Annotation of /MITgcm_contrib/gael/comm/course-idma2016/guidelines/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. Here it is assumed that you have 
completed the set up of the activities as explained in guidelines/iap-idma-instructions.

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

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.).

MITgcm exercises:
—————————

1) increase experiment duration for adjustment.cs-32x32x1, 
   tutorial_held_suarez_cs, and tutorial_plume_on_slope

   tips : - if using testreport for longer runs then use the -fast option
          - for tutorial_held_suarez_cs/input/data:
                <  nTimeSteps=16,
                >  nTimeSteps=69120,
          - for tutorial_plume_on_slope/input/data:
                <  nTimeSteps=20,
                >  nTimeSteps=8640,
          - for adjustment.cs-32x32x1/input/data:
                <  nTimeSteps=24,
                >  endTime=172800.,

2) compile, run, and display utorial_held_suarez_cs with diagnostic package 

   tips : - adjustment.cs-32x32x1 provides a working example using pkg/diagnostics
          - pkg/diagnostics needs to be activated at compile time (packages.conf), and
            at run time (data.pkg), and then configured at run time (data.diagnostics)
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	gforget	1.2	and tools discussed over the course of the IAP activity. Here it is assumed that you have
7			completed the set up of the activities as explained in guidelines/iap-idma-instructions.
8	gforget	1.1
9			tips : - look for answers/examples in the programs we ran together in class #1 and #2
10			- type ‘help read_nctiles’ in matlab and similarly for all other functions
11			- use the matlab debugger to go through computations step by step
12
13			Argo profile data exercises:
14			————————————
15
16			1) modify idma_float_plot.m to display salinity rather than temperature records
17
18			2) starting from the output of idma_float_plot.m (“p”) interpolate the data to
19			daily values, the apply convn to filter out sub-monthly fluctuations
20			tip : use 30 days as the practical definition of “monthly”
21
22			3) extract all profiles from argo_feb2013_2008_to_2010_model.nc
23			that are located in the box defined by 10N-30N and 180W-120W
24			tip : use MITprof_subset in analogy with idma_float_plot.m
25
26			4) compute 12 monthly mean temperature profiles for one float (“p”) or for
27			all profiles in the 10N-30N and 180W-120W box (see exercise #2).
28			tip: get profiles’ month from the prof_date or prof_YYYYMMDD fields
29
30			5) compute normalized model-data differences
31			d=(p.prof_Testim-p.prof_T).*sqrt(p.prof_Tweight)
32			then compute its # of entries for each depth level,
33			and plot its histogram for a chosen level
34
35			6) compute vertical gradients of temperature profiles, compute temperature
36			anomalies from the same profiles, and combine the two to infer vertical
37			displacements of isotherms.
38
39			ECCO/gcmfaces exercises:
40			————————————
41
42			1) modify example_transports.m to compute the oceanic mass transport
43			between Boston and Paris using gcmfaces_lines_transp.m and calc_transports.m
44
45			2) identify the Surface Height Anomaly variable from the files in nctiles_climatology,
46			read its series of monthly fields into memory using read_nctiles.m, and display
47			its temporal standard deviation
48
49			3) identify the grid cell area and land mask variables in mygrid, load the ETAN
50			monthly time series, and compute the area weighted average of monthly
51			Surface Height Anomaly between 10E and 10W
52
53			4) load the UVELMASS and VVELMASS variables, compute the global
54			overturning stream function for each month, and display its evolution at 25N
55
56			5) load the UVELMASS and VVELMASS variables, extract one vertical level,
57			then compute its zonal and meridional components, divergence and rotational
58			tip : routines that do such computations are located in gcmfaces_calc/ with
59			examples in gcmfaces_diags/diags_set_*.m
60
61			6) modify example_smooth.m to apply the diffusive smoother to the following field :
62			unity at one point, zeros everywhere else, NaN over land. Run this computation
63			via the matlab debugger and visualize the intermediate operations (gradients,
64			flux convergence, exchanges, etc.).
65
66			MITgcm exercises:
67			—————————
68
69			1) increase experiment duration for adjustment.cs-32x32x1,
70			tutorial_held_suarez_cs, and tutorial_plume_on_slope
71
72			tips : - if using testreport for longer runs then use the -fast option
73			- for tutorial_held_suarez_cs/input/data:
74			< nTimeSteps=16,
75			> nTimeSteps=69120,
76			- for tutorial_plume_on_slope/input/data:
77			< nTimeSteps=20,
78			> nTimeSteps=8640,
79			- for adjustment.cs-32x32x1/input/data:
80			< nTimeSteps=24,
81			> endTime=172800.,
82
83			2) compile, run, and display utorial_held_suarez_cs with diagnostic package
84
85			tips : - adjustment.cs-32x32x1 provides a working example using pkg/diagnostics
86			- pkg/diagnostics needs to be activated at compile time (packages.conf), and
87			at run time (data.pkg), and then configured at run time (data.diagnostics)