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Kuchera snow method map7/2/2023 Should the vertical motion be focused in a particular layer, some adjustment of the forecast may be required. The probability of snow being in the heavy, average or light categories, given that snow occurs, is displayed.ฤก) The forecasts are based upon average vertical motions (more info here). Then, choose the sounding valid time, enter the forecast liquid equivalent QPF and surface wind speed. In either case, select the desired forecast model from the pull-down menu and click continue. Alternatively, simply enter the site id in the box to go directly to the forecast. Then click on the blue triangle to obtain a forecast for the site. On this page, you simply click on the map to view all potential forecast sites, based on the output from the NOAA operational models. However, usage of these products remains entirely at the discretion of the user and the responsibility for decisions made (good or bad) based upon the forecasts rests entirely with the user. In addition, the output from the system is provided on this web page as a service to the operational forecast community and other interested persons. The intent of the system is to provide a test bed for ongoing research into the predictability of snow ratio. The UWM realtime snow-ratio forecast page is designed as a quasi-operational system. Programming was accomplished by Richard Hozak of KFGF. This realtime system was made possible by an informal collaboration between UWM and the National Weather Service (NWS) office at Grand Forks, North Dakota ( KFGF). For your convenience, this technique has been adapted on this website such that given a model forecast sounding (obtained from the WRF/NAM and GFS), you can determine the likelihood that the snow will fall into one of three density classes (heavy, with ratios up to 9:1 average, with ratios from 9:1 up to 15:1 light, with ratios exceeding 15:1). In a recent study of snow ratio ( Roebber, Bruening, Schultz and Cortinas 2003 Weather and Forecasting), a method for producing superior forecasts of depth of snow was developed, based on artificial neural networks. The familiar 10:1 rule is unreliable in many circumstances and guidelines based on surface temperature alone are flawed. ![]() Operational guidance on the ratio of snowfall depth to liquid water is severely limited. Hopefully this clarifies any misconceptions about the actual skill of the most common SLR approaches, and gives users confidence that the snowfall output using especially the Kuchera SLR or NCEP SREF SLR is reliable.Welcome to the University of Wisconsin - Milwaukee (UWM) realtime snow ratio forecast page To the contrary, while my sample size is somewhat small, on average the Kuchera SLR method has a slight *negative* bias when compared to observed SLRs! In other words, at least for this sample, its snowfall output was slightly *lower* than reality, not too high (or the progging excessive snowfall)! That claim is obviously patently false and based on no real, objective evidence. What I find interesting is that many people in this forum accuse the Kuchera SLR method of being too "aggressive" or "over-doing it." One former Omaha TV meteorologist (who shall remain unnamed) even called it the worst SLR method he's ever seen. (2003), is probably the most well-known of the dynamic SLR methods because both pivotalweather and WeatherBELL have it as an option in their snowfall products. The Kuchera SLR method, developed by a good friend and colleague of mine as a function of snowfall density research in Roebber et al. I probably couldn't claim that either was superior with statistical significance. As you can see, the three dynamic SLRs are far superior to the three chosen constant SLRs, and of the three dynamic SLRs, the Kuchera method and the NCEP SREF method have very similar error statistics. ![]() The results are attached in the Excel screen capture. I calculated three dynamic SLR methods for statistical comparison with the observed SLRs: Kuchera SLR method, NCEP SREF SLR method, and the AWIPS SLR method, as well as three constant SLRs 10:1 SLR, 11:1 SLR, and 12:1 SLR. Which approach is best? Are the "dynamic" approaches really better than using a constant SLR during a given event (e.g., 12:1)? In March, 2017 I compiled nearly 200 distinct cases of liquid precip commensurate with associated snowfall, which permitted me to calculate the SLR for each case. A frequent topic of discussion in this forum pertains to the skill of the most common "dynamic" snow-to-liquid ratio (SLR) methods, or those SLRs that *change* throughout the course of a snow event.
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