Short-Term Convective Storm Forecasting

Precipitation from convective storms has a significant impact on the global, regional and local hydrological cycle.  Our understanding of how convective storms form, grow, and dissipate remains a scientific challenge that HAP scientists are attacking by means of field programs and high resolution modeling through the STEP and Water System programs.  Short term forecasting (0-8 hours) of thunderstorms is a particular focus for the applied research on convective storms.  The AutoNowcaster system is a RAL developed decision support system focused on 0-2 hour nowcasts of thunderstorms, including growth, dissipation, and especially initiation.  The system is currently being transitioned to the National Weather Service for operational use at Weather Forecast Offices. The system was used operationally by the Chinese Weather Bureau during the 2008 summer Olympics.

Program Goals

The research and development of the Convective Weather Program are geared towards very short-term forecasting of high-impact weather.  The objective is to enhance existing and facilitate new capabilities of monitoring (i.e., analysis), nowcasting (< 2 h), and forecasting (> 2 h) weather-related conditions that pose a hazard or threat to human safety, transportation on land, water, and in the air, and to infrastructure.

The program strives to advance a basic understanding of dynamic, thermodynamic, and microphysical processes related to severe weather, including the initiation of storms and their subsequent evolution, by focusing on observations, data assimilation, numerical modeling, forecasting, and diagnostic evaluation.  Integration of short-term weather prediction with user applications is a high priority. 

Challenge

High-resolution, short-term forecasts of thunderstorms provide critical information for a wide range of users, including the aviation community, ground transportation, urban emergency and water resources management groups, recreation facilities, construction industries, and the military, that assists them to safely and efficiently deploy resources.  However, achieving reliable and accurate convective weather forecasts remains a scientific challenge due to uncertainty in grasping initial conditions, shortcomings in model physics and computational capabilities, and limitations of our understanding of how nature works.  The forecast skill of observation-driven expert systems decreases rapidly with increasing lead-time, while numerical weather prediction models exhibit a limited forecast ability within the first few hours after initialization primarily due to spin-up problems.  Furthermore, forecast methodology and display systems have to be tuned to the needs of different users—for example, a line of severe convective storms predicted in the wrong place may be perceived as a bad forecast from a water resources manager (e.g., dam operator), yet the same forecast might be quite good for an en-route air traffic control manager. 

Project Sponsors:

The research and development activities of the Convective Weather Program are supported by both national and international sponsors, including:

Army Test and Evaluation Command (ATEC) | Federal Aviation Administration (FAA) | National Aeronautics and Space Administration (NASA) | National Atmospheric and Oceanic Administration (NOAA) | National Science Foundation (NSF) | United States Navy

Collaborative Storm Prediction for Aviation (CoSPA)

Weather exerts a disruptive influence on aviation, both in the terminal area and en-route air traffic flow.  Weather-related delays clearly increase during the convective weather season (approximately April through September), but also winter weather can cause havoc throughout the national airspace system.  Reliable detection of hazardous weather and predictions thereof several hours in advance are essential for aviation users to achieve safe and efficient use of the airspace.  Currently, weather avoidance is largely done in real-time responding to existing weather hazards (i.e., on a tactical basis) rather than through planning ahead based on anticipated weather (i.e., strategic decision).  This is partly because of the weather forecast uncertainty and also because of a limited integration of weather information into automated air traffic management decision support tools.

The development of a Collaborative Storm Prediction for Aviation (CoSPA) product has been initiated by the Federal Aviation Administration (FAA) in order to replace a plethora of currently available weather forecast products by a single forecast to be used in all government-provided aviation weather systems.  CoSPA will embody the best techniques available today, with an open modular architecture that enables easy exchange of algorithm modules, as new or upgraded techniques become available.  CoSPA will build upon a mixture of observation-based expert systems and numerical weather prediction model to provide seamless 0 – 8 h forecasts of convective hazards and heavy snowfall.  An initial forecast demonstration experiment of a CoSPA prototype will begin in summer 2008.  CoSPA is a collaborative effort between the National Center for Atmospheric Research (NCAR), Massachusetts Institute of Technology (MIT) Lincoln Laboratory, and the NOAA Earth System Research Laboratory (ESRL) under sponsorship of the FAA.

> more | > MIT Aviation Weather | > NOAA

Probabilistic Weather Integration with Air Traffic Management

Probabilistic scenario forecast concept Weather hazards are a major cause of aviation delays and flight cancellations.  Strategic flight planning requires weather forecasts several hours into the future, which draws heavily upon numerical weather prediction.  In particular, aviation users need 0 – 12 h forecasts that provide not only details about the likely weather outcome, but also information about storm structure, intensity,organization, and associated forecast uncertainty.  This emphasizes the need for short-range (0 – 2 days), high-resolution (<10 km spatial resolution) ensemble weather forecasting systems.  Optimization of air traffic management, especially under future scenarios of anticipated much increased demand, has to build upon automated decision support tools that integrate probabilistic weather information to estimate airspace capacity and provide guidance for managing air traffic flows under consideration of the associated prediction uncertainties. 

Under NASA sponsorship, the National Center for Atmospheric Research has been developing and refining new concepts of how probabilistic weather forecasts can be tailored for aviation needs and integrated with automated decision support tools.  The novel approach entails a translation of ensemble weather forecasts into probabilistic air traffic capacity impact predictions. Although the focus has been on convective storms, primarily because of their disruptive influence on air traffic flows, the same concept may be applicable to other en-route weather hazards, such as turbulence and icing, as well. 

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Nowcasting for Army Test and Evaluation Command (ATEC) Ranges

The weather forecasting challenge for meteorologists at the ATEC ranges is to provide guidance for both tactical and strategic decisions.  Ensuring safety of personnel and test operations is the primary focus of very short-term, 0 – 2 h weather forecasting (i.e., nowcasting).  Another important aspect of weather forecasting is to find “windows of opportunity” throughout a day when range customers may expect weather conditions favorable for conducting their tests and minimizing weather-related downtime periods.  Forecasters also assist their range customers in scheduling tests months in advance, which requires providing climatological information about the local weather conditions. For further details see Saxen et al. (2008).

The AutoNowCaster (ANC) system is the primary tool used operationally by forecasters for monitoring and nowcasting of thunderstorm activity at the White Sands Missile Range (WSMR).  The ANC system is a component of the ATEC Four-Dimensional Weather (4DWX) system, which is used by Army meteorologists to provide overall meteorological support.  Partial ANC systems (so-called AN-Lite systems) that provide extrapolation-only forecasts are operational at other ATEC test centers as well, including the Aberdeen Test Center (ATC) in Maryland, the Redstone Technical Test Center (RTTC) in Alabama, Dugway Proving Ground (DPG) in Utah, and the Electronic Proving Ground (EPG) and Yuma Proving Ground (YPG) in Arizona.  The forecast skills of the AN-Lite systems does not reach the performance level of a full ANC system, but a lack of or limited access to relevant observations currently prevents upgrades to full ANC systems at ATEC ranges other than WSMR. 

Lightning potential forecast Lightning is a particular safety concern at the ranges.  A forecast product for the potential of storms to produce lightning has been developed and deployed at WSMR and EPG.  This product provides lead times of approximately 5 – 15 min before lightning occurs.  An example of this product is depicted together with vertical cross sections through storms.  The red polygons enclose storms deemed capable of producing lightning (the lightning strikes that occurred within the next 20 min are shown by the red crosses).

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Forecaster Over-the-Loop Evaluation

The objective of this demonstration project under sponsorship by the National Weather Service (NWS) is to assess the potential benefits of human-added information to automatically generated short-term forecasts by the AutoNowCaster (ANC) for the Dallas/Ft. Worth aviation weather centers.  In particular, the value of a forecaster entering boundaries and selecting the anticipated type of convective weather regime (i.e., specifically tuned weights for predictor fields) for improved overall system performance is assessed.  A similar evaluation has been carried out over the Illinois/Indiana domain in collaboration with WSI.  Efforts are underway by NCAR and the NWS to begin transfer of ANC components to the Advanced Weather Interactive Processing System (AWIPS) in order to make it available to forecasters at NWS Weather Forecast Offices (WFO) across the country. The AWIPS-ANC will also be part of the NextGen capability demonstration in Florida in 2009-10.
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Beijing Meteorological Bureau (BMB) and Olympics 2008 Forecast Demonstration Project

The Institute of Urban Meteorology (IUM) of the Beijing Meteorological Bureau (BMB) and NCAR work collaboratively to transfer the AutoNowCaster (ANC) system to BMB, and develop it further to adapt to the local terrain and climate.  As part of this multi-year project, NCAR scientists are collaborating with meteorologists from BMB to study the local characteristics of thunderstorm initiation and evolution for modifying and tuning the ANC algorithms to optimize its performance in the Beijing area, and to train the IUM staff on thunderstorm nowcasting techniques and the use of the ANC.  VDRAS is a vital component of the BMB ANC installation.  This is the first time for VDRAS to be installed in a domain with complex terrain.  Preliminary results seem to indicate that VDRAS is able to handle complex terrain well.  As part of this overall effort, NCAR staff will participate in the World Meteorological Organization (WMO) Beijing Olympics 2008 Forecast Demonstration Project (FDP).  In addition to the ANC, also NCAR’s new Niwot blending system will be employed and operated as part of this project. 
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Oceanic Weather

Remote, oceanic regions have severely limited data availability and, therefore, have few, if any, high resolution weather products that indicate current or future locations of convection.  Convective hazards impact the safety, efficiency and economic viability of oceanic aircraft operations by producing turbulence, icing and lightning, and by necessitating aircraft rerouting while inflight, leading to higher fuel costs and delays.  To improve convective products for the oceanic aviation community, the NASA-sponsored Oceanic Convection Diagnosis and Nowcasting project is focused on developing short-term forecast products of convective storms over oceanic regions. In addition, these forecast products will be combined with turbulence forecasts to create a global aviation weather hazard prediction capability.  Resulting products focus on the needs of pilots, dispatchers, air traffic managers and forecasters within the oceanic aviation community.  Collaborators in this research include the National Center for Atmospheric Research (NCAR), the Naval Research Laboratory (NRL) at Monterey, and the Massachusetts Institute of Technology (MIT) Lincoln Laboratory. 
>annual report | >more

Winter Weather

Research activities related to winter weather are designed to improve the nowcast and forecast of winter weather conditions that impact aviation operations (e.g., deicing of aircraft, runway clearing, sanding, and plowing, and air traffic operations) at airports.  These activities have focused on development of a machine to produce snow in a reliable and repeatable fashion in order to test deicing fluids; development of a hotplate snowgauge; evaluation of snowgauges; operation of a ground-based winter test facility at the Marshall field site; and support for operational Weather Support for Deicing Decision Makers (WSDDM) systems at several U.S. airports.  Current sources of funding are the FAA Aviation Weather Research Program (AWRP), FAA Technical Center, NSF, and the City of Denver. 
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Refractivity Experiment For H20 Research And Collaborative operational Technology Transfer (REFRACTT)

The lack of detailed, high-resolution water vapor measurements in the atmospheric boundary layer is one of the primary limiting factors in being able to predict convection initiation and produce accurate quantitative precipitation forecasts from numerical weather prediction models.  During the summer of 2006, scientists took an important first step in addressing the need for an improved national, high-resolution moisture field by conducting the Refractivity Experiment For H20 Research And Collaborative operational Technology Transfer (REFRACTT).  This effort is directed, not only toward improving our understanding of near-surface water vapor variability and the role it plays in the initiation of thunderstorms, but also on building operational advocacy for installing a new refractivity moisture retrieval technique on the national network of NEXRAD radars.  This novel technique is based on measuring changes in the speed of radar signals to fixed targets caused by refraction, which in turn reveals variations in the atmospheric moisture content.  REFRACTT yielded a wealth of data that are being analyzed now. 
>news release | >annual report | >more

Conceptual Approach

Short-term storm prediction builds upon several components, as outlined in the figure below, including both observations and numerical modeling.  The analysis of a wide range of observations is key to monitoring storms.  Weather radar plays a chief role in the identification and tracking of storm cells.  At very short lead times a storm forecast is obtained primarily by advection of existing radar echoes based on their recent movement (i.e., extrapolation).  The skill of extrapolation forecasts, however, decreases rapidly with increasing lead times, thus making it important to account for storm evolution (i.e., growth and/or decay).  Moreover, new storms may initiate, which requires recognition and monitoring of characteristic features in the boundary layer (e.g., frontal boundaries created by a storm outflow or advancing sea breeze) and the thermodynamic conditions of the storm environment.  Model-based information provides crucial information for identification of regions ripe for new storms to initiate.  The ultimate storm forecast is generated based on blending extrapolation, evolution, initiation and modeling information, where the weight shifts from extrapolation at very short lead times to mostly model for longer outlooks.   

Expert Systems

AutoNowCaster (ANC)

The ANC provides regional analyses and 0 – 2 h nowcasts of thunderstorms, their initiation, growth and decay.  The ANC distinguishes itself from other nowcasting systems that primarily focus on storm extrapolation and trending (e.g., TITAN), in that the ANC is able to forecast initiation of new storms.  The ANC is an expert system that mimics much of what is normally done by a human (albeit without the time stress)—i.e., review and assimilate a wide range of disparate observations and model results within the context of a forecaster’s knowledge of how the atmosphere works.  The ANC uses a data fusion procedure to assimilate data from radar, satellite, surface stations, soundings, and numerical weather prediction (NWP) models for analysis and calculation of predictor fields.  These predictor fields provide information about the current storms and environmental conditions, including: cumulus cloud detection and vertical development based on satellite data; boundary-layer convergence and stability based on radar, surface stations and NWP information; and storm characteristics based on radar data.  A fuzzy logic routine is used to combine the predictor fields that are based on observations, a numerical boundary layer model and its adjoint (i.e., VDRAS), feature detection algorithms, and optional forecaster input, to create nowcasts that are issued at regular intervals (typically every 5 min) and that are based on a conceptual understanding of how storms initiate, grow and dissipate.  For further details see Mueller et al. (2003) and Saxen et al. (2008). 
>annual report | >more

Thunderstorm Identification, Tracking, Analysis, and Nowcasting (TITAN)

TITAN is a real-time algorithm for automated identification, tracking, and extrapolation based short-term forecasting of thunderstorms utilizing volume-scan weather radar data.  For each time step, defined by the radar scanning strategy, TITAN identifies a “storm” as a contiguous region exceeding tunable thresholds for reflectivity (e.g., 35 or 40 dBZ) and size (either area or volume).  A combinatorial optimization scheme is employed to match the storms at one time with those at the following time, with some geometric logic to deal with mergers and splits.  The short-term forecast of both position and size is based on a weighted linear fit to the storm track history data.  Besides identification and tracking of storm cells, TITAN also calculates a wide range of storm attributes, including echo area extent and volume, echo top, height of the maximum reflectivity, and storm motion (speed and direction), among many others more.  A detailed description of TITAN is provided by Dixon and Wiener (1993). 
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Data Assimilation and modeling

VARIATIONAL DOPPLER RADAR ASSIMILATION SYSTEM (VDRAS)

VDRAS uses a cloud-scale model with its adjoint to retrieve boundary-layer winds and thermodynamics from Doppler radar, surface stations, and sounding data by means of a four-dimensional variational (4DVar) analysis procedure.  VDRAS is used as a data assimilation and analysis system, but can also be employed as a short-term forecasting tool for convective storms.  VDRAS is an essential component of the ANC to provide analyses of the boundary-layer wind field characteristics, which has been highly successful in capturing regions of new storm initiation.  The high-resolution (2 – 5 km in the horizontal, 100 – 400 m in vertical) VDRAS analyses are available every 5 – 10 min, depending on the temporal resolution of radar volume scans, and provide the ANC system with information needed to characterize boundary-layer stability and convergence that may lead to thunderstorm development.  VDRAS is the first real-time to diagnose low-level wind and temperature over a wide region using four-dimensional data assimilation of Doppler radar data.  A detailed description can be found in Sun and Crook (2001). 
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VARIATIONAL LIDAR ASSIMILATION SYSTEM (VLAS)

VLAS represents the Doppler lidar variant of VDRAS.  VLAS provides very high-resolution wind information at the neighborhood scale and has been used to study atmospheric transport and diffusion in urban environments. 
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RT-FDDA: REAL-TIME FOUR-DIMENSIONAL DATA ASSIMILATION

The RT-FDDA system was developed to provide high-resolution analyses and short-term (0 – 12 h) forecasts, although recent advances in computing power enable longer outlooks to be generated.  RT-FDDA employs a time-continuous assimilation of a variety of synoptic and asynoptic observation data to provide real-time local-scale analyses and short-term forecasts in a cycling fashion.  The RT-FDDA is built upon a high-resolution MM5 numerical weather prediction model (future versions will be based on the WRF model) and the data assimilation makes use of a Newtonian relaxation (i.e., nudging) scheme.  The characteristics of the RT-FDDA system generally contribute to superior analysis and forecasts compared to a twice daily MM5 forecast system, especially for shorter forecast lead times.  The RT-FDDA system is used in a variety of both winter and summer weather hazard applications. 
>annual report | >more

Blending of expert system and model output

NCWF

The NCWF product combines meteorological observations, feature detection algorithms, and numerical weather prediction model output to provide a diagnosis of the current locations of convective hazards to aircraft as well as a probabilistic depiction of future locations of existing convective hazards for lead times of 30, 60, 90, and 120 min.  Both the convective hazard detection field and the forecasts update every 5 minutes.  The current operational version of NCWF runs at the NWS Aviation Weather Center (AWC) and shows the convective hazard detection field and a binary forecast of storm location with a 1 h lead time (NCWF-1).  This operational product was first available in 1998 as an experimental product and became operational in 2000; it is available on the Aviation Digital Data Service (ADDS) Convection web page.  Current efforts are geared toward extending the forecast lead time to two hours (NCWF-2) and a continuous 0 – 6 h probabilistic forecast (NCWF-6).  These preliminary products are available on the Experimental Aviation Digital Data Service (ADDS) web site. 
>more ncwf | >ncwf2 | >annual report | >adds | >experimental adds

Niwot

Named after the Native American Chief Niwot, this new short-term forecasting system is being developed based on a blending of observation extrapolation technology and numerical weather prediction model output fields.  The promise is that the blending of numerical forecasts with expert system-based extrapolations will benefits from the skills of the latter at short time scales while weighing in more on the numerical prediction skills for extended outlooks, and by doing so yield improved short-term predictions.  This tool, which is aimed at 0 – 6 h forecasts of aviation impacting convection, will be flexible enough to accommodate improved extrapolation algorithms and numerical model output as they become available, and allow for forecaster input.  It is anticipated that the Niwot and NCWF efforts will eventually be combined into one next-generation short-term prediction system. 

Repository for Software and Documentation

Coming Soon

Publications in Refereed Journals

Cai, H., W.-C. Lee, T. M. Weckwerth, C. Flamant, and H. V. Murphey, 2006:  Observations of the 11 June dryline during IHOP 2002—A null case for convection initiation.  Monthly Weather Review, 134(1), 336 – 354. 

Crook, N. A., and J. B. Klemp, 2000:  Lifting by convergence lines.  Journal of the Atmospheric Sciences, 57(6), 873 – 890. 

Crook, N. A., and J. Sun, 2002:  Assimilating radar, surface, and profiler data for the Sydney 2000 forecast demonstration project.  Journal of Atmospheric and Oceanic Technology, 19(6), 888 – 898. 

Crook, N. A., and J. Sun, 2004:  Analysis and forecasting of the low-level wind during the Sydney 2000 forecast demonstration project.  Weather and Forecasting, 19(1), 151 – 167. 

Dixon, M., and G. Wiener, 1993:  TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A radar-based methodology.  Journal of Atmospheric and Oceanic Technology, 10(6), 785 – 797. 

Fox, N. I., R. Webb, J. Bally, M. W. Sleigh, C. E. Pierce, D. M. L. Sills, P. I. Joe, J. Wilson, and C. G. Collier, 2004:  The impact of advanced nowcasting systems on severe weather warning during the Sydney 2000 Forecast Demonstration Project: 3 November 2000.  Weather and Forecasting, 19(1), 97 – 114. 

Fritsch, J. M., R. A. Houze Jr., R. Adler, H. Bluestein, L. Bosart, J. Brown, F. Carr, C. Davis, R. H. Johnson, N. Junker, Y.-H. Kuo, S. Rutledge, J. Smith, Z. Toth, J. W. Wilson, E. Zipser, and D. Zrnic, 1998:  Quantitative precipitation forecasting: Report of the eighth Prospectus Development Team, U.S. Weather Research Program.  Bulletin of the American Meteorological Society, 79(2), 285 – 299. 

Keenan, T., P. Joe, J. Wilson, C. Collier, B. Golding, D. Burgess, P. May, C. Pierce, J. Bally, A. Crook, A. Seed, D. Sills, L. Berry, R. Potts, I. Bell, N. Fox, E. Ebert, M. Eilts, K. O'Loughlin, R. Webb, R. Carbone, K. Browning, R. Roberts, and C. Mueller, 2003:  The Sydney 2000 World Weather Research Programme Forecast Demonstration Project: Overview and current status.  Bulletin of the American Meteorological Society, 84(8), 1041 – 1054. 

May, P. T., T. D. Keenan, R. Potts, J. W. Wilson, R. Webb, A. Treloar, E. Spark, S. Lawrence, E. Ebert, J. Bally, and P. Joe, 2004:  The Sydney 2000 Olympic Games Forecast Demonstration Project: Forecasting, observing network infrastructure, and data processing issues.  Weather and Forecasting, 19(1), 115 – 130. 

Mueller, C., T. Saxen, R. Roberts, J. Wilson, T. Betancourt, S. Dettling, N. Oien, and J. Yee, 2003:  NCAR Auto-Nowcast system.  Weather and Forecasting, 18(4), 545 – 561. 

Pierce, C. E., E. Ebert, A. W. Seed, M. Sleigh, C. G. Collier, N. I. Fox, N. Donaldson, J. W. Wilson, R. Roberts, and C. K. Mueller, 2004:  The nowcasting of precipitation during Sydney 2000: An appraisal of the QPF algorithms.  Weather and Forecasting, 19(1), 7 – 21. 

Roberts, R. D., and S. Rutledge, 2003:  Nowcasting storm initiation and growth using GOES-8 and WSR-88D data.  Weather and Forecasting, 18(4), 562 – 584. 

Roberts, R. D., D. Burgess, and M. Meister, 2006:  Developing tools for nowcasting storm severity.  Weather and Forecasting, 21(4), 540 – 558. 

Saxen, T. R., Cynthia K. Mueller, Thomas T. Warner, Matthias Steiner, Edward E. Ellison, Eric W. Hatfield, Terri L. Betancourt, Susan M. Dettling, and Niles A. Oien, 2008: The Operational Mesogamma-Scale Analysis and Forecast System of the U.S. Army Test and Evaluation Command. Part IV: The White Sands Missile Range Auto-Nowcast System. Journal of Applied Meteorology and Climatology, 47(4), 1123–1139.

Sharif, H. O., D. Yates, R. Roberts, and C. Mueller, 2006:  The use of an automated nowcasting system to forecast flash floods in an urban watershed.  Journal of Hydrometeorology, 7(1), 190 – 202. 

Sills, D. M. L., J. W. Wilson, P. I. Joe, D. W. Burgess, R. M. Webb, and N. I. Fox, 2004:  The 3 November tornadic event during Sydney 2000: Storm evolution and the role of low-level boundaries.  Weather and Forecasting, 19(1), 22 – 42. 

Sun, J., and N. A. Crook, 1997:  Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments.  Journal of the Atmospheric Sciences, 54(12), 1642 – 1661. 

Sun, J., and N. A. Crook, 1998:  Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm.  Journal of the Atmospheric Sciences, 55(5), 835 – 852. 

Sun, J., and N. A. Crook, 2001:  Real-time low-level wind and temperature analysis using single WSR-88D data.  Weather and Forecasting, 16(1), 117 – 132. 

Warner, T. T., E. A. Brandes, J. Sun, D. N. Yates, and C. K. Mueller, 2000:  Prediction of a flash flood in complex terrain. Part I: A comparison of rainfall estimates from radar, and very short range rainfall simulations from a dynamic model and an automated algorithmic system.  Journal of Applied Meteorology, 39(6), 797 – 814. 

Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon, 1998:  Nowcasting thunderstorms: A status report.  Bulletin of the American Meteorological Society, 79(10), 2079 – 2099. 

Wilson, J. W., R. E. Carbone, J. D. Tuttle, and T. D. Keenan, 2001:  Tropical island convection in the absence of significant topography. Part II: nowcasting storm evolution.  Monthly Weather Review, 129(7), 1637 – 1655. 

Wilson, J. W., E. E. Ebert, T. R. Saxen, R. D. Roberts, C. K. Mueller, M. Sleigh, C. E. Pierce, and A. Seed, 2004:  Sydney 2000 Forecast Demonstration Project: Convective storm nowcasting.  Weather and Forecasting, 19(1), 131 – 150. 

Xiao, Q., Y.-H. Kuo, J. Sun, W.-C. Lee, D. M. Barker, and E. Lim, 2007:  An approach of radar reflectivity data assimilation and its assessment with the inland QPF of Typhoon Rusa (2002) at landfall.  Journal of Applied Meteorology and Climate, 46(1), 14 – 22. 

Yates, D. N., T. T. Warner, and G. H. Leavesley, 2000:  Prediction of a flash flood in complex terrain. Part II: A comparison of flood discharge simulations using rainfall input from radar, a dynamic model, and an automated algorithmic system.  Journal of Applied Meteorology, 39(6), 815 – 825. 

Links

• Short Term Explicit Prediction (STEP)
• MIT/LL Aviation Weather
• NOAA Earth System Research Laboratory (ESRL)
• NOAA National Severe Storms Laboratory (NSSL)
• NOAA National Weather Service (NWS)
• NASA Short-term Prediction Research and Transition (SPoRT) Center 

Convective Weather Program Lead:
Dr. Matthias Steiner
Phone: (303) 497-2720
Fax: (303) 497-8401

Web Developer:
Lara Ziady

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