Nowcasting Thunderstorms: a Status Report

Nowcasting Thunderstorms: A Status Report James W. Wilson, N. Andrew Crook, Cynthia K. Mueller, Juanzhen Sun, and Michael Dixon National Center for Atmospheric Research,* Boulder, Colorado ABSTRACT This paper reviews the status of forecasting convective precipitation for time periods less than a few hours (nowcasting). Techniques for nowcasting thunderstorm location were developed in the 1960s and 1970s by extrapolating radar echoes. The accuracy of these forecasts generally decreases very rapidly during the first 30 min because of the very short lifetime of individual convective cells. Fortunately more organized features like squall lines and supercells can be successfully extrapolated for longer time periods. Physical processes that dictate the initiation and dissipation of convective storms are not necessarily observable in the past history of a particular echo development; rather, they are often controlled by boundary layer convergence features, environmental vertical wind shear, and buoyancy. Thus, suc- cessful forecasts of storm initiation depend on accurate specification of the initial thermodynamic and kinematic fields with particular attention to convergence lines. For these reasons the ability to improve on simple extrapolation techniques had stagnated until the present national observational network modernization program. The ability to observe small-scale boundary layer convergence lines is now possible with operational Doppler radars and satellite imagery. In addition, it has been demonstrated that high-resolution wind retrievals can be obtained from single Doppler radar. Two methods are presently under development for using these modern datasets to forecast thunderstorm evolution: knowledge- based expert systems and numerical forecasting models that are initialized with radar data. Both these methods are very promising and progressing rapidly. Operational tests of expert systems are presently taking place in the United King- dom and in the United States. 1. Introduction The primary tools for detecting convective storms are weather radar, lightning detectors, and satellite imag- This paper will review the history and status of ery. Very short period forecasting of the future loca- forecasting thunderstorms for very short time periods tion of convective storms has historically been based (nowcasting). The term nowcasting is used to empha- primarily on the extrapolation of radar reflectivity ech- size that the forecasts are time and space specific for oes. As will be discussed later the majority of indi- periods less than a few hours. Forecasts of this type vidual thunderstorms have lifetimes less than ~20 min, are particularly important to commercial and general thus forecast techniques based on the extrapolation of aviation, outdoor sporting events, the construction existing conditions are limited. For forecast periods industry, power utilities, and ground transportation. beyond ~20 min, techniques for forecasting the initiation, growth, and dissipation of convective storms are essential. Numerical simulation studies have contrib- *The National Center for Atmospheric Research is partially spon- uted significantly to our understanding of storm orga- sored by the National Science Foundation. nization and lifetime. This understanding is just Corresponding author address: Dr. James W. Wilson, Atmo- beginning to be used in modern nowcasting systems. spheric Technology Division, NCAR, P.O. Box 3000, Boulder, Two methods are presently under development for CO 80307-3000. E-mail: [email protected] forecasting storm evolution: knowledge-based expert In final form 14 May 1998. systems and explicit numerical forecast models that ©1998 American Meteorological Society are initialized with radar data. Section 2 discusses the Bulletin of the American Meteorological Society 2079 lifetime, organization, and motion of convective and 47% lived longer than 1 h (see the right panel storms from both a numerical and observational view- of Fig. 1). point. Section 3 discusses forecasting techniques that Battan (1959) discussed how the individual ech- include extrapolation of existing storms, forecasting oes in a squall line generally moved to the left of the storm initiation and dissipation, and numerical predic- movement of the squall line itself. While the individual tion. Operational forecasting systems are described in echoes had lifetimes of tens of minutes, the squall line section 4 and the accuracy of the various techniques itself might have a lifetime of many hours. Newton and is discussed in section 5. Possible future directions are Fankhauser (1964) showed that echo motion varied contained in section 6. with storm size. In agreement with the above observational studies Wilson (1966) showed that lifetime and echo motion was dependent on the scale of the 2. Lifetime, organization, and motion of convective phenomena. Figure 2 from this study shows convective storms that well-organized systems (supercells and cyclones) have long lifetimes compared to unorganized thunder- Prior to describing nowcasting techniques and sys- storms and weak showers. Figure 3 shows that lifetime tems it is useful to review what is known from numeri- is highly related to scale size and that different scales cal and observational studies about the organization, have different motions. In this study small-scale fea- lifetime, and motion of convective storms. tures tended to move with the mean wind and larger A number of observational studies have shown that scales tended to move slower and to the right of the individual convective cells have mean lifetimes of mean wind. Thus when extrapolating echoes it is es- about 20 min. Battan (1953) and Foote and Mohr sential to consider the scale of the convective feature (1979) found mean durations of 23 and 21 min, re- and the length of the forecast period. For forecast pe- spectively. However, Battan reported that cells that riods greater than 20 min, extrapolation of individual merged with one another had longer durations. This cells within a multicellular system or alone is gener- agrees with Henry’s (1993) results from the High ally not reliable. However, large thunderstorms, super- Plains regions near Denver, Colorado. The left panel cells, storm complexes (mesoscale convective of Fig. 1 shows that 83% of the individual storm systems), and large frontal rainbands (Hill et al. 1977) cells lived < 30 min. However, if storms merged or can often be extrapolated for much longer time periods. split during their lifetime (i.e., were multicellular) only There is a significant body of knowledge based on 12% of the storm complexes existed for < 30 min numerical simulations that have a direct bearing on our physical understanding of storm type, organization, and lifetime. The group of Simple Tracks Complex Tracks papers of Thorpe et al. (1982), Weisman and Klemp (1986), Weisman et al. 80 568 complete simple 80 68 complete complex storm tracks storm tracks (1988), and Rotunno et al. (1988) dis- 83% lived < 30 minutes cusses how wind shear profiles and buoy- 12% lived < 30 minutes 60 60 ancy considerations can be used to estimate storm type (single cell, multi- cell, and supercell), storm initiation, and 40 40 storm longevity. Weisman and Klemp Percent Percent (1986) show the bulk Richardson num- 20 20 ber, which combines convective avail- able potential energy (CAPE) and the surface to 6 km wind shear, can be used to 0 0 0.0 1.0 2.0 3.0 4.0 0.0 1.0 2.0 3.0 4.0 differentiate storm type, organization, and Duration (hrs.) Duration (hrs.) lifetime. Values of the bulk Richardson number between 10 and 40 favor super- FIG. 1. Histogram showing the lifetime of simple and complex storms ob- cells, while increasingly higher numbers served during the summer of 1991 near Denver, CO, based on data from an au- tomated cell tracking system called TITAN. A simple storm is one that does not favor unsteady multicellular storms. merge or split during its lifetime and a complex storm is one that does (from CAPE and low-level shear have gained Henry 1993). wide acceptance by the forecaster com- 2080 Vol. 79, No. 10, October 1998 1.0 ) 1.0 ) a) max max 0.8 > 40 mi 0.8 Supercell > 20 mi 0.6 20 - 40 mi > 10 mi Ea 0.6 st C oa st Cy 0.4 cl one 10 - 20 mi original L 5 - 10 mi i n e 0.2 0.4 o fWe U no East Coast Cyclone ak S rgan how ized er T 0 40 80 120 160 s hunderstorms 0.2 Max cross-correlation coefficient (r Time (min) Maximum cross-correlation coefficient (r 20 b) 0 2060 100 140 Time (min) IG F . 2. Magnitude of the maximum cross correlation (rmax) between radar echo patterns as a function of time for different types 10 of storms (from Wilson 1966). Here rmax is a measure of echo lifetime and was obtained by cross-correlating echo patterns by the indicated time period. 5 - 10 mi munity as a means to identify days with potential for 0 > 20 mi severe weather. North & South (knots) Thorpe et al. (1982) and Rotunno et al. (1988) show that the low-level vertical wind shear profile (surface to 2.5 km) directed normal to the gust front ∆ -10 ( u) is related to the extent and longevity of thunder- 0 5 10 15 storms. They find that long-lived, intense convection East (knots) results when an optimal shear occurs. The Rotunno IG et al. (1988) simulations show this optimal condition F . 3.(a) Magnitude of the maximum cross correlation (rmax) occurs when the import of positive vorticity associated between radar echo patterns as a function time for different spa- with the low-level shear just balances the net buoyant tial scales within a storm system. The data are for the unorganized thunderstorm case in Fig. 2. (b) Average motion for the 5–10 and generation of negative vorticity by the cold pool. This > 20-mile scales shown in (a) (from Wilson 1966). allows deep, vertical updrafts at the gust front. The depth of the updrafts are shown to increase consider- ably as ∆u increases from 0 to 20 m s−1.

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