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The Real Time Mission Monitor (RTMM) is a visualization and information system that fuses multiple Earth science data sources, to enable real time decision-making for airborne and ground validation experiments. Developed at the NASA Marshall Space Flight Center, RTMM is a situational awareness, decision-support system that integrates satellite imagery, radar, surface and airborne instrument data sets, model output parameters, lightning location observations, aircraft navigation data, soundings, and other applicable Earth science data sets. The integration and delivery of this information is made possible using data acquisition systems, network communication links, network server resources, and visualizations through the Google Earth virtual globe application. RTMM is extremely valuable for optimizing individual Earth science airborne field experiments. Flight planners, scientists, and managers appreciate the contributions that RTMM makes to their flight projects. A broad spectrum of interdisciplinary scientists used RTMM during field campaigns including the hurricane-focused 2006 NASA African Monsoon Multidisciplinary Analyses (NAMMA), 2007 NOAA-NASA Aerosonde Hurricane Noel flight, 2007 Tropical Composition, Cloud, and Climate Coupling (TC4), plus a soil moisture (SMAP-VEX) and two arctic research experiments (ARCTAS) in 2008. Improving and evolving RTMM is a continuous process. RTMM recently integrated the Waypoint Planning Tool, a Java-based application that enables aircraft mission scientists to easily develop a pre-mission flight plan through an interactive point-and-click interface. Individual flight legs are automatically calculated "on the fly". The resultant flight plan is then immediately posted to the Google Earth-based RTMM for interested scientists to view the planned flight track and subsequently compare it to the actual real time flight progress. We are planning additional capabilities to RTMM including collaborations with the Jet Propulsion Laboratory in the joint development of a Tropical Cyclone Integrated Data Exchange and Analysis System (TC IDEAS) which will serve as a web portal for access to tropical cyclone data, visualizations and model output.

The goal of the Sensor Management Applied Research Technologies (SMART) On-Demand Modeling project is to develop and demonstrate the readiness of the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) capabilities to integrate both space-based Earth observations and forecast model output into new data acquisition and assimilation strategies. The project is developing sensor web-enabled processing plans to assimilate Atmospheric Infrared Sounding (AIRS) satellite temperature and moisture retrievals into a regional Weather Research and Forecast (WRF) model over the southeastern United States.

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.

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.

Thermal remote sensing can provide environmental measuring tools with capabilities for measuring ecosystem development and integrity. Recent advances in applying principles of nonequilibrium thermodynamics to ecology provide fundamental insights into energy partitioning in ecosystems. Ecosystems are nonequilibrium systems, open to material and energy flows, which grow and develop structures and processes to increase energy degradation. More developed terrestrial ecosystems will be more effective at dissipating the solar gradient (degrading its energy content). This can be measured by the effective surface temperature of the ecosystem on a landscape scale. A series of airborne thermal infrared multispectral scanner data were collected from several forested ecosystems ranging from a western US douglas-fir forest to a tropical rain forest in Costa Rica. Also measured were agriculture systems. These data were used to develop measures of ecosystem development and integrity based on surface temperature.

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.

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over die tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-H effort is also examined. This integration is run with specified SSTs, but with no data assimilation. Our analysis focuses on two aspects of inter-annual variability. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean beat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability in precipitation than do the satellite observations.

Thunderstorms are high impact weather phenomena. They also pose an extremely challenging forecast problem. The National Oceanic and Atmospheric Administration (NOAA), the Federal Aviation Administration (FAA), the National Aeronautic and Space Administration (NASA), and the Air Force Weather Agency (AFWA), have decided to pool technology and scientific expertise into an unprecedented effort to better observe, diagnose, and forecast thunderstorms. This paper describes plans for an operational field test called the THunderstorm Operational Research (THOR) Project beginning in 2002, the primary goals of which are to: 1) Reduce the number of Thunderstorm-related Air Traffic Delays with in the National Airspace System (NAS) and, 2) Improve severe thunderstorm, tornado and airport thunderstorm warning accuracy and lead time. Aviation field operations will be focused on the prime air traffic bottleneck in the NAS, the airspace bounded roughly by Chicago, New York City and Washington D.C., sometimes called the Northeast Corridor. A variety of new automated thunderstorm forecasting applications will be tested here that, when implemented into FAA-NWS operations, will allow for better tactical decision making and NAS management during thunderstorm days. Severe thunderstorm operations will be centered on Northern Alabama. NWS meteorologists from the forecast office in Birmingham will test the utility of experimental lightning, radar, and profiler data from a mesoscale observing network being established by NASA's Marshall Space Flight Center. In addition, new tornado detection and thunderstorm nowcasting algorithms will be examined for their potential for improving warning accuracy. The Alabama THOR site will also serve as a test bed for new gridded, digital thunderstorm and flash flood warning products.

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.

BACKGROUND: The amount of oxalate excreted in urine has a significant impact on calcium oxalate supersaturation and stone formation. Dietary oxalate is believed to make only a minor (10 to 20%) contribution to the amount of oxalate excreted in urine, but the validity of the experimental observations that support this conclusion can be questioned. An understanding of the actual contribution of dietary oxalate to urinary oxalate excretion is important, as it is potentially modifiable. METHODS: We varied the amount of dietary oxalate consumed by a group of adult individuals using formula diets and controlled, solid-food diets with a known oxalate content, determined by a recently developed analytical procedure. Controlled solid-food diets were consumed containing 10, 50, and 250 mg of oxalate/2500 kcal, as well as formula diets containing 0 and 180 mg oxalate/2500 kcal. Changes in the content of oxalate and other ions were assessed in 24-hour urine collections. RESULTS: Urinary oxalate excretion increased as dietary oxalate intake increased. With oxalate-containing diets, the mean contribution of dietary oxalate to urinary oxalate excretion ranged from 24.4 +/- 15.5% on the 10 mg/2500 kcal/day diet to 41.5 +/- 9.1% on the 250 mg/2500 kcal/day diet, much higher than previously estimated. When the calcium content of a diet containing 250 mg of oxalate was reduced from 1002 mg to 391 mg, urinary oxalate excretion increased by a mean of 28.2 +/- 4.8%, and the mean dietary contribution increased to 52.6 +/- 8.6%. CONCLUSIONS: These results suggest that dietary oxalate makes a much greater contribution to urinary oxalate excretion than previously recognized, that dietary calcium influences the bioavailability of ingested oxalate, and that the absorption of dietary oxalate may be an important factor in calcium oxalate stone formation.

Hysteresis and wavenumber vacillation are studied numerically in a weakly stratified quasigeostrophic model. In general, the amplitude of the most unstable wave increases, as the flow becomes more unstable. When the wave becomes saturated, the next longer wave will grow at the expanse of the most unstable wave and becomes the dominant wave. However, once the longwave state is established, it may remain in that regime even as the instability is decreased beyond the threshold where it first developed, thus constituting a hysteresis loop. In a highly unstable case, the flow may not show a preference for any single wave. Instead, the dominant wave aperiodically varies among several long waves. This phenomenon is known as wavenumber vacillation. Hysteresis is further examined in terms of eddy heat flux. It is shown that total eddy heat flux increases as the flow becomes more unstable, but displays a sharp drop when transition to a longer wave occurs. However, in a longwave state, the heat flux always decreases with decreasing instability even pass the threshold when wave transition first occurs.

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.

Large-scale divergent circulations are part of the atmospheric dynamic response to diabatic heating from condensation, radiative processes, and surface energy fluxes. Vertical motion and the associated divergent wind is thus intimately tied to the hydrologic cycle and the global beat balance. Despite its importance, the divergent circulation is too small in comparison to the rotational flow to measure directly with any accuracy. Vertical motions are recovered diagnostically from reanalyses and, as such, are subject to shortcomings in model physics, numerics, and data availability. While reanalysis estimates of tropical divergent circulations are much improved over those from the Global Weather Experiment, there are still substantial differences between products from operational centers. This is because these circulations are still forced largely by model physics and only secondarily by observations. In order to produce a refined estimate of tropical divergence and its interannual variability we have used a number of remotely-sensed data sets along with variational constraints to improve upon reanalysis estimates. Among these are: precipitation from SSM/I and GPCP, TOVS Path-A vertical cloud distributions; ISCCP radiative cooling rates; TOA radiative fluxes from ERBS, surface radiative fluxes from the SRB project, and surface latent and sensible flux estimates from SSM/I. The TOVS Path-A data constrain the divergent outflow in precipitating regions to have the same vertical structure as observed cloudiness. Using integral constraints for moisture, heat, and mass balance, we retrieve consistent divergent wind flows. We examine the ability of this type analysis to capture regional details of ENSO related perturbations to the divergent wind and associated tropical energy balance. Precipitation from satellite is found to be the major constraint in supplying this horizontal structure. We also consider the ability of this analysis to quantify integrated (land vs. ocean) fluctuations in the global monsoonal flow and energy balance. This depends, of course, on the error characteristics of the data constraints. Some estimate of these properties and their relative importance is provided.

Between 1973 and 1992, an average of 20 ha of forest was lost each day to urban expansion of Atlanta, Georgia. Urban surfaces have very different thermal properties than natural surfaces-storing solar energy throughout the day and continuing to release it as sensible heat well after sunset. The resulting heat island effect serves as catalysts for chemical reactions from vehicular exhaust and industrialization leading to a deterioration in air quality. In this study, high spatial resolution multispectral remote sensing data has been used to characterize the type, thermal properties, and distribution of land surface materials throughout the Atlanta metropolitan area. Ten-meter data were acquired with the Advanced Thermal and Land Applications Sensor (ATLAS) on May 11 and 12, 1997. ATLAS is a 15-channel multispectral scanner that incorporates the Landsat TM bands with additional bands in the middle reflective infrared and thermal infrared range. The high spatial resolution permitted discrimination of discrete surface types (e.g., concrete, asphalt), individual structures (e.g., buildings, houses) and their associated thermal characteristics. There is a strong temperature contrast between vegetation and anthropomorphic features. Vegetation has a modal temperature at about 20 C, whereas asphalt shingles, pavement, and buildings have a modal temperature of about 39 C. Broad-leaf vegetation classes are indistinguishable on a thermal basis alone. There is slightly more variability (+/-5 C) among the urban surfaces. Grasses, mixed vegetation and mixed urban surfaces are intermediate in temperature and are characterized by broader temperature distributions with modes of about 29 C. Thermal maps serve as a basis for understanding the distribution of "hotspots", i.e., how landscape features and urban fabric contribute the most heat to the lower atmosphere.

The Advanced Microwave Sounding Unit (AMSU)-A instruments on the NOAA-15 and NOAA-16 satellites provide information on the warm cores of tropical cyclones from oxygen channel brightness temperature (Tb) measurements near 55 GHz. With appropriate assumptions, cyclone-scale Tb gradients can be directly related to middle-to-lower tropospheric height gradients. We have developed a method for diagnosis of maximum sustained winds (Vmax) from radially averaged Tb gradients in several of the AMSU channels. Calibration of the method with recon-based (or other in situ) winds results in better agreement than with Dvorak wind estimates. Gradient wind theory shows that the warm core Tb gradient signal increases non-linearly with wind speed, making microwave temperature sounders useful for diagnosing high wind speeds, but at the expense of a minimum useful detection limit of about 40 knots. It is found that accurate wind diagnoses depend upon (1) accounting for hydrometeor effects in the AMSU channels, and (2) maximizing signal-to-noise, since the 50 km resolution data cannot fully resolve the temperature gradients in the Vmax region, typically 10-20 km in scale. AMSU imagery and max diagnoses from specific hurricanes will be shown, including independent tests from the 2000 hurricane season.

We constructed and analyzed wet and dry soil moisture composites for the mid-latitude GCIP region of the central US using long climate model simulations made with the NCAR CCM3 and reanalysis products from NCEP. Using the diagnostic composites as a guide, we have completed a series of predictability experiments in which we imposed soil water initial conditions in CCM3 for the GCIP region for June 1 from anomalously wet and dry years, with atmospheric initial conditions taken from June 1 of a year with 'near-normal' soil water, and initial soil water from the near-normal year and atmospheric initial conditions from the wet and dry years. Preliminary results indicate that the initial state of the atmosphere is more important than the initial state of soil water determining the subsequent late spring and summer evolution of sod water over the GCIP region. Surprisingly, neither the composites or the predictability experiments yielded a strong influence of soil moisture on the atmosphere. To explore this further, we have made runs with extreme dry soil moisture initial anomalies imposed over the GCIP region (the soil close to being completely dry). These runs did yield a very strong effect on the atmosphere that persisted for at least three months. We conclude that the magnitude of the initial soil moisture anomaly is crucial, at least in CCM3, and are currently investigating whether a threshold exists, below which little impact is seen. In a complementary study, we compared the impact of the initial condition of snow cover versus the initial atmospheric state over the western US (corresponding to the westward extension of the GAPP program follow-on to GCIP). In this case, the initial prescription of snow cover is far more important than the initial atmospheric state in determining the subsequent evolution of snow cover. We are currently working to understand the very different soil water and snow cover results.

Recent studies at the Global Hydrology and Climate Center (GHCC) have shown that the assimilation of land skin temperature (LST) tendencies into a mesoscale model can significantly improve short term forecasts of near surface temperature and moisture. The high spatial and temporal resolution of GOES derived land surface products provide valuable information about the spatial and temporal variability of the land surface forcing simulated in the model. In the GHCC studies LST was derived using a split window technique requiring at least two longwave infrared window channels and thus, utilized the 11 and 12 micron channels found on the GOES-8 Imager with a nadir spatial resolution of 4km. However, beginning with the launch of GOES-M (scheduled for mid 2001 ) and subsequent satellites the 12 micron channel will be removed from the Imager leaving only one longwave window channel. The GOES Sounder will continue to have more than one longwave infrared window channel (including a 12 micron channel) but, with a spatial resolution of 10 km nadir. LST retrievals from the newer GOES satellites will thus be derived from Sounder measurements at a reduced spatial resolution. This paper intercompares the LST retrievals from the GOES-8 Imager and Sounder. The effects on the LST retrievals due to the Sounder's reduced resolution from that of the Imager and its different longwave infrared channel characteristics are examined. The effects of transitioning from Imager to Sounder LST products on the results from model assimilation of these products are also examined.

Since the beginning of the World Climate Research Program's Global Precipitation Climatology Project (GPCP) satellite remote sensing of precipitation has made dramatic improvements, particularly for tropical regions. Data from microwave and infrared sensors now form the most critical input to precipitation data sets and can be calibrated with surface gauges to so that the strengths of each data source can be maximized in some statistically optimal sense. Recent availability of the TRMM (Tropical Rainfall Measuring Mission) has further aided in narrowing uncertainties in rainfall over the tropics and subtropics. Although climate modeling efforts have long relied on space-based precipitation estimates for validation, we now are in a position to make more quantitative assessments of model performance, particularly in tropical regions. An integration of the CCM3 using observed SSTs as a lower boundary condition is used to examine how well this model responds to ENSO forcing in terms of anomalous precipitation. An integration of the NCEP spectral model used for the Reanalysis-11 effort is also examined. This integration is run with specified SSTs, but no data assimilation. Our analysis focuses on two aspects. First are the spatial anomalies that are indicative of dislocations in Hadley and Walker circulations. Second, we consider the ability of models to replicate observed increases in oceanic precipitation that are noted in satellite observations for large ENSO events. Finally, we consider a slab ocean version of the CCM3 model with prescribed ocean heat transports that mimic upwelling anomalies, but which still allows the surface energy balance to be predicted. This less restrictive experiment is used to understand why model experiments with specified SSTs seem to have noticeably less interannual variability than do the satellite precipitation observations.

This study compares the lightning locations reported by the National Lightning Detection Network (NLDN) with the lightning locations determined by the Lightning Imaging Sensor (LIS). The NLDN system identifies the rf signature of cloud-to-ground lightning. The LIS data is the top level of a hierarchy of optical data objects. The centroid and timing of each LIS lightning activity center are compared with each flash in a subset of the NLDN long range lightning location data in a portion of the Atlantic Ocean and the Caribbean Sea consisting of those locations more than 625 km from any sensor. This subset is produced by analyzing each reported NLDN location to determine if that location is within the LIS field of view at the time of the reported flash. The Tropical Rainfall Measuring Mission Satellite (TRMM) orbit limits the cross-sensor comparison to tropical and sub-tropical regions. Because the rf-detection system depends on ionospheric propagation conditions, a separate analysis was made for daylight conditions at both source and sensor as well as nighttime at both places. A full year of data is compared to provide an adequate sample of each data set. Confirmation of lightning in the general location of the NLDN report is established when LIS detected one or more centers of lightning activity within a 2 degree radius from the NLDN location.

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/NCAR MM5. Assimilation has been performed on a grid centered over the Southeastern US since November 1998. Results from the past year show that assimilation of the satellite data reduces both the bias and RMSE for simulations of surface air temperature and relative humidity. These findings are based on comparison of assimilation runs with a control using the simple 5-layer soil model available in MM5. A significant development in the past several months was the inclusion of the detailed Oregon State University land surface model (OSU/LSM) as an option within MM5. One of our working hypotheses has been that the assimilation technique, although simple, may provide better short-term forecasts than a detailed LSM that requires significant number initialized parameters. Preliminary results indicate that the assimilation out performs the OSU/LSM as it is incorporated in MM5. Details will be presented at the symposium.

Airborne lidar systems are used to determine wind velocity and to measure aerosol or cloud backscatter variability. Atmospheric aerosols, being affected by local and regional sources, show tremendous variability. Continuous wave (cw) lidar can obtain detailed aerosol loading with unprecedented high resolution (3 sec) and sensitivity (1 mg/cubic meter) as was done during the 1995 NASA Multi-center Airborne Coherent Atmospheric Wind Sensor (MACAWS) mission over western North America and the Pacific Ocean. Backscatter variability was measured at a 9.1 micron wavelength cw focused CO2 Doppler lidar for approximately 52 flight hours, covering an equivalent horizontal distance of approximately 30,000 km in the troposphere. Some quasi-vertical backscatter profiles were also obtained during various ascents and descents at altitudes that ranged from approximately 0.1 to 12 km. Similarities and differences for aerosol loading over land and ocean were observed. Mid-tropospheric aerosol backscatter background mode was approximately 6 x 10(exp -11)/ms/r, consistent with previous lidar datasets. While these atmospheric measurements were made, the lidar also retrieved a distinct backscatter signal from the Earth's surface from the unfocused part of the focused cw lidar beam during aircraft rolls. Atmospheric backscatter can be highly variable both spatially and temporally, whereas, Earth-surface backscatter is relatively much less variant and can be quite predictable. Therefore, routine atmospheric backscatter measurements by an airborne lidar also give Earth surface backscatter which can allow for investigating the Earth terrain. In the case where the Earth's surface backscatter is coming from a well-known and fairly uniform region, then it can potentially offer lidar calibration opportunities during flight. These Earth surface measurements over varying Californian terrain during the mission were compared with laboratory backscatter measurements using the same lidar of various Earth surfaces giving good agreement, suggesting that the lidar efficiency, and thus a lidar calibration factor for detection, can be estimated fairly well using Earth's surface signal.

The NASA Tropical Rainfall Measuring Mission (TRMM) Observatory made an overpass during the Central Oklahoma tornado outbreak at approximately 04:00 LTC on 4 May, 1999 (23:00 CDT on 3 May). At the time of the TRMM snapshot there were 25 individual storms in Oklahoma that could be identified and cross-correlated between the TRMM measurements and the Twin Lakes (KTLX) NEXRAD radar. Of these, six were significant supercells that could be trended for 30-min prior and subsequent to (1 hr total) the TRMM overpass. Thus, we are able to provide a context for the TRMM snapshot with respect to each individual supercell's recent growth, decay, and severe weather producing history. The most vigorous and electrically active storm at the time of the overpass is the Stroud, OK supercell whose F3 tornado ended only about 10 minutes earlier. Another F3 tornado north of Crescent, OK (- 150 km west of Stroud) is on the ground and a supercell with a developing, yet still weak mesocyclone is just south of the Stroud storm. This latter storm produces an F1 tornado an hour later near Sapulpa, OK. The objective of this study is to provide additional insight into the characteristics of the supercell storms through the unique capabilities and vantagepoint of the TRMM science instruments.