NCEP PTYPE Algorithms

1. NCEP PTYPE Algorithms Fred H. Glass NOAA/NWS St. Louis LSX Winter Weather Workshop – November 19, 2008

2. Why Have Ptype Algorithms? • Forecasting winter weather is a significant challenge • A variety of precipitation algorithms have been developed in an effort to address this challenge!

3. Algorithms 101 • Ptype from the algorithms is derived by post-processing of the model output • Ptypes are generated when even just a trace of precipitation is generated by the model • No single algorithm handles all ptypes in a sufficient manner • Always examine soundings; even when they are accurate the output from the various algorithms may generate conflicting ptypes due to their methodologies and assumptions • An ensemble approach should be considered by comparing the output of the different algorithms

4. Algorithms/Techniques • NCEP Baldwin-Schichtel • NCEP Revised • Ramer • Bourgouin • Czys

5. NCEP Baldwin-Schichtel(aka NCEP, Baldwin, or BTC) • Developed based on MS research at Univ. of Oklahoma by Schichtel, utilizing ETA model vertical thermodynamic profiles and hourly precipitation reports • Utilizes a decision tree approach • Identifies warm and cold layers by calculating the area between the 0°C or -4°C isotherm and the wet-bulb temperature • Compares magnitude of warm/cold layers (area) with the surface temperature to identify ptype

6. NCEP Baldwin-Schichtel(the steps) • First identifies the highest saturated layer (considered to be the precipitation generation layer) • Next determines the initial state of these hydrometers • T < -4°C → assumed to be ice crystals • T ≥ -4°C → assumed to be supercooled water drops • If supercooled water droplets, then checks the surface temperature (lowest model layer) • Tsfc ≤ 0°C → freezing rain • Tsfc > 0°C → rain

7. NCEP Baldwin-Schichtel(the steps) • If ice crystals, then the magnitude of the area between the -4°C isotherm and the wet-bulb temperature profile in the sounding is computed • if area ≤ 3000 deg m → snow • if area > 3000 deg m → ice crystals melted → checks to see if hydrometers re-freeze into ice pellets or if they fall to the surface as rain or freezing rain

8. NCEP Baldwin-Schichtel(reviewing the process)

9. NCEP Baldwin-Schichtel(Strengths and Weaknesses) Strengths • Easily applied and widely used • Initial check for hydrometer state • Utilizes the wet-bulb temperature • Forecasting freezing rain and sleet Weaknesses • Will forecast freezing or liquid precipitation with deep isothermal layer near the surface with Tw between 0°C and -4°C • Ignores impact of dry layers • Tendency to over-forecast freezing rain and sleet

10. NCEP Baldwin-Schichtel(problem sounding)

11. NCEP Revised • A modified version of the NCEP Baldwin-Schichtel • Attempts to balance the freezing rain and sleet bias of the regular version by having a bias towards snow • Instead of the -4°C area check, it computes the area in the sounding with a wet-bulb temperature greater than 0°C

12. NCEP Revised(modified step) • If ice crystals, then the magnitude of the area in the sounding with a wet-bulb temperature > 0°C is computed • if area ≤ 500 deg m → snow • if area > 500 deg m → ice crystals melted → checks to see if hydrometers re-freeze into ice pellets or if they fall to the surface as rain or freezing rain

13. NCEP Revised(Strengths and Weaknesses) Strengths • Easily applied • Initial check for hydrometer state • Utilizes the wet-bulb temperature • Eliminates the near surface isothermal layer problem with the original algorithm • Removes the freezing rain and sleet bias Weaknesses • Not readily available • NCEP website • Part of dominant technique • Ignores impact of dry layers

14. Ramer • Developed in the early 1990s utilizing over 2000 cases of collocated surface precipitation observations and upper air soundings • Utilizes T, RH, and the Tw at different pressure levels as input • Based on the pressure level data, it identifies layers where precipitation is likely and calculates an ‘ice fraction’ • Follows the idealized precipitation parcel down to the ground from a ‘precipitation generating level’, anticipating the state of the hydrometer

15. Ramer(the steps) • Two preliminary checks are completed before the method performs a full calculation • if surface Tw > 2°C → rain is diagnosed • if surface Tw ≤ 2°C and the Tw < -6.6°C at all other levels → snow is diagnosed • If these checks fail then a full calculation of the ‘ice fraction’ of the hydrometer is computed

16. Ramer(the steps) • Determine the ‘precipitation generating level’ • highest level with RH > 90% • level must be located at or below 400 mb • Determine the initial hydrometer state at the generating level • if Tw < -6.6°C → completely frozen (ice fraction=1) • if Tw ≥ -6.6°C → completely liquid (ice fraction=0) • The algorithm then determines the amount of freezing and melting and the resultant ‘ice fraction’ of the hydrometer is computed

17. Ramer(the steps) • The algorithm then determines the amount of freezing and melting and the resultant ‘ice fraction’ as the hydrometer descends from the ‘generation level’ • Identifying layers warmer/colder that 0°C based on the depth of the layer and average Tw • Assigning an ice fraction at each level • Ptype is determined by the final ice fraction of the hydrometer at the surface • > 85% = sleet, <4% and TWsfc < 0 = freezing rain • Between 4-85% = mixed, 100% = snow

18. Ramer(Strengths and Weaknesses) Strengths • Developed utilizing observed data • Initial check for hydrometer state • Utilizes the wet-bulb temperature • Verifies well - high POD for snow (90%) and freezing rain (60%) Weaknesses • Hard to visualize • Does not account for impact of dry layers • Low POD for sleet (low FAR also)

19. Bourgouin • Developed in the early 1990s in Canada utilizing a dataset from two winters of collocated surface precipitation observations and upper air soundings • Based on the premise that the temperature variation of a hydrometer and its phase changes are predominately driven by the temperature of the environment through which it falls; assumes a constant vertical motion and terminal velocity • Calculates the areas above and below freezing, and the magnitude of the freezing and melting energy then determine ptype.

20. Bourgouin(Strengths and Weaknesses) Strengths • Based on observed data and associated ptype • Can be applied to any region or model data • High POD for freezing rain Weaknesses • Assumes ice crystals are present • Does not account for dry layers or impacts • Uses T rather than Tw • Assumes a constant terminal velocity of hydrometers

21. Czys • A non-dimensional parameter developed to distinguish ice pellet and freezing rain environments • Not derived from any observed data, but rather on the established condition that most incidents of freezing rain and ice pellets are associated with an elevated warm layer above a layer of sub-freezing air adjacent to the surface, and any cloud ice must completely melt for freezing rain

22. Czys • Initially tested with excellent results using data from the 1990 Valentine’s Day Ice Storm in the Midwest, and several other events during the winter of 1995-96. • Ptype is determined primarily by computing the ratio of the residence time that an ice sphere remains in a warm layer, to the time required for complete melting • Minor modifications by Cortinas et. al (2000) to also predict snow and rain

23. Czys(Strengths and Weaknesses) Strengths • Can be applied to any region or model data • Limited skill in forecasting sleet and delineating rain Weaknesses • Not based on observed data • Poor with snow • Overall is the worst performing algorithm • Limited availability

24. Dominant Ptype Ensemble • Approach utilized by the WRF-NAM output available in both AWIPS and Bufkit and the GFS in AWIPS • WRF-NAM uses 5 schemes – NCEP BS, NCEP Revised, Ramer, Bourgouin, and explicit cloud microspyhysics • GFS uses 4 schemes - NCEP BS, NCEP Revised, Ramer, and Bourgouin • Ties result in the most dangerous ptype (ZR, S, IP, R) • SREF – like WRF-NAM → dominant ptype of 5 schemes for each member (21 members) → ptype with most members wins • Dominant algorithm approach (SREF NCEP BS, Cysz)

25. Availability • NCEP Baldwin-Schichtel • EMC website/NAM meteogram (www.emc.ncep.noaa.gov/mmb/precip_type) • Part of dominant ptype in SREF (SPC or NCEP Winter System) (www.emc.ncep.noaa.gov/mmb/SREF/FCST/COM_US/winter_js/html/prob_prcptype.html) or (www.spc.noaa.gov/exper/sref/) • Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in AWIPS/Bufkit • HPC Winter Weather Diagnostics (WWD) website • NCEP Revised • EMC website/NAM meteogram • Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in AWIPS/Bufkit • Ramer • EMC website/NAM meteogram • Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in AWIPS/Bufkit • LAPS in AWIPS • GFS Bufr data in Bufkit

26. Availability • Bourgouin • EMC website/NAM meteogram • Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in AWIPS/Bufkit • Any model in Bufkit • Cysz • SPC SREF dominant ptype

27. AWIPS(Dominant of NCEP BS, NCEP Revised, Ramer, & Bourgouin)

28. Winter Weather Diagnostics (WWD)(Baldwin-Schichtel – NCEP)

29. SPC SREF(NCEP Dominant)

30. SPC SREF(Czys Dominant)

31. NCEP SREF Winter System(NCEP Dominant)

32. NAM PTYPE Meteograms

33. Bufkit

34. Summary of strengths & weaknesses • NCEP Baldwin-Schichtel • Good for ZR and IP; utilizes Tw • Problem with near surface isothermal layers • NCEP Revised • Better for snow – eliminates isothermal layer problem • Does not account for dry layers • Ramer • Strongest from statistical approach; utilizes Tw • Does not account for dry layers; difficult to understand • Bourgouin • Easy to visualize; observed data used in creation • Does not check initial hydrometer state • Cysz • Limited skill with IP and ZR • Overall the worst performing