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CADS Toolbox template application
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Datasets at different spatial resolution #98

Closed akankshabalha closed 1 year ago

akankshabalha commented 1 year ago

Notebook description

SpatiaResol.txt

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I am using two datasets - surface albedo and ERA5 t2m. Both the datasets have different spatial resolution. As I need both the datasets at same spatial resolution, I have resampled t2m (ds_t2m_resampled) according to the spatial resolution of albedo (i.e 0.01 degree )but I am not able to plot time-weighted raster nor a spatial-weighted time-series of resampled t2m dataset. Could you please help me with it.

Environment

# packages in environment at /data/common/mambaforge/envs/wp5: # # Name Version Build Channel _libgcc_mutex 0.1 conda_forge conda-forge _openmp_mutex 4.5 2_gnu conda-forge affine 2.4.0 pyhd8ed1ab_0 conda-forge aiohttp 3.8.5 py310h2372a71_0 conda-forge aiosignal 1.3.1 pyhd8ed1ab_0 conda-forge alsa-lib 1.2.9 hd590300_0 conda-forge ansiwrap 0.8.4 py_0 conda-forge antlr-python-runtime 4.7.2 py310hff52083_1003 conda-forge anyio 4.0.0 pyhd8ed1ab_0 conda-forge argon2-cffi 23.1.0 pyhd8ed1ab_0 conda-forge argon2-cffi-bindings 21.2.0 py310h5764c6d_3 conda-forge arrow 1.2.3 pyhd8ed1ab_0 conda-forge asciitree 0.3.3 py_2 conda-forge asttokens 2.2.1 pyhd8ed1ab_0 conda-forge async-lru 2.0.4 pyhd8ed1ab_0 conda-forge async-timeout 4.0.3 pyhd8ed1ab_0 conda-forge attr 2.5.1 h166bdaf_1 conda-forge attrs 23.1.0 pyh71513ae_1 conda-forge aws-c-auth 0.7.3 he2921ad_3 conda-forge aws-c-cal 0.6.2 hc309b26_0 conda-forge aws-c-common 0.9.0 hd590300_0 conda-forge aws-c-compression 0.2.17 h4d4d85c_2 conda-forge aws-c-event-stream 0.3.2 h2e3709c_0 conda-forge aws-c-http 0.7.12 hc865f51_1 conda-forge aws-c-io 0.13.32 h019f825_2 conda-forge aws-c-mqtt 0.9.5 h3a0376c_1 conda-forge aws-c-s3 0.3.14 h1678ad6_3 conda-forge aws-c-sdkutils 0.1.12 h4d4d85c_1 conda-forge aws-checksums 0.1.17 h4d4d85c_1 conda-forge aws-crt-cpp 0.23.0 h40cdbb9_5 conda-forge aws-sdk-cpp 1.10.57 h6f6b8fa_21 conda-forge babel 2.12.1 pyhd8ed1ab_1 conda-forge backcall 0.2.0 pyh9f0ad1d_0 conda-forge backports 1.0 pyhd8ed1ab_3 conda-forge backports.functools_lru_cache 1.6.5 pyhd8ed1ab_0 conda-forge beautifulsoup4 4.12.2 pyha770c72_0 conda-forge black 23.7.0 py310hff52083_1 conda-forge bleach 6.0.0 pyhd8ed1ab_0 conda-forge blosc 1.21.5 h0f2a231_0 conda-forge bokeh 3.2.2 pyhd8ed1ab_0 conda-forge boost-cpp 1.78.0 h6582d0a_3 conda-forge bottleneck 1.3.7 py310h0a54255_0 conda-forge branca 0.6.0 pyhd8ed1ab_0 conda-forge brotli 1.0.9 h166bdaf_9 conda-forge brotli-bin 1.0.9 h166bdaf_9 conda-forge brotli-python 1.0.9 py310hd8f1fbe_9 conda-forge bzip2 1.0.8 h7f98852_4 conda-forge c-ares 1.19.1 hd590300_0 conda-forge c3s-eqc-automatic-quality-control 0.1.2.dev85+gc94905b pypi_0 pypi ca-certificates 2023.7.22 hbcca054_0 conda-forge cached-property 1.5.2 hd8ed1ab_1 conda-forge cached_property 1.5.2 pyha770c72_1 conda-forge cacholote 0.4.1 pypi_0 pypi cads-toolbox 0.0.2b0 pypi_0 pypi cairo 1.16.0 hbbf8b49_1016 conda-forge cartopy 0.22.0 py310h7cbd5c2_0 conda-forge cdsapi 0.6.1 pyhd8ed1ab_0 conda-forge certifi 2023.7.22 pyhd8ed1ab_0 conda-forge cf-units 3.2.0 py310h278f3c1_0 conda-forge cf_xarray 0.8.4 pyhd8ed1ab_0 conda-forge cffi 1.15.1 py310h255011f_3 conda-forge cfgrib 0.9.10.4 pyhd8ed1ab_0 conda-forge cfitsio 4.3.0 hbdc6101_0 conda-forge cftime 1.6.2 py310hde88566_1 conda-forge cgul 0.0.4 pypi_0 pypi charset-normalizer 3.2.0 pyhd8ed1ab_0 conda-forge click 8.1.7 unix_pyh707e725_0 conda-forge click-plugins 1.1.1 py_0 conda-forge cligj 0.7.2 pyhd8ed1ab_1 conda-forge cloudpickle 2.2.1 pyhd8ed1ab_0 conda-forge colorama 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jsonschema-specifications 2023.7.1 pyhd8ed1ab_0 conda-forge jsonschema-with-format-nongpl 4.19.0 pyhd8ed1ab_1 conda-forge jupyter-lsp 2.2.0 pyhd8ed1ab_0 conda-forge jupyter_client 8.3.1 pyhd8ed1ab_0 conda-forge jupyter_core 5.3.1 py310hff52083_0 conda-forge jupyter_events 0.7.0 pyhd8ed1ab_2 conda-forge jupyter_server 2.7.3 pyhd8ed1ab_0 conda-forge jupyter_server_terminals 0.4.4 pyhd8ed1ab_1 conda-forge jupyterlab 4.0.5 pyhd8ed1ab_0 conda-forge jupyterlab_pygments 0.2.2 pyhd8ed1ab_0 conda-forge jupyterlab_server 2.24.0 pyhd8ed1ab_0 conda-forge kaleido 0.2.1 pypi_0 pypi kealib 1.5.1 hcd42e92_5 conda-forge keyutils 1.6.1 h166bdaf_0 conda-forge kiwisolver 1.4.5 py310hd41b1e2_0 conda-forge krb5 1.21.2 h659d440_0 conda-forge lame 3.100 h166bdaf_1003 conda-forge lcms2 2.15 haa2dc70_1 conda-forge ld_impl_linux-64 2.40 h41732ed_0 conda-forge lerc 4.0.0 h27087fc_0 conda-forge libabseil 20230125.3 cxx17_h59595ed_0 conda-forge libaec 1.0.6 hcb278e6_1 conda-forge libarchive 3.6.2 h039dbb9_1 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libspatialindex 1.9.3 h9c3ff4c_4 conda-forge libspatialite 5.0.1 h15f6e67_28 conda-forge libsqlite 3.43.0 h2797004_0 conda-forge libssh2 1.11.0 h0841786_0 conda-forge libstdcxx-ng 13.1.0 hfd8a6a1_0 conda-forge libsystemd0 254 h3516f8a_0 conda-forge libthrift 0.18.1 h8fd135c_2 conda-forge libtiff 4.5.1 h8b53f26_1 conda-forge libutf8proc 2.8.0 h166bdaf_0 conda-forge libuuid 2.38.1 h0b41bf4_0 conda-forge libvorbis 1.3.7 h9c3ff4c_0 conda-forge libwebp-base 1.3.1 hd590300_0 conda-forge libxcb 1.15 h0b41bf4_0 conda-forge libxkbcommon 1.5.0 h5d7e998_3 conda-forge libxml2 2.11.5 h0d562d8_0 conda-forge libzip 1.10.1 h2629f0a_1 conda-forge libzlib 1.2.13 hd590300_5 conda-forge llvmlite 0.40.1 py310h1b8f574_0 conda-forge locket 1.0.0 pyhd8ed1ab_0 conda-forge lz4 4.3.2 py310h0cfdcf0_0 conda-forge lz4-c 1.9.4 hcb278e6_0 conda-forge lzo 2.10 h516909a_1000 conda-forge mapclassify 2.5.0 pyhd8ed1ab_1 conda-forge markdown-it-py 3.0.0 pyhd8ed1ab_0 conda-forge markupsafe 2.1.3 py310h2372a71_0 conda-forge 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notebook-shim 0.2.3 pyhd8ed1ab_0 conda-forge nspr 4.35 h27087fc_0 conda-forge nss 3.92 h1d7d5a4_0 conda-forge numba 0.57.1 py310h0f6aa51_0 conda-forge numcodecs 0.11.0 py310heca2aa9_1 conda-forge numpy 1.24.4 py310ha4c1d20_0 conda-forge numpy_groupies 0.9.22 pyhd8ed1ab_0 conda-forge openjpeg 2.5.0 hfec8fc6_2 conda-forge openssl 3.1.2 hd590300_0 conda-forge orc 1.9.0 h385abfd_1 conda-forge overrides 7.4.0 pyhd8ed1ab_0 conda-forge packaging 23.1 pyhd8ed1ab_0 conda-forge pandas 2.1.0 py310hcc13569_0 conda-forge pandoc 3.1.3 h32600fe_0 conda-forge pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge papermill 2.4.0 pyhd8ed1ab_0 conda-forge parso 0.8.3 pyhd8ed1ab_0 conda-forge partd 1.4.0 pyhd8ed1ab_0 conda-forge pathspec 0.11.2 pyhd8ed1ab_0 conda-forge patsy 0.5.3 pyhd8ed1ab_0 conda-forge pcre2 10.40 hc3806b6_0 conda-forge pexpect 4.8.0 pyh1a96a4e_2 conda-forge pickleshare 0.7.5 py_1003 conda-forge pillow 10.0.0 py310h582fbeb_0 conda-forge pip 23.2.1 pyhd8ed1ab_0 conda-forge pixman 0.40.0 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pyhd8ed1ab_0 conda-forge pyproj 3.6.0 py310h24ef57a_1 conda-forge pyqt 5.15.9 py310h04931ad_4 conda-forge pyqt5-sip 12.12.2 py310hc6cd4ac_4 conda-forge pyshp 2.3.1 pyhd8ed1ab_0 conda-forge pysocks 1.7.1 pyha2e5f31_6 conda-forge python 3.10.12 hd12c33a_0_cpython conda-forge python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge python-dotenv 1.0.0 pyhd8ed1ab_0 conda-forge python-eccodes 1.6.0 py310h1f7b6fc_1 conda-forge python-fastjsonschema 2.18.0 pyhd8ed1ab_0 conda-forge python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge python-tzdata 2023.3 pyhd8ed1ab_0 conda-forge python_abi 3.10 3_cp310 conda-forge pytz 2023.3 pyhd8ed1ab_0 conda-forge pyyaml 6.0.1 py310h2372a71_0 conda-forge pyzmq 25.1.1 py310h5bbb5d0_0 conda-forge qt-main 5.15.8 h7fe3ca9_15 conda-forge rasterio 1.3.8 py310hd227816_0 conda-forge rdma-core 28.9 h59595ed_1 conda-forge re2 2023.03.02 h8c504da_0 conda-forge readline 8.2 h8228510_1 conda-forge referencing 0.30.2 pyhd8ed1ab_0 conda-forge regionmask 0.10.0 pyhd8ed1ab_0 conda-forge 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Note: you may need to restart the kernel to use updated packages.
malmans2 commented 1 year ago

Hi @akankshabalha,

I created a template for your use case to show you how to use the various functions we have developed so far. At the end, there's a plot of both original and interpolated temperature.

As usual, keep working on a small subset. We'll fix the details (e.g., chunking) and run the notebook on the whole dataset when you are done developing your analysis.

Here is the template: https://github.com/bopen/c3s-eqc-toolbox-template/blob/main/notebooks/wp5/WIP_satellite_albedo.ipynb Here is the notebook executed: https://gist.github.com/malmans2/0f720911280cc49a96175117f9ce1e50

Please let me know if everything is OK.

akankshabalha commented 1 year ago

Hi @malmans2 Thanks but it is not the desired output. I want the pixel size of t2m to be of the same size as albedo. Plotting subset albedo and new t2m together would be helpful for comparison purpose (as you did for original and regridded t2m).

malmans2 commented 1 year ago

Sorry but I don't understand the request?

You just need to plot albedo and t2m side by side? I've only interpolated the subset because in the code you shared you only download the subset of t2m. Doing it for the larger area is exactly the same.

akankshabalha commented 1 year ago

My request is - if the t2m pixel size is 0.1 0.1 degree and albedo pixel size is 0.01 0.01 degree, then I need t2m pixel to be divided into 100 small pixels (0.1/0.01) to obtain 0.01 degree spatial resolution - same as that of albedo.

akankshabalha commented 1 year ago

These links might be helpful to understand what I want.

https://desktop.arcgis.com/en/arcmap/latest/manage-data/raster-and-images/resample-function.htm

http://pirate.shu.edu/~mckenndo/Resampling.htm

malmans2 commented 1 year ago

I'm still confused.

Do you want to interpolate from t2m grid to albedo grid? If yes, isn't that what is done at the end of the notebook? da_t2m_regrid has the exact same coordinates as da_albedo_subset

I used nearest neighbour interpolation as in your code, are you looking for another interpolation?

akankshabalha commented 1 year ago

What you did till end of the notebook is fine. But I want further processing on da_t2m_regrid. Please see and compare the pixel size of da_albedo_subset and da_t2m_regrid, they are not same. da_t2m_regrid has much bigger pixel size. For example, in da_t2m_regrid, between 77.4 - 77.5 longitudes, there are only two pixels whereas da_albedo_subset has almost 10 pixels. I want da_t2m_regrid to contain same number of pixels between 77.4-77.5 as da_albedo_subset has. (To obtain this, yes you need to do interpolation at some point but before that resampling function needs to be run. I do not know what is equivalent to the desired resampling function in Jupyter)

akankshabalha commented 1 year ago

In simplest terms, I need the spatial resolution of da_t2m_regrid same as da_albedo_subset.

malmans2 commented 1 year ago

Sorry, but I don't understand what you are looking for.

What you did till end of the notebook is fine. But I want further processing on da_t2m_regrid. Please see and compare the pixel size of da_albedo_subset and da_t2m_regrid, they are not same. da_t2m_regrid has much bigger pixel size. For example, in da_t2m_regrid, between 77.4 - 77.5 longitudes, there are only two pixels whereas da_albedo_subset has almost 10 pixels.

If with number of pixel you mean the number of grid cells, they are identical. As you are interpolating from low res to high res using a nearest neighbour interpolation, there are blocks of 10x10 with identical grid cells. If that's not what you are looking for, then probably you want to apply some different interpolation (for example, bilinear).

(To obtain this, yes you need to do interpolation at some point but before that resampling function needs to be run. I do not know what is equivalent to the desired resampling function in Jupyter)

If you need to apply some kind of resampling before interpolating, I need to know exactly what you need to do. Ideally, you can show me the python code to do it. If you don't know how to do it with python, you can send me the equation/algorithm and we will code it in python. (Jupyter is just the computing platform, the language we are using for EQC is python)

In simplest terms, I need the spatial resolution of da_t2m_regrid same as da_albedo_subset.

That's exactly what is done in the notebook. da_t2m_regrid has the same spatial resolution as da_albedo_subset.