Mesoscale Deterministic and Probabilistic Prediction over the Northwest: An Overview
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Transcript of Mesoscale Deterministic and Probabilistic Prediction over the Northwest: An Overview
Mesoscale Deterministic and Probabilistic Prediction over the
Northwest: An Overview
Cliff MassUniversity of Washington
University of Washington Mesoscale Prediction Effort
• An attempt to create an end-to-end deterministic and probabilistic prediction system.
• On the deterministic side, examine the benefits of high resolution
• Identify major issues with physics parameterizations
Deterministic Prediction
• WRF ARW Core run at 36, 12, 4, and now 4/3 km grid spacing
• Extensive verification• Variety of applications running off the
deterministic forecasts.
Major Elements• Two mesoscale ensembles systems UWME
(15 members) and EnKF (60 members, 36 and 4 km grid spacing).
• Sophisticated post-processing to reduce model bias and enhance reliability and sharpness of resulting probability density functions (PDFs) for UWME.
• Stand-alone bias correction• Bayesian Model Averaging (BMA)• Ensemble MOS (EMOS)
Major Elements
• Psychological research to determine the best approaches for presenting uncertainty information.
• Creation of next-generation display products providing probabilistic information to a lay audience. Example: probcast.
Inexpensive Commodity Clusters
• This effort has demonstrated the viability of doing such work on inexpensive Linux clusters.
• Proven to be highly reliable
The Summary
Verification
Precip Verification
High Resolution
• Attempt to answer questions:– What is the payoff in getting the land-water
boundaries and smaller scale terrain much better
– Does ultra high resolution improve objective verification or subjective structures?
– Do physics problems get better or worse?
1.3
4 km
6-hr forecast, 10m wind speed and direction
1.3 km
4 km
Boundary Layer Physics: A Current Achilles Heel of
Mesoscale NWP
• Well known issues:– Winds too strong and geostrophic near surface– Excessive low-level mixing– Inability to maintain shallow cold PBL
During the past few months we have continued our testing program of various PBL schemes, vertical
diffusion options, etc.• A test case has been one in which the 4 and
1.3 km created unrealistic roll circulations.
http://www.atmos.washington.edu/~ovens/wrf_1.33km_striations/
1 km visible
Problem
• Instead of getting open cellular convection, there are these period cloud streets.
• Look like roll circulation, but of too large a scale (if you look at sat pics you can see hints of them).
• Sometimes apparent (but less so) in 4-km.• Occurs only in unstable, post-frontal
conditions.
Through the kitchen sink at it and consulted heavily with Dave
Stauffer at Penn State• Tried a range of PBL schemes (YSU, QNSE, ACM2,
MYNN, MYJ, MYJ with Stauffer mods)• Added 6th order diffusion and played with diffusion
coefficent.• Fully, interactive nesting• Upper level diffusion and gravity wave drag• Monotonic advection• Varying vertical diffusion, both more and less
Results
• ACM2 (Pleim PBL and LSM) was the only thing that helped reduce the rolls.
• It created this stratiform cloud mass that wasn’t very realistic.
Excessive Geostrophy and Mixing at Low Levels
• Tried every PBL option in ARW…not the solution!
• Trying other things: decreasing model diffusion and increasing surface drag by increasing ustar.
Example: Cut vertical diffusion 10 1/8th of normal value
Vertical Diffusion cut to 1/4
Standard Low Diffusion
Mesoscale Ensembles at the UW
UWMECore Members
• 8 members, 00 and 12Z• Each uses different synoptic scale initial and boundary
conditions from major international centers• All use same physics• MM5 model, will be switching to WRF.• 72-h forecasts
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
“Native” Models/Analyses Available
UWME– Physics Members
• 8 members, 00Z only• Each uses different synoptic scale initial and
boundary conditions• Each uses different physics• Each uses different SST perturbations• Each uses different land surface characteristic
perturbations
– Centroid, 00 and 12Z• Average of 8 core members used for initial and
boundary conditions
36 and 12-km domains
Post-Processing
• Post-processing is a critical and necessary step to get useful PDFs from ensemble systems.
• The UW has spent and is spending a great deal of effort to perfect various approaches that are applicable on the mesoscale.
Post-Processing• Major Efforts Include
– Development of grid-based bias correction– Successful development of Bayesian Model
Averaging (BMA) postprocessing for temperature, precipitation, and wind
– Development of both global and local BMA– Development of ensemble MOS (EMOS)
Grid-Based Bias Correction
• Use previous observations, land-use categories, elevation, and distance to determine and reduce bias in forecasts
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BSS
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*ACMEcoreACMEcore*ACMEcore+ACMEcore+Uncertainty
*UW Basic Ensemble with bias correction UW Basic Ensemble, no bias correction*UW Enhanced Ensemble with bias cor. UW Enhanced Ensemble without bias cor
Skill forProbability of T2 < 0°C
BSS: Brier Skill Score
Bias Correction Substantially Improves Value of Ensemble Systems
BMA
BMA
• Testing both global BMA (same weights over entire domain) and local BMA (ensemble weights vary spatially).
EMOS
EMOS Test
EMOS Verification
Communication and Display
• Considerable work by Susan Joslyn and others in psychology and APL to examine how forecasters and others process forecast information and particularly probabilistic information.
• One example has been their study of the interpretation of weather forecast icons.
The Winner
PROBCAST
UW EnKF System
• Can we produce a superior 3D analysis?• Can we use it to produce good short-term
predictions…a major missing element in most systems?
• Can we use a significant proportion of the numerous observations that are now available?
UW EnKF Data Assimilation
• Now using a 36-4 km system with 3hr update• Previously, used 36-12 km and 6h update• Future: move to 1 hr update
EnKF 12km Surface Observations
EnKF 12-km vs. GFS, NAM, RUC
RMS analysis errors
GFS 2.38 m/s 2.28 KNAMRUCEnKF 12km
Wind Temperature
2.30 m/s2.13 m/s1.85 m/s
2.54 K2.35 K1.67 K
The END
Future Evaluation
• Improving PBL and surface drag may preferentially help 1.3 km (more later)
• Using 1.3 km as a testbed (some problems are more acute at higher resolution)
A Major Issue Has Been Excessive Wind Speeds Over Land and Excessive Geostrophy at the Surface –either
too much mixing in vertical or not enough drag. Winds over land and water too similar
• No magic bullet in PBL tests.• Recently, we tried something that really looks like
it has potential to help…increasing the friction velocity….ustar.
• Essentially adds drag, without messing other things up.
• Perhaps it is realistic, mimicking the effects of hills.