Transcript NWOverviewFeb10.ppt

Mesoscale Deterministic and
Probabilistic Prediction over the
Northwest: An Overview
Cliff Mass
University 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?
4 km
1.3
6-hr forecast, 10m wind
speed and direction
4 km
1.3 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
UWME
Core 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
“Native” Models/Analyses Available
Resolution (~ @ 45 N )
Abbreviation/Model/Source
Type
avn, Global Forecast System (GFS),
Spectral T254 / L64
~55 km
National Centers for Environmental Prediction
cmcg, Global Environmental Multi-scale (GEM),
Computational
Distributed
1.0 / L14
~80 km
Objective
Analysis
SSI
3D Var
Finite
Diff
0.90.9/L28 1.25 / L11
~70 km
~100 km
3D Var
Finite
Diff.
32 km / L45
90 km / L37
SSI
3D Var
Spectral T239 / L29
~60 km
1.0 / L11
~80 km
3D Var
Spectral T106 / L21
~135 km
1.25 / L13
~100 km
OI
Spectral T239 / L30
Fleet Numerical Meteorological & Oceanographic Cntr.
~60 km
1.0 / L14
~80 km
OI
tcwb, Global Forecast System,
1.0 / L11
~80 km
OI
Canadian Meteorological Centre
eta, limited-area mesoscale model,
National Centers for Environmental Prediction
gasp, Global AnalysiS and Prediction model,
Australian Bureau of Meteorology
jma, Global Spectral Model (GSM),
Japan Meteorological Agency
ngps, Navy Operational Global Atmos. Pred. System,
Taiwan Central Weather Bureau
ukmo, Unified Model,
United Kingdom Meteorological Office
Spectral T79 / L18
~180 km
Finite
Diff.
5/65/9/L30 same / L12
~60 km
3D Var
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
B
Skill for
Probability of T2 < 0°C
0.2
*ACMEcore
*UW Basic Ensemble with bias correction
ACMEcore
UW Basic Ensemble, no bias correction
*ACMEcore+
*UW Enhanced Ensemble with bias cor.
ACMEcore+
UW Enhanced Ensemble without bias cor
Uncertainty
0.1
0.0
-0.1
00
0.6
03
06
09
12
15
18
21
24
42
45
48
27
30
33
36
0.5
BSS
0.4
0.3
0.2
0.1
0.0
-0.1
00
03
06
09
12
15
18
21
24
27
30
33
36
39
BSS: Brier Skill Score
Bias Correction Substantially Improves Value of Ensemble Systems
39
42
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
Wind
Temperatur
e
RMS analysis errors
GFS
NAM
RUC
EnKF 12km
2.38 m/s
2.30 m/s
2.13 m/s
1.85 m/s
2.28 K
2.54 K
2.35 K
1.67 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.