20
You can use raster package to download WorldClim data, see ?getdata to know about resolution, variables and coordinates.
As example:
library(raster)
library(sp)
r <- getData("worldclim",var="bio",res=10)
Bio 1 and Bio12 are mean anual temperature and anual precipitation:
r <- r[[c(1,12)]]
names(r) <- c("Temp","Prec")
I create random points as ...
9
A respected source of global climate classification information can be found here: http://koeppen-geiger.vu-wien.ac.at/present.htm. The data are in KMZ rather than shapefile format, but should be easy to convert. The value of using these is that the methodology for their creation is well documented (see citations here).
8
There are a number of sites which provide varying ranges of climatic data at a broad range of spatial scales.
I often use WorldClim for global data and if I need higher resolution data for the USA I use data from the PRISM group. You could also look at MODIS data, which is very detailed, with many derivative datasets generated for ease of use.
I am not ...
6
I doubt that you'll find a free database for climatic data, which contains literately every point in the world. I assume that even the most exact climate data is usually computed from models and already interpolated.
For instance: How do you plan to get exact climate information for locations somewhere in central Africa? In places where there has never ...
6
You can likely get a reasonable interpolation using a linear regression (assuming your 30 weather stations are a representative sample) using elevation, latitude and distance from the coast as independent variables with the day as a factor. I've done this using ArcGIS and R previously.
Daily 9am and 3pm temperatures over 10 days in 2003 from weather ...
6
An approach would be creating a style for each raster, ranging from -40°C to +20°C (or whatever you want).
Firstly, right-click on the layer and, from Properties >> Style, try to set it following these steps:
Then, save the style (as a .qml file) going to Style >> Save Style... from the same dialog above, but remember to save it with the same ...
5
I would write an R script that worked as a client, but will run on the database server. This will save the complication of trying to hook into PostGIS's backend and using PL/R (as I said in comments).
The script will look something like this (which is practically pseudo-code here):
> con = dbConnect(PG,"localhost","weather") # connect to local DB
>...
5
A very simple approach which springs to mind is to export the tiff to an ascii grid format such as ESRI's .asc file. You will then have a space delimited plain text file. It will have a few header lines which describe the origin, resolution and NoData values etc and you can easily skip over these for the sake of you calculations. You can do the ...
5
These data are not in a netcdf format but, rather a space delimited ASCII format. This data is a bit difficult to deal with because of the lack of headers and any type of unique station identifier.
Returns a vector of file names on disk and pulls associate years from file name.
f <- list.files(getwd())
y <- as.numeric(unlist(lapply(strsplit(f, "[.]")...
4
European Reanalysis Interim (ERA-Interim) | European Centre for Medium-Range Weather Forecasts
State-of-the-art 3rd generation reanalysis (1979-present) with robust
physics and data assimilation.0.75°x0.75° global grid with 60 vertical
levels. Well suited for climate study within the satellite era.
European Reanalysis 40-year (ERA-40) | European ...
4
Take a look at: http://www.cgiar-csi.org/data/uea-cru-ts-v3-10-01-historic-climate-database
Quote: "In January 2010, the University of East Anglia officially released the CRU-TS 3.0 Climate Database (See the official data release at http://badc.nerc.ac.uk/data/cru). This new version of database covers from 1901 to 2006*, globally at 0.5 degree spatial ...
4
Interpolating climate data, you have two options (i see you need ready to use tutorials, I will give reference, but also some theoretical aspects you have here):
simple interpolation using a kriging approach is the best option, cause you will have a statistical sounding relation. You can use this tutorial: In Romanian, but you can use Google Translate (use ...
4
The PRISM Climate Group's data is exceptional. Their raster products include precipitation, max temp, min temp, dewpoint and historic data.
NASA's MODIS site has a wealth of data as does this USGS site. You will find a wide range of products there from vegetation indices to emissivity and burn data.
4
The WorldClim dataset has a lot of the data you want. It is free for non-commercial use and has interpolated 1 km resolution data on:
average monthly mean temperature (°C * 10)
average monthly minimum temperature (°C * 10)
average monthly maximum temperature (°C * 10)
average monthly precipitation (mm)
4
for rainfall you can download the TRMM datasets: http://trmm.gsfc.nasa.gov/
4
The available free gridded global data mostly relates to near-surface wind speed and direction over global oceans. Free global land surface wind speed datasets are few.
None that I know come in GIS format, as each cell has two data values, one for speed and one for vector - but more digging might provide disaggregation and the possibility of mapping at ...
4
NetCDF is incredibly general and writing slow code is easy. I routinely deal with tens of thousands of NetCDF files in R, using some combination of packages raster, ncdf, ncdf4, RNetCDF, or rgdal. The key is to leverage the cell index tools in raster so that the "cell-in-polygon" test occurs only once, then you can apply the extraction across all files. Many ...
4
The short answer is that the docker-based command doesn't output any netCDF data into the shell pipeline. It does launch local docker instance with the configuration you've created via the Web UI.
Here is stdout actually looks like
https://gist.github.com/dobrych/2cf416d05608c30851f9f7d325d3de19
Since most of the users were interested in getting data as ...
4
Following your question on reading and formatting this specific climate data:
First, add libraries and country boundaries.
library(sp)
library(raster)
library(maptools)
data(wrld_simpl)
germany<- wrld_simpl[wrld_simpl@data$NAME =="Germany",]
Now, assuming we have the data.frame "stations" created in your previous question (at this point, not sure ...
4
I did some reading around and came across obscure CDO documentation for the collgrid function. With that, I was able to finally perform this series of merges in python by walking through files and performing a subprocess call to CDO using something similar to the following:
#!/bin/python3
import os
import subprocess
source = "/path/to/source"
destination =...
4
Here is some example code for R. I think there are many examples out there.
library(raster)
b <- brick("climfile.nc")
points <- shapefile("ptsfile.shp")
d <- extract(b, points)
write.csv(d, "file.csv", rownames=FALSE)
4
The file is just a zipped text file without headers.
First you need to unzip the file, I would suggest 7-zip if on Windows.
If you open it up in notepad you would see:
-179.750 89.750 -32.2 -34.6 -32.4 -27.2 -12.5 -1.9 -0.5 -1.4 -9.9 -18.9 -27.0 -33.2 -19.3
-179.750 89.250 -32.4 -34.8 -32.3 -27.1 -12.4 -1.9 -0.6 -1.5 -9.9 -...
4
The error does not occur in the part of the code you present here. Later, you try to retrieve the system:time_start property with getNumber(), while you have set it as an ee.Date() in line 12. Probably, setting it to the standard GEE long number format (using millis() on an ee.Date()) will help you:
.set({year: year, 'system:time_start':date_start....
3
Perhaps this website would be useful for future climate data to meet your specific needs:
http://www.ccafs-climate.org/
I haven't downloaded data from here yet, but it does appear to have the very latest available data.
3
Check the website climate.gov from NOAA (http://www.climate.gov/#dataServices).
These are the available datasets for the world: http://www.climate.gov/#dataServices/mapServices_global
Includes:
Global Hourly Surface Data (*)
Global Hourly Summaries (*)
Global Summary of the Day (GSOD) (*)
GHCN-Daily Data (*)
GHCN-D ...
3
Yes, it is appropriate. Prediction by kriging can theoretically only get better when you bring in more correlated information, and that is what you do when moving from kriging to co-kriging. In practice, the gain can be disappointing, considering the effort it takes.
There can also be other reasons to favor co-kriging. An example is when you need the ...
3
The finest resolution of Bioclim is 30 arc-seconds, which translates to approximately 1 kilometer (1000m).
You can certainly resample the data to 30 meters but this would be quite invalid because the assumption in a model is that you are representing a functional resolution with the data. Since you are not adding any additional information to the climate ...
3
Use the spatial analysts PLUS tool to add 0.3 or 0.8 to your climate grid. If you read the help file (syntax section) the second grid which you add to your input grid can actually be a constant and not just another grid. So putting 0.3 into that will add 0.3 to all the cells in your input raster.
3
From wikipedia:
For the purpose of measuring the height of objects on land, the usual
datum used is mean sea level (MSL). This is a tidal datum which is
described as the arithmetic mean of the hourly water elevation taken
over a specific 19 years cycle. This definition averages out tidal
highs and lows (caused by the gravitational effects of the ...
3
The methods in the 3rd paragraph of your link state:
The data layers were generated through interpolation of average
monthly climate data from weather stations on a 30 arc-second
resolution grid (often referred to as "1 km2" resolution). Variables
included are monthly total precipitation, and monthly mean, minimum
and maximum temperature, and 19 ...
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