Convective Weather - Hydrometeorology
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.
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.
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.
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
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.
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
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).
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).
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.
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
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
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
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.