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

CMAR NWP 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

CMAR NWP Downscaling CCAM uses nudging to ‘step down’ the forecast resolution from 60km  8km  1km. 60km 8km 1km Nudging Initial forecast

CMAR NWP GFS analysis Summary of the CCAM system User latitude/ longitude Data Products (web pages, etc) Initial conditions Local terrain and vegetation CCAM

CMAR NWP GFS analysis Summary of the CCAM system User latitude/ longitude Topography and vegetation Initial conditions CCAM 60km forecast Data Products (web pages, etc) Extract forecast data Topography and vegetation Downscaling CCAM 8km forecast Extract forecast data Topography and vegetation Downscaling CCAM 1km forecast Extract forecast data

CMAR NWP 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

CMAR NWP 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 km forecast 8 km forecast

CMAR NWP 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

CMAR NWP 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

CMAR NWP 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)

CMAR NWP 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

CMAR NWP 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

CMAR NWP 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

CMAR NWP 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)

CMAR NWP 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

CMAR NWP 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 define Pressure levels (instead of sigma levels) Output variables (also just use “all”)

CMAR NWP 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

CMAR NWP 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 km Jakarta forecast (jog48jaka) 8 km Bali forecast (jog48bali)

CMAR NWP 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)

CMAR NWP Sigma levels kbotdav=4 (typically NWP) kbotdav=10 (typically climate)

CMAR NWP 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

CMAR NWP 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

CMAR NWP CCAM output Typical output area

CMAR NWP GFS analysis Data store Delivery 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 synchronization with parallel forecast systems

CMAR NWP CCAM Validation (Valencia) Wind speed and direction

Thank You Marine and Atmospheric Research NameDr Marcus Thatcher TitleResearch Scientist Contact CSIRO Phone Webwww.csiro.au