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527 | 527 | "metadata": {}, |
528 | 528 | "outputs": [], |
529 | 529 | "source": [ |
530 | | - "for label, ds in zip([\"ORAS5\", \"ARGO\"], [ds_reanalysis_trend, ds_argo_trend]):\n", |
531 | | - " da = ds.to_dataarray()\n", |
532 | | - " facet = plot.projected_map(da, col=\"variable\", robust=True)\n", |
533 | | - " for ax in facet.axs.flatten():\n", |
534 | | - " ax.set_extent(\n", |
535 | | - " [lon_slice.start, lon_slice.stop, lat_slice.start, lat_slice.stop]\n", |
536 | | - " )\n", |
537 | | - " facet.fig.suptitle(label)\n", |
538 | | - " plt.show()" |
| 530 | + "ds_reanalysis_trend_interp = diagnostics.regrid(\n", |
| 531 | + " ds_reanalysis_trend, ds_argo_trend, method=\"nearest_s2d\"\n", |
| 532 | + ")\n", |
| 533 | + "ds_trend = xr.concat(\n", |
| 534 | + " [\n", |
| 535 | + " ds_reanalysis_trend_interp.expand_dims(product=[\"ORAS5\"]),\n", |
| 536 | + " ds_argo_trend.expand_dims(product=[\"ARGO\"]),\n", |
| 537 | + " ],\n", |
| 538 | + " \"product\",\n", |
| 539 | + ")\n", |
| 540 | + "da = ds_trend.to_dataarray()\n", |
| 541 | + "seconds_per_year = 365 * 24 * 360\n", |
| 542 | + "with xr.set_options(keep_attrs=True):\n", |
| 543 | + " meters_to_millimeters = 1000\n", |
| 544 | + " da = da * meters_to_millimeters * seconds_per_year\n", |
| 545 | + " da.attrs[\"units\"] = \"mm/yr\"\n", |
| 546 | + " bias = da.diff(\"product\")\n", |
| 547 | + "plot.projected_map(da, col=\"variable\", row=\"product\", robust=True)\n", |
| 548 | + "\n", |
| 549 | + "\n", |
| 550 | + "plot.projected_map(bias, col=\"variable\", row=\"product\", robust=True)\n", |
| 551 | + "_ = plt.suptitle(f\"Bias {'-'.join(da['product'].values.tolist())}\")" |
539 | 552 | ] |
540 | 553 | }, |
541 | 554 | { |
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