Mesoscale NWP: Applications to Africa
Dr. Cody Kirkpatrick
NCAR ISP Colloquium
July 28, 2011
Numerical Weather Prediction (NWP)
Tuesday, reviewed the fundamentals of NWP
Here, talk about NWP applied to mesoscale weather phenomena
The equations are the same!*
*For the most part.
Resolve features with more (and different) details
Different “things” may need to be parameterized.
Fundamental issue is really computing power/speed.
Idealized NWP forecast process
(Warner 2011)
What are “mesoscale” phenomena?
What are “mesoscale” phenomena?
Forced by surface features
Terrain
Land use boundaries
Examples
Sea breezes
Mountain valley winds
Urban circulations
Airflow over rough terrain
Forced by synoptic waves
Examples
Large thunderstorms
Squall lines
Hurricanes
Other “cloud clusters”
Less predictability
Initiation, motion, persistence
all more difficult
Figure 1.1 of “Mesoscale Meteorologyin Midlatitudes”
The difficulties of mesoscale modeling
Can rarely neglect any terms!
▲ Plus an equation for each hydrometeor species (normally, 5 or more)!
▲
Hydrostatic vs. nonhydrostatic
Today, most local models are nonhyrostatic by default
Computing “overhead,” at least in WRF-EMS, is only
about 5%
But if you run your own model, compare the results!
Biggest advantage: more realistic vertical motions
Convection; any buoyancy-driven flow
Vigorous flow over terrain
What this talk is not…
Next presentation will talk about ensemble modeling
and probabilistic model output
A single model’s output: deterministic
Fig. 7.3 from
Warner (2011).
ECMWF forecasts
of 2m temperature
for London.
General modeling issues
Always ask: “what is it, exactly, that you want your model to represent?”
Midlatitude waves, cyclones, and fronts?
Hurricanes?
Tropical, African easterly waves?
Individual thunderstorms?
Gravity waves, cold pools, thunderstorm outflows?
Each of these have major implications for:
Choices of horizontal, vertical, and temporal resolution of the model
Horizontal resolution
How many grid points are needed to adequately
resolve a feature? What do you see here?
Horizontal resolution
What about now?
Horizontal resolution
What about now?
Horizontal resolution
That’s it! Need at least 6 grid points to truly observe a
wave. (And this is a pretty crude representation.)
Horizontal resolution
Extreme values are better resolved at finer resolution
(more grid points = smaller grid spacing)
Resolution: terrain
36 km terrain, central Africa
Resolution: terrain
12 km terrain, central Africa
Resolution: terrain
12 km terrain, southern South Africa
Resolution: terrain
4 km terrain, southern South Africa
Vertical resolution
Same principles apply here
Greater resolution is needed where physics are more
important – near the surface!
Boundary layer heat fluxes into the troposphere
Surface fluxes (heat and moisture)
Surface friction
Some models (such as WRF-EMS) have good
“default” options – but always review them first
Vertical resolution
Numerical schemes
Also a trade-off between numerical accuracy and
computational speed
Type of numerical scheme used:
higher order � more accuracy � slower
Example: first derivative at a point (h is the grid spacing)
Second formula is more accurate, but also more complex
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Resolution and model representation
Animation based on the
COMET module: “A
Convective Storm Matrix”
(www.meted.ucar.edu)
Parameterization
We must “parameterize,” or approximate, processes:
that we don’t understand.
that operate at scales smaller than our grid.
that are too complex/time consuming for us to model.
What are some examples of processes that we need
to parameterize?
What needs to be parameterized?
Lots of things.
Surface heating
Surface heating
Solar Radiation
Moisture Fluxes
Turbulence
Convection
Evaporation CondensationFluxes
Fluxes
Solar Radiation
Convection parameterization (CP) scheme
One of the most important parameterization choices
you will make is for convection.
Objectives of a subgrid-scale CP scheme
Define convection in the right place and at the right time
…with correct evolution and intensity
Make appropriate modifications to large-scale environment
Trick question: what are CP schemes not designed
to do?
Convection parameterization (CP) scheme
CP schemes relieve grid-scale instability (T, RH, qv)
Keep the model from “blowing up”
Precipitation is a by-product!
General guidelines:
∆x > 10 km: need a CP scheme
∆x <≈ 4 km: probably don’t need CP; but need to pay
special attention to microphysics parameterization!
In between: no schemes designed for this range. Be very
careful if your grid spacing is 5 to 10 km.
Convection parameterization (CP) scheme
“But precipitation is the output we want!!!”
Examples of precip. calculations:
Grell: P = C*m*Peff� Condensate in the updraft; mass flux; precip. efficiency & shear
Anthes-Kuo: P = f(RH)*M� Warning, overly simple and not used much anymore
BMJ: P = integral of specific humidity excess� Popular scheme in modern models
� Adjusts T, q profiles toward a reference (climatology) profile
Kain-Fritsch: P = Peff*S� Precip. Efficiency and vertical vapor flux above LCL
Different parameterizations
Performance of some
parameterizations can
depend on:
season
meteorology of the
region
…Why?
From the Encyclopedia of World Climatology
Resolution vs. speed
Very important considerations at the mesoscale:
memory and computing speed
There is a trade-off between speed and resolution
Many gridpoints needed to cover a small geographic area
at high resolution
CFL criterion requires that time step be related to grid
spacing
Resolution vs. speed
Assume we double the horizontal resolution. What
effect would this have on computation time?
Computation time will increase by a factor of eight.
Modeling with WRF
Simulation time example:
12 km domain + 4 km nest, for a 24-hour forecast, on 10
processors: took me
about 3 hours
If I also include a 1-km
nest: total 10 hours!
(That’s 10 hours just
to generate a 24-hour
forecast!!)
12 km
4 km
Benefits of mesoscale modeling
Localized heavy rainfall, floods
Terrain-induced flows
Conditions for blowing dust and sand
Other possible ideas
Wildfire mitigation forecasting
Short-term disease vectors?
Shipping (waves, winds, etc.); pirate attacks?
Case studies! Regional influences, etc.
Weather Research and Forecast Model
Also known as “WRF”
Many partners in development: NCAR, NCEP, others
Suitable for research (case studies) and operational,
“real time” simulation
Useful websites
strc.comet.ucar.edu/wrf/
www.wrf-model.org
www.wrfems.info
Initial conditions (ICs)
What is the “starting point” of your model forecast?
Most common methods:
Assimilate the data yourself
� Observations (surface, upper
air, aircraft)
� Remote sensing
Use an analysis or a forecast
from another model
� Not as bad as it sounds!
� Dynamically balanced; no “bad observations” to deal with
If the ICs are bad, the forecast will be bad.
Boundary conditions (BCs)
For any model that does not cover the entire globe, the simulation domain has edges!
Also, there is a lower boundary (earth’s surface) and an upper boundary
Lower boundary takes care of itself
Upper boundary� Usually placed in the stratosphere above any vertical accelerations
� Some models include a “sponge layer” to make sure vertical waves don’t reflect back down
“Lateral” boundaries on the sides
Lateral boundary conditions
How are these handled? Typically use data from
another model
In the USA: “Global”
Forecast System” or
GFS (~30 km resol.)
Canada: GEM
Europe: ECMWF
model
Basically, BCs probably come from a model with a
larger domain than yours
Example 1: July 25–26
36 km horizontal resolution
Example 1: July 25–26
36 km horizontal resolution
Example 1: July 25–26
36 km horizontal resolution
Example 1: July 25–26
12 km horizontal resolution
Example 1: July 25–26
36 km horizontal 12 km horizontal
4 km horizontal
Example 2: Nigeria/Bight of Benin
Total rainfall (mm) 12Z July 13 to 12Z July 14
12 km horizontal resolution
Example 2: Nigeria/Bight of Benin
Total rainfall (mm) 12Z July 13 to 12Z July 14
4 km horizontal resolution – only in the gray box
Example 2: Nigeria/Bight of Benin
Total rainfall (mm) 12Z July 13 to 12Z July 14
4 km horizontal resolution (zoomed in on the box)
Example 3: July 18–19
Example 3: July 18–19
Example 3: South Africa
MSLP (hPa) and maximum wind gust (m/s) 00Z July 18 to 00Z July 19
Elevation
(meters)
Example 3: South Africa
Maximum wind gust (m/s) 00Z July 18 to 00Z July 19
Elevation
(meters)
Example 3: Interior
Animation available at http://theupdraft.com/weatherimages/dustloop.gif
Example 3: Western Coast
U-wind component (m/s) at Dakar, Senegal
Sea breeze (onshore)
Land breeze (offshore)
Actual mesoscale
model results
Results sampled
only every 6 hours
Summary
Questions to ask
What am I simulating?
What spatial resolution do I
need?
How will I “verify” that my
output is realistic?
Do I have the resources to
run a mesoscale model in
“real time”?
Implementation
Careful selection of domains
“Tune” your model to get the
best results
Parameterizations
Grid spacing
“The sooner the model is
used in the process, the
longer the study will take.”
Contact Info. & Additional Resources
COMET Modules (www.meted.ucar.edu)
“How Models Produce Clouds and Precipitation”
“Effective Use of High-Resolution Models”
Also, check the Numerical Modeling section for many
modules on NWP models and usage (including things like
dust forecasting, ocean waves, convection, etc.)
Contact information:
Dr. Cody Kirkpatrick
Phone: 1 (303) 497-8349
Email: [email protected]