Due Friday Jan 27
In this exercise you will learn about the purpose of running multiple ensemble members to investigate error in a forecast associated with errors in the initial conditions. You will also learn about error from model resolution. Our baroclinic wave case study is an idealized, so our verification or "truth" is another run but at 0.5 degree resolution. This 0.5 deg run was initialized with the same Gaussian perturbation that you used last week. You will run a 2 deg resolution simulation with the Gaussian perturbation plus some random noise, which represents errors in the observations and or the method of constructing model initial conditions (called "analysis").
I. Make your very own initial condition files with a random perturbation to the baroclinic wave initial conditions (~5-10 min)
II. Build and CAM (~5 min, plus some wait time)
III. Meanwhile analyze CAM output in MATLAB and write up answers to a few questions (a couple of hours max)
I. Make your initial conditions
Go to your camruns directory and make a subdirectory for this exercise called perturbed2deg and another subdirectory for matlab files for this exercise.
mkdir perturbed2deg/run only do this if you didn't run the build script first
In this mfiles directory. Copy the matlab script to make the initial conditions and run it in matlab. It is meant to be self explanatory, but ask Cecilia if it is not. Remember, the dot at the end of the cp command puts the file in the current directory (dot = here). The & sign puts matlab in the background so you can continue to use the window to do other things.
cp /home/disk/p/atms380/scripts/mk_cam_ICs_perturbed.m .
II. Build and run CAM
In the "work" directory. Copy the build script from the class script directory here. The dot at the end of the command puts the file in the current directory (dot = here).
cp /home/disk/p/atms380/scripts/bld-cam4-perturbed.csh .
Execute the script:
Wait a few minutes. When done submit the job.
Verify your job is in the queue with either
qstat -u "*"
the latter lets you see all the jobs in the queue.
If for some reason you wish to kill your job.
qdel xxxxx fill in the x's with the job-ID and this will cancel your job
III. Analyze CAM - turn in about a page on a-d below, plus figures. Next week, you will need to edit the matlab file that you use to remake a figure with your own output so it reads your data file not mine. Otherwise you don't need to edit them much at all. Feel free though.
Make a supdirectory of your "work" directory for this case and call it something like mfiles. Go to that directory and copy the analysis files to your directory for this exercise. Start matlab
cp /home/disk/p/atms380/scripts/analyze_ex3* .
a) Run analyze_ex3_a in matlab. The Anomaly Correlation is a measure of how well the patterns match. The script by default is comparing 850hPa temperature, which is a 2-dimensional field. Thus the Anomaly Correlation is high if the fronts are in the same location in the horizontal plane. This script has you examine the role of resolution. This has nothing to do with the integration you are doing with the random perturbation (that is used in part b). Run the script and look at the output in figure 1. To turn in: Try a range of days and variables and discuss your observations about what controls Anomaly Correlation.
b) Run analyze_ex3_b in matlab. This script plots information about the wind component in the eastward direction or U-wind in a series of ensemble members that had initial conditions perturbed with a random number. All runs are 2 deg resolution. When your job is done increment the variable ENS_MEMS to 10 by editing the script. You probably won't notice one more line. Figure 1 is the Anomaly Correlation for the ensemble. Figure 2 is the ensemble mean of U and Figure 3 is like Figure 2, but for each ensemble memeber. Figures 2 and 3 also have a black line showing the location of the U-wind maximum in the northern hemisphere. This is the jet maximum. To turn in: Try a range of days for Figs 2 and 3 (edit the script where it says "VARY THIS") and discuss your observations about how the ensemble members differ.
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