Short%20Range%20Ensemble%20Forecasts

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Short Range Ensemble Forecasts

• A NEW NWP TOOL FOR THE 0-3 DAY TIME RANGE

JUN DU NCEP/EMC WASHINGTON, DC USA
Where America's Climate and Weather Services
Begin
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Acknowledgements

• Steve Tracton (NCEP/EMC)
• Bill Bua and Stephen Jascourt (NCEP/COMET/UCAR)
• Zoltan Toth (NCEP/EMC)

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What we will cover?

• Definition of an ensemble forecast system
• Rational/justification for ensemble forecasting
• Atmosphere as a chaotic system
• Initial and boundary condition uncertainty
• Model errors/uncertainty
• NCEP Short-Range Ensemble Forecast (SREF) System
  pronounced as serf
• Interpretation of SREF products
• Product examples
• Case studies

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What is an ensemble?

• A set of multiple predictions valid at the
  same time generated from reasonably different
  initial conditions and/or with various credible
  versions of models, the objective being to
  improve skill through ensemble averaging, which
  eliminates non-predictable components, and to
  provide reliable information on forecast
  uncertainties (e.g., probabilities) from the
  spread (diversity) amongst ensemble members

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Example An Ensemble of Three NWP Models
If youve used more than one model in the
forecast process, youve done ensemble
forecasting!

• AVN, UKMET, ECMWF
• All initialized 12Z 6 May 2002
• 72-hr forecasts valid 12Z 9 May 2002

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ENSEMBLE FORECASTING
SPAGHETTI
CLUSTERS

• SLIGHTLY DIFFERENT INITIAL CONDITIONS OR MODEL
  FORMULATIONS PRODUCE A NUMBER OF POSSIBLE
  FORECASTS AND THE SPREAD OF THESE FORECASTS
  QUANTIFIES UNCERTAINTY

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What is the rationale for ensemble prediction?

• Atmosphere is essentially a chaotic system
• Result Small perturbations (errors) in the
  atmosphere can potentially grow into large
  differences in atmospheric evolution
• Uncertainties (perturbations) in analyses
  inevitable now and forever gt even a perfect NWP
  model can yield large errors (uncertainties) in
  forecasts
• NWP models not perfect only estimate dynamical
  and physical behavior gt added source of error
  (uncertainties) in forecasts

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How chaotic can the atmosphere be?
8 day forecast
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Chaos can reign even in the short-range!
3.5 day forecast
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Schematic of Ensemble Prediction The initial
probability PDF(D) rep-resents the initial
uncertainties. From the best estimate of the
initial state a single deterministic forecast
(blue solid curve) is performed. This single
deterministic forecast fails to predict correctly
the future state (red dotted curve). An ensemble
of perturbed forecasts (thin blue solid curves)
start- ing from perturbed initial conditions
designed to sample the initial un- certainties
can be used to estimate the probability PDF(Dn)
at future time, Dn.
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What do we want an ensemble prediction system
(EPS) to do?

• Encompass the case dependent range of possible
  forecast scenarios by region, circulation system,
  sensible weather elements, etc.
• Provide the most skillful forecast probability
  distribution (PDF) within the range of
  possibilities
• Facilitate the communication of forecast
  uncertainty - probabilistic forecast products -
  to the end-users (public, emergency managers,
  government agencies, etc.)

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How is an EPS made?

• In principle, can use one or a combination of
• Different initial atmospheric states
• Different models or variations on the same model
  (perturbed physics, dynamics, numerics)
• Different lateral or lower boundary conditions

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Initial condition ensembles

• Ideally
• Sufficient ensemble members to adequately
  prescribe the PDF of the initial condition
  uncertainty
• A sufficiently skillful model to reliably predict
  the PDF of all possible outcomes

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Initial condition ensembles

• Realistically
• Can only have a relatively few ensemble members,
  because of computational and operational time
  constraints
• We have imperfect NWP models, so even if we get
  the initial condition uncertainty right, we may
  not get the right forecast PDF (spread,
  probability of events) from them

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So, whats an EPS developer to do?

• Find the initial condition uncertainties that
  matter (not all errors in analyses are important)
• Make use of the initial conditions that project
  onto the most rapidly growing atmospheric modes
• This gives us the largest spread given the
  limited number of individual ensemble members
  possible.

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How do we find the initial condition errors
that will grow?

• Singular vectors (ECMWF)
• Seeks out non-linear growing atmospheric modes
• Breeding method for initial condition
  perturbations (NCEP, Toth and Kalnay, 1993)
• Works out mathematically and practically to be
  roughly equivalent to singular vector method, but
  at a much lower cost

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How do we breed perturbations?

• Begins with analysis/forecast cycle which differs
  only in initially prescribed random distribution
  ("seed") of analysis errors.
• Initially random perturbations added and
  subtracted from the control (not operational, but
  at less resolution) analysis (applies to all
  model parameters!)
• Each breeding cycle generates a pair of perturbed
  analyses (10 in all).
• Goal Lets see what grows!

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How do we breed perturbations?

• Continue process of breeding cycles over
  consecutive periods.
• Procedure
• Find differences between the ensemble members and
  the control forecast
• Scale them back so they are about the size of
  analysis errors
• Add and subtract from control analysis and then
  run ensemble members again

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How do we breed perturbations?

• After several cycles, the growing modes dominate
• Scaled ensemble perturbations at this point (and
  in the future) estimate the analysis errors that
  result in the most rapidly growing modes

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EMC
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NCEP Global EPS Configuration

• Current ensemble configuration (all
  perturbations developed from breeding method)
• 00Z
• T170 high resolution control out to 7 days, then
  out to 16 days at T62 resolution
• T126 control that is started from truncated T170
  analysis truncated to T62 after 84 hr
• 10 perturbed forecasts at T126 out to 84 hrs,
  then T62

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NCEP Global EPS Configuration

• 12Z
• T170 control out to 126 hrs ("Aviation
  forecast"), then T62 out to 16 days
• 10 perturbed forecasts generated and run the same
  way as at 00Z
• Results in 23-member ensemble over 24 hours

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Global model ensemble products

• On web
• Spaghetti diagrams
• NH ensemble mean/spread
• NH control fcst and normalized spread
• Relative measure of predictability
• Probabilistic precipitation forecasts

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NCEP Global EPS Configuration

• Future plans
• Add 06Z and 18Z ensembles (10 perturbations and
  control)
• Would give 45 ensemble members per day
• Increase ensemble control and member resolution
  concurrent with increases in operational model
  resolution
• First 5 days or so only, no demonstrated benefit
  beyond the medium range

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• Why We Need Ensembles for Mesoscale)
• Deal with uncertainties in analyses and model
  formulation
• But Requires tradeoffs when computer resources
  limited (e.g., model resolution)
• But But Mesoscale predictability often
  substantially controlled by synoptic
  predictability (and uncertainties therein)
• gt Subjective or statistically based
  downscaling possible to get
  uncertainties in mesoscale weather
• Ideal Ensembles with highest resolution
  justifiable (Issue point of diminishing return?)
• Compromise Combination of single (or few) high
  resolution and coarser resolved ensemble

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CONSIDER!!!

• High Resolution Mesoscale models
• allow us to see features not in coarser models
• But even small timing and placement errors can
  be significant in attempt to accurately forecast
  details (see Mass, et al., 3/02 BAMS!!!).
• But But Forcaster judgement could mitigate

• One model (even with forecaster input)
• is an all or nothing proposition gt

One detailed mesoscale model based
forecast could allow the user to make
highly specific and detailed inaccurate
forecasts. (after Grumm)
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• NWS Vision 2005 Goals
• NWS will move towards adding probabilistic
  forecast products
• Move from subjective model to model
  comparison to the use of more objective
  ensemble prediction systems

• American Meteorological Society (AMS)
  Statement - Enhancing Weather Information with
  Probability Forecasts (3/02 BAMS!)
• The AMS endorses probability forecasts and
  recommends their use be substantially
  increased.

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ROLE IN SEAMLESS PRODUCT SUITE Together,
Medium Range Ensemble Forecasting (MREF) and
Short Range Ensemble Forecasting (SREF) systems
are viewed as integral in a seamless suite of
products that enable estimates in the forecast
confidence of specific weather threats - first,
in the context of the larger-scale circulation
patterns and associated weather at medium ranges
(3-10 days) and, then, in the details of
specific weather systems and sensible weather at
short ranges (0-3days).
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Current Short Range Ensemble Forecast (SREF)
System

• Meso Eta Model (4 members 1 control)
• Eta coordinate, 48-km resolution, 45 layers
• Updated in January 21, 2002 to have the same
  model physics as the operational Eta-12
• Regional Spectral Model (RSM, 4 members 1
  control)
• Sigma coordinate, 48-km equivalent resolution,
  42 layers
• Has old AVN/MRF large scale precipitation and
  convective schemes (inferred clouds, SAS using
  tallest convective cloud)
• Moving toward having same precipitation/convection
  physics as operational AVN/MRF
• Other physics essentially the same as operational
  AVN
• /- perturbations from separate Meso Eta and RSM
  breeding cycles

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Current Short Range Ensemble Forecast, Cont.

• Forecasts initialized from 21z and 09z so that
  SREF output available by time 00z and 12z Meso
  Eta Model (12 Km) run completed
• SREF guidance available for use with the Meso Eta
  run
• Domain is full North American continent
• GRIB files at 40-km resolution (Grib-212)
• Forecast range is 0-63 hours.

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Short Range Ensemble Forecast (Near-)Future
plans

• Eta ensemble runs with Kain-Fritsch (KF)
  convection, all else left the same, now being
  tested, operational by end of June 2002
• Control and 4 ensemble perturbations
• A model physics-perturbed ensemble of 10 when
  combine with SREF Eta with Betts-Miller-Janjic
  (BMJ)
• Multi-Model - initial condition and physics
  perturbed ensemble of 15 when combined with SREF
  Eta-BMJ and RSM

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(Longer Term) Future Work

• System Development
• Test and evaluate possible enhancements
  (relative contributions and any necessary
  tradeoffs)
• PERTURB PHYSICS (e.g.,
  Convection, BL processes)
• ADDITIONAL MEMBERS (40-50)
• ADDITIONAL MODEL DIVERSITY
  (E.G., RUC, ETA/KF)
• INCREASED MODEL RESOLUTION
• TRANSITION TO WRF (LONGER
  TERM)
• Product Development
• Generic
• User Specific
• Statistical Post Processing (Generic
  Ensemble MOS)
• Extensive Forecaster/User Training and Education

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Ensemble Products on Web

• Challenge
• Avoid information overload!
• Condense information from inidvidual ensemble
  members to useful and user friendly form
• Assure users make appropriate interpretations of
  SREF products

http//lnx48.wwb.noaa.gov/SREF/SREF.html
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General SREF (and Global Ensemble) Product
Categories

• Mean and spread
• Spaghetti diagrams of one contour line from all
  ensemble members
• Probability charts (based on how many ensemble
  members meet or exceed certain thresholds)

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Mean and Spread Interpretation
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3 day forecast from 00 UTC 11/2/01
Depth uncertainty - how strong will trough be?
Phase uncertainty - where will the trough axis be?
SD - meters
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Uncertainty in strength of system
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3 day forecast from 00 UTC 11/2/01
Uncertainty in intensity
Uncertainty in forward speed/location
980, SD14
SD - hPa
Hurricane Michelle
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Mean and Spread Advantages

• Compact communication of ensemble forecast
  information
• Can see field over entire domain
• Ensemble mean on average has greater skill than
  any individual member
• Spread (sample standard deviation) quantifies the
  degree of uncertainty

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Mean and Spread Limitations

• Assumes normal distribution of forecasts (bell
  curve with maximum likelihood at mean)
• Mean may hide important details
• Bi- or multi-modal solutions
• Timing problems in prediction of features
• Precipitation forecasts (particularly where
  convective precipitation is expected to be
  important)
• Can use spread as guide to where mean may not be
  communicating the correct information, and use
  additional tools to make further assessments

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Ridge/Trough Highly predictable
Unpredictable, Strong gradient
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010519/0000V63 SREFX-CMB 500MB 5820M
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3-day forecast from 00 UTC 11/2/01, spaghetti
diagram for ensemble
Uncertain location of incoming western trough
Uncertain amplitude of eastern trough
From CDC web site http//www.cdc.noaa.gov/map/im
ages/ens
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Spaghetti Diagram Interpretation
Phase uncertainty
Amplitude uncertainty
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Spaghetti Diagram Interpretation Clustering
Clustering
Ensemble mean
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Spaghetti Diagrams Interpretation
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Spaghetti Diagrams Advantages

• Avoids the assumption of normally distributed
  data and pitfalls thereof
• Can tell if ensemble mean, if present, is
  representative of the ensemble as a whole
• Shows spread among ensemble members and whether
  there is clustering of members around two or more
  forecasts
• Shows mode (I.e. most frequently occurring
  solution)
• Indicates outliers which may overly influence the
  ensemble mean and spread

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Spaghetti Diagrams Limitations

• Limited to one or only a few contours
• Cannot see full field of interest over the full
  domain
• May not choose the right contour (use ensemble
  mean/spread to make the best choice)

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Sequence provides info on envelope of storm tracks
010519/0000V63 SREFX-CMB SFC LOWS
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010519/0000V63 SREFX-CMB 24HR PQPF OF .25
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010519/0000V63 SREFX-CMB SHADED, IN AT LEAST
60 OF MEMBES
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Probability charts combine info from Spaghetti,
but later permits view of individual solutions
010519/0000V63 SREFX-CMB SPGHETTI .25
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010519/0000V63 SREFX-CMB LIFTED INDEX PROB 0F lt
-4
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010519/0000V63 SREFX-CMB CAPE gt 2000J/KG
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Probability charts Advantages

• Depicts probabilities for exceeding critical
  value in a compact manner
• Variable of interest is seen over the full domain
• Uses actual distribution of data from ensemble
  members to determine probabilities

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Probability charts Limitations

• Do not get information on full PDF
• Only know percentage of ensemble members that
  exceed the value (sampling problem of limited
  ensemble size)
• Need to use several threshold values for complete
  picture
• Does not depict maximum value

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EXAMPLE OF POINT DATA BOX AND WHISKER PLOTS
0 6 12 18 24 30
36 42 48 54 60
FHRgt
The blue boxes represent values from the 25
quartile (bottom of the box) to the 75 quartile
(top of the box) with the median of the ensemble
as a horizontal line in the box. The whiskers
extend to both the max and min values supplied
by the EPS output.. This allows you to instantly
ascertain the uncertainty and the median (not
the mean) in one view.
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SREF Behavior Current Configuration

• RSM and Eta tend to group into separate clusters
  (especially for QPF)
• SREF Eta tends to have smaller spread than the
  RSM
• Illustration in next three slides
• YellowRSM
• GreenSREF Eta
• Bold contours RSM and Eta means

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outliers
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SREF Behavior The Eta-KF Ensemble Subset

• Increased ensemble spread
• Could be result of using KF/mass-flux scheme or
  use of 4th order (less damping) diffusion scheme
• Sharper gradients, including in stability
  parameters
• Tendency for larger instability
• Consistent with later triggering of convective
  scheme than for BMJ, particularly in weakly
  capped regions

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SREF Eta/BMJ SLP mean/spread
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SREF Eta/KF SLP mean/spread
Increased spread
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SREF Eta/BMJ CAPE
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SREF Eta/KF CAPE
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Same Deterministic Model with Different
Convection Schemes Results In Different
Precipitation Forecasts
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EMC
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Focus on Winter Weather

ER/NCEP Winter Weather Experiment

• Goals
• Improve Winter Weather Services to the public
  through coordination of the winter weather
  watches/warnings with National guidance products
• Test short range ensemble for their applications
  to winter weather forecasting
• Results Encouraging

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WHY??

• Recent Snowstorms Creating the challenge for a
  more focused NWS effort
• January 25, 2000
• December 30, 2000
• March 4-6, 2001
• HPC-WFO Interaction
• Need for coordination as forecast capabilities
  are extended to medium range
• Need for a more unified message to the media
  (quantify uncertainty)

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MAJOR SNOWSTORM AMBUSHES WASHINGTON
Not Good- especially when effecting DC (just
after announce-ment of new Super Computer by
NWSHQ
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NCEP Winter Weather Experiment

• Time line Nov 1 May 1
• Participants
• NCEP EMC
• Provide Short Range Ensemble (SREF) Guidance
  (operational in May 2001)
• NCEP HPC
• Provide SREF- and MRF ensemble- based Winter Wx
  guidance
• Collaboration with WFOs (Chat Room Technology)
• WFOs (Eastern Region)
• Mt. Holly, State College, Sterling, Wakefield
• Use graphical guidance from NCEP to produce
  coordinated Winter Storm Watches/Warnings

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WWE Products/Services

• 2 shifts per day
• 630AM/PM 430PM/AM
• Issuing 3 graphics
• Watch/Warning Guidance Graphic
• Storm Tracks Graphic
• Conditional Probability Graphic
• Using Chat Room software
• As WFO coordination tool

Low track valid 12Z/06 00Z/08
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Winter Weather Products directly fromSREF
(graphical)

• 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

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SREF Conditional Probability Product
Example of product directly from SREF that is
available to HPC as guidance for generating
collaborative winter weather products. Storm
tracks include SREF and all other models
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Watch/Warning Guidance Graphic

• Amounts compared to threshold values from ERH
• Color coded to indicate percentage of threshold
  met
• Graphic produced for Day 1 and Day 2
• Available at T5hrs from ensemble start (14Z/01Z)
• Posted to chat room for comments by 1445Z/0245Z
• Distributed at 15Z/03Z
• Second chat session at 19Z/07Z if necessary

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24 Hour Snow Thresholds
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24 Hour Freezing Rain Thresholds
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Watch/Warning (WSW) Guidance Graphic
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Ensemble Performance January 31, 2002
Prob Snow
Prob Freezing Rain
27 hour forecasts valid 12Z January 31
9AM Radar Jan. 31, 2002
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Ensemble Performance January 31, 2002
Dominant precipitation type 27 hour forecast
valid 12Z January 31
9AM Radar January 31
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9AM Satellite January 31, 2002
Now thats a Mesoscale feature!!
Whose knife was used to slice this?
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CASE STUDIES WINTER
Jan 25-26, 2000 East Coast Surprise
Snowstrom Dec 29-30, 2000 East Coast
Millennium Snowstorm WARM SEASON April
7-8, 2002 Texas Squall Line May 3-4, 2002
Southeast U.S Precip Tropical Storm
Barry (August 2001)
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Based on Op models, official forecasts for DC
as late as 21Z 24 Jan called for only
40 chance of light snow!
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Ensemble provides a clear heads up on morning
of 24th for the possibility of a major snow
event, especially when considered in context of
independent information from satellite imagery
and radar that suggested storm track closer to
coast and precip further inland than available
operational models were indicating
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Wide range of solutions (CTL vs Best) in precip
and Storm Track (next) gt Deterministic (yes/no
forecast) very risky!
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12 hr (top) and 24 hr (bottom) MSLP from 12Z 24
Jan for worst and best SREF members.

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Official forecast gt winter storm warn-ing
with of 3-6 inches predicted in DC and 5-10
inches in Balt. Reality DC and Balt woke up
on the morning of the 30th with sunny skies and,
surprise, no snow.
Eta from 12 GMT 29 Dec 24 hour accuml precip
ending 00GMT 31 Dec 2000
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SREF 24hr spaghetti from 12Z 29 Dec for .50 12
hr precip ending 12 GMT 30 Ensemble indicated
a 30-40 chance of signif- icant snow thus, in
this case SREF gave a heads up (60) for the
chance of no snowstorm
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KEY POINTS
January 24/25, 2000 DC Snowstorm Ensembles
gave heads up for snowstorm in face of
deterministic model and official forecasts of
no snow December 30, 2000 DC
Non-Snowstorm Ensemble gave heads up of no
snow in face of deterministic model (Eta)
predicted and official forecasts of snowstorm
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EMC
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WARM SEASON CASE MAY 4-5,02
RSM
Eta KF
Eta BMJ
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WARM SEASON CASE MAY 4-5,02
Op Eta (12km)
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WARM SEASON CASE APRIL 7-8,02
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WARM SEASON CASE APRIL 7-8,02
Eta KF
Eta BMJ
OpEta (12km)
RSM
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WARM SEASON CASE APRIL 7-8,02
Eta BMJ
Eta KF
RSM
OpEta (12 km)
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60 hr Eta vt 12Z Aug 6, 2001
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63 hr SREF vt 12Z Aug 6, 2001
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Summary Due to observational and model
limitations, the ensemble method offers a
scientifically sound basis to utilize over the
standard deterministic approach for NWP.
Further, EPS output supports operational
forecasters providing both deterministic or
probabilistic forecast products. In
principle, EPS output facilitates an opportunity
to provide ranges of scenarios that may occur, as
well as helping to identify the most likely
result Research indicates that an ensemble
comprised of different models will likely offer
better results than an EPS from one model gt
combination of uncertainties related to
dynamically amplifying analysis errors and model
formulation
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Summary (cont.) There a a number of other ways
to display data EPS output, and each user can
decide which method will benefit them most gt
learning process and continual interaction
between forecasters and EPS developers An EPS
comprised of members lower in resolution can
provide more useful information and user value
than a single higher resolution deterministic
run. Future Directions This is an evolving
science and as computing power and verification
efforts increase and change, so too will the
techniques to produce and visualize ensemble
output ? Stay Tuned!!
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EMC
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Additional Ensemble Links
http//www.wmo.ch/web/www/DPS/WS-S/PROCEEDINGS/
Introduction.htm http//www.euromet.met.ed.ac.uk/
ucisa/teachers/english/nwp/n7400/ n7400003.htm
http//eyewall.met.psu.edu/mos/index.html
http//sgi62.wwb.noaa.gov8080/ens/training/ncep
wks/ncepwks.html http//sgi62.wwb.noaa.gov8080/
ens/enshome.html http//lnx48.wwb.noaa.gov/SREF/
SREF.html http//eyewall.met.psu.edu/ensembles2/
index.html http//eyewall.met.psu.edu/SREF/index
.html http//www.meteo.psu.edu/gadomski/ewall.h
tml http//www.met.utah.edu/jhorel/html/models/m
odel_ens.html http//www.wmo.ch/web/www/DPS/ET-E
PS-TOKYO/Documentation-plan.html
http//www.hpc.ncep.noaa.gov/ensembletraining
http//www.ecmwf.int/newsevents/training/rcourse
_notes/GENERAL_CIRCULATION/CHAOS/Chaos.html