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

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

Algorithms 101 Ptype from the algorithms is derived by post- processing of the model output 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 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 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 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 An ensemble approach should be considered by comparing the output of the different algorithms

Algorithms/Techniques NCEP Baldwin-Schichtel NCEP Baldwin-Schichtel NCEP Revised NCEP Revised Ramer Ramer Bourgouin Bourgouin Czys Czys

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 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 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 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 Compares magnitude of warm/cold layers (area) with the surface temperature to identify ptype

NCEP Baldwin-Schichtel (the steps) First identifies the highest saturated layer (considered to be the precipitation generation layer) First identifies the highest saturated layer (considered to be the precipitation generation layer) Next determines the initial state of these hydrometers 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) If supercooled water droplets, then checks the surface temperature (lowest model layer)  T sfc ≤ 0°C → freezing rain  T sfc > 0°C → rain

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 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

NCEP Baldwin-Schichtel (reviewing the process)

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

NCEP Baldwin-Schichtel (problem sounding)

NCEP Revised A modified version of the NCEP Baldwin- Schichtel 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 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 Instead of the -4°C area check, it computes the area in the sounding with a wet-bulb temperature greater than 0°C

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 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

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

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

Ramer (the steps) Two preliminary checks are completed before the method performs a full calculation Two preliminary checks are completed before the method performs a full calculation  if surface T w > 2°C → rain is diagnosed  if surface T w ≤ 2°C and the T w < -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 If these checks fail then a full calculation of the ‘ice fraction’ of the hydrometer is computed

Ramer (the steps) Determine the ‘precipitation generating level’ 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 Determine the initial hydrometer state at the generating level  if T w < -6.6°C → completely frozen (ice fraction=1)  if T w ≥ -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 The algorithm then determines the amount of freezing and melting and the resultant ‘ice fraction’ of the hydrometer is computed

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’ 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 T w  Assigning an ice fraction at each level Ptype is determined by the final ice fraction of the hydrometer at the surface Ptype is determined by the final ice fraction of the hydrometer at the surface  > 85% = sleet, 85% = sleet, <4% and T Wsfc < 0 = freezing rain  Between 4-85% = mixed, 100% = snow

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

Developed in the early 1990s in Canada utilizing a dataset from two winters of collocated surface precipitation observations and upper air soundings 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 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. Calculates the areas above and below freezing, and the magnitude of the freezing and melting energy then determine ptype. Bourgouin

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

A non-dimensional parameter developed to distinguish ice pellet and freezing rain environments 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 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 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 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 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 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 Minor modifications by Cortinas et. al (2000) to also predict snow and rain Czys

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

Approach utilized by the WRF-NAM output available in both AWIPS and Bufkit and the GFS in AWIPS 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 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) Dominant algorithm approach (SREF NCEP BS, Cysz) Dominant Ptype Ensemble

Availability NCEP Baldwin-Schichtel NCEP Baldwin-Schichtel  EMC website/NAM meteogram (  Part of dominant ptype in SREF (SPC or NCEP Winter System) ( or (  Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in AWIPS/Bufkit  HPC Winter Weather Diagnostics (WWD) website NCEP Revised NCEP Revised  EMC website/NAM meteogram  Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in AWIPS/Bufkit Ramer 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

Availability Bourgouin 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 Cysz  SPC SREF dominant ptype

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

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

SPC SREF (NCEP Dominant)

SPC SREF (Czys Dominant)

NCEP SREF Winter System (NCEP Dominant)

NAM PTYPE Meteograms

Bufkit

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

That’s It! Questions?