Physically Based Estimation of Aerodynamic Roughness Using Satellite

Physically Based Estimation of Aerodynamic Roughness Using Satellite Imagery Case Study: U. S. Southern Great Plains by Michael Jasinski/NASA/614. 3, Jordan Borak/SSAI, and Richard Crago/Bucknell U. Study: A theoretical, satellite-based approach for estimating vegetation aerodynamic roughness length for momentum has been developed. The new method employs a physical model of surface stress in conjunction with spaced-based estimates of canopy density for specific IGBP land cover types that can be obtained from NASA and other sensors including MODIS, Landsat, and AVHRR. A five-year time series for the U. S. Southern Great Plains is demonstrated using MODIS imagery. Importance: Virtually all weather, climate and air quality models used today require aerodynamic roughness length. The current method for assigning roughness in weather and climate models, used for many decades, is a simplistic look-up table approach by association with a few land classes. The new space-based approach employs more realistic and seasonally dynamic representations of the Earth's aerodynamic roughness. Benefits: Its use within weather and climate models should improve forecasts, as well as the forecasting of hazardous biological or chemical plumes that are especially sensitive to roughness. Terms and Acronyms: roughness length -used in numerical models to express the roughness of the surface. It affects the intensity of mechanical turbulence and the fluxes of varies quantities above the surface IGBP-International Geosphere-Biosphere Programme MODIS-MODerate Resolution Imaging Spectroradiometer AVHRR- Advanced Very High Resolution Radiometer

Case Study: Satellite Based Estimation Aerodynamic Roughness Importance: Improved characterization of the land surface, including aerodynamic roughness, is one of the most important research needs for atmospheric transport and diffusion modeling (Office of the Federal Coordinator of Meteorology, FCM-R 23 2004). z 0/h Fig 1. Aerodynamic roughness normalized by canopy height for June 10, 2002 using MODIS LAI. (After Jasinski, Borak and Crago, Agricultural and Forest Meteorology, 33 (2005), 55 -68) z 0/h Julian day Forested IGBP cover types Julian day Non- forested cover types Fig 2. Time Series Normalized Roughness Length, 2001 -2004. Mean for each forested and non-forested IGBP Land Cover Type in the U. S. Southern Great Plains domain shown above. (After Borak, Jasinski, and Crago, Agricultural and Forest Meteorology, 35(2005), 252 -268)

Ocean Surface Drift Revealed by Tracking SAR Images Antony K. Liu (614. 2), Yunhe Zhao (614. 2), and Ming-Kuang Hsu (NITST) History: ocean surface feature tracking analyses is based on data from a single orbital sensor collected over its revisit interval of a lone, low-Earth orbital satellite. Study: This study demonstrates that multiple SAR images overlapped in a short time can be used to derive ocean surface drift, and can help to identify oceanic processes. -SAR can penetrate clouds, smoke, haze, and darkness and acquire high quality images of the Earth’s surface. However, the coverage of a SAR sensor is always limited, and the repeating cycle of a single SAR is usually too long for ocean surface feature tracking. -With about 30 minute acquisition time offset and almost the exactly same path, the SAR images from two SAR sensors on ERS-2 and ENVISAT, respectively, have been collected and used for ocean feature tracking between them. -A case study to derive the ocean surface drift over the southern part of Luzon Strait near the Philippines. Conclusion: To validate the results, wind data from Quik. SCAT are compared with the satellite-derived flow field. The qualitative comparison shows a generally consistent pattern of ocean surface drift. The result has been accepted for publication in EOS, Transactions, American Geophysical Union (in press). Acronyms: SAR-Synthetic Aperture Radar ERS-2 -European Remote Sensing satellite -2 ENVISAT -ENVIronment SATellite Quik. SCAT-A “quick recovery” mission to fill the gap created by the loss of data from NASA Scatterometer

Ocean Surface Drift Revealed by Tracking SAR Images Antony K. Liu (614. 2), Yunhe Zhao (614. 2), and Ming-Kuang Hsu (NITST) Figure 1. The overlaid of two SAR subscenes collected over the Luzon Strait near Philippines from Envisat (in red) and ERS-2 (in green) on April 27, 2005 and separated by 28 minutes. The major oceanographic feature, a long oil slick oriented in north-south direction, can be clearly identified. Figure 2. Ocean surface drift (green arrows) derived from Envisat and ERS-2 SAR data over the Luzon Strait (Envisat image as background). The surface drift unit of 1 m/s is indicated by a white arrow at the top. The Quik. SCAT wind data are shown as red arrows.

Using Google Earth to Bring NASA Post-Katrina Data to the Public C. Wayne Wright, Instrumentation Science Branch, Code 614. 6 Charles. W. Wright(AT)NASA. gov Interested people can now use Google Earth to easily browse the entire EAARL lidar data set collected just after Hurricane Katrina made landfall along the Gulf Coast. The online dataset consists of: • digital elevation images constructed from 315 million lidar samples, including submerged and sub-vegetation topography, • nearly ¼ million high-resolution RGB and Color Infrared (CIR) photographs. This makes vast amounts of post-Katrina NASA scientific information accessible to the public through Google Earth, the easy-to -use, no cost, geospatial Earth Browser. http: //inst. wff. nasa. gov/eaarl Hurricanes Katrina and Rita data http: //Earth. Google. com Google Earth EAARL color-coded topographic map (high elevations red, low elevations blue) and digital photography superimposed on Google Earth imagery near Pensacola, Florida.

Using Google Earth to Bring NASA Post. Katrina Data to the Public C. Wayne Wright, Instrumentation Science Branch, Code 614. 6 Charles. W. Wright(AT)NASA. gov • The EAARL (Experimental Advanced Airborne Research Lidar) system was used at the request of the U. S. Army Corps of Engineers, U. S. Geological Survey, National Park Service, and the Bureau of Reclamation to acquire data over Post. Katrina and Post-Rita hurricane landfall regions. • The results have been made available to the public via the no-cost Google Earth browsing client. The Google Earth client allows users to easily browse and view all of the nearly 100 gb of online photography and processed lidar data in full resolution and from any perspective view. It also allows the user to overlay NASA lidar digital elevation maps along with the EAARL digital photographs. • The EAARL sensor suite is an airborne lidar that provides unprecedented capabilities to simultaneously survey multiple environments including coral reefs, nearshore benthic habitats, coastal vegetation, and sandy beaches. : • For more information please visit: http: //inst. wff. nasa. gov/eaarl