active Check to include Clear and return to Menu Close Closed captions. Press the spacebar to toggle captions on and off. Continue Continue Exit Filter FINISH finish Definition definition Glossary Terms Menu Please rotate your device NEXT next Notes Notes Bio More info Captions pause play Send an email PREV previous Question List replay Resources resources Restart Resume Search... search Search in: Search Results Skip navigation. Press enter to return to the slide. Slide Text SUBMIT ALL submit all SUBMIT submit volume warning decision training division (wdtd) radar & applications course (rac) principles of doppler weather radar snow accumulation algorithm introduction in order for nws forecasters to receive credit for this course in the nws learning center, you will need to take the following steps. review lesson take your time and review the lesson content provided in this presentation. complete the quiz at the end of this lesson, there is an embedded quiz. complete this quiz by selecting the best answer for each question. you need to correctly answer 70% of the quiz questions to receive completion credit in the lms. technical problems?? if you encounter any technical problems with this lesson, please contact the rac team directly by e-mail (nws.wdtd.rachelp@noaa.gov) or you can use the wdtd feedback forum web page (https://training.weather.gov/wdtd/contact/feedback.php) to send us a message. roadmap learning objectives identify strengths & limitations of the snow accumulation algorithm snow accumulation algorithm (saa) uses hybrid scan data from epregenerates snow water equivalent & depth products6 products displayable in awipsoverview saa here; product details later saa design 6 research locationssnow gages used as ground truthresults applied for region albany, ny cleveland, oh minneapolis, mn denver, co grand junction, co reno, nv albany, ny cleveland, oh minneapolis, mn denver, co grand junction, co reno, nv saa designed for dry snow z-s relationships reflectivity (z) to rate of snow water equivalent (s)same default z-s relationship for each region research location relationship albany, ny z = 120 s2 cleveland, oh z = 180 s2 minneapolis, mn z = 180 s2 denver, co z = 130 s2 grand junction, co z = 40 s2 reno, nv z = 222 s2 research location relationship albany, ny z = 120 s2 cleveland, oh z = 180 s2 minneapolis, mn z = 180 s2 denver, co z = 130 s2 grand junction, co z = 40 s2 reno, nv z = 222 s2 saa and snow ratio snow ratio water equivalent to snow depthsame default snow ratio for each region research location snow ratio albany, ny 11.8 cleveland, oh 16.7 minneapolis, mn 11.8 denver, co 13.3 grand junction, co 14.3 reno, nv 8.0 research location snow ratio albany, ny 11.8 cleveland, oh 16.7 minneapolis, mn 11.8 denver, co 13.3 grand junction, co 14.3 reno, nv 8.0 saa adaptable parameters default values based on regionseven urc adaptable parameters:z-s, snow ratio, height correction saa products 1km x 1°; 16 data levels; range of 124 nmosw: one hour snow water equivalentosd: one hour snow depthssw: storm total snow water equivalentssd: storm total snow depthusw: user selectable snow water equivalentusd: user selectable snow depth osd begin and end of snowfall accumulations no automatic reset of snow accumulationsmust be manually reset prior to event saa strengths only source of real time high resolution snowfall accumulationsuses best possible reflectivity (close to ground) to convert to snowfallaccumulations can be reset to zero as neededavailable z-s relationships and snow ratios are editable saa limitations saa designed for dry snowground truth likely needed:to verify precipitation typeto determine onset of accumulationno automatic reset of accumulations; must be done manuallyavailable z-s relationships and snow ratios may not be representative potential limitation #1: assumption of dry snow rising terminal velocity of melting drops lowers reflectivity below peak of bright band reflectivity factor reflectivity factor reflectivity rises due to particle coalescence reflectivity rises due to melting total reflectivity reflectivity areas impacted bybright band potential limitation #2: beam overshooting -3° c height seeder -3° c distance -12° c height -12° c distance vertical reflectivity profile reflectivity feeder potential limitation #3: sub-beam evaporation/sublimation precipitation not reaching ground:saa overestimates potential limitation #4: horizontal displacement of falling precipitation radar seessnow here snow falls atsurface here beam blockage preventsradar from seeing thisprecipitation potential limitation #5: unusual precipitation particle shapes changes in shape, size can impact z, le differentlyevents can contain mix of stratiform, convective elementsvariations can be difficult to detect, let alone predictsaa doesn’t account for these variations yes yes yes yes yes yes no no no no no no you are expecting a mixed-phase precipitation event across your cwa. would using the saa to measure snowfall during the transition from rain to snow be advisable? the albany, new york site was used to create the z-s ratio for which region(s)? northeast northeast northeast northeast northeast northeast inter-mountain west inter-mountain west inter-mountain west inter-mountain west inter-mountain west inter-mountain west mid-west mid-west mid-west mid-west mid-west mid-west great lakes great lakes great lakes great lakes great lakes great lakes west coast west coast west coast west coast west coast west coast south/southeast south/southeast south/southeast south/southeast south/southeast south/southeast true true true true true true false false false false false false the z-s relationship can be changed if needed. true true true true true true false false false false false false the saa will automatically reset snowfall accumulations once reflectivities over 20 dbz are no longer dected. true true true true true true false false false false false false the saa should be used in lieu of snowfall reports from spotters. roadmap Welcome to the lesson on the Snow Accumulation Algorithm. Here is the Roadmap with your current location. There is one objective for this lesson. We now focus on the generation of snow water equivalent and snow depth products from the Snow Accumulation Algorithm (or SAA). The SAA uses hybrid scan data from the Enhance Preprocessing Algorithm (EPRE)   After data processing, this algorithm produces 6 total products in AWIPS that represents values for snow water equivalent and depth over various time ranges.   Currently 6 products in AWIPS and more information on the products will be presented in a later lesson. The SAA was developed from data collected at 6 different research locations. At each of these locations, a network of high quality snow gages was used as ground truth against the radar snowfall estimates. The output of this research provided default adaptable parameters that are used at each of the regions on the map. Note that there was not a research site selected in the southern United States. The data for Albany was selected for use in both the Northeast and Southern United States. One of the most important assumptions with the SAA is that it was designed for dry snow events. Similar to how there are Z-R relationships to estimate rainfall rates from reflectivity, there are Z-S relationships developed to estimate snow water equivalent from reflectivity. Using the EPRE Hybrid Scan data as input, the returned power is plugged into a Z-S relationship using coefficients developed from one of the regional research locations.   This table lists the default Z-S relationships for each research location. The snow ratio used for converting snow water equivalent to snow depth is another adaptable parameter and the default values for each region are listed here. Even within a given region, it is expected that the appropriate ratio will vary from event to event. All the sites within a given region have the same default values for the SAA adaptable parameters. There are seven SAA adaptable parameters that are editable under URC guidelines in the RPG. Here you can edit the coefficients in the Z-S relationship, and modify the snow-water ratio. There are six snow products generated, all with 16 data levels, a resolution of 1km by 1 degree and a range of 124 nm. There are three durations: one hour, storm total and user selectable. For each of these durations, there is a snow water equivalent and a snow depth product. Examples of the products will be shown with more information in a later lesson. The SAA is designed to be event driven, and there is no automatic reset of the accumulations. This means the snow accumulations must be reset to zero at the beginning of an event. Resetting is done at the RPG Control window, just as with a reset of the PPS or QPE storm total accumulations. The first strength of the SAA is that it is the only source for real time high resolution snowfall accumulations. Since the SAA also uses EPRE as input, the SAA uses a reflectivity value closest to the ground that is not from clutter and is not blocked by the beam. At the RPG, snow accumulations can be reset to zero as needed. The Z-S relationships and snow ratios are editable. Perhaps the most important thing to remember about the SAA is that it designed for dry snow, snow that does not melt as it falls or when it hits the ground. Ground truth will likely be needed to verify precipitation type and to determine the onset of snow accumulation. The onset must be known in order to reset the snow accumulations, which must be done manually. Finally, the default Z-S relationship were developed at specific locations and applied across an entire region. So both the Z-S relationship and the snow ratio may not be representative for your CWA and may require adjusting. The Z-S equation in the Snow Accumulation Algorithm is based on the assumption that the precipitation at the surface is dry snow. We know this assumption isn’t true in the bright band, and here is a closer look at what’s going on. As snow flakes approach the melting layer, liquid resides on their ice surfaces. The increased water coating helps colliding ice particles to stick together and snow flakes begin to increase in size. Larger particles form and the radar reflectivity increases. The liquid water coating itself also helps to increase radar reflectivity. An offset to the increasing reflectivity occurs when the terminal velocity of these particles increases with melting. Increasing terminal velocity increases the separation between hydrometeors and lowers the reflectivity. Due to these mechanisms, overestimation can occur in areas where the radar samples the bright band because wet snow has a higher reflectivity than dry snow. This overestimation can occur even when only portions of the beam (i.e., the top or bottom) are sampling the bright band. Assume we have a cloud pictured here actively generating precipitation so the intensity increases from top to bottom. Reflectivity begins to degrade once the top of the radar beam is above this precipitation production layer, and the signal is lost once the bottom of the beam is above the cloud. The dendritic growth zone (i.e., -12 to -18 degrees Celsius) is usually the region of maximum growth. However, high cloud liquid water content between the dendritic growth zone and the bright band can contribute significant amounts of riming and needles. Collision-coalescence becomes more active in warmer, saturated clouds. Any of these precipitation production zones can be shallow, causing reflectivity to degrade quickly as range from the radar increases. A particularly acute example of this problem is with orographic precipitation. With precipitation very close to upslope terrain, it’s difficult to separate ground returns from real precipitation. So, even when the beam does sample the precipitation generation regions, reflectivity can be degraded. Another data quality issue to consider is sub-beam evaporation (or sublimation). When sub-beam evaporation/sublimation occurs, expect the Snow Accumulation Algorithm to overestimate the liquid equivalent precipitation at the surface. The problem is most common when the near-surface airmass is dry (such as in areas of downslope winds or valleys/basins with locally low elevations). These areas may have lower annual precipitation totals such as areas north of the White Mountains in Northern New Hampshire and adjacent parts of Maine shown in the figure on the right. Under strong, sub-beam horizontal wind conditions (or areas of strong sub-beam vertical wind shear), precipitation may drift horizontally a long distance before reaching the ground. In some cases, the radar may not observe the precipitation that reaches the ground, such as when there is lee side spillover of orographic precipitation. Precipitation particle shape and size can significantly alter reflectivity without a corresponding change in the liquid equivalent precipitation rate. If you experience snow events, take a look at how the shapes and sizes of the snowflakes change over short periods of time.   Many precipitation systems can contain a mixture of stratiform and convective elements with variations in vertical velocity profiles with respect to the thermal profile. The result is a rapid change in particle shapes.    This error source can be very difficult to detect, let alone predict. You may notice areas of precipitation particles that vary significantly from the “average” for your region using dual-pol base data. While you could identify these areas as likely deviating from the Z-S equation being used, you can’t edit for localized areas within the radar coverage area.   This concludes the lesson and here is the Roadmap with your current location.