One single Julia code gridL2_Dates.jl to grid all kinds of satellite data onto rectangular grid at arbitrary spacing (and determined spatial and temporal resolution). Input defined just via json files, which makes it generic as long as you have corner coordinates for your satellite dataset (you can grid XCO2, methane CO, etc from TROPOMI or SIF from OCO-2, OCO-3, TROPOMI, GOME-2, etc)..
The main program, gridL2_Dates.jl, can compute gridded averages and save everything into a netCDF4 file that can be read via tools such as python, Julia, Panoply, etc ( 3D dataset with time/lat/lon). Most importantly, it can oversample, i.e. the gridding takes the actual footprint overlap with the final grid into account. This is done by splitting a footprint via its corner coordinates into a set of points nxn within that footprint (typicall n=10, i.e. 100 points). For these, a simple "in-the-box" gridding routine will be applied, i.e. tha algorithm finds in which grid boxe each sub-pixel of a footprint falls. This results in fractional "samples" per grid box.
The program was written as a first step into Julia (as everything else was too slow and C++ development time just too long). So, some things are still clumsy and not really "Julian". Feel free to make things more elegant and create a pull request if you have done so.
The current version works well with Julia versions > 1.0 but I didn't test all of them. Download it for your platform from Julia's old releases.
You might have to install a couple of Julia packages, this can done like in Julia using the built-in package manager (press ] at the Julia prompt):
julia> ]
(v1.3) pkg> add ArgParse, Statistics, Glob, JSON, Dates, PrintfHint for first time users, you need to use the console/terminal to use it properly. On a Mac, it is good to add an alias, e.g.
alias julia /Applications/Julia-1.3.app/Contents/Resources/julia/bin/julia
in your .bash_profile or whatever you are using.
Once this is done, you can test out what options there are (I often do this myself):
$ julia ./gridL2_Dates.jl --help
usage: gridL2_Dates.jl [--Dict DICT] [-o OUTFILE] [--monthly]
[--compSTD] [--latMin LATMIN] [--latMax LATMAX]
[--lonMin LONMIN] [--lonMax LONMAX]
[--dLat DLAT] [--dLon DLON]
[--startDate STARTDATE] [--stopDate STOPDATE]
[--dDays DDAYS] [-h]
optional arguments:
--Dict DICT JSON dictionary file to use (default:
"/home/cfranken/code/gitHub/Gridding/gridding/tropomi_all.json")
-o, --outFile OUTFILE
output filename (default OCO2_SIF_map.nc)
(default: "OCO2_SIF_map.nc")
--monthly Use time-steps in terms of months (not days)
--compSTD compute standard deviation within dataset
--latMin LATMIN Lower latitude bound (type: Float32, default:
-90.0)
--latMax LATMAX Upper latitude bound (type: Float32, default:
90.0)
--lonMin LONMIN Lower longitude bound (type: Float32, default:
-180.0)
--lonMax LONMAX Upper longitude bound (type: Float32, default:
180.0)
--dLat DLAT latitude resolution (type: Float32, default:
1.0)
--dLon DLON longitude resolution (type: Float32, default:
1.0)
--startDate STARTDATE
Start Date (in YYYY-MM-DD) (default:
"2018-03-07")
--stopDate STOPDATE Stop Date (in YYYY-MM-DD) (default:
"2018-10-31")
--dDays DDAYS Time steps in days (or months if --monthly is
set) (type: Int64, default: 8)
-h, --help show this help message and exit
The most important part of this script is --Dict as most description about the files you want to grid are within the json file you will use here. You can also change the spatial resolution (e.g. --dLat), the lat/lon boundaries if you don't want the globe and the date ranges and temporal resolution you are interested in (--startDate, --stopDate, --dDays defines the number of days you want to aggregate together (if you set --monthly, this will be the step in months). If you set --compSTD, the standard deviation of the data within a grid box will be computed (the original variable name will be kept with an _std added to it and currently discarding all attributes). This is not necessarily an uncertainty estimate but the true spread of data within a grid box.
An example on how to run this is
julia ./gridL2_Dates.jl --dLat 1 --dLon 1 --dDays 16 --startDate 2019-02-01 --stopDate 2020-01-30 --Dict oco2_all.json -o ~/oco2_16day_1deg_scf.nc
Now to the most important part, the json structure makes the code very general (check out https://www.json.org/json-en.html). An example file content is below:
{
"basic":{
"lat": "latitude",
"lon": "longitude",
"lat_bnd": "Latitude_Corners",
"lon_bnd": "Longitude_Corners",
"time": "time"
},
"grid":{
"sif_757": "Daily_SIF_757nm",
"sif_771": "Daily_SIF_771nm",
"sif_757_relative": "Science/SIF_Relative_757nm",
"sif_771_relative": "Science/SIF_Relative_771nm",
"dcCorr": "Science/daily_correction_factor"
},
"filePattern": "YYYY/MM/oco2_LtSIF_??MMDD_*.nc4",
"folder": "/net/fluo/data2/groupMembers/cfranken/data/kurosu/test_data/daily/oco2/new/"
}
The structure will be used to define dictionaries in Julia, with a key (the left side, pointing to internal variables in the code) and a value (this is actually the path to the respetive dataset within the files you want to grid). What is needed for sure is the key/value pair for lat_bnd and lon_bnd as these corner coordinates in the files are used to perform proper gridding and over-sampling (I think time is not even used right now). The other part is in the grid group, eveything you will add here will be gridded, i.e. all variables on the left side of the key/value pairs (and saved how you call it on the left).
Last but not least, we need a filePattern, which defines where to look for the data (YYYY, MM and DD are keywords, which will be internally used to find the right matching years, months and days). Wildcards can be used (?, *, etc). Then you need to provide the main folder where the data is located (the full path is folder/filePattern).
You can add filter criteria as well, as we sometimes want to make sure that quality filters are applied, angles are within a specific range, and so forth. Within the json file, this can be done by adding groups called filter_eq, filter_gt, or filter_lt). These test for equalities, greater than or lower than. An example is here:
{
"basic":{
"lat": "PRODUCT/latitude",
"lon": "PRODUCT/longitude",
"lat_bnd": "PRODUCT/SUPPORT_DATA/GEOLOCATIONS/latitude_bounds",
"lon_bnd": "PRODUCT/SUPPORT_DATA/GEOLOCATIONS/longitude_bounds",
"time": "PRODUCT/time_utc"
},
"grid":{
"ch4": "PRODUCT/methane_mixing_ratio_bias_corrected",
"altitude": "PRODUCT/SUPPORT_DATA/INPUT_DATA/surface_altitude"
},
"filter_gt":{
"PRODUCT/qa_value": 0.3,
"PRODUCT/methane_mixing_ratio_bias_corrected": 1600
},
"filter_lt":{
"PRODUCT/methane_mixing_ratio_bias_corrected": 2200
},
"filePattern": "S5P_*_L2__CH4____YYYYMMDD*.zip",
"folder": "/net/fluo/data2/projects/TROPOMI_GASES/ch4/"
}
here, we want to make sure that the qa_value is larger than 0.3 and that the methane mixing ratio is within a reasonable range (be careful doing things like this, just added for demonstration).
Please feel free to use this tool but make sure that you help the community if you find bugs, improve it etc. Any modifications that are useful should be made publicly available, you can fork and create a pull request. Also, let us know if you find bugs. On top of that, please acknowledge the tool if you use it in publications.
We use these as well but I have no time to document all of them right now. Contact us if you want to use them (cfranken"at"caltech.edu).