Effective Mesoscale, Short-Range Ensemble Forecasting Tony Eckel** and Clifford F. Mass Presented By: Eric Grimit University of Washington Atmospheric

Effective Mesoscale, Short-Range Ensemble Forecasting

Tony Eckel** and Clifford F. MassPresented By: Eric Grimit

University of Washington Atmospheric Sciences Department

**AFWA, Offutt AFB, NE

This research was supported by…

- The United States Air Force

- The DoD Multidisciplinary University Research Initiative (MURI) program administered by the Office of Naval Research under Grant N00014-01-10745.

- The National Weather Service

Overview Questions

Can an effective mesoscale, short-range ensemble forecast (SREF) system be designed?

Effective: skillful forecast probability

How should analysis uncertainty be accounted for?

How much does model bias impact a SREF system and can it be easily corrected?

How should model uncertainty be accounted for?

FP = 93%

Event Threshold

FP = ORF = 72%

Fre

qu

en

cy

Initial State

Forecast Probability from an Ensemble

EF provides an estimate (histogram) of truth’s Probability Density Function (PDF).

In a large, ideal EF system, Forecast Probability (FP) = Observed Relative Frequency (ORF)

24 h Forecast State 48 h Forecast State

True

PDF

0 5 10 15 20 25 30 35 40 kt0 5 10 15 20 25 30 350 5 10 15 20 25 30 35Surface Wind Speed (kt)

Fre

qu

ency

Fre

qu

en

cy

In practice, things go awry from…• Undersampling of the PDF (too few ensemble members)• Poor representation of initial uncertainty• Model deficiencies

-- Model bias causes a shift in the estimated mean-- Sharing of model errors between EF members leads to reduced variance

EF’s estimated PDF does not match truth’s PDF, and FP ORF

Fre

qu

ency

Probability of Surface Winds > 18.0 kt

0

0

0

0

0

0

0

0

0

0

0

0

0

0

100

100

UW’s Ensemble of Ensembles

# of EF Initial Forecast Forecast Name Members Type Conditions Model(s) Cycle Domain

ACME 17 SMMA 8 Ind. Analyses, “Standard” 00Z 36km, 12km1 Centroid, MM58 Mirrors

UWME 8 SMMA 8 Independent “Standard” 00Z 36km, 12km Analyses MM5

UWME+ 8 PMMA 8 Independent 8 MM5 00Z 36km, 12km Analyses variations

8 PME 8 MMMA 8 Independent operational, 00Z, 12Z 36km

Analyses large-scale

Hom

egro

wn

Impo

rted

ACME: Analysis-Centroid Mirroring Ensemble

PME: Poor Man’s Ensemble MM5: 5th Generation PSU/NCAR Mesoscale Modeling System

SMMA: Single Model Multi-Analysis

PMMA: Perturbed-model Multi-Analysis

MMMA: Multi-model Multi-Analysis

Total of 129, 48-h forecasts (31 Oct 2002 – 28 Mar 2003) all initialized at 00z• Incomplete forecast case days are shaded

Parameters:• 36-km Domain: Mean Sea Level Pressure (MSLP), 500mb Geopotential Height (Z500 )• 12-km Domain: Wind Speed at 10 m (WS10 ), Temperature at 2 m (T2 )

Research Dataset

Verification:

• 36-km Domain: centroid analysis (mean of 8 independent analyses, available at 12 h increments)

• 12-km Domain: ruc20 analysis (NCEP 20-km mesoscale analysis, available at 3 h increments)

November December January

February March

3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 1 1 1 1 11 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 1 2 3 4 5 6 7 8 9 0 1 2 3 4

1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 25 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

36-km Domain (151127)

12-km Domain(103100)

Note: Global PME data was fitted to the 36-km domain

MM5 Nested Model Domains

TrainingPeriod

Bias-correctedForecast Period

TrainingPeriod

Bias-correctedForecast Period

Gridded, Mean Bias Correction

N

n nji

tjitji o

f

Nb

1 ,

,,,,

1 N number of forecast cases (14) fi,j,t forecast at grid point (i, j ) and lead time (t )oi,j observation (centroid-analysis or ruc20 verification)

For the current forecast cycle:

1) Calculate bias at every grid point and lead time using previous 2 weeks’ forecasts

2) Postprocess current forecast to correct for bias:

tji

tjitji b

ff

,,

,,*,, fi,j,t bias-corrected forecast at grid point (i, j ) and lead time (t)*

November December January

February March

3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 1 1 1 1 11 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 1 2 3 4 5 6 7 8 9 0 1 2 3 4

1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 25 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9

Ave

rage

RM

SE

(C

)an

d

(sh

aded

) A

vera

ge B

ias

Uncorrected UWME+ T2

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Ave

rag

e R

MS

E a

nd

Bia

s (C

)

plus01 plus02 plus03 plus04 plus05 plus06 plus07 plus08 mean -2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Ave

rag

e R

MS

E a

nd

Bia

s (m

b)

12 h

24 h 36

h 48 h

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Ave

rag

e R

MS

E a

nd

Bia

s (m

b)


-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

Ave

rag

e R

MS

E a

nd

Bia

s (m

b)

Ave

rage

RM

SE

(C

)an

d

(sh

aded

) A

vera

ge B

ias

Bias-Corrected UWME+ T2

12 h

24 h 36

h 48 h

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0


-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

MultimodelVs.

Perturbed Model

PMEVs.

UWME+

36-km

Domain

*PME exhibits more dispersion than *UWME+ because

*PME (a multi-model system) has more model diversity

*PME is better at capturing growth of synoptic-scale errors

1 2 3 4 5 6 7 8 936h PME0.0

0.1

0.2

0.3

0.4

Verification Rank

36h PME

36h ACMEcore+

*PME*UWME+

Pro

bab

ilit

y

Verification Rank Histogram

Record of where verification fell (i.e., its rank) among the ordered ensemble members:

Flat Well calibrated EF (truth’s PDF matches EF PDF)

U’d Under-dispersive EF (truth “gets away” quite often)

Humped Over-dispersive EF

*Bias-corrected, 36-h MSLP

Comparison of VRHs

“Nudging” MM5 outer domain may improve SREF

Skill vs. Lead Time for FP of the event: MSLP < 1001 mb

BSS = 1, perfect

BSS < 0, worthless

Comparison of Skill

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

*ACMEcoreACMEcore*ACMEcore+ACMEcore+Uncertainty

*UWME+

UWME+

*PME

PME

0.75

0.80

0.85

0.90

0.95

0 12 24 36 48

Lead Time (h)

BSS

0.75

0 12 24 36 48

*PME

PME

0.75

0 12 24 36 48

* Bias-corrected

Value ofModel Diversity

For a Mesoscale SREF

UWMEVs.

UWME+

12-km

Domain

s

eVVZ

Standardized Verification

VerificationOutlierPercentage

% of |VZ| > 3

Pro

bab

ilit

y

*UWME 36 h MSLP Verification Rank Histogram

MRE = 14.4%

VOP = 9.0%

How Much & Where Does Truth “Get Away”?

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

ilit

y

Verification Rank

Case Study Initialized at 00Z, 20 Dec 2002

rank 9 Vz < 3 Vz > 3Z500 EF Mean and Vzrank 1Z500 EF Mean and

VRH Outside Ranks

MRE = 25.7% VOP = 18.5%

*UWME

36-h

s

eVVZ



% of |VZ| > 31 2 3 4 5 6 7 8 9

36h ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

ilit

y

Verification Rank

Pro

bab

ilit

y

*UWME 36 h MSLP Verification Rank Histogram

MRE = 14.4%

VOP = 9.0%


1 2 3 4 5 6 7 8 936h *ACMEcore0.0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 936h *ACMEcore0.0

0.1

0.2

0.3

0.4

36h *ACMEcore

36h *ACMEcore+

1 2 3 4 5 6 7 8 936h *ACMEcore0.0

0.1

0.2

0.3

0.4

36h *ACMEcore

36h *ACMEcore+

1 2 3 4 5 6 7 8 936h *ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

ilit

y

Verification Rank

(d) T2

(c) WS10

(b) MSLP

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

Verification Rank

(a) Z500

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

Verification Rank

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

*UWME*UWME+

VOP 5.0 %

4.2 %

9.0 %

6.7 %

25.6 %

13.3 %

43.7 %

21.0 %

Surface/Mesoscale Variable

( Errors Depend on Model Uncertainty )

SynopticVariable

( Errors Depend on Analysis Uncertainty )

Comparison of 36-h VRHs

*UWME*UWME+

*UWME*UWME+

*UWME*UWME+

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48


*UWME

UWME

*UWME+

UWME+

Comparison of Skill

Skill vs. Lead Time for FP of the event: WS10 > 18 kt

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

Lead Time (h)

BSS

-0.05

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

BSS = 1, perfect

BSS < 0, worthless

* Bias-corrected

0.000

0.001

0.002

0.003

0.004

Re

liab

ility

0.002

0.003

0.004

0.005

0.006

0.007

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

Lead Time (h)

Re

so

luti

on

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

BSS

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48


*UWME

UWME

*UWME+

UWME+

Uncertainty

Skill forP(WS10 > 18 kt)

Conclusions

Bias Correction

• Particularly important for mesoscale SREF in which model biases are often large

• Significantly improves SREF utility by correctly adjusting the forecast PDF

• Allows for fair and accurate analysis

Multianalysis Approach for Representing Analysis Uncertainty

• Contributes to a skilled SREF

• Analyses too highly correlated at times—miss key features

• Limits EF size to number of available analyses- Mirroring produces additional, valid samples of the approximate forecast PDF (i.e., from UWME) but can not correct deficiencies in original sample

• More rigorous approach would be beneficial to SREF

Including Model Diversity in SREF is Critical for Representation of Uncertainty

• Provides needed increase in spread

• Degree of importance depends on the variable or phenomenon considered

• Improves SREF utility through better estimation of the forecast PDF

Conclusions

The Value of Mesoscale SREF May be Further Increased by . . .

• Nudging MM5 outer domain solutions toward the PME solutions

- A PME (multimodel) system is great at capturing large-scale model uncertainty

• Increasing model resolution of SREF members to increase variance at small-scales

• Introducing more thorough model perturbations

• Improving the model to reduce need for model diversity

The End

Backup Slides

Resolution (~ @ 45 N ) ObjectiveAbbreviation/Model/Source Type Computational Distributed Analysis

avn, Global Forecast System (GFS), Spectral T254 / L64 1.0 / L14 SSINational Centers for Environmental Prediction ~55 km ~80 km 3D Var cmcg, Global Environmental Multi-scale (GEM), Finite 0.90.9/L28 1.25 / L11 3D VarCanadian Meteorological Centre Diff ~70 km ~100 km eta, limited-area mesoscale model, Finite 32 km / L45 90 km / L37 SSINational Centers for Environmental Prediction Diff. 3D Var gasp, Global AnalysiS and Prediction model, Spectral T239 / L29 1.0 / L11 3D VarAustralian Bureau of Meteorology ~60 km ~80 km

jma, Global Spectral Model (GSM), Spectral T106 / L21 1.25 / L13 OIJapan Meteorological Agency ~135 km ~100 km ngps, Navy Operational Global Atmos. Pred. System, Spectral T239 / L30 1.0 / L14 OIFleet Numerical Meteorological & Oceanographic Cntr. ~60 km ~80 km

tcwb, Global Forecast System, Spectral T79 / L18 1.0 / L11 OITaiwan Central Weather Bureau ~180 km ~80 km ukmo, Unified Model, Finite 5/65/9/L30 same / L12 3D VarUnited Kingdom Meteorological Office Diff. ~60 km

Operational Models/Analyses of the PME

Perturbed surface boundary parameters according to their suspected uncertainty

Assumed differences between model physics options approximate model error

Design of UWME+

vertical Cloud 36-km 12-km shlw. SST Land UseIC ID# Soil diffusion Microphysics Domain Domain cumls. Radiation Perturbation Table

MRF 5-Layer Y Simple Ice Kain-Fritsch Kain-Fritsch N cloud standard standard

avn plus01 MRF LSM Y Simple Ice Kain-Fritsch Kain-Fritsch Y RRTM SST_pert01 LANDUSE.plus1

cmcg plus02 MRF 5-Layer Y Reisner II Grell Grell N cloud SST_pert02 LANDUSE.plus2

eta plus03 Eta 5-Layer N Goddard Betts-Miller Grell Y RRTM SST_pert03 LANDUSE.plus3

gasp plus04 MRF LSM Y Shultz Betts-Miller Kain-Fritsch N RRTM SST_pert04 LANDUSE.plus4

jma plus05 Eta LSM N Reisner II Kain-Fritsch Kain-Fritsch Y cloud SST_pert05 LANDUSE.plus5

ngps plus06 Blackadar 5-Layer Y Shultz Grell Grell N RRTM SST_pert06 LANDUSE.plus6

tcwb plus07 Blackadar 5-Layer Y Goddard Betts-Miller Grell Y cloud SST_pert07 LANDUSE.plus7

ukmo plus08 Eta LSM N Reisner I Kain-Fritsch Kain-Fritsch N cloud SST_pert08 LANDUSE.plus8Perturbations to

moisture availability,

albedo, and

roughness length

ACMEcore+

CumulusPBL

ACME

1) Albedo

2) Roughness Length

3) Moisture Availability

Plus01 SST PerturbationPlus01 SST Perturbation

VOP = 8.0% MR = 37.3%MRE = 15.1%

*PME


rank 9 Vz < 3 Vz > 3Z500 EF Mean and Vzrank 1Z500 EF Mean and

VRH Outside Ranks

s

eVVZ



% of VZ > 31 2 3 4 5 6 7 8 9

36h ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

ilit

y

Verification Rank

Pro

bab

ilit

y

*ACMEcore 36 h MSLP Verification Rank Histogram

MR = 36.6%MRE = 14.4%

VOP = 9.0%

36-h


VOP = 13.8%

VOP = 8.0%

*ACMEcore+

*PME Z500 Cent Analysis 12Z, 21 Dec 2002


36 h Forecast

Z500 EF Mean and Standardized Verification, Vz

Vz < 3

Vz > 3s

eVVZ

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

BSS

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48


0.00

0.02

0.04

0.06

Re

liab

ility

0.04

0.06

0.08

0.10

0.12

0.14

00 03 06 09 12 15 18 21 24 27 30 33 36 39 42 45 48

Lead Time (h)

Re

so

luti

on

*ACMEcore

ACMEcore

*ACMEcore+

ACMEcore+

Uncertainty

Skill forP(T2 < 0°C)

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

0 12 24 36 48

Lead Time (h)

EF

Sp

rea

d

or

M

SE

of

EF

Me

an

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 12 24 36 48

Lead Time (h)

EF

Sp

rea

d

or

M

SE

of

EF

Me

an

(m2 /

s2 )

(C2 )

WS10 T2

*ACMEcore Spread

MSE of *ACMEcore Mean

*ACMEcore+ Spread

MSE of *ACMEcore+ Mean

c2 11.4 m2 / s2 c

2 13.2 ºC2

Ensemble Dispersion*Bias Corrected

0.0

0 12 24 36 48

ACMEcore Spread (Uncorrected)

ACMEcore+ Spread (Uncorrected)

Ensemble Dispersion at Smaller Scales

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0 12 24 36 48

Lead Time (h)

EF

Sp

rea

d

or

M

SE

of

EF

Me

an

En

sem

ble

Var

ian

ce (

m2 /

s2 )

UWME WS10

36-km12-km

36 km Grid Spacing

12 km Grid Spacing

3 h Cumulative Precipitation

An analysis is just one possible initial condition (IC) used to run a numerical model (such as NCEP’s Eta).

T

The true state of the atmosphere exists as a single point in phase space that we never know exactly.

A point in phase space completely describes an instantaneous state of the atmosphere.For a model, a point is the vector of values for all parameters (pres, temp, etc.) at all grid points at one time.

e

Trajectories of truth and model diverge:

1) IC error grows nonlinearly

2) Model error adds to the problem

PHA

SE

SPACE

12 hForecast

36 hForecast

24 hForecast

48 hForecast

T

48 hVerification

T

36 hVerification

T

24 hVerification

T

12 hVerification

Eta Analysis

e

a

uc

j

tg

n

2 1 0 1 2 34

2

0

2

4

6

7

-2.96564

Core ,i 2

Cent ,1 2

32.5 ,Core ,i 1 Cent ,1 1

T

T

Analysis Region

8 “Core” Analyses

Plug each analysis into MM5 to create an ensemble of mesoscale forecasts (cloud of future states encompassing truth).

1) Reveal uncertainty in forecast 2) Reduce error by averaging to M 3) Yield probabilistic information

M

48h forecast Region

PHA

SE

SPACE

Success and Failureof

ACME

ACMEcore

Vs.ACME

36-km

Domain

12-km

Domain

cmcg*

The ACME Process

STEP 1: Calculate best guess for truth (the centroid) by averaging all analyses.

STEP 2: Find error vector in model phase space between one analysis and the centroid by differencing all state variables over all grid points.

STEP 3: Make a new IC by mirroring that error about the centroid.

cmcgC cmcg*

Sea

Lev

el P

ress

ure

(mb)

~1000 km

1006

1004

1002

1000

998

996

994

cent

170°W 165°W 160°W 155°W 150°W 145°W 140°W 135°W

eta

ngps

tcwbgasp

avn

ukmo

cmcg

e

a

uc

j

tg

n

c

2 1 0 1 2 34

2

0

2

4

6

7

-2.96564

Core ,i 2

Cent ,1 2

32.5 ,Core ,i 1 Cent ,1 1

T

M

T

Analysis Region

48h forecast Region

ACME’s Centroid Analysis

Eta Analysis

Centroid Analysis

PHA

SE

SPACE

PHA

SE

SPACE

e

n

ac

u

tg

T

j

T

Analysis RegionM

48h Forecast Region

e

a

uc

j

tg

n

c

2 1 0 1 2 34

2

0

2

4

6

7

-2.96564

Core ,i 2

Cent ,1 2

32.5 ,Core ,i 1 Cent ,1 1

ACME’s Mirrored Analyses

Eta Analysis

Eta´ Analysis

Centroid Analysis

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1836h *ACMEcore0.0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1836h *ACMEcore0.0

0.1

0.2

0.3

0.4

0.4

0.3

0.2

0.1

0.01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

36-h T2

36-h MSLP

*ACMEcore *ACME0.3

0.2

0.1

0.0

Comparison of Verification Rank Histograms

Verification Rank

Pro

bab

ilit

yP

rob

abil

ity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Verification Rank

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

36h *ACMEcore0.0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1836h *ACMEcore0.0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 1836h *ACMEcore0.0

0.1

0.2

0.3

0.4

36-h WS10

MR = 52.5%MRE = 30.3%

VOP = 25.9%

MR = 44.0%MRE = 32.9%

VOP = 24.7%

*ACMEcore

*ACME

MR = 68.1%MRE = 45.9%

VOP = 44.4%

MR = 62.3%MRE = 51.2%

VOP = 44.2%

*ACMEcore*ACME

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

ilit

y

0.4

0.3

0.2

0.1

0.01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

Verification Rank

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

ilit

y

0.3

0.2

0.1

0.01 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

36-h T2

MR = 36.5% MRE = 14.2%

VOP = 9.1%36-h MSLP

MR = 26.1%MRE = 15.0%

VOP = 8.7%

*ACMEcore

*ACME

1 2 3 4 5 6 7 8 936h ACMEcore0.0

0.1

0.2

0.3

0.4

Pro

bab

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Effective Mesoscale, Short-Range Ensemble Forecasting Tony Eckel** and Clifford F. Mass Presented By: Eric Grimit University of Washington Atmospheric