NCEP PTYPE Algorithms

Fred H. GlassNOAA/NWS St. Louis

LSX Winter Weather Workshop – November 19, 2008

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!

Algorithms 101Ptype from the algorithms is derived by post-

processing of the model outputPtypes are generated when even just a trace of

precipitation is generated by the modelNo single algorithm handles all ptypes in a sufficient

mannerAlways 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

Algorithms/Techniques

NCEP Baldwin-SchichtelNCEP RevisedRamerBourgouinCzys

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 approachIdentifies 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

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

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

NCEP Baldwin-Schichtel(reviewing the process)

NCEP Baldwin-Schichtel(Strengths and Weaknesses)

StrengthsEasily applied and

widely usedInitial check for

hydrometer stateUtilizes the wet-bulb

temperatureForecasting freezing

rain and sleet

WeaknessesWill 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

NCEP Baldwin-Schichtel(problem sounding)

NCEP RevisedA modified version of the NCEP Baldwin-

SchichtelAttempts 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

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

NCEP Revised(Strengths and Weaknesses)

StrengthsEasily appliedInitial check for hydrometer

stateUtilizes the wet-bulb

temperatureEliminates the near surface

isothermal layer problem with the original algorithm

Removes the freezing rain and sleet bias

WeaknessesNot readily available

NCEP websitePart of dominant

techniqueIgnores impact of dry

layers

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

Ramer

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 diagnosedIf these checks fail then a full calculation of the

‘ice fraction’ of the hydrometer is computed

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

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

Ramer(Strengths and Weaknesses)

StrengthsDeveloped utilizing

observed dataInitial check for

hydrometer stateUtilizes the wet-bulb

temperatureVerifies well - high POD

for snow (90%) and freezing rain (60%)

WeaknessesHard to visualizeDoes not account for

impact of dry layersLow 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

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.

Bourgouin

Bourgouin(Strengths and Weaknesses)

StrengthsBased on observed

data and associated ptype

Can be applied to any region or model data

High POD for freezing rain

WeaknessesAssumes ice crystals

are presentDoes not account for

dry layers or impactsUses T rather than Tw

Assumes a constant terminal velocity of hydrometers

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

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

Czys

Czys(Strengths and Weaknesses)

StrengthsCan be applied to any

region or model dataLimited skill in

forecasting sleet and delineating rain

WeaknessesNot based on

observed dataPoor with snow Overall is the worst

performing algorithmLimited availability

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)

Dominant Ptype Ensemble

AvailabilityNCEP 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) websiteNCEP Revised

EMC website/NAM meteogram Part of dominant ptype ‘ensemble’ for GFS in AWIPS and WRF-NAM in

AWIPS/BufkitRamer

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

AvailabilityBourgouin

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

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 & weaknessesNCEP Baldwin-Schichtel

Good for ZR and IP; utilizes Tw

Problem with near surface isothermal layersNCEP Revised

Better for snow – eliminates isothermal layer problemDoes not account for dry layers

RamerStrongest from statistical approach; utilizes Tw

Does not account for dry layers; difficult to understandBourgouin

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