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

Acknowledgements

• Steve Tracton (NCEP/EMC) • Bill Bua and Stephen Jascourt

(NCEP/COMET/UCAR)

• Zoltan Toth (NCEP/EMC)

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

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

If you’ve used more than one model in the forecast process,

you’ve done ensemble forecasting

!

Example: An Ensemble of Three NWP Models

• • •

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

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

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 => even a perfect

NWP model can yield large errors (uncertainties) in forecasts

–

NWP models not perfect: only estimate dynamical and physical behavior => added

source of error (uncertainties) in forecasts

How chaotic can the atmosphere be?

8 day forecast

Chaos can reign even in the short-range!

3.5 day forecast

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(D+n) at future time, D+n.

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.)

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

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

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

So, what’s 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.

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

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: Let’s see what grows!

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

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

EMC

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

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

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

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

•

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) ==> 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

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 =>

“ One detailed mesoscale model based forecast could allow the user to make highly specific and detailed inaccurate forecasts.” (after Grumm )



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

.”

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).

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

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.

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

(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

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

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)

Mean and Spread: Interpretation

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 meter s

Uncertainty in strength of system

Uncertainty in intensity

3 day forecast from 00 UTC 11/2/01

Uncertainty in forward speed/location 980, SD~14

Hurricane Michelle SD hPa

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

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

Unpredictable, Strong gradient Ridge/Trough Highly predictable

010519/0000V63 SREFX-CMB 500MB; 5820M

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/images/ens

“Spaghetti” Diagram Interpretation

Phase uncertainty Amplitude uncertainty

“Spaghetti” Diagram Interpretation: Clustering

Clustering Ensemble mean

“Spaghetti” Diagrams: Interpretation

“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

“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)

Sequence provides info on envelope of storm tracks

010519/0000V63 SREFX-CMB; SFC LOWS

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

010519/0000V63 SREFX-CMB; SHADED, IN AT LEAST 60% OF MEMBES

Probability charts combine info from Spaghetti, but later permits view of individual solutions 010519/0000V63 SREFX-CMB; SPGHETTI .25”

010519/0000V63 SREFX-CMB; LIFTED INDEX PROB 0F < -4

010519/0000V63 SREFX-CMB; CAPE > 2000J/KG

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

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

EXAMPLE OF POINT DATA – “BOX AND WHISKER PLOTS”

0 6 12 18 24 30 36 42 48 54 60

FHR=>

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.

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

– – –

Yellow=RSM Green=SREF Eta Bold contours = RSM and Eta means

outliers

SREF Behavior: The Eta-KF Ensemble Subset

• • •

Increased ensemble spread

–

Could be result of using KF/mass-flux scheme or use of 4 th 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

SREF Eta/BMJ SLP mean/spread

SREF Eta/KF SLP mean/spread

Increased spread

SREF Eta/BMJ CAPE

SREF Eta/KF CAPE

Same Deterministic Model with Different Convection Schemes Results In Different Precipitation Forecasts

EMC

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”

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)

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

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

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

Winter Weather Products directly from SREF (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

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

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 T+5hrs 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

24 Hour Snow Thresholds

24 Hour Freezing Rain Thresholds

Watch/Warning (WSW) Guidance Graphic

Ensemble Performance January 31, 2002 Prob Snow Prob Freezing Rain 27 hour forecasts valid 12Z January 31 9AM Radar Jan. 31, 2002

Ensemble Performance January 31, 2002 Dominant precipitation type 27 hour forecast valid 12Z January 31 9AM Radar January 31

9AM Satellite January 31, 2002 Now that’s a Mesoscale feature!!

Whose knife was used to slice this?

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)

Based on Op models, official forecasts for DC as late as 21Z 24 Jan called for only 40% chance of

light

snow!

Ensemble provides a clear “heads up” on morning of 24 th 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

Wide range of solutions (CTL vs Best) in precip and Storm Track (next) => Deterministic (yes/no forecast) very risky!

12 hr (top) and 24 hr (bottom) MSLP from 12Z 24 Jan for “worst” and “best” SREF members.

Eta from 12 GMT 29 Dec 24 hour accuml precip ending 00GMT 31 Dec 2000 Official forecast => 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 30 th with sunny skies and, surprise, snow .

no

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

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

EMC

Eta BMJ WARM SEASON CASE: MAY 4-5,02 RSM Eta KF

WARM SEASON CASE: MAY 4-5,02 Op Eta (12km)

WARM SEASON CASE: APRIL 7-8,02

Eta BMJ WARM SEASON CASE: APRIL 7-8,02 Eta KF RSM OpEta (12km

)

Eta BMJ WARM SEASON CASE: APRIL 7-8,02 Eta KF RSM OpEta (12 km)

60 hr Eta vt 12Z Aug 6, 2001

63 hr SREF vt 12Z Aug 6, 2001

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 => combination of uncertainties related to dynamically amplifying analysis errors and model formulation

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 => 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!!

EMC

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.gov:8080/ens/training/ncepwks/ncepwks.html

http://sgi62.wwb.noaa.gov:8080/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.html

http://www.met.utah.edu/jhorel/html/models/model_ens.html

http://www.wmo.ch/web/www/DPS/ET-EPS-TOKYO/Documentation-plan.html

http://www.hpc.ncep.noaa.gov/ensembletraining http://www.ecmwf.int/newsevents/training/rcourse_notes/GENERAL_CIRCUL ATION/CHAOS/Chaos.html