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Winter Weather Refresher Stephen Jascourt and Bill Bua COMET NWP resources [email protected] [email protected] at NCEP OUTLINE 1. Global Forecast System – what did we learn last winter? 2. Global Forecast System – what’s new for 2003-2004?

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Winter weather refresher l.jpg

Winter Weather Refresher

Stephen Jascourt and Bill Bua

COMET NWP resources

[email protected]

[email protected] at NCEP

Slide2 l.jpg


1. Global Forecast System – what did we learn last winter?

2. Global Forecast System – what’s new for 2003-2004?

3. Eta Model – what did we learn last two winters?

4. Eta Model – what’s new?

5. Nonhydrostatic Mesoscale Model (NMM)

6. Short range ensembles (SREF)

7. RUC-20


Slides with this blue background indicate transition to next item in above outline

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Global Forecast


What did we learn last winter?

October 2002 and august 2003 implementations l.jpg
October 2002 and August 2003 Implementations

  • Increased resolution from T170L42 to T254L64 for first 84 hours of forecast (10/02)

  • Replaced long wave radiation scheme 8/02 (now called RRTM)

    • Results in warmer troposphere, colder stratosphere

      • Should mitigate the GFS cold bias in troposphere

      • Colder stratosphere may impact data assimilation of satellite radiances

Anecdotal evidence about the gfs l.jpg
Anecdotal evidence about the GFS

  • GFS deepens mid-tropospheric troughs too much in eastern North America, particularly past day 3-4

    • Evidence that the GFS prefers positive PNA pattern (ridge west, trough east North America)

  • GFS often has better storm tracks along the Gulf and Atlantic coasts than the Eta-12

  • Reasons for above are unknown and may be regime-dependent

  • Likely will continue in winter 2003-04

    • Forecasters should assess anew this winter

Contrasting winter regimes 01 02 vs 02 03 l.jpg
Contrasting Winter Regimes: ‘01-’02 vs ‘02-’03

Negative PNA in winter 2001-02 (positive height anomalies over eastern US)

Positive PNA in winter 2002-03 (positive height anomalies over Alaska/Yukon, negative over east coast)

Comparison of 5 day 500 hpa height error djf 01 02 to djf 02 03 l.jpg
Comparison of 5 day 500-hPa height error: DJF 01-02 to DJF 02-03

Too much troughing over eastern US, not enough across north Pacific and Hudson Bay

Too much ridging over northern oceans, too much trough over NH mid-latitude continents

Gfs lower tropospheric cold bias in cold season l.jpg
GFS lower tropospheric cold bias in cold season 02-03

00 UTC GFS analysis and forecast biases for 850-hPa temperatures at 12-h intervals for January 2002




Cold bias over west, spreads across northern plains during forecast.


Magnitude may depend on flow regime



Compare january 2002 to 2003 850 hpa for 1 st 48 hrs of forecast l.jpg
Compare January 2002 to 2003 02-03850-hPa for 1st 48 hrs of forecast

00 UTC GFS analysis and forecast biases for 850-hPa temperatures at 12-h intervals for January 2003




Overall forecast bias still gets colder with time but pattern of bias different under different regime


PNA regime changes biases in upper Midwest by about 1-2ºC?



Cold air damming and propagation along barrier bad due to sigma coordinate 2001 02 at t170 l.jpg

Model topo. (m) 02-03

Cold air damming and propagation along barrierbad due to sigma coordinate: 2001-02 at T170

Contours=temperature error (surface, deg C) Colors=model terrain height

24-hour GFS fcst of 2-m temperature was 8°C too warm over High Plains during start of arctic outbreak (T170)!

Cold air damming and propagation along barrier bad due to sigma coordinate 2002 03 at t254 l.jpg
Cold air damming and propagation along barrier 02-03bad due to sigma coordinate: 2002-03 at T254

Contours=temperature error (surface, deg C) Colors=model terrain height

Model topo. (m)

At T254, 10ºC too warm at 2-m in GFS 48-hr forecast near DEN!

Avn generally too wet too large precip area and with a generally wet bias l.jpg
AVN generally too wet 02-03too large precip area and with a generally wet bias

AVN 36-h forecast of 24-h precipitation

verifying 12z 3 March 2002

24-h gage analysis of precipitation

verifying 12z 3 March 2002

T254 still has same precipitation bias l.jpg
T254 still has same precipitation bias 02-03

Grid 211: 24, 48, 72-hr fcsts of 24-hr accum prec











Equitable Threat Score

Equitable Threat Score

JAN 2003: Dry month

FEB 2003: Wet month











Gfs tends not to be able to remove enough elevated cape l.jpg
GFS tends not to be able to remove enough elevated CAPE 02-03

Why do we care about this in the cold season?

  • Can result in overdevelopment of frontal waves

    • Waves move too far into the cold air

    • Overdevelopment results in problems with amount (too much), location, and type of precipitation

  • Example follows from winter 2001-2002

    • Note: This problem is not expected to improve with resolution increase on October 29, 2002

    • The source of the problem is physics, rather than dynamics

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GFS tends not to be able to remove enough elevated CAPE - example

FORECAST versus ANALYSISlow positions and pressures at 6 hour intervals from 00z7Dec01 through 12z9Dec01from GFS run of 00 UTC 7 Dec 2001








12z08Dec01 (lows collocated)

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GFS tends not to be able to remove enough elevated CAPE - example

Verification, 12z8Dec01

Verification, 12z9Dec01

Low tracks

Forecast Analysis

Reasons for overdevelopment in this gfs forecast l.jpg

GFS tends not to be able to remove enough elevated CAPE example

Reasons for overdevelopment in this GFS forecast:

  • Deep, moist, conditionally unstable elevated layer

  • Convective scheme cannot remove this instability (or enough of it)

  • Grid-scale scheme convects instead, which results in too much:

    • Latent heating in 850-500 hPa layer

    • Vorticity spin-up at low- and mid-levels

    • Frontal wave intensification

    • Moisture convergence in lower troposphere (results in even more moisture entering the grid column!)

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Ensembles more consistent run to run than operational higher-resolution GFS

Yellow = operational MRF, same valid times

Initial=00 UTC 8 April 2002 Initial=00 UTC 9 April 2002

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3-day forecast from 00 UTC 11/2/01, spaghetti diagram for ensemble


Ensembles help assess forecast confidence and range of scenarios

Uncertain location of

incoming western trough

Uncertain amplitude

of eastern trough

From CDC web site:

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Relative Measure of Predictability (RMOP) ensemblemeasure of how likely the ensemble mean is (note: wwwt may become www)

  • Based on last 30 days of ensemble performance to take into account regime predictability and general model performance

  • Ensemble mean and each ensemble member placed in equally likely climatological bins (bins vary seasonally and geographically to account for typical variability)

    • RMOP colors with percentage below color bar show the percentile rank of today’s forecast compared to the last 30 days for number of ensemble members agreeing with their ensemble means (“agreeing with” = in the same bin)

      • For example, red (90%) means the ensemble distribution has more members in the same bin as the mean than 90% of the cases in the past 30 days, suggesting this is among the most predictable forecasts in the last month

    • RMOP probability numbers (above the color bar)

      • Calibrated probability that ensemble mean will verify based on how often the ensemble mean verified when the same number of ensemble members were in the bin containing the ensemble mean during the past 30 days

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Unpredictable heights in ensemble

Strong gradient


Highly predictable

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Global Forecast ensemble


What’s new for 2003-2004?

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Resolution through boreal winter 2003-04 ensemble


00, 06, 12, 18 UTC

“MRF” are same as fields labeled “AVN”. MRF fields to be discontinued







84h 180h 384h

3½d 7½d 16d

2003 until …. Planned change (may only be resolution)

by 12/6/03






T62 L28

T62 L28

84h 180h 384h

3½d 7½d 16d

84h 180h 384h

3½d 7½d 16d

11 members (1 control, 10 perturbations) 11 members (1 cont., 10 pert)

00 UTC, 12 UTC 00 UTC, 06 UTC, 12 UTC, 18 UTC

Topography comparison t254 topography 0 3 5 days l.jpg
Topography comparison ensembleT254 topography (0-3.5 days)

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Resolution of topography affects precip forecast ensemble




Sharper precipitation maxima, slightly better placement of precipitation as a consequence of increased horizontal resolution (first 3 1/2 days only!)

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Topography comparison ensembleT170 topography (3.5-7.5 days)

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Topography comparison ensembleT126 topography (7.5-16 days), also ensembles 0-3.5 days until 12/06/03, then 0-7.5 days

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Topography comparison ensembleT62 topography(ensembles 3.5-16 days until 12/06/03, then 7.5-16 days)

New long wave radiation scheme and changes to cloud long wave radiation interaction l.jpg
New Long Wave Radiation Scheme and ensembleChanges to Cloud-Long Wave Radiation Interaction

  • More efficient (runs twice as fast)

  • More accurate (by a factor of 5 to 10!)

  • Decreased lower tropospheric cold bias and upper tropospheric/lower stratospheric warm bias in parallel experiments

  • High stratosphere cold bias occurs (may affect data assimilation of radiances?)

Details at: and

Slide30 l.jpg

GFS warmer with new long wave scheme ensemble

Skin T

Skin and 2-meter temperatures with RRTM long wave are higher than old GFS LW radiation early in the forecast at high latitudes

2-meter T

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GFS warmer with new long wave scheme ensemble

Near-surface temperature increase in RRTM over old GFS LW radiationincreases through 5 days (average difference around 1oC)

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Eta Model: ensembleWhat did we learn last two winters?

Model has been stable (no major changes, several minor fixes) from December 2001 through June 2003

Slide33 l.jpg

  • Examine the analysis! ensemble

  • Compare against satellite, radar, surface data, etc.!

  • Large scale features set the forecast scenario

  • Model details and high resolution topography and coastlines will not help forecast accuracy if the large-scale winds are not well forecast or the cyclone track or intensity is off.

  • Look off the coasts – is the Atlantic ridge too weak in the model? Is the trough off the west coast sharp enough? Is the jet core, where parcels are peeling anticyclonically into, through, and out of, in the right position? How do you expect errors in such features will affect the strength of a cold surface high or the amplitude and timing of a major wave in the model forecast?

Slide34 l.jpg

Sensitivity to multiple factors ensemble

B, F (in)

A,C, D, E, G, H (mm)

Initial conditions

  • GFS vs. Eta initial state in same model:

  • compare B vs. F

  • compare D vs. G






  • Different convective parameterizations in same model:

  • compare C vs. D vs. H




  • Different resolutions in same model:

  • compare A vs. C vs. B



  • Also, different models with same initial condition:

  • compare E vs. F

Look at: different models and ensembles!

Check for: bad init. cond. and unphysical behavior in forecast

Slide35 l.jpg

Remember, saturation for ice occurs at much lower RH with respect to water

  • Affects your interpretation of cloud base/top and cloud coverage

(new 32 km SREF is between brown and pink curves)

Model cloud top of overcast deck

Forecast sounding

(new 32 km SREF is between brown and pink curves)

Ice saturation

threshold in 12 km

Eta model

Model is saturated with respect to ice

Model cloud base

Slide36 l.jpg

  • precipitable water becomes steadily drier during forecast compared to verification

  • QPF also dries up at increasing forecast range compared to verification

    • Monthly total 24-h forecast minus observed precipitation for Feb 2003

    • lower (or more negative) values at later times into forecast period

    Valid at 36 h

    Valid at 60 h

    Valid at 84 h

    Slide37 l.jpg

    Watch for moisture stream getting intercepted by convection respect to water

    • Convective scheme drops too little precip, leaving moisture stream free to reach area where dynamics are causing grid-scale lift in colder air.

    • In reality, convection intercepts moisture stream.

    Moist inflow




    Moist inflow

    Moist inflow

    Slide38 l.jpg

    Snow drifting downwind while falling respect to water

    Most falls on upwind side but some advects downwind of ridges

    Slide39 l.jpg

    Snow drifting downwind while falling respect to water

    Most falls on upwind side but some advects downwind of ridges

    Slide40 l.jpg

    Lake Effect respect to water

    • Lake-effect band placement excellent

    • precip intensity too weak by factor of at least 3 overall and 10 for peak local amounts

    • can’t resolve multiple bands and waves

    Slide41 l.jpg

    Isothermal layers form at 0 respect to wateroC

    Hourly BUFR sounding


    Slide42 l.jpg

    Precipitation Type: microphysics vs. diagnostic output respect to water

    Hourly BUFR sounding

    now in BUFKIT

    Slide43 l.jpg

    Precip type grids are not from model microphysics respect to water

    Baldwin-Schichtel diagnostic algorithm

    • Tends to have bias against SN in Eta; overforecasts ZR

    • Purpose is to alert forecaster to potential hazardous weather (ZR is most hazardous) so that forecaster inspects situation carefully and determines for him/herself the precip type

    Slide44 l.jpg

    Precipitation Type: microphysics vs. diagnostic output respect to water

    Overrunning case at LEX (Kentucky)

    Hourly BUFR sounding

    Both all liquid


    Slide45 l.jpg

    Precipitation Type: microphysics vs. diagnostic output respect to water

    Overrunning case at LEX (Kentucky)

    Hourly BUFR sounding

    Microphysics=72% frozen precip



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    Precipitation Type: microphysics vs. diagnostic output respect to water

    Overrunning case at LEX (Kentucky)

    Hourly BUFR sounding

    Microphysics=mix of 21% frozen



    Slide47 l.jpg

    Precipitation Type: microphysics vs. diagnostic output respect to water

    Overrunning case at MSL (Alabama)

    Hourly BUFR sounding

    Microphysics=94% frozen



    Slide48 l.jpg

    Precipitation Type: microphysics vs. diagnostic output respect to water

    Overrunning case at MSL (Alabama)

    Hourly BUFR sounding

    Microphysics=mix with only 13% frozen



    Slide49 l.jpg

    Patchy snow cover with bare ground spots changed 26 Feb 2002 respect to water

    • Before model fix: (as in soundings to the left)

    • 2-meter temperatures too cold over snow

    • 850 temperatures too warm over Canada

    • arctic boundary layer poorly handled

    • After model fix (as in schematic below):

    • 2-meter temps warmer, 850 temps cooler so verifies better

    • arctic boundary layer structure still poor, seldom makes very stable even when it should

    Too warm

    (before fix)

    Too cold

    Land surface upgrade summer 2001 l.jpg
    Land surface upgrade summer 2001 respect to water

    Cold season processes (Koren et al 1999)

    • Patchy snow cover

    • Frozen soil (new state variable)

    • Variable snow pack density (new state variable)

    • Soil heat flux under snowpack (Lunardini 1981)

    • New maximum snow albedo database (Robinson & Kukla 1985)

      • Takes into account observed effect of vegetation on the albedo of grid box

    Slide51 l.jpg

    Effect on high temperature with thin snow cover respect to water

    NEW: snowcover - ground > freezing

    OLD: snowcover ground=freezing

    Ground holds at freezing




    Model  0 C

    First day better but still too cool

    Second day worse because not all snow melted yet

    2 m AIR TEMP

    North Platt, Neb.

    previous model formulation (until snow completely melts):=> all incoming energy melts/sublimates snow => skin temp held at freezing

    => 2-m air temp held near freezing

    Current formulation:=> patchy snow cover for snow depth less than threshold depth (veg-type dependent) => reduces surface albedo => accelerates melting => more available energy at sfc

    => skin temp can exceed 0 C => 2-m air temp rises further above freezing.

    0 C


    Slide52 l.jpg

    Problems with light, fluffy snowcover respect to water

    If daily satellite snow analysis (from Satellite Analysis Branch) has snowcover where model first guess has none, then snow pack is added to the Eta analysis:

    • Depth assumed to be 1.5”, 5:1 snow:water ratio (yields water equivalent of 0.3”)

    • If actual snow cover has less average water content than 0.3”, it will melt sooner and the ground will heat faster in reality than in the model forecast

    9-hr 850-hPa temp fcst

    9-hr 2-m temp fcst

    Forecast is <4ºC


    Verification is 8º-12ºC!

    Slide53 l.jpg

    • Model outputs: respect to water


    • Extended below model topography using standard atmosphere lapse rate, but this will miss cold air trapped below valley inversion

    • 12-km grids getting into AWIPS:

    • NCEP sends the following fields on 12-km grid for distribution over SBN:

      • precipitation and convective precipitation (3 h accumulation)

      • T, RH at 2 meters

      • U, V at 10 meters

      • MSLP and EMSL (Shuell and Mesinger reductions to sea level)

      • station pressure on model terrain

      • precipitable water

      • CAPE/CIN based on parcel from lowest model layer

      • LI based on most unstable of the 6 bottom 30-hPa-thick average parcels

      • helicity (0-3 km using storm motion from Bunker’s method)

    Slide54 l.jpg

    Eta Model: respect to water

    What’s new for 2003-2004?

    Slide55 l.jpg

    • Summer 2003 change bundle (5 items) respect to water

    • Forecast impact mostly from item #3, especially better holding in low clouds. Other items combine for slight improvement in winds and moisture early in forecast (up to 12 hours)

    • Convection and fundamentals of data assimilation unchanged, therefore overall forecast character will be same as before


    • Superobs (combining individual data points) to 1 km radial x 6o azimuth

    • Not used where VAD quality flag says bad data (including birds) or where radar beam runs into model topography

    • 32 km test shows no forecast impact

    • 10 km test but without assimilation cycling shows improved fits to raob initially but no overall forecast impact

    • 2) Radiance processing

    • Upgrade to global model processing methods, including new emissivity model over land and use NOAA-17 polar orbiter data (not previously used, should improve analysis over Pacific though test error stats show no change)

    • Allows channels used previously only over water to be used over land.

    • Hardly any overall forecast impact (32 km test)

    Slide56 l.jpg

    • Summer 2003 change bundle (5 items) respect to water

    • 3)Output and Radiation made consistent with the new (Nov 2001) cloud microphysics

    • Microphysics fields cycled

    • Longwave effects of clouds updated hourly instead of every 2 hours

    • Convective cloud fraction increased; shallow convection  10% cloudcover

    • Formulation for cloud fraction changes to Xu and Randall (1996), instead of Randall (1994)

    • RH for cloud fraction calculated consistently with Nov 2001 microphysics

    • Cloud optical properties changed

    • Output changed (modified or new variables):

    • Precipitable water field now includes only vapor (had included condensate, resulting in excessive values in regions of very heavy precipitation)

    • Cloud water, cloud ice, rain, snow, and total condensate separately output

    • Output cloud-base and cloud-top pressures from shallow nonprecipitating convection, deep convection, and grid-scale clouds separately

    • Visibility calculations have been changed to use the new cloud fields, responding to mixing ratios of cloud water, cloud ice, rain, and snow

    • Low-level clouds in the lowest 100 mb and upper-level clouds above the tropopause are no longer ignored

    Slide57 l.jpg

    • Summer 2003 change bundle (5 items) respect to water

    • 3) Output and Radiation made consistent with the new (Nov 2001) cloud microphysics

      Forecast impact in 32 km test:

      • <0.5” precip improved, >0.5” even drier

      • Improved slightly all forecast hours fit to raobs and 2-meter temperatures

        General forecast impact usually seen in individual forecasts:

      • Increase in partly cloudy and overcast conditions (was too little)

      • Smaller diurnal range when low clouds are present (was too large)

      • Cooler daytime temperatures where shallow boundary-layer cumulus form

      • Fog/low clouds burn off slower during the morning (occurred much too early; still somewhat too early)

      • Consistently better positioning of warm fronts during the daytime in moist situations when low clouds keep the surface cool, preventing mixing from advancing the surface warm front

    Slide58 l.jpg

    • Summer 2003 change bundle (5 items) respect to water

    • 4) GOES cloud-top assimilation

    • Remove moisture above observed cloud tops

    • Add moisture at observed cloud levels

    • Use as top anchor point for precip assimilation if have to add precipitating cloud

    • Forecast impact on overall error statistics mixed, but viewing individual cases shows improvement in structure and pattern of precipitation early in the forecast (32 km test)

    • 5) Stage IV precipitation assimilation

    • Upgraded from Stage II to Stage IV – adds quality control

    • Forecast impact small but improvement measured in precipitation during EDAS cycle, which improves soil moisture a little (32 km test)

    • Also, many additional output variables in grib files on NCEP server

    • Hourly output to 36 hours, and 6, 18 UTC runs out to 84 hours

    • New land surface variables including snow depth and percentage of snow cover

    • More discussion on the model change bundle at


    • More technical details in the Technical Procedures Bulletin at


    No metar surface temperatures in eta analysis l.jpg
    No METAR surface temperatures in Eta analysis respect to water

    12 UTC analysis = 00 UTC EDAS analysis (includes late data) plus

    {3 h forecast and assimilate data for new analysis} at 03, 06, 09, 12 UTC

    Why METAR temperatures removed? Creates bad analysis soundings!

    Starts with good 00 UTC EDAS sounding

    3h forecast from

    9 UTC analysis much too cold above nocturnal inversion

    12 UTC raob

    00 UTC raob

    How? By spreading influence upward

    Times are valid times of 1, 2, and 3 hour forecasts

    Arrows show when data added

    • Hourly soundings starting with 00 UTC EDAS.

    • Small radiative cooling in bottom 75 hPa every hour

    • Large cooling through lowest 150 hPa after data added at 3, 6, and 9 UTC.

    Sounding jumps when data added

    Slide60 l.jpg

    No METAR surface temperatures in Eta analysis respect to water

    • RUC still uses METAR temperatures. RUC’s terrain-following coordinate system allows it to take advantage of good ways to handle surface data.

    • Eta still uses other parts of METARs (winds, dewpoints, pressure)

      • Eta stopped using all surface temperature observations over land on Sep 10, 2003

      • Eta uses only surface observations within 6 minutes of analyses times as of Sep 10, 2003

    • GFS never used METAR temperatures, only uses METAR pressures

    What’s the forecast impact?

    Precip: 3 weeks summer – improved over east, especially 24 h amounts > 0.50”, west=no change

    Surface T, Td, wind: nearly same as with METAR temperatures

    Aloft (heights, T, wind): nearly same, slight improvement in mid-troposphere

    Overall: large improvement in 32-km reanalysis fits to obs, but brief 12-km test =smaller changes

    RMS differences from raob temperature profiles:

    No T from METAR

    With T from METAR

    Analyses 12 h forecast 60 h forecast

    60 h forecast fits raob T same except worse near ground

    12 h forecast fits raob T same except worse near ground

    Fits raob T profile better except near ground

    RMS temperature difference from raobs

    32-km reanalysis, winter (1 month) 12-km test, summer (3 weeks) 12-km test, summer (3 weeks)

    Slide61 l.jpg

    • Changes testing for respect to waterpossible mid-winter implementation (late January or February)

    • Follow the comet.eta newsgroup for updates in case things don’t go as planned

    • 1) Overhaul of short wave radiation – but may not get in

    • Will be like GFS except will ignore short-wave absorption by oxygen and carbon dioxide

    • GFS solar radiation described at

    • Was the oldest component of the model and needed catching up

    • Forecast impact: will reduce the present high bias in incoming solar radiation, both clear sky and through clouds. Forecast is consistently colder; preliminary tests show increased errors because too cold – will not be included in change package unless can be corrected

    • 2) Land surface changes

    • Will reduce snow depth threshold for patchy snow (presently assumes bare patches if depth less than 5” over grass up to 16” over forest)

    • Albedo will vary with solar zenith angle, so more solar energy is reflected and less available for heating at low sun angles (e.g. morning, evening; and all day during winter at high latitudes)

    • Precip adds to snowpack if model microphysics indicates frozen fraction > 50% (presently, adds to snowpack if lowest layer air temperature is below freezing)

    • Numerous other technical and numerical refinements

    • Forecast impact: expected to reduce diurnal range (which has been too large) and probably cause a net overall cooling compared to present model

    Slide62 l.jpg

    • Changes testing for respect to waterpossible midwinter implementation (late January or February)

    • 3) Bias adjustment of precipitation assimilation

    • Multisensor precipitation analyses are currently assimilated into the EDAS:

      • Assimilated starting 12 hours before the model initial time

      • Has affect of stimulating precipitation during 12-hour assimilation cycle where observations show it should occur and inhibiting it elsewhere

        • Affects soil moisture and thus later evaporation

      • Helps spin up microphysics and vertical motion in right places based on observed precip

      • NO PRECIP ASSIMILATION WHERE SNOW – only where rain

      • Currently the precip amounts assimilated have a low bias

    • Bias as determined by large-scale several-week comparison of gauge and multisensor analyses will be removed – this is the only change

    • Forecast impact: primarily warm season, not winter. Should slightly reduce the tendency of the model to dry out during the forecast

    • 4) GOES 12 radiances added

    • These will substitute for GOES-8 radiances. Eastern North America and offshore waters have had no GOES radiance coverage in the Eta model since GOES-8 was replaced by GOES-12 in April, 2003

    • OVERALL: temperatures cooler and matches diurnal curve better

    Slide63 l.jpg

    N respect to waterONHYDROSTATIC



    • New hybrid vertical coordinate

    • Replaced the Eta model for high-resolution windows runs in 2002

    • May replace Eta completely during 2005

    • Provides operational support for fire weather and dispersion/emergency

    • Similar physics to Eta

    • No assimilation cycling yet, starts with Eta-12 analysis

    • BEGINS THE TRANSITION TOWARD WRF (Weather Research and Forecasting Model)

      • Modular design: different model options “plug in”

      • WRF will allow collaborators outside NCEP to run the same model and to supply parameterizations that will more readily work in the NCEP operational environment (a great difficulty now)

      • NMM will be one version of WRF

      • WRF ensemble will run in high-resolution window slot Fall 2004

    High resolution window runs l.jpg
    High-resolution Window Runs respect to water

    • Eta used for initial and boundary conditions

    • Once per day 10 km over Alaska, 8 km over west, central, east CONUS

    • Twice per day 8 km over Hawaii, Puerto Rico

    • Not operational system – reliability pretty good but not 100%

    Slide65 l.jpg

    Small-domain specialty runs respect to water

    • 26 small domains

    • One operational 8-km run each at 00, 06, 12, 18 UTC

      • Supports fire weather operations during fire season

      • SPC selects domain if severe threat high

      • HPC can select domain for winter weather event

    • 4-km runs “on call” operational service to feed dispersion model in emergency

    Slide67 l.jpg

    ground respect to water


    Hybrid and Eta Coordinates

    Ptop (constant pressure)

    Ptop (constant pressure)

     = 0

    Pressure domain

    400 hPa

     = 0

    Sigma domain

     = 1


     = 1

    Slide68 l.jpg

    Vertical resolution and vertical coordinate respect to water

    Layers slope with terrain 60 flat layers,

    60 everywherefewer above high terrain

    • Tick marks are actual model levels from BUFR sounding files

    • Same stations used on left (NMM) and right (Eta)

    • Note difference in station elevation. NMM usually lower (stations usually in valleys)

    • Note difference in vertical resolution with increasing station elevation


    scoordinate up to 400 hPa

    p coordinate above 400 hPa

    Slide69 l.jpg

    NMM Forecast Characteristics respect to water(compared to Eta)

    • Large-scale conditions same as Eta

    • Nocturnal cold bias, esp. clear/calm

    • Cold bias with old snow because treated same as fresh snow in NMM

    • 10-m winds slightly too weak over western states

    • More flow through valleys

      • Not as good for valley inversion trapping

      • Better for downslope (and diurnal upslope too)

    • More flow over ridges instead of around ridges

    • Better gravity waves – affects winds and lee-wave temperatures

    • More detail

    • Catches more of the spotty mountain convection missed by Eta

    • Too much orographic precip in heavy precip episodes

    • Features closer to true amplitude

      • Small displacement error gives bigger (worse) RMS error stats than too-smooth Eta model yet more physically descriptive forecast

    Topography nmm vs eta l.jpg
    Topography NMM vs. Eta respect to water

    30” data (approx 1 km)

    • Eta = 12 km silhouette topo

      • Valleys must be at least 2 grid boxes = 24 km wide because step coordinate requires zero wind at valley walls

    • NMM = 8 km mean topo

      • Peaks about same, valleys deeper and sharper

    Slide71 l.jpg

    Topography NMM vs. Eta respect to water

    • Peaks not generally higher in NMM

    • Valleys deeper in NMM

    • More detail in NMM

    • Even more detail over high plains in NMM

    Zoomed view

    Slide72 l.jpg

    Topographic influence on winds respect to water

    NMM Eta

    Flow into/through valley Flow over valley

    - good if well-mixed - good for trapping/damming

    Downslope follows terrain Downslope doesn’t reach bottom

    or corners

    Overhead view, Topo contours

    Flow over Flow around

    Dissemination l.jpg
    Dissemination respect to water

    • GRIB and BUFR data available for ftp

    • Fields do not go out over SBN

    • Model will not run if hurricane model needs to run (they share same time slot on computer)

      How do I get it?

      The model grib files are accessed on

      In these directories:









    Short range ensemble forecasts sref l.jpg
    Short-Range Ensemble Forecasts (SREF) respect to water

    • What? (caution: changes planned by December, details a few slides later)

    • 5 Eta 48 km (control + 2 perturbation pairs)

    • 5 Regional Spectral Model 48 km (control + 2 perturbation pairs, based on GFS analysis) [RSM currently has old AVN/MRF physics]

    • 5 Eta members using Kain-Fritsch convective parameterization

    • When?

    • 21, 09 UTC in time for your use with 00, 12 UTC Eta

    • Status?

    • Officially operational (24x7 computer support/reliability)

    • Output might get into AWIPS in fall 2004 (OB-4)

    • New user-friendly web interface linked from SREF home page,

    • which is

    Slide75 l.jpg respect to water

    Note: wwwt address may change to www

    Go here,

    and it brings up this

    Slide76 l.jpg respect to water

    NCEP Short range ensembles on the web. Note wwwt address may change to www

    Slide77 l.jpg

    Mean and Spread charts – Dominant Precip Type respect to water

    • Shows the precipitation type diagnosed in the largest number of ensemble members

    Precipitation type determined by Baldwin-Schichtel algorithm

    Slide79 l.jpg

    Probability Charts: percentage of members exceeding threshold


    Percentage of members with QPF > .25”/24h

    010519/0000V63 SREFX-CMB; 24HR PQPF OF .25”

    Slide80 l.jpg

    Probability Charts: threshold exceeded by specified percentage


    Highest QPF at each point exceeded by

    60% of the ensemble members


    Slide81 l.jpg

    Individual Station Plots: Meteograms percentageEnsemble mean and all members. Experimental, available by fall 2003linked on

    Slide82 l.jpg

    Precip-type algorithms percentage“ensemble” of ptype from operational Eta. Available during fall/winter linked on

    Tentative new sref configuration by 12 03 l.jpg

    Model percentage





    Betts-Miller-Janjic (BMJ)

    Operational Ferrier

    1 positive (p1),1 negative (n1)


    BMJ-SAT (Saturated reference profile: convection causes vertical transport but precip is handled by grid-scale)

    Experimental Ferrier?

    p1, n1


    Kain-Fritsch (KF)

    Operational Ferrier

    p1, n1


    KF w/ full cloud detrainment

    Experimental Ferrier

    p1, n1


    Relaxed Arakawa-Schubert (RAS)

    Operational Ferrier

    p1, n1


    Shallow Convection only

    Experimental Ferrier



    Simple Arakawa Schubert (SAS)

    Operational GFS



    Relaxed Arakawa Schubert

    Operational GFS?

    p1, n1

    Tentative New SREF Configuration (by 12/03)

    • 15 members (same number as now)

    • More physics diversity (convection scheme and microphysics)

    • Less initial condition diversity – one positive-negative perturbation pair (2 now)

    • 32-38km or equivalent (RSM) horizontal resolution with 60 levels

    • Upgrade RSM physics to current GFS physics

    • BUFR sounding and surface data to be available for all members

    Reasons for proposed change l.jpg

    KF members percentage

    BMJ members

    Eta Analysis location of low center

    Reasons for proposed change

    • Insufficient spread (verifying analyses falling outside range of solutions provided by the SREF)

    • Evidence that physics diversity increases spread in solutions (even in cold season, when dynamics typically more important)

    Contours around surface low

    Slide85 l.jpg

    Distribution verifies better with physics diversity percentage

    • Analyze distribution with Talagrand diagram:

    • Rank each ensemble member at each grid point from lowest to highest forecast value. 15 ensemble members means rankings are 1, 2, 3, …, 15

    • Identify verification as less than #1, between #1 and #2, …, between #14 and #15, higher than #15

    • Make climatology of how many times verification falls into each position

    Ensemble spread slightly better: verification less than smallest forecast or bigger than largest forecast value 25% of the time

    Ensemble spread too small: verification less than smallest forecast or bigger than largest forecast value 37% of the time

    SREF with 15 members:

    5 Eta using BMJ convection,

    5 Eta using KF convection,

    5 RSM

    SREF with 10 members:

    5 Eta using BMJ convection,

    5 RSM

    Percentage with verification value > largest forecast value

    Percentage with verification value < smallest forecast value

    • New SREF, 15 members:

    • More physics diversity

    • fewer initial condition perturbations

    Ensemble spread nearly perfect! Verification almost equally likely in every slot.

    When will wwe iii occur l.jpg
    When will WWE-III occur? percentage

    • Begins Oct. 1, 2003 (Intermountain WFOs)

      • Test and evaluation period conducted Sept. 15 - Sept. 30, 2003

    • Nov. 1, 2003 (Remaining WFOs)

      • Test and evaluation period conducted Oct. 15 - Oct. 31, 2003

    • Occurs twice daily

    • HPC routinely produces enhanced graphics in support of WWE

    • WFOs participate only when impacted by an event

      • View graphics and participate in a 15 minute (maximum) collaboration call

    • Collaboration occurs after arrival of updated operational and ensemble guidance, but still early enough in the forecast process to foster different opinions among participants

    • Ends April 1, 2004

      • May be extended to no later than May 1, 2004 depending on how active the winter has been

    Wwe iii hpc will provide l.jpg
    WWE-III: HPC will provide….. percentage

    • Accumulation Graphics

      • Loops of precipitation in 6 hour increments out to 72 hours (CONUS)

      • Melted QPF, Snow, Freezing Rain, and the precip type grid used to convert melted to winter precip

    • HPC forecasters chooses a QPF source (HPC, Eta, GFS, or SREF) and a precip type grid to convert the QPF to accumulations (from the Eta, GFS, or SREFs)

      • EVENT Total Accumulation Graphic (ETAG)

      • Manually edited accumulations over an event

      • Separate graphics for Combined Snow/Sleet and Freezing Rain

      • Valid from issuance time through end of event or 72 hours max

      • For non Intermountain Region WFOs only

    • Watch/Warning Exceedance Potential Graphics

      • Shows by how much the ETAG exceeds watch/warning criteria

      • Separate graphics of both combined snow/sleet and freezing rain for both 12 and 24 hour thresholds

      • Not viable in the Intermountain region

        • Criteria by elevation does not allow this strategy to work

    • Low Tracks Graphic

      • HPC forecast of surface low position and track over the CONUS

      • Forecast of central pressure of low in 12 hour increments out through 72 hours

      • Clustering of available model solutions displayed on same map

    • 500 mb Heights Graphic

      • HPC Forecast of 500 mb heights centered over the Intermountain region

      • 12 hour increments out through 72 hours, with corresponding brief descriptive

    Hpc winter weather experiment products l.jpg
    HPC Winter Weather Experiment Products percentage

    • Products are available on WWE Web Site


      • Password-protected to prevent general viewing of “not-ready-for-prime-time” products

      • Contact Pete Manousos ([email protected]) to obtain password to site

    Winter weather products directly from sref graphical l.jpg
    Winter Weather Products directly from percentageSREF (graphical)

    (Products on NAWIPS at HPC)

    • Probability of freezing rain for each 3, 6, 12 and 24 hour period

    • Joint probability of freezing rain and PQPF exceeding specified criteria

    • Mean, maximum and minimum snow amounts and freezing rain for 3, 6, 12 and 24 hour periods

    Sref case examples 31 jan 2002 l.jpg
    SREF case examples: 31 Jan 2002 percentage

    Probability of Snow

    Probability of Freezing Rain

    27 hour forecasts valid 12 UTC January 31

    Dominant Precipitation Type

    9AM Radar Jan. 31, 2002

    Slide92 l.jpg

    SREF case examples: 31 Jan 2002 percentage

    9AM Satellite January 31, 2002

    Where’s the rain-snow line?

    Slide93 l.jpg

    SREF case examples: 30 Dec 2000 percentage(“Millenium Storm”)

    24 h accumulated precipitation

    Eta 36 h forecast from 12 UTC 29 Dec 2000

    SREF spaghetti diagram

    24 h accum. precip > 0.5 in

    24 h fcst from 12 UTC 29 Dec 2000

    • Operational Eta forecast heavy snow across

    • Washington, D. C. and Baltimore metro areas and

    • southeast PA.

    • Most SREF members kept the heavy precipitation offshore

    • Official forecast: Winter Storm Warning, 3-6” DC, 5-10” Baltimore

    • Verification: clear skies, no precipitation across DC/Baltimore/northern Maryland but

    • heavy snow fell from the northeast end of Philadelphia northeastward

    6 7 jan 2002 observed snowfall l.jpg
    6-7 JAN 2002, observed snowfall percentage

    SREF case examples: 6-7 Jan 2002

    Slide95 l.jpg

    SREF case examples: 6-7 Jan 2002 percentage

    3 h QPF = 0.2”

    Sea-level pressure = 1002 hPa

    • Spaghetti diagrams from SREF run just hours

    • before snowstorm began:

    • Forecast heavy precip and forecast snow area do not intersect - no forecast of heavy snow!

    • Previous SREF runs had precip further south, hardly any where heavy snow verified

    • Why was SREF forecast so bad? Continue…

    Contours outline precip type = snow

    Slide96 l.jpg

    SREF case examples: 6-7 Jan 2002 percentage

    Raob wind, raob hght, analysis wind, hght

    Notice winds in trough axis, especially sharpness of observed vs. analysis wind shift and 100 knots observed at Atlanta.

    Eta analysis just as bad

    Slide97 l.jpg

    SREF case examples: 6-7 Jan 2002 percentage

    Every individual ensemble member initial condition which includes perturbations, such as the member shown here (labeled rsmp1), still did not come close to matching the observed strength and sharpness of the winds in the trough axis

    When the analysis has large errors, even ensemble perturbations won’t include reality, and forecast verification will lie outside the ensemble envelope! Always look out for bad analyses!

    Slide98 l.jpg

    SREF case examples: 6-7 Jan 2002 percentage

    Convection over Gulf in sharp trough – often trouble for analysis!

    Ruc 20 l.jpg
    RUC-20 percentage

    • 3D-VAR analysis implemented (yes it finally happened in May 2003)

    • See info on COMET NWP matrix page at

    • and the

    • RUC page at (esp. see online TPB)

      • Large improvement in error stats (precip, raobs, METARS, sensible weather, visibility, cloud patterns, everything!)

      • 2-level snow model

      • GOES cloud top assimilation, boundary layer profilers

      • many many model changes.

    • Still uses theta-sigma hybrid coordinate.

    Expected effects of new ruc 3 d var l.jpg
    Expected Effects of New RUC 3-D Var percentage

    • Slight improvement or about equal skill overall in 3-h and 12-h forecasts compared to those from the previous RUC OI analysis as verified against rawinsondes.

    • Closer fit to observations than the RUC OI analysis.

    • A smoother analysis increment (correction to 1-h forecast field) resulting in less noise in short-range forecasts.

    • Capability for assimilation of indirect observations such as radial winds, satellite radiances and wind speeds.

    Precipitation type in ruc l.jpg
    Precipitation Type in RUC percentage

    • RUC Precip type is

    • notderived from the Baldwin-Schichtel algorithm used for Eta p-type grids

    • not always exactly the same as in the model’s microphysics, though it’s close

    • RUC Precip type output can be mixed (includes multiple types)

    • [define surface T based on “minimum orography”, same as used for 2-meter temperatures; fits METAR elevations more closely than topography used for dynamics and has deeper valleys]

    • Includes rain when microphysics rain rate at ground is not too small and surface T >= 0oC

    • Includes freezing rain same as rain but surface T < 0oC and at any higher level T > 0oC

    • Includes sleet when microphysics graupel rate at ground is big enough and bigger than the snowfall rate at the ground and there is enough rainwater at some level and surface T < 0oC

    • Includes snow when

      • Microphysics snowfall rate at ground is not exceedingly small, or

      • Microphysics graupel rate at ground is big enough but smaller than snowfall rate, or

      • Microphysics graupel rate at ground is big enough and 0oC < surface T < 2oC, or

      • Microphysics graupel rate at ground is big enough and there isn’t much rainwater at any level

    • For details on RUC output variables, see

    Ruc land surface process parameterization l.jpg
    RUC Land-surface Process Parameterization percentage

    • Updated in 20 km for fall 2002 (last year)

    • change in thermal conductivity – better diurnal cycle

    • frozen soil physics, 2-layer snow model

    Purpose –

    Improve near-sfc,

    precip, cloud fcsts

    Ongoing cycle

    of soil moisture,

    soil temp, snow


    2-layer snow model

    Slide103 l.jpg

    RUC: 2-layer snow model update percentage

    Problem – Too cold temps at night (with clear skies, low winds) over thin snow layer. Similar to Eta patchy snow problem solved with crisis fix – Feb 02

    Solution – couple thin layer of soil with thin layer of snow cover (implemented October 2002)

    7.5 cm

    S n o w

    2-layer snow model

    4 cm

    5 cm

    1-layer snow model

    S o i l

    coupled snow-soil layer

    Temperature over snow cover comparison between operational ruc experimental ruc w thin snow fix l.jpg
    TEMPERATURE OVER SNOW COVER percentagecomparison between operational RUC & experimental RUC w/thin snow fix

    RUC: 2-layer snow model update

    Valid 1200 UTC 5 March 2002

    Difference big


    Control (21-h fcst)

    Experimental – Control difference


    Control - 19 C

    Experiment - 10 C

    Observed - 11 C

    (12 F)

    Experimental (21-h fcst)

    Slide105 l.jpg
    After thin snow fix, area of snow cover better matches the NESDIS snow coverthough still not perfect.

    RUC: 2-layer snow model update

    RUC-40, old

    RUC-20, new

    10 February 2001

    1500 UTC

    More realistic snow cover with fix

    Oct 2002 thin snow fix improves surface temperature forecasts in areas with shallow snow cover l.jpg

    RUC: 2-layer snow model update NESDIS snow cover

    Oct 2002 thin snow fix improves surface temperature forecasts in areas with shallow snow cover

    Bias results from 4-14 February 2001

    with thin snow fix, reduced cold bias

    Avg surface tempbiases from 3-h fcst for stations with snow depth < 10 cm

    Feb 4 5 6 7 8 9 10 11 12 13 day

    0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 hour

    with thin snow fix, reduced cold bias

    Avg surface tempbiases from 3-h fcst for stations for all stations with snow cover

    0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 12 0 time

    Ruc good for light fluffy snow cover l.jpg
    RUC: good for light, fluffy snow cover NESDIS snow cover

    Remember the case shown in the Eta section when thin snow melted early in the forecast period, but not in the model, so Eta forecast 2-meter temperatures were much too cold?

    Here’s the same case, RUC vs. Eta comparison. RUC did well.





    Slide108 l.jpg

    • COMET NESDIS snow cover

    • Keeps you informed through the Eta and AVN newsgroups.

    • Read it like email, but it doesn’t get mixed up with your

    • regular email

    • Post questions, get answers

    • Read questions from other forecasters

    • Traffic light, not too much to read

    • Discuss/explain important model error characteristics when

    • they are happening, timely information!

    • Start at

    Slide109 l.jpg

    COMET NESDIS snow cover ensemble training

    Ensemble Module


    Coming soon, might be ready by the time you read this

    Ensemble Forecasting Powerpoint

    From WDTB Winter Weather Workshop, July 2003

    Two versions:

    Condensed version:

    Full version:

    The comet nwp model matrix l.jpg
    The NESDIS snow coverCOMET NWP Model Matrix

    Slide111 l.jpg NESDIS snow cover

    More cases being added, some under development now