www.csiro.au

CCAM Numerical Weather Prediction

Dr Marcus Thatcher

Research Scientist

December 2007

CMAR NWP

Overview

Numerical Weather Prediction with CCAM

Example:

Processing NCEP GFS analyses

Indonesia 60 km resolution 8 day forecast

Jakarta 8 km resolution 4 day forecast

Bali 8 km resolution 4 day forecast

Post processing forecast output

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Overview

CCAM NWP forecasts are constructed in two stages

The first stage creates a 60 km forecast for 8 days into the future

The second stage downscales the 60 km forecast to 8 km resolution. Normally this is only done for 3 days into the future

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Downscaling

CCAM uses nudging to ‘step down’ the forecast resolution from 60km 8km 1km.

60km

8km

1km

Nudging

Nudging

Initial forecast

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GFS analysis Summary of the CCAM system

Userlatitude/longitude

DataProducts(web pages, etc)

Initial conditions

Local terrain

and vegetationCCAM

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GFS analysis Summary of the CCAM system

Userlatitude/longitude

Topographyand vegetation

Initial conditionsCCAM 60km

forecast

DataProducts(web pages, etc)

Extract forecast data

Topographyand vegetation

DownscalingCCAM 8km

forecast

Extract forecast data

Topographyand vegetation

DownscalingCCAM 1km

forecast

Extract forecast data

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CCAM NWP Example

The scripts for the CCAM NWP example are located under:

$HOME/ccam/scripts/nwp

The script that runs the simulation is called:

startforecast.sh

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Process model output

CCAM NWP Example

The startforecast.sh script performs four main functions:

Downloads NCEP GFS analyses and processes the analysis for initial conditions

Runs a 60 km resolution forecast for Indonesia

Runs two 8 km forecasts that are nested in the 60 km forecast (Jakarta and Bali as examples)

Processes the output from CCAM so that it can be used

Download and prepare IC

Run 60 km forecast

8 kmforecast

8 kmforecast

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1) Downloading initial conditions

CCAM can use various analysis products for initial conditions, including:

NCEP GFS 0.5 deg

NCEP GFS 1 deg

Australian BoM GASP 1 deg

CMC 1 deg

NOGAPS 1 deg

For this example we will use the NCEP GFS analysis

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1) Downloading initial conditions

Summary:

NCEP GFS analysis is downloaded using getanalysis.sh

Process the GRIB file using procgfs2.sh

CCAM initial conditions are then located in $HOME/ccam/scripts/nwp/obs/avn

CCAM initial conditions can be inspected using GrADS or Ferret

Download analysis(getanalysis.sh)

Process GRIB(procgfs2.sh)

CCAM initial conditions

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2) 60 km resolution forecast

Next, the CCAM 60 km resolution forecast is run.

The runall script simply starts the Indonesian forecast if the last forecast is older than the analysis

jog48indon contains all the information needed to run CCAM

Start forecasts(runall)

Indonesia 60 km forecast(jog48indon)

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2) 60 km resolution forecast

jog48indon converts the initial conditions to a conformal cubic (CC) grid using cdfvidar

Soil initial conditions are generated using smclim from a soil climatology dataset. It is also possible to use soil data from the last forecast.

Pre-generated topography and land-use datasets are located in the ~/ccam/scripts/data directory

Convert initial conditions to CC(cdfvidar)

Prepare soil initial conditions(smclim)

Topography and land-use

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2) 60 km resolution forecast

Then CCAM namelist file (called input) is prepared

Some example CCAM namelist switches are provided

dt = time step (20mins)

nwt = output interval

ntau = total number of steps

kdate_s = start date

ktime_s = start time

leap = use leap year?

io_in = interpolate initial conditions from input?

mfix = mass conservation

mfix_qg = moisture conservation

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2) 60 km resolution forecast

ifile = intial conditions file

mesonest = boundary conditions file

albfile = albedo file

zofile = roughness file

rsmfile = rsmin file

vegfile = vegetation file (SiB)

soilfile = soil data file (Zobler)

ofile = CCAM output file

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2) 60 km forecast

After running CCAM, we need to convert the output back to a regular grid using cc2hist

Different output variables can be specified in the cc2hist namelist

It is also possible to obtain the output in pressure levels

Run CCAM(globpea.q1)

Process output(cc2hist)

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3) cc2hist – processing CCAM output

A typical cc2hist command looks like:

cc2hist –r 0.5 ccout.nc llout.nc < cc.nml

Where

-r determines the output resolution in deg

ccout.nc is the CCAM output (on the CC grid)

llout.nc is the CCAM output converted to a regular grid

cc.nml is the namelist that specifies the output variables and the output domain.

Instructions for cc2hist can be obtained by:

cc2hist -h

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3) cc2hist – processing CCAM output

Below is an example cc2hist namelist

&input

kta=0, ktb=99999, ktc=-1

minlat = -20., maxlat = -10., minlon = 90., maxlon = 120.

use_plevs = T

plevs = 1000, 900, 800, 700, 600, 500, 400, 300, 200

&end

&histnl

hnames = "temp","u","v","psl","rnd24","tscrn","zs","mixr","zg","tmaxscr","tminscr"

hfreq = 1, htype = "inst", hbytes=2

&end

Output all timesteps

Output domain

Use and definePressure levels(instead of sigmalevels)

Output variables(also just use “all”)

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3) cc2hist – processing CCAM output

CCAM output from the 60 km forecast can be found at

$HOME/ccam/scripts/nwp/save/indon-0701/indon_60km

Once processed by cc2hist, we can examine the CCAM NWP forecast using GrADS or Ferret

It is also possible to examine the ‘raw’ CCAM 60 km forecast on the conformal cubic grid

$HOME/ccam/scripts/nwp/wdir/indon/indon_60km

By looking at the ‘raw’ output shows what variables you can process with cc2hist

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4) 8 km nested forecasts

Once the 60 km forecast is complete, we can downscale to 8 km resolution forecast for multiple locations

For example, here we downscale to 8 km resolution forecasts for Jakarta and Bali

As before, the output is controlled by cc2hist

60 km Indonesian forecast(jog48indon)

8 kmJakartaforecast

(jog48jaka)

8 kmBali

forecast(jog48bali)

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4) 8 km nested forecasts

For nested forecasts, the CCAM namelist is slightly different

mesonest = CCAM 60 km output filename

io_in = -1 to interpolate the 60km BC to the 8km CC grid

dt = 3 mins

nbd = -3 (far field nudging)

nud_uv = nudge winds

nud_p = nudge surface pressure

nud_t = nudge temperature

nud_q = nudge mixing ratio

nud_hrs = efolding time

kbotdav = lowest model level to nudge (1 = all levels)

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Sigma levels

kbotdav=4 (typically NWP)

kbotdav=10 (typically climate)

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5) cc2hist – processing 8 km CCAM output

CCAM output from the 8 km forecast can be found at

$HOME/ccam/scripts/nwp/save/indon-0701/jaka_8km

$HOME/ccam/scripts/nwp/save/indon-0701/bali_8km

The ‘raw’ CCAM 8 km forecast on the conformal cubic grid is located at

$HOME/ccam/scripts/nwp/wdir/indon/jaka_8km

$HOME/ccam/scripts/nwp/wdir/indon/bali_8km

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CCAM output

Typically the forecast is stored in 3 hour (60km) or

1 hour (8km) intervals.

This is because the radiation scheme is normally updated once every hour.

The default areas of the forecast are: 60km ±15deg ~ ±1700kms

8km ±2deg ~ ±220kms

1km ±0.25deg ~ ±30kms

Since CCAM is a global model, the output can be also global. However, usually the output is for the high resolution cubic panel only

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CCAM output

Typical output area

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GFS analysis

Data storeDelivery platform(web pages, FTP, etc)

Example forecast system

GFS download and archive

system

Operational forecasting

system

Data integrity system

Hindcast system

Generator for client data products

CCAM

Validation and verification system

Archive and synchronizationwith parallel forecast systems

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CCAM Validation (Valencia)Wind speed and direction

www.csiro.au

Thank You

Marine and Atmospheric Research

Name Dr Marcus Thatcher

Title Research Scientist

Email Marcus.Thatcher@csiro.au

Contact CSIRO

Phone 1300 363 400

+61 3 9545 2176

Email enquiries@csiro.au

Web www.csiro.au