fire Algorithm QA Mask: 32-bit unsigned integerBits Description Description 0-1 Surface Type (water=0, coastal=1, land=2) 2-3 N/A 4Day/Night (daytime = 1, nighttime = 0) 5Potential fire (0/1) 6-10N/A 11Fire Test 1 valid (0 - No, 1 - Yes) 12Fire Test 2 valid (0 - No, 1 - Yes) 13Fire Test 3 valid (0 - No, 1 - Yes) 14Fire Test 4 valid (0 - No, 1 - Yes) 15Fire Test 5 valid (0 - No, 1 - Yes) 16Fire Test 6 valid (0 - No, 1 - Yes) 17-19N/A 20Adjacent clouds (0/1) 21Adjacent water (0/1) 22-23Sun Glint Level (0-3) 24N/A 25 False Alarm 1 (excessive rejection of legitimate background pixels) 26False Alarm 2 (water pixel contimination) 27Amazon forest-clearing rejection test 28-31N/A NameTypeDescription FP_line16 bit Integer Fire pixel line Sparse data array (unit-less) FP_sample16 bit Integer Fire pixel sample Sparse data array (unit-less) FP_latitude32 bit Float Fire pixel latitude Sparse data array (unit: degrees) FP_longitude32 bit Float Fire pixel longitude Sparse data array (unit: degrees) FP_power32 bit Float Fire radiative power Sparse data array (unit: MW) FP_confidence8 bit Integer Fire detection confidence Sparse data array (unit: %) FP_land8 bit Integer Land pixel flag Sparse data array (unit-less) Fire Mask: 8-bit unsigned integer Missing – 0Brightness temperatures for M13 or M15 unavailable Scan – 1Not processed (obsolete) Other – 2Not processed (obsolete) Water – 3Pixel classified as non fire water Cloud – 4Pixel classified as cloudy No Fire – 5Pixel classified as non fire land Unknown – 6Pixel with no valid background pixels Fire Low – 7Fire pixel with confidence strictly less than 20% fire Fire Medium – 8Fire pixel with confidence between 20% and 80% Fire High – 9Fire pixel with confidence greater than or equal to 80% Inputs: HDF5/BLOB Outputs: NetCDF4 for fire pixels Outputs: QA Mask for each pixel in the granule Outputs: Fire Mask for each pixel in the granule