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Snow Nowcasting Using a Real-Time Correlation of Radar Reflectivity

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Snow Nowcasting Using a Real-Time Correlation of Radar Reflectivity 20 JOURNAL OF APPLIED METEOROLOGY VOLUME 42 Snow Nowcasting Using a Real-Time Correlation of Radar Re¯ectivity with Snow Gauge Accumulation ROY RASMUSSEN AND MICHAEL DIXON National Center for Atmospheric Research, Boulder, Colorado STEVE VASILOFF National Severe Storms Laboratory, Norman, Oklahoma FRANK HAGE,SHELLY KNIGHT,J.VIVEKANANDAN, AND MEI XU National Center for Atmospheric Research, Boulder, Colorado (Manuscript received 21 November 2001, in ®nal form 13 June 2002) ABSTRACT This paper describes and evaluates an algorithm for nowcasting snow water equivalent (SWE) at a point on the surface based on a real-time correlation of equivalent radar re¯ectivity (Ze) with snow gauge rate (S). It is shown from both theory and previous results that Ze±S relationships vary signi®cantly during a storm and from storm to storm, requiring a real-time correlation of Ze and S. A key element of the algorithm is taking into account snow drift and distance of the radar volume from the snow gauge. The algorithm was applied to a number of New York City snowstorms and was shown to have skill in nowcasting SWE out to at least 1 h when compared with persistence. The algorithm is currently being used in a real-time winter weather nowcasting system, called Weather Support to Deicing Decision Making (WSDDM), to improve decision making regarding the deicing of aircraft and runway clearing. The algorithm can also be used to provide a real-time Z±S relationship for Weather Surveillance Radar-1988 Doppler (WSR-88D) if a well-shielded snow gauge is available to measure real-time SWE rate and appropriate range corrections are made. 1. Introduction and runway deicing ¯uids is signi®cantly affected by dilution from the water content of snow (Bernadin et Snowstorms affect a variety of human activities such al. 1997; Rasmussen 1999a). In the case of aircraft de- as transportation (aviation and highways), commerce, energy, and communications. Snow accumulating on icing (or anti-icing), accurately measured SWE is need- highways and runways must be cleared to ensure safe ed by pilots and ground personnel to assess whether the operation of surface vehicles and aircraft. The operation snow accumulation on an aircraft wing since deicing/ of aircraft during snowy conditions is a signi®cant safety anti-icing has exceeded the capability of the deicing/ issue because of the rapid loss of lift and increase in anti-icing ¯uid to keep the surface ice free. The Society drag for relatively thin coatings of snow on a wing (25% of Automotive Engineering (SAE) Ground Deicing loss of lift and increase in drag for rough ice coatings Committee produces Holdover Tables that indicate the of only 0.8-mm thickness on the wing), requiring re- amount of time a deicing/anti-icing ¯uid can prevent moval before takeoff. Runways are often treated with ice formation on an aircraft for a given precipitation an anti-icing ¯uid to prevent the accumulation of snow. type, rate, and temperature. The tables are based on Snow on highways must also be removed as quickly as testing of deicing ¯uids at 1 and 2.5 mm h21 liquid possible to ensure safe and ef®cient operations of both equivalent snowfall rates and at various temperatures. commercial and public vehicles. Thus, knowledge of the occurrence of rates less than 1 A key quantity in all of the above applications is the mm h21, between 1 and 2.5 mm h21, or greater than liquid equivalent amount of snow, or snow water equiv- 2.5 mm h21 are critical in the proper use of this table. alent (SWE). For instance, the performance of aircraft Current National Weather Service snow intensities are based on visibility, which has been shown to be a poor indicator of the actual liquid equivalent snowfall rate Corresponding author address: Dr. Roy Rasmussen, NCAR, P.O. Box 3000, Boulder, CO 80307. (Rasmussen et al. 1999b). Thus, the current hourly ME- E-mail: [email protected] TAR (a French acronym that translates as aviation rou- q 2003 American Meteorological Society JANUARY 2003 RASMUSSEN ET AL. 21 tine weather report) reports of snow intensity do not TABLE 1. Various Z±S and Ze±S relationships from literature (adopt- ed from Gray and Male 1981). Conversion to Z ±S is based on Z 5 meet this need. e e 0.224Z for melted snow¯akes (Smith 1984). The method of runway and taxiway snow removal also depends on whether the snow is dry and ¯uffy (low Precipitation type ZZe water content) or wet and dense (high water content). Hail 320S 1.6 72S 1.6 The formation of ice on a highway critically depends Graupel 900S 1.6 202S 1.6 on the water content of the snow. Single crystals (Carlson and Marshall 1972) 160S 2.0 36S 2.0 SWE rates at speci®c locations such as an airport or Snow¯akes consisting of the following crystal types: along a highway can be measured using accurate snow (Ohtake and Henmi 1970) gauges with appropriate wind shielding (Goodison Plates and columns 400S 1.6 90S 1.6 1978; Yang et al. 1998; Rasmussen et al. 2001). The Needle crystals 930S 1.9 208S 1.9 Stellar crystals 1800S 1.5 403S 1.5 recent study by Rasmussen et al. (2001) has shown that Spatial dendrites 3300S 1.7 739S 1.7 real-time, accurate snow measurements can be made if Snow¯akes (Imai 1960) the sidewalls of the snow gauges are heated to 12.08C Dry (T , 08C)* 540S 2 120S 2 and appropriate corrections are made to account for the Wet (T . 08C) 2100S 2 470S 2 undercatch of snow due to wind effects. In principle, a Snow¯akes (Puhakka 1975) network of these heated and shielded snow gauges could Dry (T , 08C)* 1050S 2 235S 2 be deployed to cover a wide region, such as a metro- Wet (T . 08C) 1600S 2 358S 2 politan area or large airport. However, such a network Snow¯akes (Gunn and Marshall 1958) 2000S 2.0 448S 2 is costly to deploy and maintain, and thus an attractive Snow¯akes (Sekon and Srivastava 1970) 1780S 2.21 399S 2.21 alternative is to use radars to estimate liquid equivalent snowfall rate. Radar measures the equivalent radar re- * T 5 mean air temperature. 1 ¯ectivity, Ze, rather than the SWE rate. Thus, a method to correlate radar re¯ectivity Ze with SWE rates is need- ter weather nowcasting system (Rasmussen et al. 2001) ed. Rasmussen et al. (2001) present a method to perform developed with Federal Aviation Administration (FAA) this correlation in real time using snow gauges on the funding. Section 2 provides a background on the use of ground and radar re¯ectivity measurements from aloft Z±S relationships to estimate snowfall rate and the prob- that will be further described in this paper. lems therein. The real-time technique is brie¯y de- In addition to current SWE rates, nowcasts of SWE scribed in section 3, and an evaluation of the technique rates are important. Forecasts with lead times as short is given in section 4. Conclusions are presented in sec- as 30 min are of value to decision makers, who require tion 5. forecasts of SWE amounts for particular locations, such as the ramps, taxiways, runways, and highway sections. Techniques such as the cross-correlation method (Tuttle 2. Background and Foote 1990; Rinehart and Garvey 1978) enable de- The estimation of liquid equivalent snowfall rate us- termination of the motion of radar echoes. This method ing radar has been studied for the past 50 years by many was adapted to track winter storm echoes by Rasmussen investigators (e.g., Fujiyoshi et al. 1990; Langville and et al. (2001). An evaluation of the technique by Turner Thain 1951; Ohtake and Henmi 1970; Yoshida 1975; et al. (1999) showed it provided reasonably accurate Sekhon and Srivastava 1970; Marshall and Gunn 1952; point re¯ectivity forecasts out to 30 min. Therefore, a Carlson and Marshall 1972; Boucher and Weiler 1985; 30-min forecast of SWE rates could be made if a re- Harimaya 1978). These studies have attempted to relate lationship existed between radar re¯ectivity aloft and the liquid equivalent snowfall rate, S (mm h21), to radar snow gauge accumulation at the ground. Rasmussen et re¯ectivity factor Z (mm6 m23) using a power law of al. (2001) present a method to properly time-lag and the form wind-adjust the re¯ectivity and snow gauge data to Z aS b (mm6 m23 ). (1) achieve this relationship, and they use it to perform a 5 snow nowcast. In this paper we present a statistical eval- The values of the coef®cient a and exponent b are uation of this technique using data from New York City usually determined through a regression ®t of Z and S 6 snowstorms. for a number of snowstorms, in which Z (5SnDm, n is The main motivation for this work is to provide airline number concentration and Dm is the diameter of a melted and airport decision makers with accurate point now- snow¯ake) is calculated from melted snow¯akes (e.g., casts of SWE rates in support of aircraft deicing/anti- Marshall and Gunn 1952). These studies have found icing and runway snow removal operations. The tech- that the values of a and b are highly variable, depending nique is currently being used operationally in the Weath- on various parameters such as the crystal type, degree er Support to Deicing Decision Making (WSDDM) win- of riming and aggregation, density, and terminal veloc- ity (Table 1). 1 In many cases the goal of such studies is to determine Here, Ze is the radar-determined radar re¯ectivity factor that as- sumes particles with a dielectric constant of water.
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