Annotation of /MITgcm_contrib/gael/comm/course-idma2015/computing/iap-idma-exercises



Below is a list of proposed, self guided exercises. I generally tried to order 
the exercises by increasing complexity. While none of them is really challenging, 
the various exercises aim to give you with first hand experience with the data sets
and tools discussed over the course of the IAP activity.

tips : - look for answers/examples in the programs we ran together in class #1 and #2 
         - type ‘help read_nctiles’ in matlab and similarly for all other functions
         - use the matlab debugger to go through computations step by step

notes on matlab software and exercises:
——————————————————

Having the up-to-date matlab software (gcmfaces and MITprof) set-up is pre-requisite.

1) if you did this set-up by following steps 1 and 2 of computing/iap-idma-readme (i.e. 
    using setup_gcmfaces_and_mitprof.csh) then the ‘software exercise’ #1 is for you

2) If you operate on a windows PC where shell scripting, cvs, etc cannot be relied upon, 
    then ‘software exercise’ #2 below is for you.

software exercises:
—————————

1) in a terminal window, go to your copy of gcmfaces and update it using cvs,
    then do the same thing with MITprof.
    tip : see http://mitgcm.org/public/using_cvs.html

2) set-up the matlab software manually
   tip : see instructions provided at the beginning of
   http://mitgcm.org/viewvc/*checkout*/MITgcm/MITgcm_contrib/gael/matlab_class/gcmfaces.pdf

Argo profile data exercises:
———————————— 

1) modify idma_float_plot.m to display salinity rather than temperature records

2) starting from the output of idma_float_plot.m (“p”) interpolate the data to 
    daily values, the apply convn to filter out sub-monthly fluctuations
    tip : use 30 days as the practical definition of “monthly”

3) extract all profiles from argo_feb2013_2008_to_2010_model.nc 
    that are located in the box defined by 10N-30N and 180W-120W
    tip : use MITprof_subset in analogy with idma_float_plot.m

4) compute 12 monthly mean temperature profiles for one float (“p”) or for 
    all profiles in the 10N-30N and 180W-120W box (see exercise #2).
    tip: get profiles’ month from the prof_date or prof_YYYYMMDD fields

5) compute normalized model-data differences 
    d=(p.prof_Testim-p.prof_T).*sqrt(p.prof_Tweight)
    then compute its # of entries for each depth level,
    and plot its histogram for a chosen level

6) compute vertical gradients of temperature profiles, compute temperature
    anomalies from the same profiles, and combine the two to infer vertical
    displacements of isotherms.

ECCO/gcmfaces exercises:
———————————— 

1) modify example_transports.m to compute the oceanic mass transport
    between Boston and Paris using gcmfaces_lines_transp.m and calc_transports.m

2) identify the Surface Height Anomaly variable from the files in nctiles_climatology,
    read its series of monthly fields into memory using read_nctiles.m, and display 
    its temporal standard deviation

3) identify the grid cell area and land mask variables in mygrid, load the ETAN 
    monthly time series, and compute the area weighted average of monthly 
    Surface Height Anomaly between 10E and 10W

4) load the UVELMASS and VVELMASS variables, compute the global 
    overturning stream function for each month, and display its evolution at 25N
     
5) load the UVELMASS and VVELMASS variables, extract one vertical level,
    then compute its zonal and meridional components, divergence and rotational
    tip : routines that do such computations are located in gcmfaces_calc/ with 
           examples in gcmfaces_diags/diags_set_*.m

6) modify example_smooth.m to apply the diffusive smoother to the following field : 
    unity at one point, zeros everywhere else, NaN over land. Run this computation 
    via the matlab debugger and visualize the intermediate operations (gradients, 
    flux convergence, exchanges, etc.).
1	gforget	1.1
2
3			Below is a list of proposed, self guided exercises. I generally tried to order
4			the exercises by increasing complexity. While none of them is really challenging,
5			the various exercises aim to give you with first hand experience with the data sets
6			and tools discussed over the course of the IAP activity.
7
8			tips : - look for answers/examples in the programs we ran together in class #1 and #2
9			- type ‘help read_nctiles’ in matlab and similarly for all other functions
10			- use the matlab debugger to go through computations step by step
11
12			notes on matlab software and exercises:
13			——————————————————
14
15			Having the up-to-date matlab software (gcmfaces and MITprof) set-up is pre-requisite.
16
17			1) if you did this set-up by following steps 1 and 2 of computing/iap-idma-readme (i.e.
18			using setup_gcmfaces_and_mitprof.csh) then the ‘software exercise’ #1 is for you
19
20			2) If you operate on a windows PC where shell scripting, cvs, etc cannot be relied upon,
21			then ‘software exercise’ #2 below is for you.
22
23			software exercises:
24			—————————
25
26			1) in a terminal window, go to your copy of gcmfaces and update it using cvs,
27			then do the same thing with MITprof.
28			tip : see http://mitgcm.org/public/using_cvs.html
29
30			2) set-up the matlab software manually
31			tip : see instructions provided at the beginning of
32			http://mitgcm.org/viewvc/checkout/MITgcm/MITgcm_contrib/gael/matlab_class/gcmfaces.pdf
33
34			Argo profile data exercises:
35			————————————
36
37			1) modify idma_float_plot.m to display salinity rather than temperature records
38
39			2) starting from the output of idma_float_plot.m (“p”) interpolate the data to
40			daily values, the apply convn to filter out sub-monthly fluctuations
41			tip : use 30 days as the practical definition of “monthly”
42
43			3) extract all profiles from argo_feb2013_2008_to_2010_model.nc
44			that are located in the box defined by 10N-30N and 180W-120W
45			tip : use MITprof_subset in analogy with idma_float_plot.m
46
47			4) compute 12 monthly mean temperature profiles for one float (“p”) or for
48			all profiles in the 10N-30N and 180W-120W box (see exercise #2).
49			tip: get profiles’ month from the prof_date or prof_YYYYMMDD fields
50
51			5) compute normalized model-data differences
52			d=(p.prof_Testim-p.prof_T).*sqrt(p.prof_Tweight)
53			then compute its # of entries for each depth level,
54			and plot its histogram for a chosen level
55
56			6) compute vertical gradients of temperature profiles, compute temperature
57			anomalies from the same profiles, and combine the two to infer vertical
58			displacements of isotherms.
59
60			ECCO/gcmfaces exercises:
61			————————————
62
63			1) modify example_transports.m to compute the oceanic mass transport
64			between Boston and Paris using gcmfaces_lines_transp.m and calc_transports.m
65
66			2) identify the Surface Height Anomaly variable from the files in nctiles_climatology,
67			read its series of monthly fields into memory using read_nctiles.m, and display
68			its temporal standard deviation
69
70			3) identify the grid cell area and land mask variables in mygrid, load the ETAN
71			monthly time series, and compute the area weighted average of monthly
72			Surface Height Anomaly between 10E and 10W
73
74			4) load the UVELMASS and VVELMASS variables, compute the global
75			overturning stream function for each month, and display its evolution at 25N
76
77			5) load the UVELMASS and VVELMASS variables, extract one vertical level,
78			then compute its zonal and meridional components, divergence and rotational
79			tip : routines that do such computations are located in gcmfaces_calc/ with
80			examples in gcmfaces_diags/diags_set_*.m
81
82			6) modify example_smooth.m to apply the diffusive smoother to the following field :
83			unity at one point, zeros everywhere else, NaN over land. Run this computation
84			via the matlab debugger and visualize the intermediate operations (gradients,
85			flux convergence, exchanges, etc.).