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EUR-11 + dtype='float64', name='rlat', length=412))
  • CORDEX_domain :
    EUR-11

    • The dummy='topo' argument means, we want a dummy variable in the dataset to see how the domain looks like. For the dummy topography, we use the cdo topo operator in the background. So maybe you have to install python-cdo, e.g., conda install -c conda-forge python-cdo. Working with xarray datasets means, that we can use all the nice functions of xarray including plotting. The latest versions of py-cordex also come with an xarray accessor that allows you, e.g., to get a quick overview of the domain, using, e.g.

    @@ -2014,7 +2014,7 @@

    EUR-11

    -_images/da8b772f377f73bdcf77da70cccee05bd530c3a6ad46f9cffd52600932f154a3.png +_images/7e1ea413e7fd377606bd9e0019495cfb6485d89013c0a7889a1db62c041e4eb2.png

    We can also use the dummy topography to plot an overview:

    @@ -2025,10 +2025,10 @@

    EUR-11
    -
    <matplotlib.collections.QuadMesh at 0x14e837520>
+
    <matplotlib.collections.QuadMesh at 0x144da9cc0>
 
    
 
    
-_images/bfe4eb438d3daf2723a42025e4529de8d7835a1316696315f89293c952a3a8c8.png
+_images/e211f9edb4990d7f33888bf922ae5d64fd7f0e734e0d9f8ca1ecaf979c33532f.png
    @@ -2038,10 +2038,10 @@

    EUR-11

    -
    <matplotlib.collections.QuadMesh at 0x14e8ff550>
+
    <matplotlib.collections.QuadMesh at 0x144e61c30>
 
    
 
    
-_images/a1d27b44b1c6f95995ca21af79cce5bc4160e60cc67d0ea6e548d6d42016100e.png
+_images/2907ce25b108ebf2778c04a147b599ce4cf58e65cd6d0e6218c6c0a9a06ae1f4.png

    Let’s define a slightly more sophisticated plotting function that uses cartopy for the right projection with a rotated pole:

    @@ -2111,7 +2111,7 @@

    EUR-11

    -_images/1e1738944a815c13fadb907e08cc749971ba1ebf1599c01064a01b34535a729d.png +_images/843012da33d062844654203d925bde1ce3b0162f93ac802b45b69c81683c690e.png @@ -2327,7 +2327,7 @@

    Plot all cordex-core domains +_images/503abed6bb41586386abc15dc720fbea50bc29fdf38ebe422f083aa49e6f624b.png @@ -2342,23 +2342,24 @@

    Using the grid information for interpolation -
    --2024-04-02 15:21:09--  https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc
+
    --2024-04-09 16:29:42--  https://raw.githubusercontent.com/remo-rcm/pyremo-data/main/orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc
 Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
 Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
 HTTP request sent, awaiting response... 
 
    
 
    
 
    200 OK
-Length: 299137 (292K) [application/octet-stream]
+Length: 
 
    
 
    
-
    Saving to: ‘orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc.1’
+
    299137 (292K) [application/octet-stream]
+Saving to: ‘orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc.1’
 
 
           orog_fx_M   0%[                    ]       0  --.-KB/s               
-orog_fx_MPI-ESM1-2- 100%[===================>] 292.13K  --.-KB/s    in 0.03s   
+orog_fx_MPI-ESM1-2- 100%[===================>] 292.13K  --.-KB/s    in 0.006s  
 
-2024-04-02 15:21:10 (10.4 MB/s) - ‘orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc.1’ saved [299137/299137]
+2024-04-09 16:29:42 (48.5 MB/s) - ‘orog_fx_MPI-ESM1-2-HR_historical_r1i1p1f1_gn.nc.1’ saved [299137/299137]
 
    
 
    
 
    
@@ -2766,7 +2767,7 @@ 
    Using the grid information for interpolation
 
    
@@ -3242,10 +3243,10 @@ 
    Interpolation using 
-
    <matplotlib.collections.QuadMesh at 0x14f02c6d0>
+
    <matplotlib.collections.QuadMesh at 0x145562c20>
 
    
 
    
-_images/a37eb179ba0d721ef48714a8c66673f78eb8c0cda03a9a3d3ca67cc86f7e17cc.png
+_images/2f8cc3edd325058dc20fcaf0aa2cb29e3d514bdff471e83adc004313235c9ca5.png
 
    
 
    
 
@@ -3292,10 +3293,10 @@ 
    Alternative interpolation methods
-
    <matplotlib.collections.QuadMesh at 0x14ff2e470>
+
    <matplotlib.collections.QuadMesh at 0x146476aa0>
 
    
 
    
-_images/30644bfe6b28e6fa9a95213bf73a6a587c18d7f35fe7e1ca8ddde4374a2ace51.png
+_images/7170b68778e58985cddc27ff10c7332fdb506e9ac887ff846a3f6c692bfc2925.png
 
    
 
    
 
    We can easily compare both approaches
    
@@ -3306,10 +3307,10 @@ 
    Alternative interpolation methods
-
    <matplotlib.collections.QuadMesh at 0x14ffa8fd0>
+
    <matplotlib.collections.QuadMesh at 0x1464f9840>
 
    
 
    
-_images/8cd3b609ab55ec74e6181b6e674e6aeddb89523eb02b92b171e9a799ff6d3bac.png
+_images/efaab9a5cdc59f34ca99c542d0bf3697bd1c600e8cab4e55987b8ec3fac2d19f.png
 
    
 
    
 
    The nice thing about xesmf is that it works together with xarray and will keep all meta information. Another consequence is that xesmf works well with dask and it’s vectorization. That means, if we have a long time axis along we want to regrid, this can easily be parallelized using, e.g., dask.distributed and will also work nicely with large datasets that don’t fit into memory. The xesmf regridder can also store and reuse regridding weights for faster interpolation.
    
diff --git a/searchindex.js b/searchindex.js
index 4622a4c..5a78f2b 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["aws-access", "domains", "intro", "regional"], "filenames": ["aws-access.ipynb", "domains.ipynb", "intro.md", "regional.ipynb"], "titles": ["AWS Access", "Cordex domains", "Welcome to the CORDEX Tutorials!", "Regional means"], "terms": {"A": [0, 1], "grow": 0, "number": [0, 1], "cordex": [0, 3], "dataset": [0, 1, 3], "i": [0, 1, 2, 3], "now": [0, 1, 3], "avail": [0, 1, 2], "from": [0, 1, 2, 3], "s3": [0, 1, 3], "In": [0, 3], "thi": [0, 1, 2, 3], "exampl": [0, 3], "we": [0, 1, 3], "show": [0, 1, 3], "how": [0, 1, 3], "deriv": 0, "dask": [0, 1, 3], "distribut": [0, 1, 3], "import": [0, 1, 3], "client": [0, 3], "4a5976e3": 0, "0183": 0, "11ee": [0, 3], "928a": 0, "080038c05de1": 0, "connect": [0, 1, 3], "method": [0, 3], "cluster": [0, 3], "object": [0, 1, 3], "type": [0, 1, 3], "localclust": [0, 3], "dashboard": [0, 3], "user": [0, 3], "g300046": 0, "levant": 0, "spawner": 0, "preset": 0, "proxi": [0, 3], "8787": [0, 3], "statu": [0, 3], "info": [0, 3], 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