ALBERT

Description: An assimilation technique is described in which satellite-observed surface skin temperature tendencies are used in a model surface energy budget so that the predicted rate of temperature change in the model more closely agrees with the satellite observations. Both visible and infrared GOES satellite data are used in the assimilation. The technique is based on analytically recovering surface moisture from similarity expressions derived from an evapotranspiration residual obtained as a difference between the unadjusted model evapotranspiration and the satellite-inferred evapotranspiration. The technique has application in regional-scale models where surface parameters such as root zone moisture, soil moisture, etc., are unknown. It is assumed that the largest error in the surface energy budget is in the evapotranspiration term. Two tests are given for the technique, first, a one-dimensional test against FIFE data and, second, a three-dimensional test over Oklahoma. In these cases the technique appears to correctly adjust the model response to agree better with observations.

Description: A case is presented in which clouds are observed to form first over a mesoscale-size area (100 x 300 km) of harvested wheat in Oklahoma, where the ground temperature is warmer than adjoining areas dominated by growing vegetation. In addition, clouds are suppressed over relatively long bands downwind of small man-made lakes and areas characterized by heavy tree cover. The observed variability of cloud relative to landscape type is compared with that simulated with a one-dimensional boundary-layer model. Clouds form earliest over regions characterized by high, sensible heat flux, and are suppressed over regions characterized by high, latent heat flux during relatively dry atmospheric conditions. This observation has significance in gaining understanding of the feedback mechanisms of land modification on climate, as well as understanding relatively short-range weather forecasting.

Description: The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V' signature, has been observed to occur during and preceding severe weather in previous studies. This study describes an algorithmic approach to objectively detect enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of cross correlation statistics of pixels and thresholds of enhanced-V quantitative parameters. The effectiveness of the enhanced-V detection method will be examined using Geostationary Operational Environmental Satellite, MODerate-resolution Imaging Spectroradiometer, and Advanced Very High Resolution Radiometer image data from case studies in the 2003-2006 seasons. The main goal of this study is to develop an objective enhanced-V detection algorithm for future implementation into operations with future sensors, such as GOES-R.

Description: The area of coldest cloud tops above thunderstorms sometimes has a distinct V or U shape. This pattern, often referred to as an "enhanced-V signature, has been observed to occur during and preceding severe weather. This study describes an algorithmic approach to objectively detect overshooting tops, temperature couplets, and enhanced-V features with observations from the Geostationary Operational Environmental Satellite and Low Earth Orbit data. The methodology consists of temperature, temperature difference, and distance thresholds for the overshooting top and temperature couplet detection parts of the algorithm and consists of cross correlation statistics of pixels for the enhanced-V detection part of the algorithm. The effectiveness of the overshooting top and temperature couplet detection components of the algorithm is examined using GOES and MODIS image data for case studies in the 2003-2006 seasons. The main goal is for the algorithm to be useful for operations with future sensors, such as GOES-R.

Description: Activities of the Center for Space Environmental Health (CSEH), one of several NSCORTs supported by NASA in order to advance knowledge in environmental health in space habitats, are reviewed. Research in environmental health will define the standards or requirements needed to protect human health. This information will affect mission plans and the design of space habitats. This reseach will study unique contaminant stresses and lead to risk models for human health and performance.

Description: The key mission of the Satellite Proving Ground is to demonstrate new satellite observing data, products and capabilities in the operational environment to be ready on Day 1 to use the GOES-R suite of measurements. Algorithms, tools, and techniques must be tested, validated, and assessed by end users for their utility before they are finalized and incorporated into forecast operations. The GOES-R Proving Ground for the Geostationary Lightning Mapper (GLM) focuses on evaluating how the infusion of the new technology, algorithms, decision aids, or tailored products integrate with other available tools (weather radar and ground strike networks; nowcasting systems, mesoscale analysis, and numerical weather prediction models) in the hands of the forecaster responsible for issuing forecasts and warning products. Additionally, the testing concept fosters operation and development staff interactions which will improve training materials and support documentation development. Real-time proxy total lightning data from regional VHF lightning mapping arrays (LMA) in Northern Alabama, Central Oklahoma, Cape Canaveral Florida, and the Washington, DC Greater Metropolitan Area are the cornerstone for the GLM Proving Ground. The proxy data will simulate the 8 km Event, Group and Flash data that will be generated by GLM. Tailored products such as total flash density at 1-2 minute intervals will be provided for display in AWIPS-2 to select NWS forecast offices and national centers such as the Storm Prediction Center. Additional temporal / spatial combinations are being investigated in coordination with operational needs and case-study proxy data and prototype visualizations may also be generated from the NASA heritage Lightning Imaging Sensor and Optical Transient Detector data. End users will provide feedback on the utility of products in their operational environment, identify use cases and spatial/temporal scales of interest, and provide feedback to the developers for adjusted or new products.

Description: Throughout the 1980s, ESA and the space agencies of Canada, Germany, France, Japan, and the U.S. have pursued cooperative projects bilaterally and multilaterally to prepare for, and to respond to, opportunities in space life sciences research previously unapproachable in scale and sophistication. To cope effectively with likely future space research opportunities, broad, multilateral, coordinated strategic planning is required. Thus, life scientists from these agencies have allied to form the International Space Life Sciences Strategic Planning Working Group. This Group is formally organized under a charter that specifies the purpose of the Working Group as the development of an international strategic plan for the space life sciences, with periodic revisions as needed to keep the plan current. The plan will be policy-, not operations-oriented. The Working Group also may establish specific implementation teams to coordinate multilateral science policy in specific areas; such teams have been established for space station utilization, and for sharing of flight equipment.

Description: Using the WETNET database, existing methods for the estimation of surface wetness from SSM/I data have been assessed and further developed. A physical-statistical method for optimal estimation of daily surface heat flux and Bowen ratio on the mesoscale has been developed and tested. This method is based on observations of daytime planetary boundary layer (PBL) growth from operational ravansonde and daytime land-surface temperature amplitude from Geostationary Operational Environmental (GOES) satellites. The mesoscale patterns of these heat fluxes have been compared with an AVHRR-based vegetation index and surface wetness (separately estimated from SSM/I and in situ observations). Cases of the 1988 Midwest drought and a surface/atmosphere moisture gradient (dry-line) in the southern Plains were studied. The analyses revealed significant variations in sensible heat flux (S(sub 0), and Bowen ratio, B(sub 0)) associated with vegetation cover and antecedent precipitation. Relationships for surface heat flux (and Bowen ratio) from antecedent precipitation and vegetation index have been developed and compared to other findings. Results from this project are reported in the following reviewed literature.