The 5 step program to a blissful OpenDAP Life
- Inspiration: grab the mexcdf_dap binary for your platform
and "get_roms_dap.m" at:
http://cove.whoi.edu/~rsignell/mexcdf_dap Make sure that
"which mexcdf53" points to your new OpenDAP-enabled mexcdf53
mex file, and then run the "get_roms_dap" demo script.
- Kick Ass: Get the IDV http://my.unidata.ucar.edu/content/software/idv/.
Replace the "idv.jar" file in your IDV installation
directory with this updated "idv.jar":
ftp://ftp.unidata.ucar.edu/pub/idv/untested/idv.jar
Then get http://cove.whoi.edu/~rsignell/john/DefaultIdv_rps.tar.gz
and unpack it from your "~/.metapps" directory so that it
overwrites the files in the ~/.metapps/DefaultIdv directory. Start IDV,
and in the Displays menu, select: Displays=>Favorite
Bundles=>Adriatic=Test01
- Good Citizen: Serve your netcdf data via OpenDAP: Grab the "CGI Server
Base" and "CGI Server NetCDF module" for your web server from
http://opendap.org/download/index.html and then follow the install
instructions at: http://opendap.org/server/install-html/install.html
Basically all you do is unpack the binaries into your cgi-bin directory, edit
a conf file to point to the places where you have netcdf files, and who can
have access. It took me 35 minutes to get our OpenDAP server going!
- Ultimate Citizen: Make a THREDDS catalog
http://my.unidata.ucar.edu/content/projects/THREDDS
of your DODS-served data so folks can view it with tools like the IDV.
These are simple XML files that just tell tools like IDV the protocol and
location of the data. Check out an example at:
http://cove.whoi.edu/~rsignell/adria-models.xml
- Go Uptown: If you want to get a bit fancier, install an aggregation server.
http://www.opendap.org/server/agg-html/, which just makes it possible for
groups of files to appear as one.
Wednesday, November 17, 2004
How to get OpenDAP working for ROMS model output
Rich Signell sent me this advice on getting DODS/OpenDAP working for my model output:
Tuesday, November 16, 2004
Office of Coast Survey - Historical Maps and Charts
A huge array of historical charts are available online:
Office of Coast Survey - Historical Maps and Charts
They use a goofy format, called MrSid, that must be decoded into something more sensible to use. I use the linux decoder created by Lizardtech found here. Once you decompress the image, you see why they use this format which compresses the image using wavelets.
Below is an image of a historical chart of the Mississippi River Delta first published in 1874 found by searching for key word 'Mississippi'. Click the image for a higher resolution image (still only 1/4 of full resolution).
Office of Coast Survey - Historical Maps and Charts
They use a goofy format, called MrSid, that must be decoded into something more sensible to use. I use the linux decoder created by Lizardtech found here. Once you decompress the image, you see why they use this format which compresses the image using wavelets.
Below is an image of a historical chart of the Mississippi River Delta first published in 1874 found by searching for key word 'Mississippi'. Click the image for a higher resolution image (still only 1/4 of full resolution).
Tuesday, November 9, 2004
TGLO skill estimates
Skill estimates for the mooring locations were calculated from a simulation lasting one year: summer 2001 to summer 2002. The largest skill is for buoy D, away from the Mississippi, and near the coast. Most of the skill estimates are around, or beneath zero, indicating that the model error is of a similar magnitude to the variance. The first two columns of numbers are the skill for the east-west and north-south velocities. The third column is the skill for speed. This particular model run had no buoyancy forcing, and represents the 'base' model skill, to which future, improved runs will be compared.
All timeseries use the mean of the data for the 'climatology', which produces the lowest skill estimates (see paper on skill linked below). Also, if the timeseries are filtered more, say with a 3-day boxcar, the skill is increased so that 4 to 5 of the skill estimates are positive.
B : -1.3335 : -0.3682 :-1.4042
D : -0.5348 : 0.2566 :-0.2602
F : -1.2726 : -2.9699 : -2.0598
J : -4.2723 : -0.9684 : -3.1124
K : -0.5815 : -1.4996 : -1.5841
N : -2.9575 : -1.5863 : -0.5991
R : -0.0772 : -0.5407 : -0.3376
S : -2.9290 : -1.9349 : -9.9881
V : -3.6531 : -5.8301 : -0.9598
W : -0.4621 : -0.3207 : -0.3583
All timeseries use the mean of the data for the 'climatology', which produces the lowest skill estimates (see paper on skill linked below). Also, if the timeseries are filtered more, say with a 3-day boxcar, the skill is increased so that 4 to 5 of the skill estimates are positive.
B : -1.3335 : -0.3682 :-1.4042
D : -0.5348 : 0.2566 :-0.2602
F : -1.2726 : -2.9699 : -2.0598
J : -4.2723 : -0.9684 : -3.1124
K : -0.5815 : -1.4996 : -1.5841
N : -2.9575 : -1.5863 : -0.5991
R : -0.0772 : -0.5407 : -0.3376
S : -2.9290 : -1.9349 : -9.9881
V : -3.6531 : -5.8301 : -0.9598
W : -0.4621 : -0.3207 : -0.3583
Wednesday, November 3, 2004
Skill paper.
I just submitted this paper to Ocean Modelling. The paper discusses estimating model skill when the primary feature is a descrete event.
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