C3S_34e
Subsetting and Regridding
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C3S_34e News from Copernicus
Subsetting and Regridding
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Copernicus Climate Data Store
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Copernicus Climate Data Store
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Copernicus Climate Data Store: Datasets by Product Type
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Copernicus Climate Data Store: Download Datasets
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Copernicus Climate Data Store: CDS API
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Copernicus Climate Data Store: Rook WPS
CLISOPS
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Remapping, illustration.
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Outline
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A Zoo of Grids
Schematic of a Discrete Climate Model Grid, from Earth Magazine and Kotamarthi et al. [2021]
https://utcdw.physics.utoronto.ca/UTCDW_Guidebook/Chapter2/section2.2_climate_modeling.html
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A Zoo of Grids
Climate model experiments are simulated on various grids
→ connectivity information required (not provided in CMIP)
Overview of grids that ocean output was submitted on (CMIP6):
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Remapping Methods
Regridding method | Preferably applied for |
Bilinear | Smoothly varying variables (eg. air temperature) |
Conservative / Area weighted | Upscaling, discontinuous variables, fluxes |
Patch recovery (least square fit over patch of cells) | Accurate computation of derivatives |
Nearest neighbour | Categorical data (eg. land surface type) |
Remapping is mostly necessary when comparing data from different sources.
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Remapping Tools
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General Constraints & ECMWF Requirements
eg. the grids used for the IPCC Atlas
→ to be selected by the user
No extrapolation to avoid unscientific/unrealistic results.
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xESMF
xESMF - ESMF/ESMPy regridding capabilities
applied on xarray.Datasets
Remapping in two steps (common to all Regridders):
n x m source grid → (p*o) x (n*m) weight matrix
p x o target grid
Weight matrices are quite sparse (a target cell value depends only on few source grid cells) and therefore can be efficiently stored by neglecting 0-values.
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Problems - Masking
Unmapped (out-of-source-domain)
grid cells
Target grid cells with no contribution from
a source grid cell will be masked.
This does not work for “nearest neighbour” remapping (by definition - there is always a nearest neighbour):
Here, such a mask would have to be generated manually (example):
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Problems - Masking
Adaptive masking
Target grid cells that lie partly outside of the source domain (or when at least one of the contributing source grid cells is masked) have to be (re-)normalized for many applications.
Adaptive masking allows the user to set a maximum contribution threshold of unmapped / masked source grid cells to the target grid cell.
Conservative remapping - at the bounds of the original domain cells are partly unmapped - unmapped area “contributes” with value of 0.
Conservative remapping with applied adaptive masking (re-normalization).
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Problems - Masking
Adaptive masking - example: threshold 50 %
Target grid cells that lie partly outside of the source domain (or when at least one of the contributing source grid cells is masked) have to be (re-)normalized for many applications.
Adaptive masking allows the user to set a minimum contribution threshold of source grid cells to the target grid cell.
Source Domain / Grid
Target Domain / Grid
masked
unmasked
and
renormalized
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Problems - Masking
Adaptive masking
Advantages:
masks during weight
generation are not needed
→ reusage of weights
methods (besides nearest
neighbour where
renormalization
is not applicable).
value for the maximum
fraction that masked
source grid cells may
overlap with a target grid cell.
adaptive masking - threshold 1
adaptive masking - threshold 0.8
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Problems - Halos
Grid halos
(“degenerated cells”) and no exact copies
Scatter plot of the latitude and longitude coordinates.
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Problems - Unstructured Grids
Unstructured Grids
Example AWI FESOM
Scatter plot of the latitude and longitude coordinates.
AWI FESOM data remapped with nearest neighbour method.
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Problems
Further problems
Each vector component has to be remapped individually - problematic for grids where the vector components do not represent geographic N-S and E-W
On a staggered grid the scalar variables (pressure, density, total enthalpy etc.) are stored in the cell centers of the control volumes, whereas the velocity or
momentum variables are located at the cell faces. Allows easy calculation of derivatives of eg. wind velocity at the cell centers.
Staggered grid (Arakawa C-Grid), Delandmeter and van Sebille (2019)
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Outlook
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Material
Today’s TGIF GitHub Repository
https://github.com/sol1105/tgif_copernicus_clisops_21-11-12
Jupyter Notebook (NBViewer link)
Feel free to take a look the provided notebooks
or use the binder link to try it out yourself!
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Copernicus Climate Data Store - Beta
EarthKit by ECMWF
Python API
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Copernicus Climate Data Store - Beta
STAC Catalog
Python tool to download data
Datasets download
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Copernicus Climate Data Store - Beta
Planned to support Notebooks to access Datasets.
But … search API with STAC for Datasets in missing.
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… thanks for your attention!
If you made it this far …
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Copernicus quality checks
for CMIP5, CMIP6 and CORDEX
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Copernicus Quality Checks - Workflow
Quality Checks
ESGF
CDS
WPS
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Copernicus Quality Checks
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