Wr f Workshop 2009

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IS WRF REALLY IMPROVING?A COMPREHENSIVE VERIFICATION OVER THE

PACIFIC NORTHWEST

Cliff Mass and David Ovens

University of Washington


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A lot of effort has been expended

We have all worked hard over the pastten years transitioning from MM5 to

WRF. In addition, a great deal of effort has

gone into improving physicsparameterizations, numerics, and addingadditional modeling options.


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But

Does WRF with all its improvements verifybetter than MM5 for key case studies and overextended verification periods?

Do we even have the tools and capabilities tomonitoring the evolving quality of our modelingsystems?

Is it possible that some of the improvementshave actually detracted from modeling systemskill when used with other components?


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In general, we dont havesatisfactory answers for these

questions.

Neither NCEP nor DTC nor any national entityappears to have such information.

We need the mechanisms and capabilities in

place to evaluate and guide our modeldevelopment


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What has been the results over the PacificNorthwest where much of this information is

available?


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Northwest U.S. MM5 and WRF Real-time since 1995 Now running:

MM5 (36-12 km) nested in NWS NAM WRF ARW 3.0 (36-12-4 km) nested in NWS GFS WRF currently uses Thompson microphysics, YSU PBL,

NOAH LSM, RRTM LW, Dudhia SW, K-F PBL MM5 uses MRF PBL, K-F.

Extensive multi-year verification on QC data. Have run extensive tests of WRF V3.1, MM5 driven by GFS,

and a collection of varying physics, including with and withoutLSM


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The Analysis

Based on this extensive series of runs, let ustry to answer (for the NW) the followingquestions:

What have we gained by moving to WRF? What have we lost? What advantages can one realize from V3.1? Is the NOAH LSM a plus or minus for the key

parameters? Are we making progress?


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0000 UTC (5 PM) MAE, July-August 2008

With LSM


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1200 UTC (5 AM) MAE, July-August 2008

With LSM


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0000 UTC (5 PM) MAE, Jan-Feb 2009

With LSM


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1200 UTC (5 AM) MAE, Jan-Feb 2009

With LSM


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What do verification scores tell usabout MM5 and WRF?

The LSM greatly improves the dewpointtemperature forecast. So WRF with LSM ismuch better for dewpoint than MM5 without.

For temperature, the LSM helps in theafternoon, but hurts in the morning.

WRF is better than MM5 for wind direction. For precipitation, summer is better for MM5,

winter for WRF. Very little difference in wind speed.


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Scatter Diagrams Can Reveal the

Subtleties of Model Performance


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Cold Bias


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Model Warm Bias for Cold Temps


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Model Cold Bias


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Model Warm Bias


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A National Effort for WRF Verificationis Required to Guide Our Work

We have pieces of the puzzle: The Developmental Testbed Center (DTC) is a natural center

for such activities. Powerful verification capabilities have been developed

(Model Evaluation Tools, MET) We need a long-term baseline of model

performance for best combinations of modelphysics options or promising optioncombinations.

DTC should take on this key responsibility as anhonest and unbiased evaluator of modelperformance.


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What do verification scores tell usabout WRF?

The LSM greatly improves the dewpointtemperature forecast.

For temperature, the LSM helps in theafternoon, but hurts in the morning.

CAM and the new RRTMG schemes have very

similar verification scores.


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PBL tests with WRF 3.1 3 new PBL schemes (MYNN, QNSE, Pleim-Xiu) have

been tested for a stable case, 17-18 Jan 2009. Our current scheme: YSU = Yonsei University non-local-K scheme with explicit

entrainment layer and a parabolic K profile. MYNN = Mellor-Yamada Nakanishi and Nino Level 2.5 PBL. Predicts sub-grid

TKE terms. QNSE = Quasi-Normal Scale Elimination PBL. A TKE-prediction option that uses

a new theory for stably stratified regions. Pleim-Xiu = Asymmetric Convective Model with non-local upward mixing and

local downward mixing. BouLac = Bougeault-Lacarrere PBL (new in 3.1, not yet tested) designed for use

with BEP urban model.


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Cases without LSM compared to the case with the best 2-m


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Cases without LSM compared to the case with the best 2 mtemperature score.



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LSM with best 2-m Temperature



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2-m Temperatures, LSM vs no LSM

LSM No LSM


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LSM No LSM


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Some Conclusions

LSM greatly improves dewpoint temperatureforecasts.

LSM improves maximum temperatureforecasts, but degrades minimum temperatureforecasts.


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM

PBL i h WRF 3 1


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PBL tests with WRF 3.1 3 new PBL schemes (MYNN, QNSE, Pleim-Xiu) have

been tested for a stable case, 17-18 Jan 2009. Our current scheme: YSU = Yonsei University non-local-K scheme with explicit

entrainment layer and a parabolic K profile. MYNN = Mellor-Yamada Nakanishi and Nino Level 2.5 PBL. Predicts sub-grid

TKE terms. QNSE = Quasi-Normal Scale Elimination PBL. A TKE-prediction option that uses

a new theory for stably stratified regions. Pleim-Xiu = Asymmetric Convective Model with non-local upward mixing and

local downward mixing. BouLac = Bougeault-Lacarrere PBL (new in 3.1, not yet tested) designed for use

with BEP urban model.


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PBL tests with WRF 3.1 The LSM is a bigger contributor to temperature

errors than the PBL schemes. No obvious improvement has been noted. Further tests and analysis is needed.


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temperature score.



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temperature score.




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temperature score.



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temperature score.


2 T LSM LSM


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LSM No LSM

2 T t LSM LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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LSM No LSM


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Wr f Workshop 2009

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