ALBERT

Description: Antenna temperatures and the corresponding geolocation data from the five sources of the Special Sensor Microwave/Imager data from the Defense Meteorological Satellite Program F11 satellite have been characterized. Data from the Fleet Numerical Meteorology and Oceanography Center (FNMOC) have been compared with data from other sources to define and document the differences resulting from different processing systems. While all sources used similar methods to calculate antenna temperatures, different calibration averaging techniques and other processing methods yielded temperature differences. Analyses of the geolocation data identified perturbations in the FNMOC and National Environmental Satellite, Data and Information Service data. The effects of the temperature differences were examined by generating rain rates using the Goddard Scattering Algorithm. Differences in the geophysical precipitation products are directly attributable to antenna temperature differences.

Description: Data from both 27 sites in the Atlanta mesonet surface meteorological network and eight National Weather Service sites were analyzed for the period from 26 July to 3 August 1996. Analysis of the six precipitation events over the city during the period (each on a different day) showed that its urban heat island (UHI) induced a convergence zone that initiated three of the storms at different times of the day, i.e., 0630,0845, and 1445 EDT. Previous analysis has shown that New York City (NYC) effects summer daytime thunderstorm formation and/or movement. That study found that during nearly calm regional flow conditions the NYC UHI initiates convective activity. Moving thunderstorms, however, tended to bifurcate and to move around the city, due to its building barrier effect. The current Atlanta results thus agree with the NYC results with respect to thunderstorm initiation.

Description: A set of global, monthly rainfall products has been intercompared to understand the quality and utility of the estimates. The products include 25 observational (satellite-based), four model and two climatological products. The results of the intercomparison indicate a very large range (factor of two or three) of values when all products are considered. The range of values is reduced considerably when the set of observational products is limited to those considered quasi-standard. The model products do significantly poorer in the tropics, but are competitive with satellite-based fields in mid-latitudes over land. Over ocean, products are compared to frequency of precipitation from ship observations. The evaluation of the observational products point to merged data products (including rain gauge information) as providing the overall best results.

Description: A technique has been developed for assimilating Geostationary Operational Environmental Satellite (GOES)-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite-observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The assimilation technique has been applied to the Oklahoma-Kansas region during the spring-summer 2000 time period when dynamic changes in vegetation cover occur. In April, central Oklahoma is characterized by large NDVI associated with winter wheat while surrounding areas are primarily rangeland with lower NDVI. In July the vegetation pattern reverses as the central wheat area changes to low NDVI due to harvesting and the surrounding rangeland is greener than it was in April. The goal of this study is to determine if assimilating satellite land surface data can improve simulation of the complex spatial distribution of surface energy and water fluxes across this region. The PSU/NCAR NM5 V3 system is used in this study. The grid configuration consists of a 36-km CONUS domain and a 12-km nest over the area of interest. Bulk verification statistics (BIAS and RMSE) of surface air temperature and dewpoint indicates that assimilation of the satellite data results reduces both the bias and RMSE for both state variables. In addition, comparison of model data with ARM/CART EBBR flux observations reveals that the assimilation technique adjusts the bowen ratio in a realistic fashion.

Description: The first Advanced Microwave Sounding Unit temperature sounder (AMSU-A) was launched on the NOAA-15 satellite on 13 May 1998. The AMSU-A's higher spatial and radiometric resolutions provide more useful information on the strength of the middle and upper tropospheric warm cores associated with tropical cyclones than have previous microwave temperature sounders. The gradient wind relationship suggests that the temperature gradient near the core of tropical cyclones increases nonlinearly with wind speed. We recast the gradient wind equation to include AMSU-A derived variables. Stepwise regression is used to determine which of these variables is most closely related to maximum sustained winds (V(sub max)). The satellite variables investigated include the radially averaged gradients at two spatial resolutions of AMSU-A channels 1 through 10 T(sub b) data (delta(sub r)T(sub b)), the squares of these gradients, a channel 15 based scattering index (SI-89), and area averaged T(sub b). Calculations of Tb and delta(sub r)T(sub b) from mesoscale model simulations of Andrew reveal the effects of the AMSU spatial sampling on the cyclone warm core presentation. Stepwise regression of 66 AMSU-A terms against National Hurricane Center (NHC) V(sub max) estimates from the 1998 and 1999 Atlantic hurricane season confirms the existence of a nonlinear relationship between wind speed and radially averaged temperature gradients near the cyclone warm core. Of six regression terms, four are dominated by temperature information, and two are interpreted as correcting for hydrometeor contamination. Jackknifed regressions were performed to estimate the algorithm performance on independent data. For the 82 cases that had in situ measurements of V(sub max), the average error standard deviation was 4.7 m/s. For 108 cases without in situ wind data, the average error standard deviation was 7.5 m/s. Operational considerations, including the detection of weak cyclones and false alarm reduction are also discussed.

Description: A set of global, monthly rainfall products has been intercompared to understand the quality and utility of the estimates. The products include 25 observational (satellite-based), four model and two climatological products. The results of the intercomparison indicate a very large range (factor of two or three) of values when all products are considered. The range of values is reduced considerably when the set of observational products is limited to those considered quasi-standard. The model products do significantly poorer in the tropics, but are competitive with satellite-based fields in mid-latitudes over land. Over ocean, products are compared to frequency of precipitation from ship observations. The evaluation of the observational products point to merged data products (including rain gauge information) as providing the overall best results.

Description: The National Aeronautics and Space Administration established the framework for the Science Investigator-led Processing Systems (SIPS) to enable the Earth science data products to be produced by personnel directly associated with the instrument science team and knowledgeable of the science algorithms. One of the first instantiations implemented for NASA was the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) SIPS. The AMSR-E SIPS is a decentralized, geographically distributed ground data processing system composed of two primary components located in California and Alabama. Initial science data processing is conducted at Remote Sensing Systems (RSS) in Santa Rosa, California. RSS ingests antenna temperature orbit data sets from JAXA and converts them to calibrated, resampled, geolocated brightness temperatures. The brightness temperatures are sent to the Global Hydrology and Climate Center in Huntsville, Alabama, which generates the geophysical science data products (e.g., water vapor, sea surface temperature, sea ice extent, etc.) suitable for climate research and applications usage. These science products are subsequently sent to the National Snow and Ice Data Center Distributed Active Archive Center in Boulder, Colorado for archival and dissemination to the at-large science community. This paper describes the organization, coordination, and production techniques employed by the AMSR-E SIPS in implementing, automating and operating the distributed data processing system.

Description: The germination of this special Computers & Geosciences (C&G) issue began at the Royal Geographical Society (with the Institute of British Geographers) (RGS-IBG) annual meeting in January 1997 held at the University of Exeter, UK. The snow and cold of the English winter were tempered greatly by warm and cordial discussion of how to stimulate and enhance cooperation on geostatistical and geospatial research in remote sensing 'across the big pond' between UK and US researchers. It was decided that one way forward would be to hold parallel sessions in 1998 on geostatistical and geospatial research in remote sensing at appropriate venues in both the UK and the US. Selected papers given at these sessions would be published as special issues of C&G on the UK side and Photogrammetric Engineering and Remote Sensing (PE&RS) on the US side. These issues would highlight the commonality in research on geostatistical and geospatial research in remote sensing on both sides of the Atlantic Ocean. As a consequence, a session on "Geostatistics and Geospatial Techniques for Remote Sensing of Land Surface Processes" was held at the RGS-IBG annual meeting in Guildford, Surrey, UK in January 1998, organized by the Modeling and Advanced Techniques Special Interest Group (MAT SIG) of the Remote Sensing Society (RSS). A similar session was held at the Association of American Geographers (AAG) annual meeting in Boston, Massachusetts in March 1998, sponsored by the AAG's Remote Sensing Specialty Group (RSSG). The 10 papers that make up this issue of C&G, comprise 7 papers from the UK and 3 papers from the LIS. We are both co-editors of each of the journal special issues, with the lead editor of each journal issue being from their respective side of the Atlantic. The special issue of PE&RS (vol. 65) that constitutes the other half of this co-edited journal series was published in early 1999, comprising 6 papers by US authors. We are indebted to the International Association for Mathematical Geology for allowing us to use C&G as a vehicle to convey how geostatistics and geospatial techniques can be used to analyze remote sensing and other types of spatial data. We see this special issue of C&G. and its complementary issue of PE&RS. as a testament to the vitality and interest in the application of geostatistical and geospatial techniques in remote sensing. We also see these special journal issues as the beginning of a fruitful. and hopefully long-term relationship, between American and British geographers and other researchers interested in geostatistical and geospatial techniques applied to remote sensing and other spatial data.

Description: A technique has been developed for assimilating GOES-derived skin temperature tendencies and insolation into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. A critical assumption of the technique is that the availability of moisture (either from the soil or vegetation) is the least known term in the model's surface energy budget. Therefore, the simulated latent heat flux, which is a function of surface moisture availability, is adjusted based upon differences between the modeled and satellite observed skin temperature tendencies. An advantage of this technique is that satellite temperature tendencies are assimilated in an energetically consistent manner that avoids energy imbalances and surface stability problems that arise from direct assimilation of surface shelter temperatures. The fact that the rate of change of the satellite skin temperature is used rather than the absolute temperature means that sensor calibration is not as critical. The technique has been employed on a semi-operational basis at the GHCC within the PSU/CAR MM5 since 1 November 1998. A one-way nested grid configuration was employed with a 75 kin CONUS domain and a 25 km grid over the southeastern United States. Initial conditions were obtained from the 12 UTC Early Eta Data Assimilation System analyses and lateral boundary conditions from the Early Eta forecast available at 3 hour intervals. The satellite-derived land surface temperature tendencies and insolation were assimilated between two and five hours (1400 and 1650 UTC) of the forecast. We performed the assimilation on the Southeastern domain only. In addition, a control run without assimilation was performed to provide insight into the performance of the assimilation technique.

Description: An Observing System Simulation Experiment (OSSE) was conducted to study the impact of airborne lidar wind sounding on mesoscale weather forecast. A wind retrieval scheme, which interpolates wind data from a grid data system, simulates the retrieval of wind profile from a satellite lidar system. A mesoscale forecast system based on the PSU/NCAR MM5 model is developed and incorporated the assimilation of the retrieved line-of-sight wind. To avoid the "identical twin" problem, the NCEP reanalysis data is used as our reference "nature" atmosphere. The simulated space-based lidar wind observations were retrieved by interpolating the NCEP values to the observation locations. A modified dataset obtained by smoothing the NCEP dataset was used as the initial state whose forecast was sought to be improved by assimilating the retrieved lidar observations. Forecasts using wind profiles with various lidar instrument parameters has been conducted. The results show that to significantly improve the mesoscale forecast the satellite should fly near the storm center with large scanning radius. Increasing lidar firing rate also improves the forecast. Cloud cover and lack of aerosol degrade the quality of the lidar wind data and, subsequently, the forecast.