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Satellite/Multi-Sensor Data Integration
Incorporating satellite data into operational and research analysis systems
Including satellite-based data into a multi-sensor national system for Precipitation Estimation (QPE)
NSSL is working to improve operational techniques for rainfall estimation from radar by including other measurement systems such as satellite. A technique has been developed which identifies brightbands in the radar data and utilizes cloud top temperature from GOES to improve precipitation estimation. In addition, techniques under development by NESDIS are being used where radar data is unreliable or unavailable due to terrain blockage, common in the Western U.S.
Western US Storms Comparison of Precipitation Products
Web-based storm tracking tool
StormTracker is an interactive tool for automatically tracking thunderstorm clusters from satellite and radar data. It combines real-time data from GOES, the RUC-2, radar and a lightning detection network, and provides time series plots of data pertinent to the moving storm cluster.
Rabin, R. M., T. Whittaker, 2006: Tool for Storm Analysis Using Multiple Data Sets. Advances in Visual Computing, G. Bebis, R. Boyle, D. Koracin, B. Parvin, Ed(s)., Springer, 571-578.
Applications of satellite derived winds to convective weather
NSSL studied the upper level wind fields derived from water vapor imagery for a variety of winter storms and summertime convective events. Scientists were looking for clues in the wind fields that would detect the development of convection. These derived wind fields have been made available to the SPC and the NESDIS Synoptic Analysis Branch on an experimental basis.
Rabin, R.M., S.F. Corfidi, J. C. Brunner, C. E. Hane, 2004: Detecting winds aloft from water vapour satellite imagery in the vicinity of storms. Weather, 59, 251-257.
Upper Level Wind Analysis Web Page 
Studies of the evolution of atmospheric moisture and precipitation
New imagery to depict storm overshoot into the stratosphere and to understand their role as a source of water vapor in the stratosphere
Storm intensity can often be indicated on satellite by the observation of penetrating convective clouds and the monitoring of the temperature of the anvil. NSSL explored a technique combining temperature data from satellite and operational models that could be converted into color-enhanced imagery to help identify these features. In addition, the presence of stratospheric moisture above storms has been inferred from multi-spectral satellite imagery.
M. Setvak, R. Rabin, P. Wang, 2006: Contribution of MODIS instrument to the observations of deep convective storms and stratospheric moisture detection in GOES and MSG imagery, J. Atm. Res., to be published (page numbers not yet available).
Using GOES satellite monitoring of surface temperature to detect land-surface wetness
NSSL scientists studied the relationship between the observed daytime rise in surface radiative temperature and modeled soil moisture over the continental U.S. to provide an infrared (IR) satellite-based index for soil moisture.
R. Rabin, T. Schmit, 2006: Estimating Surface Wetness from GOES. J.Atmos. Ocean. Tech., 23, 991-1003.
Surface and Atmospheric Wetness—Monthly Means From GOES
Relating remotely-sensed vegetation and soil moisture indices to surface energy fluxes in vicinity of an atmospheric dryline
A physical-statistical method for optimal estimation of daily surface heat flux and Bowen ratio on the mesoscale is applied to a portion of the US southern Great Plains, where a strong surface/atmosphere moisture gradient (dryline) was present. The derived heat flux is found to decrease by nearly an order of magnitude with the transition from prairie to forest eastward across Oklahoma. A subtle maximum in heat flux in the Texas panhandle is observed upwind of an eastward bulge in the dryline where enhanced downward mixing of dry air was occurring.
Rabin, R.M., B. Burns, C. Collimore, G.R. Diak, W. Raymond, 2000: Relating remotely-sensed vegetation and soil moisture indices to surface energy fluxes in vicinity of an atmospheric dryline. Remote Sens. Rev., 18, 53-83.
The use of shortwave IR reflectance to detect intensity of thunderstorms
NSSL scientists, in collaboration with the Cooperative Institute for Research of the Atmosphere (CIRA - Colorado State University) and the CHMI, used GOES-12 data to explore the differences in cloud top reflectance of individual storms during their development. They also investigated differences in the vertical profiles of solar reflectance and their evolution between storms of varying intensity, and determined if the spatial resolution of the GOES data is sufficient to resolve the vertical profiles of mid-IR solar reflectance from cloud clusters near storms.
Setvák, M., R.M. Rabin, C.A. Doswell III, V. Levizzani, 2003: Satellite observations of convective storm tops in the 1.6, 3.7 and 3.9 micron spectral bands. Atmospheric Research, 67-68, 607-627.
GOES Cloud Top Properties of Convective Clouds
Exploration of the role of soil moisture anomalies on severe weather outbreaks in the U.S.
Data was collected to analyze soil moisture anomalies for specific significant severe weather outbreaks. NSSL scientists looked at soil moisture patterns and their possible relationship to atmospheric stability and forcing mechanisms. They also compared storm tracks in El-Nino versus La-Nina springs in the U.S.
Studies of the climatology of convective and extreme weather events
Applications of GOES satellite fire detection at the SPC
The Storm Prediction Center issues daily Fire Weather Outlooks to indicate areas where the combination of dry dead fuels, such as grass and timber, and meteorological conditions (wind, relative humidity, temperature, and dry thunderstorms) might contribute to potentially dangerous wild fire behavior. In support of this effort, NSSL, in collaboration with Oklahoma State University, SPC, and CIMMS, used spatially compared statewide burn patterns to explain the geographic pattern of wildfires in OK. The group targeted wildfires occurring across the Southern Plains from late December 2005 through mid-March 2006. Numerous grass fires burned in this region during this period, causing loss of property and even loss of life. Conditions leading to a high risk of wild-fires at this time included extremely dry soil, combined with periodic episodes of strong winds, unseasonable warmth, and very low relative humidity.
A website was developed to show the resulting spatial extent of the fires as mapped by satellite, and to provide comparison of surface conditions which may have been factors in the observed fire patterns.
GOES Winter Precipitation efficiency algorithm
Recent studies have shown the importance of snow microphysics for heavy snowfall. Specifically, snow production and accumulation appears to be highly efficient when two parameters are collocated within a narrow temperature range (centered at -15ºC). In addition the dominant crystal type formed in this temperature range is dendrites which have been shown to be conducive for high snow to liquid ratios.
To highlight areas conducive for this highly efficient snowfall a GOES Winter Precipitation Efficiency Algorithm was developed by NSSL. The algorithm uses cloud products derived from Geostationary Operational Environmental Satellite (GOES) Sounder radiances to create an analysis of the height of the pressure level at -15ºC. Further refinement of the analysis is conducted by including vertical velocity output from the Rapid Update Cycle (RUC) to highlight areas where the -15ºC pressure level is collocated with moderate lift.
R. Rabin and Jay Hanna, GOES Winter Precipitation efficiency algorithm, submitted to NWA annual meeting 2006.
Precipitation Efficiency Inferred From GOES Sounder Products
The enhanced-V signature in relation to severe weather
Enhanced IR satellite imagery of deep convection sometimes displays a cloud-top V-shaped feature known as the enhanced V-signature. Enhanced-V features signify strong tropospheric shear and intense updrafts, both of which are also essential for severe thunderstorms. Using enhanced-V features from satellite in addition to radar-based algorithms will increase confidence in the detection of severe weather.
Article submitted to Weather and Forecasting, J. Brunner et al., currently in review.
Applications of Super Rapid Scan Data
Data from the GOES-10 satellite collected 1-minute intervals in August-September 2006 is being used to explore future applications of the GOES-R imager planned for the next decade.
