30 January 2003 13:00 NWSFO-Seattle An Update on Local MM5 Products at NWSFO-Seattle Eric P. Grimit SCEP NOAA/NWS-Seattle Ph.D. Candidate, University of

30 January 2003 13:00 NWSFO-Seattle30 January 2003 13:00 NWSFO-Seattle

An Update on Local MM5 Products at NWSFO-Seattle

Eric P. GrimitSCEP NOAA/NWS-Seattle

Ph.D. Candidate, University of Washington


Overview

Update on the deterministic MM5 runs

Update on the expanded MM5 short-range ensemble forecast system

Examples of new products available in AWIPS and IFPS

Timing & Availability

Forecaster Feedback


Two Deterministic Mesoscale Forecasts

New MM5gfs format:AWIPS/IFPS name change from MM5avn to MM5gfs Forecast 0-72 h (36-/12-km); 6-48 h (4-km)Outer 36-km domain nudged toward GFS forecast4-km nest split out and run separately after 36/12 finishesMM5gfs-36km, MM5gfs-12km at 0000/1200 UTC

Delivery: ~10:00 AM/PM (0600/1800 UTC)

MM5eta:No changes, still the early run; 0-72 h (36-/12-km)MM5eta-12km at 0000/1200 UTC

Delivery: ~9:30 AM/PM (0530/1730 UTC)

Future Implementations:MM5gfs-36km will be sent to WRHQ for distribution among all WR FOs; MM5gfs-12km to all Northwest FOs


The Extended Run

New MM5ext run:Forecast 72-168 h (36-/12-km) with GFS LBCsOuter 36-km domain nudged toward GFS forecast to prevent synoptic-scale “drift” caused by the limited-area MM5 modelMM5ext-36km, MM5ext-12km at 0000/1200 UTC

Delivery: ~2 AM/PM (1000/2200 UTC)

May need to go to 192 h (day 8) due to lag-time to cover NDFD 7-day forecast requirement; will add ~30 min to delivery time

Future Implementations:MM5ext-36km will be sent to WRHQ for distribution among all WR FOs; MM5ext-12km to all Northwest FOsPaired with WOCSS diagnostic model to downscale surface winds to 5-km grid for IFPS

Fre

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Initial State

Ensemble Forecasting, Theory vs. Application

- Start with an “analysis PDF” made up of many equally likely analyses of the atmosphere (i.e., initial conditions, ICs). We can think of truth as a random sample from this PDF.

- Create “forecast PDFs” by running each IC in a model, producing many possible forecasts. The chaotic nature of the atmosphere leads to non-linear error growth.

24hr Forecast State 48hr Forecast State

Fre

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Initial State 24hr Forecast State 48hr Forecast State

- Difficult to produce the analysis PDF. Errors in mean and spread lead to poor estimates of the forecast PDF. We are therefore uncertain about our uncertainty prediction!

anal

ysis

PD

F EF H

istogram


Factors to be Considered forMesoscale Short-Range Ensembles

Compared to medium range EF, successful SREF is elusive:Predominantly linear error growth (Gilmour et al., 2001) so cannot count on perturbations diverging non-linearly

Predictability of sensible wx parameters on mesoscale is largely unknown

Limited-area domain may constrain error growth

Model uncertainty must be included since it likely plays a significant role

Local factors for the Pacific NWUncertainty upstream over the Pacific can be HUGE

Significant errors in synoptic wave phase and amplitude

Regime and orography may be an advantageSynoptic flow interacts with terrain to create mesoscale features

Convection is weak and limited

phasespace

T

48hr forecast state (core)

48hr true state

Analysis pdf :

Forecast pdf :

8 “independent” atmospheric analyses, the centroid, plus 8 “mirrored” ICs17 divergent, “equally likely” solutions using the same primitive equation model, MM5

Forecast pdf

48hr forecast state (perturbation)

ngp

uk

eta

cmc

gsp

avn

Analysis pdfcwb

Cngp

eta

cmc

avn

gsp

cwb

uk

UW SREF Methodology Overview

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.

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 ~55km ~80km cmcg, Global Environmental Multi-scale (GEM), Spectral T199 / L28 1.25 / L11 3D VarCanadian Meteorological Centre ~100km ~100km eta, Eta limited-area mesoscale model, Finite 12km / L45 90km / L37 SSINational Centers for Environmental Prediction Diff. gasp, Global AnalysiS and Prediction model, Spectral T239 / L29 1.0 / L? 3D VarAustralian Bureau of Meteorology ~60km ~80km

jma, Global Spectral Model (GSM), Spectral T106 / L21 1.25 / L13 OIJapan Meteorological Agency ~135km ~100km ngps, Navy Operational Global Atmos. Pred. System, Spectral T159 / L24 1.0 / L14 OIFleet Numerical Meteorological & Oceanographic Cntr. ~90km ~80km

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

ICs/LBCs for the Analysis-Centroid Mirroring Ensemble

# of Initial Forecast UW MM5 Name Members Conditions Model(s) Cycle Domain

ACME 17 8 Ind. Analyses 1 (MM5) 00Z 36km, 12km1 Centroid8 Mirrors

ACMEcore 8 Independent 1 (MM5) 00Z, 12Z 36km, 12kmAnalyses

ACMEcore+ 8 “ “ 8 (MM5 variations) 00Z 36km, 12km

PME 8 “ “ 8 00Z, 12Z 36km

NCEP SREF 8 Regional 2 (ETA, RSM) 00Z, 12Z 36kmBreeding

Hom

egro

wn

Impo

rted

ACME: Analysis-Centroid Mirroring EnsemblePME: Poor Man’s EnsembleNCEP SREF: National Centers for Environmental Prediction Short Range Ensemble ForecastMM5: PSU/NCAR Mesoscale Modeling System Version 5


Illustration of “Mirroring”

Sea-Level Pressure Analyses

TCWB CENT C1.1T

•Disagreement with respect to the southern-most low results in large differences in the position, intensity, and structure of the low•Mirrored analysis is still “realistic” or “plausible”


0.00

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MAE(mb)

avn cmcg eta gasp jma ngps tcw b ukmo cent/mean

Member

MSLP f48poorman ACMEcore mirrors

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0.50

1.00

1.50

2.00

2.50

MAE(C)

avn cmcg eta gasp jma ngps tcw b ukmo cent/mean

Member

T850 f48poorman ACMEcore mirrors

Validation of the Mirrored Runs(Using centroid analysis verification on 65 cases from Dec01 – Mar02)

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ACME Benefits and Limitations

Strengths:Good representation of analysis/observational error

Perturbations to synoptic scale disturbances

Magnitude of perturbation(s) set by spread among analyses Bigger spread Bigger perturbations

Computationally efficient and affordable

Weaknesses:Limited by number and quality of available analyses

May miss key features of analysis error

Analyses must be independent (i.e., dissimilar biases)

Calibration difficult; no stability since analyses may change techniques


Example Ensemble Probability Product

valid 2100 UTC today

ACMEcore: 31 Oct 2002 – 20 Jan 2003

BSS: 0.527 0.559 0.474


7 mb4 mb

Example Ensemble Spread Product



Variance has a 2 distribution, use the f-statistic:f = s2 / s2

where s2 denotes the climatological median variance

~~

Evaluate the CDF: F(s2)

Transform to a normal distribution, using thepercentile obtained from the f-distribution,where: (F(s2))

Use resulting standard normal rv as a high/lowuncertainty index

Standardizing the spread

Confidence Products: Visualizing Uncertainty



MM5ens Product Timing and Availability

MM5ens-36km, MM5ens-12km at 0000 UTCEnsemble centroid deterministic forecastDelivery: ~1 AM (0900 UTC)In time for 2 AM AFD & 4:15 AM ZFP

MM5ens_prob-36km, MM5ens_prob-12km at 0000 UTCRaw ensemble probabilities for exceedance of selected criteria:

3h, 6h, 12h POP at 5 thresholds850-hPa T at 2 thresholds10-m WSPD at 2 thresholds10-m high and low T at 2 thresholds

Delivery: ~5 AM (1300 UTC)In time for 8:30 AM AFD, morning ZFP updatesUseful for IFPS POP for at least the first 24-hr period of grids

MM5ens_spread-36km at 0000 UTC500-hPa heights and MSLP standard deviationDelivery: ~5 AM (1300 UTC)


Forecaster Feedback

We really want feedback on all new (and old) products to:

Improve communication between researchers and operational forecasters

Improve the quality of local mesoscale forecast guidance, especially for NWS watch/warning criteria

Attempt to reduce the overwhelming nature of too much guidance

Create innovative ways of visualizing uncertainty

Feedback/reaction to MM5 ensemble products for PNW Workshop ’03 Poster:

“Experimental MM5 Short-Range Ensemble Products at NWSFO-Seattle”

Probability of Warning Criteria at McGuire AFB Bas e d o n 1 5 /0 6 Z MM5 En s e m b le

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%)

T S torm

W inds> 35k t

W inds> 50k t

S now> .5"/hr

Fzg Rain

15/06 12 18 16/00 06 12 18 17/00 06

Innovative Forecast Products/Tools

•Work with NWS-Seattle, Whidbey NAS forecasters(specialized products for warning criteria)

•Work with MURI visualization team at UW APL(ways to visualize uncertainty)

GOAL: VISUALIZING FORECAST UNCERTAINTYWITHOUT NEEDING A TON OF PRODUCTS


MM5 SREF Webpage:http://www.atmos.washington.edu/~emm5rt/ensemble.cgi

Email:[email protected]

[email protected]

[email protected]

[email protected]

I’m at the forecast office on most Fridays 7-3

Resources & Contact Information

} deterministic runs

} ensemble runs

EXTRA SLIDES

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1 3 5 7 9 11 13 15 17 190

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1 3 5 7 9 11 13 15 17 190

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1 3 5 7 9 11 13 15 17 19

Difficult to consistently construct the “correct” analysis/forecast pdf.Errors in mean and spread result from:

1) Model error

2) Choice of ICs

3) Under sampling due to limits of computer processing

Result: EF products don’t always perform the way they should. (especially a problem for SREF)

Limitations of EFF

requ

ency

Initial State0 5 10 15 20

0.2

0.4

.5

0.000514093

dnorm ( ),,x 10 3

200 x

0 5 10 15 20

0.2

0.4

.5

7.4336e-007

dnorm ( ),,x 10 2

200 x24hr Forecast State 48hr Forecast State0 5 10 15 20

0.2

0.4

.5

7.6946e-023

dnorm ( ),,x 10 1

200 x

analysis pdf

ensemble

pdf


Value of the Mirrored Runs(Using centroid analysis verification on 65 cases from Dec01 – Mar02)

cmcg

, avn

gasp

eta

ngps

ukm

o

tcw

b

cent

c1.0

c

c1.0

a

c1.0

g

c1.0

e

c1.0

n

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u

c1.0

t

Z850 f36 Missing Rate

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2 3 4 5 6 7 8 9 10 11 12 13 14 15

Number of Members

Ideal missing rate:

Actual missing rate:

MemberIncluded:


vertical 36km 12km shallow SST

IC ID# Soil diffusion Cloud Microphysics Domain Domain cumulus Radiation Perturbation Land Use Table

all 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.TBL.plus1

cmcg plus02 MRF LSM Y Reisner II (grpl), Skip4 Grell Grell N cloud SST_pert02 LANDUSE.TBL.plus2

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

gasp plus04 Gayno-Seaman 5-Layer N Shultz Betts-Miller Kain-Fritsch N RRTM SST_pert04 LANDUSE.TBL.plus4

jma plus05 Gayno-Seaman 5-Layer N Reisner II (grpl), Skip4 Kain-Fritsch Kain-Fritsch Y cloud SST_pert05 LANDUSE.TBL.plus5

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

tcwb plus07 Blackadar 5-Layer Y Simple Ice Betts-Miller Grell Y RRTM SST_pert07 LANDUSE.TBL.plus7

ukmo plus08 Burk-Thompson none N Reisner I (mx-phs) Kain-Fritsch Kain-Fritsch N cloud SST_pert08 LANDUSE.TBL.plus8

Perturbations to:

1) Moisture Availability

2) Albedo

3) Roughness Length

CumulusPBL

AC

ME

core

+

A

CM

E

8 5 3 2 5 3 2 2 8 8 = 921,600

Total possible combinations:

MM5 Configurations for ACMEcore+

Documents

30 January 2003 13:00 NWSFO-Seattle An Update on Local MM5 Products at NWSFO-Seattle Eric P. Grimit SCEP NOAA/NWS-Seattle Ph.D. Candidate, University of