mesoscale nwp: applications to africa · mesoscale nwp: applications to africa dr. cody kirkpatrick...

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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xfxxfxf

dx

df

hOh

xfxf

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iiii

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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: codyk@ucar.edu