1. SeaWiFS Highlights March 2003 SeaWiFS Views Dust Storm in the Middle East Gene Feldman (gene@seawifs.gsfc.nasa.gov) NASA GSFC/Earth Sciences Directorate/Laboratory for Hydrospheric Processes/Sea WiFS/SIMBIOS project Office On March 19, 2003, a large dust storm was blowing across Iraq, and the northern end of the Arabian Peninsula hiding the ground from view. This image was collected on March 21, 2003. It shows air that is still a bit dusty over the Persian Gulf, and the first hint of smoke from oil well fires.

2. SeaWiFS Views Dust Storm in the Middle East March 21, 2003

3. ARCTIC2003 EOS AquaAMSR-E Sea Ice Validation Aircraft Campaign  Successfully completed seven aircraft flights with the NASA P-3 to validate the AMSR-E standard sea ice products: - Sea ice concentration - Sea ice temperature - Snow depth on sea ice  Arctic regions covered included the Bering, Beaufort, and Chukchi seas  P-3 flights were coordinated with: - Surface measurements at Barrow, AK and at a Beaufort Sea ice camp - Satellite overpasses including: AMSR-E, Landsat 7, MODIS, RADARSAT, and ICESat  P-3 instrumentation included: - NOAA ETL Polarimetric Scanning Radiometers (PSR-A and PSR-CX) - NASA Wallops Airborne Topographic Mapper (ATM) - NASA Langley Turbulent Air Motion Measurement System (TAMMS) - NASA Wallops and NOAA ETL digital & video cameras; IR radiometers Donald J. Cavalieri (Donald.J.Cavalieri) and Thorsten Markus (Thorsten.Markus@nasa.gov) NASA GSFC/Earth Sciences Directorate/Laboratory for Hydrospheric Processes/Oceans and Ice Branch/Microwave Sensors Branch

4. March 13, 2003; Barrow (Elson Lagoon): Low altitude flights (500ft) coincident with in-situ surface measurements of snow and ice physical properties (collaboration w/ M. Sturm & J. Maslanik) March 15, 2003; Norton Sound/Bering Sea: Maps of a divergent ice cover at 4300 ft coincident with Landsat 7 coverage; a/c stacks over coastal polynya at different altitudes to measure heat and moisture fluxes (collaboration w/ B. Walter) March 16, 2003; St. Lawrence Island polynya: stacks over polynya at different altitudes to measure heat and moisture fluxes (collaboration w/ B. Walter); coincident Landsat 7 and ICESat coverage March 18, 2003; Ice edge near St. Matthew Island: Mapping of ice edge at 4300 ft; coincident Radarsat Coverage March 19, 2003; Beaufort Sea ice camp, Barrow: Mapping of area at 4300 ft, mostly multiyear ice; coincident ICESat coverage; 2nd Barrow survey March 20, 2003: Point Hope/Kotzebue Sd.: Mapping of area at 4300 ft; various sea ice types; coincident Landsat 7 and Radarsat coverage March 22, 2003: Ice edge and Kuskokwim Bay: Mapping of ice edge (coincident Landsat 7 coverage) and a/c stacks over polynya in Kuskokwim Bay to measure heat and moisture fluxes ARCTIC2003 EOS AquaAMSR-E Sea Ice Validation Aircraft Campaign

5. Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, through multivariate analysis of land surface climatology Background and relevance to NASA The climatic factors contributing to heightened malaria risk are often not observed in regions where the disease is most prevalent. There has been increasing effort in recent years to employ climatic and satellite remotely sensed data to identify and map vector habitat and malaria transmission risk. Significant potential exists for applying global EOS and DAO land surface products for assessing health risks in data sparse regions. Objective To determine the feasibility of using modeled climatology and satellite data products for the prediction of malaria intensity in the Mpumalanga Province, South Africa. Approach Monthly climatology generated from a sophisticated land surface data assimilation model is employed in short-term forecasting of infection rates in the Mpumalanga Province of South Africa, using a multivariate regression approach. The hydrology model employed is the Off-line Land-Surface Global Assimilation System (OLGA) that uses output from a global atmospheric circulation model and land surface parameters available from the International Satellite Land Surface Climatology Project (ISLSCP) to compute a time series of gridded, non-routinely observed hydrologic quantities such as soil moisture and evaporation. Multivariate model predictions are compared to long-term monthly records of clinical and microscopic diagnoses. Results The approach addresses the high degree of short-term autocorrelation in the disease and weather time series. The resulting model is able to predict 11 of the 13 months that were classified as high risk during the validation period. Although further refinement is warranted the good results obtained using the multivariate model indicates the feasibility of predicting of malaria incidence using NASA data products, at least, for regions such as this in South Africa. Hydrological Sciences Branch Goddard Space Flight Center April 2003 David Grass Lamont-Doherty Observatory of Columbia University, Palisades, NY Michael F. Jasinski (Michael.F.Jasinski@nasa.gov) NASA Goddard Space Flight Center, Laboratory for Hydrospheric Processes/Hydrological Science Branch, Greenbelt, MD John Govere Mpumalanga Department of Health, Mpumalanga District Nelspruit, South Africa

6. Fig. 2. Observed, modelled and predicted malaria cases in the Mpumalanga Province, South Africa, using autoregressive multivariate regression analysis. Fig. 1. Time series of hydroclimatic variables used in the multivariate analysis, January 1987-June 1995. Dashed lines indicate trends. Predictability of Malaria Transmission Intensity in the Mpumalanga Province, South Africa, through multivariate analysis of land surface climatology