1. Courtney D. Radley 1, Frank J. LaFontaine 2, Robbie E. Hood 3, John R. Mecikalski 4, Kevin R. Knupp 4, and Daniel J. Cecil 4 1 Universities Space Research Association, Huntsville, AL, 2 Raytheon Information Solutions, Huntsville, AL, 3 NASA Marshall Space Flight Center, Huntsville, AL, 4 University of Alabama in Huntsville, Huntsville, AL UAHUAH AMPR image characteristics  Surface water is indicated by patterns of lower T b ’s in the lower frequencies that is not displayed in the higher frequencies.  Lower T b ’s in the 85 GHz usually indicate ice scattering and can represent cloud and rain water in the 37 GHz frequency.  T b ’s in the 10.7 GHz frequency in this case, may be influenced by rain, extremely large ice mass, as well as land surface features such as soil, vegetation, and murky waters as they are not exhibiting as cool as emissivity from the ocean or other water-only surface. Leaf Area Index Analysis Instrumentation Observation Analysis Introduction  On 22 September 1998, Hurricane Georges made landfall on the Dominican Republic as a Category 2 Hurricane on the Saffir-Simpson Scale.  Georges moved steadily across the island at 8 ms -1 becoming one of the worst natural disasters in the island’s history.  As part of the Third Convection and Moisture Experiment (CAMEX-3) the Advanced Microwave Precipitation Radiometer (AMPR) aboard the NASA ER-2 flew over the Dominican Republic (DR) during George’s landfall.  AMPR images detected an area of surface water in the first NW to SE pass, but two hours and twenty minutes later, the water boundaries have obviously expanded into an area there was not previously ground water. Data and Methodology  Instruments utilized include AMPR and the ER-2 Doppler Radar (EDOP).  Data sets utilized include Topography data from United States Geological Survey (USGS) Shuttle Radar Topography Mission (SRTM) and MODIS Leaf Area Index (LAI).  AMPR data is merged with topography data to get a physical depiction of where the standing water is located.  MODIS LAI data is used to classify the vegetation contributing to the AMPR signature and is compared with AMPR and topography data to analyze the extent of surface water detection.  EDOP reflectivities provide a source for rainfall rate estimation  The first flight leg shows low reflectivites in the area of the flooded valley (denoted by two solid black lines) (~20 dBZ) and therefore low rainfall rates (.443 mmhr -1 ). The second flight leg corresponds better with the valley of interest and exhibits higher reflectivities (30-35 dBZ) and therefore higher rainfall rates (2.44 mmhr -1 – 5.72 mmhr -1 ).  An estimated 10 mm of rain fell over the region of interest in the two hour period between 2000 Z and 2200 Z on 22 Sept 1998. Comparison to topography data  Comparing the LAI with the AMPR images above, the lowering of T b ’s in the 10.7 GHz corresponds well with the boundaries of the lower LAI indices.  As expected, the T b magnitude depends strongly on vegetation density. Results  Lowering of T b ’s depend greatly on the land surface and vegetation density, in this case.  The lowest T b ’s are found where the lowest vegetation densities are found.  Depth of standing water is inconsequential for AMPR surface water detection. Black parallelogram denotes spatial coincidence shown in AMPR images. 10.7 GHz 37.1 GHz 85.5 GHz 19.35 GHz 85.5 GHz Acknowledgements AMPR and EDOP data are archived and distributed by the Global Hydrology Resource Center. MODIS LAI data is archived and distributed by Goddard Space Flight Center. SSM/I images are archived at the Naval Research Laboratory in Monterey, CA. SRTM data is provided by USGS and is conducted by the Jet Propulsion Laboratory (JPL) at the California Institute of Technology. Dr. Gerald Heymsfield of Goddard Space Flight Center is the Principle Investigator for EDOP, and EDOP images are from Geerts et al. (2000).  MODIS LAI is used to analyze how vegetation effects the T b ’s.  There is a distinctive boundary in the leaf area index where the vegetation biome changes. AMPR over Louisiana Delta – Future Case Study  No distinguished difference of surface water vs. the ocean.  Surface water looks the same to AMPR as the ocean in low vegetation density biomes.  Surface water signature became evident in the AMPR 10.7 GHz image of the DR case as soon as water began to collect on the surface, but in a broad enough area to be sensed by an AMPR footprint.  In conjunction with other instruments such as in a sensor-web network, AMPR can be a useful addition for early detection of inland flooding in real time from an aircraft platform. Wet conditions – 27 Sept 1998 – Hurricane Goerges - AA AA B B B B A = Surface Water B = Ice Scattering  AMPR 10.7GHz T b contours overlaid on topography can show where water is collecting geographically.  Lower 10.7 GHz T b ’s closely correspond to lower elevations.  This bull's-eye of lowest T b ’s correspond to the lower LAI indices above, where an area of grasses/cereal crops is shown.  T b ’s during dry conditions show the definition between the radiometrically cool background of the ocean vs. the radiometrically warm background of land.  Lake in the center of the Louisiana Delta is clearly visible in all channels in image to the right. Wet conditions – 27 Sept 1998 – Hurricane Goerges ~1530 UTC Wet conditions – 27 Sept 1998 – Hurricane Goerges ~ 1700 UTC Dry conditions – 9 April 1998  This case will be analyzed in the same manner as the DR case with a topography and LAI analysis, and a precipitation analysis using ground based and airborne radar.  Studying more cases like the DR will provide a better understanding of vegetation densities that AMPR is able to monitor the surface through, and will determine which instruments are advantageous to use in conjunction with AMPR to monitor inland flooding DURING a flooding event. Dry conditions – 18 April 1998 A A