Activity
From 03/17/2012 to 04/15/2012
04/09/2012
- 10:51 AM Revision b9fe1b70: renamed script to multiscalesmooth.R
- 10:45 AM Revision 7a06885b: numerous comment, format, and variable name changes
- 10:33 AM Revision 3abda63e: now actually refining before smoothing (GRASS version)
- 10:33 AM Revision 40cd02ba: reversed order of refine-and-smooth step
- 10:32 AM Revision c87139a7: added GRASS/Python implementation of multiscale smoother
04/03/2012
- 03:40 PM Document: Presentation E&O progress, IPLANT meeting 04032012
- This presentation reports on the prediction of tmax for the Oregon case study. There are three main analyses document...
03/27/2012
- 01:06 PM Revision 13367984: added R script to run multiscalesmooth example on Oregon SRTM
- 01:04 PM Revision e50dfd65: switched to more accurate chisq test
- 01:02 PM Revision d0cf2921: enhanced smoother to accept sd as a raster, not just a constant
- 12:48 PM Revision ae79872e: tweaked var names to match Gallant pub; added comments
- 08:57 AM Revision 6871bc85: fixed vwg step
- 06:15 AM Document: Exploration of TRMM precipitation data in Oregon
- A brief overview of a comparison between TRMM satellite derived precipitation product and station observations in Ore...
03/26/2012
- 04:03 PM Revision 64258f2c: continued R translation of multiscale smoother
- 03:58 PM Revision 2d654eeb: initial R translation of multiscale smoother
03/23/2012
- 09:54 AM Task #361: Test and compare the GAM method on several days (10 days for now) for Oregon
- * The R code has been updated to include some interaction terms. Examination of 3D surface plot with tmax on the z-ax...
03/20/2012
- 04:47 PM Revision 7967a0d1: added test script for days missing from our MOD11A1 OR holdings
- 03:48 PM Document: Presentation IPLANT meeting 03202012
- This presentation provides an update and summary of three ongoing tasks:
1) Prediction of tmax using GWR with 0,30,5...
03/19/2012
- 10:37 PM Revision 0fe2dd49: added old GLCNMO land-cover AML scripts (Tien Ming Lee)
- 03:41 PM Task #364: Integrate spatial variables and structure in the GAM methodology
- GWR predictions were produced using the sgwr package in R.
The following specifications were used to run the mode...
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