Mesoscale Ensemble Prediction System:
This presentation is the property of its rightful owner.
Sponsored Links
1 / 14

Mesoscale Ensemble Prediction System: What is it? What does it take to be effective? PowerPoint PPT Presentation


  • 78 Views
  • Uploaded on
  • Presentation posted in: General

Mesoscale Ensemble Prediction System: What is it? What does it take to be effective? What are the impacts of shortcomings?. Maj Tony Eckel, Ph.D. Asst. Professor Naval Postgraduate School, Meteorology Department (831) 656-3430, [email protected] Sep 09.

Download Presentation

Mesoscale Ensemble Prediction System: What is it? What does it take to be effective?

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Mesoscale ensemble prediction system what is it what does it take to be effective

Mesoscale Ensemble Prediction System:

What is it?

What does it take to be effective?

What are the impacts of shortcomings?

Maj Tony Eckel, Ph.D.

Asst. Professor

Naval Postgraduate School, Meteorology Department

(831) 656-3430, [email protected] Sep 09


Mesoscale ensemble prediction system what is it what does it take to be effective

Forecast + Uncertainty

and/or

Decision Recommendation

Exploitation

Products, Marketing,

Value Verification,

Forecaster & User Education,…

Ensemble

IC Perturbations, Model Perturbations,

# of Members, Skill Verification, Post-processing,…

Foundation

Observations, Data Assimilation, the Model(s), Model Resolution,…

What is it?

Users

Optimal Decision Making

Ensemble Prediction System


Mesoscale ensemble prediction system what is it what does it take to be effective

Initial Conditions

- Erred Observations

- Incomplete Observations

- Limitations to Data Assimilation

Lateral

Boundary Conditions

- Inaccuracies

- Coarse spatial & temporal resolution

Sources of Uncertaintyin NWP

ExternalInternal

Upper Boundary

Modeling Limitations

Computer

Precision

Model Core

- Mathematical Model

- Numerical Truncation

- Limited Resolution

NWP

Model

Model Physics Limitations

- Assumptions

- Parameterizations

Lower Boundary Conditions

- Incomplete and Erred Surface Temperature, Soil Moisture, Albedo, Roughness Length, …


What does it take to be effective robust ic perturbations

Goal:n dynamically consistent and equally likely analyses that span the

analysis error subspace

  • Methods:

    • Bred Modes (BM)

    • Singular Vectors (SV)

    • Ensemble Kalman Filter (EnKF)

    • Ensemble Transform Kalman Filter (ETKF)

SV

BM

EnKF

ETKF

Brier Score

Event

Z500> Z500

Descamps and Talagrand, MWR, 2007

What does it take to be effective?- - - Robust IC Perturbations - - -

Perturbation Options:

  • Questions:

    • Special considerations for mesoscale, short-range ensemble?

    • Importance of Scale?

    • Cold vs. Warm Start?


What does it take to be effective robust model perturbations

-IC Perturbations

-Stochastic Convection

Teixeira and Reynolds, MWR, 2008

What does it take to be effective?- - - Robust Model Perturbations - - -

  • Goal:Diversity in members’ attractors to attempt to span true attractor, or

    at least aim for a statistically consistent forecast PDF

  • Methods:

    • Multi-model – different models and/or different physics schemes

    • Stochastic Physics – perturb state variables’ tendency during model integration

    • Stochastic Backscatter – like stoch. phys., but scale-dependent

    • *Stochastic Parameters – randomly perturb parameters (e.g., entrainment rate) during integration

    • Perturbed Parameters – randomly perturb parameters, but hold constant during integration

    • Perturbed Field Parameters– SST, albedo, roughness length, etc.

  • Perturbed Lateral Boundary Conditions

    – Smaller domain, Longer forecast  Bigger issue

  • Questions:

    • Combinations?

      – Which methods may be used together?

    • Other Methods?

      – Stochastic Field Parameters

      – Couple to Ocean Model Ensemble and/or LSM Ensemble

      – …?


Mesoscale ensemble prediction system what is it what does it take to be effective

Brier Score

Event

T850> Tc

  • Model Configuration:

    Need to meet user requirements

    • Forecast Length

    • Forecast Update Frequency

    • Domain Coverage

    • $ $ $ Resolution $ $ $

      –Can only estimate uncertainty of resolved scales

      –Benefits of finer resolution

      1) Increased spread

      2) Lower uncertainty

      3) Increased VALUE

      –Resolving convection (grid<4km) is key

Talagrand et al., ECMWF, 1999

6-h Precip (bias-corrected)

better statistical consistency

Clark et al., WAF, 2009

Forecast Hour

What does it take to be effective?- - - Powerful Processors - - -

Earth Simulator System

122.4 teraflops

Primary Drivers…

  • Ensemble Size:Need enough members to consistently depict the PDF from which the members are drawn

    • 8-10 for decent ensemble mean

    • 20-30 for skilled forecast probability

    • 50+ to capture low probability events (PDF tails)


What does it take to be effective robust post processing

Calibration:Need to maximize skill & value

Obtain Reliability – account for systematic errors (significant model biases in meso. models)

Boost Resolution via Downscaling – can greatly improve value of information

Reforecast Dataset – needed to calibrate low frequency events

Adaptable to variety of state and derived variables

Practical – easy to maintain

Preserve Meteorological Consistency?

Event: 2m Temp<0°C

Multi-model

w/ bias cor.

BSS

*UWME

UWME

*UWME+

UWME+

Uncertainty

IC only

Multi-model

Eckel and Mass, WAF, 2005

Lead Time (h)

What does it take to be effective?- - - Robust Post-processing - - -


What does it take to be effective comprehensive verification

Parameters– emphasis on sensible weather and user requirements

Ground Truth– emphasis on observations vs. model analysis

Skill Metrics (VRH,BSS, CRPS, etc.)– focus on ensemble performance

Value Metrics (ROCSS, VS, etc.) – focus on benefit to user

Accessible to Users

– Interactive, web-based interface

– Frequent updates

– Link into products and education

Lightning % Forecasts

Cape Canaveral / Patrick AFB

Lambert and Roeder, NASA, 2007

http://science.ksc.nasa.gov/amu/briefings/

fy08/Lambert_Probability_ILMC.pps

Shuttle Endeavor

12 Jul 2009

What does it take to be effective?- - - Comprehensive Verification - - -

Both feed back into R&D


Mesoscale ensemble prediction system what is it what does it take to be effective

  • Poor Dispersion– Failure to simulate error growth

Degraded Value

for

Decision Making

  • Poor Skill– Weak Reliability & Resolution

  • High Ambiguity– Large random error in uncertainty estimate

Deficient

NCEP SREF

20090301-20090531

48-h Sfc Wind Speed

Robust Meso. EPS

Value Score

Meso.

EPS

C/L Ratio

JMA EPS

20071215-20080215

Event: 5-d 2m temp  0C

UKMet MOGREPS

20060101-20060228

Event: 36-h Cloud Ceiling < 500ft

NCEP SREF

20090301-20090531

Event: 48-h Sfc RH > 90%

Bowler et al., MetOffice, 2007

Eckel and Allen, 2009

What are the impacts of shortcomings?


Mesoscale ensemble prediction system what is it what does it take to be effective

Backup Slides


Mesoscale ensemble prediction system what is it what does it take to be effective

Not absolute -- depends on uncertainty in ICs and model (better analysis/model = sharper true PDF)

AnalysisModelTrue Forecast PDF (at a specific )

Perfect

Perfect

Only one possible true state, so true PDF is a delta function.

Erred

Perfect

Each historical analysis match will correspond to a

different true initial state, and a different true state at time  .

Perfect

Erred

While each matched analysis corresponds to only one true IC,

the subsequent forecast can match many different true states

due to grid averaging at  =0 and/or lack of diffeomorphism.

Erred

Erred

Combined effect creates a wider true PDF.

Erred model also contributes to analysis error.

Perfect  exactly accurate (with infinitely precision)

Erred  inaccurate, or accurate but discrete

True Forecast PDF

True forecast PDF recipe for lead time  in the current forecast cycle:

1) Look back through an infinite history of forecasts produced by the analysis/forecast system in a stable climate

2) Pick out all instances with the same analysis (and resulting forecast) as the current forecast cycle

3) Pool the verifying true states at  to construct a distribution


Average forecast error covariance matrix eigenvalue spectrum 24h leadtime

ICs by Ensemble Transform (ET)(from Craig Bishop, NRL)

Average forecast error covariance matrix eigenvalue spectrum, 24h leadtime

ET maintains error variance in more directions than breeding…

ET

Amplitude

Breeding

Eigenvalue


Mesoscale ensemble prediction system what is it what does it take to be effective

O. Talagrand and G. Candille, Workshop Diagnostics of data assimilation system performance

ECMWF, Reading, England, 16 June 2009


Mesoscale ensemble prediction system what is it what does it take to be effective

(c) WS10

(d) T2

c2  13.2 ºC2

c2  11.4 m2 / s2

04L

04L

22L

22L

16L

10L

16L

10L

Ens. Var. OR mse of the Ens. Mean (m2 / s2)

Ens. Var. OR mse of the Ens. Mean(ºC2)

mse of *UWMEMean

UWME Variance

*UWME Variance

mse of *UWME+Mean

UWME+ Variance

*UWME+ Variance

Eckel and Mass, WAF, 2005


  • Login