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{ | ||
"cells": [ | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "43f58c96", | ||
"metadata": {}, | ||
"source": [ | ||
"# Uncertainty in seasonal forecasts for a single model system" | ||
] | ||
}, | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "c7ce5c2e", | ||
"metadata": {}, | ||
"source": [ | ||
"## Import packages" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "48246700", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"import calendar\n", | ||
"\n", | ||
"import numpy as np\n", | ||
"import plotly.figure_factory as ff\n", | ||
"import xarray as xr\n", | ||
"from c3s_eqc_automatic_quality_control import diagnostics, download, utils" | ||
] | ||
}, | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "5586e956", | ||
"metadata": {}, | ||
"source": [ | ||
"## Define Parameters" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "3a3dfa6f", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"# Model\n", | ||
"centre = \"ecmwf\"\n", | ||
"system = \"51\"\n", | ||
"\n", | ||
"# Time\n", | ||
"year_forecast = 2023\n", | ||
"year_start_hindcast = 1993\n", | ||
"year_stop_hindcast = 2016\n", | ||
"month = 6\n", | ||
"\n", | ||
"# Region\n", | ||
"region_name = \"Southern Norway\"\n", | ||
"lat_slice = slice(64, 58)\n", | ||
"lon_slice = slice(4, 14)\n", | ||
"\n", | ||
"# Download parameters\n", | ||
"chunks = {\"year\": 1}\n", | ||
"n_jobs = 1" | ||
] | ||
}, | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "62c65518", | ||
"metadata": {}, | ||
"source": [ | ||
"## Define request" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "9ca06bda", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"collection_id = \"seasonal-monthly-single-levels\"\n", | ||
"\n", | ||
"request = {\n", | ||
" \"format\": \"grib\",\n", | ||
" \"originating_centre\": centre,\n", | ||
" \"system\": system,\n", | ||
" \"variable\": \"2m_temperature\",\n", | ||
" \"product_type\": \"monthly_mean\",\n", | ||
" \"leadtime_month\": list(map(str, range(1, 7))),\n", | ||
" \"area\": [89.5, -179.5, -89.5, 179.5],\n", | ||
" \"grid\": \"1/1\",\n", | ||
" \"month\": f\"{month:02d}\",\n", | ||
"}" | ||
] | ||
}, | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "51a4fdbf", | ||
"metadata": {}, | ||
"source": [ | ||
"## Functions to cache" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "b859c808-ea82-4f63-a6eb-bf58229c2e9d", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"def regionalised_mean(ds, lon_slice, lat_slice, weights):\n", | ||
" ds = utils.regionalise(ds, lon_slice=lon_slice, lat_slice=lat_slice)\n", | ||
" ds = diagnostics.spatial_weighted_mean(ds, weights=weights)\n", | ||
" with xr.set_options(keep_attrs=True):\n", | ||
" ds[\"t2m\"] -= 273.15\n", | ||
" ds[\"t2m\"].attrs[\"units\"] = \"°C\"\n", | ||
" return ds" | ||
] | ||
}, | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "90606022", | ||
"metadata": {}, | ||
"source": [ | ||
"## Download and transform" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "2a939f1e-7e91-4501-9ffc-1eca70253a36", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"datasets = {}\n", | ||
"for model, years in {\n", | ||
" \"hindcast\": range(year_start_hindcast, year_stop_hindcast + 1),\n", | ||
" \"forecast\": [year_forecast],\n", | ||
"}.items():\n", | ||
" ds = download.download_and_transform(\n", | ||
" collection_id,\n", | ||
" request | {\"year\": list(map(str, years))},\n", | ||
" chunks=chunks,\n", | ||
" n_jobs=n_jobs,\n", | ||
" transform_func=regionalised_mean,\n", | ||
" transform_func_kwargs={\n", | ||
" \"lon_slice\": lon_slice,\n", | ||
" \"lat_slice\": lat_slice,\n", | ||
" \"weights\": False,\n", | ||
" },\n", | ||
" backend_kwargs={\n", | ||
" \"time_dims\": (\n", | ||
" \"forecastMonth\",\n", | ||
" \"indexing_time\" if centre in [\"ukmo\", \"jma\", \"ncep\"] else \"time\",\n", | ||
" )\n", | ||
" },\n", | ||
" cached_open_mfdataset_kwargs={\n", | ||
" \"combine\": \"nested\",\n", | ||
" \"concat_dim\": \"forecast_reference_time\",\n", | ||
" },\n", | ||
" )\n", | ||
" datasets[model] = ds" | ||
] | ||
}, | ||
{ | ||
"attachments": {}, | ||
"cell_type": "markdown", | ||
"id": "5ecce3e6", | ||
"metadata": {}, | ||
"source": [ | ||
"## Density plot" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "5b2d38a8", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"def get_limits(data, xfactor, yfactor):\n", | ||
" ylim = [0, max([max(d.y) for d in data]) * yfactor]\n", | ||
" xlim = [func([func(d.x) for d in data]) for func in (min, max)]\n", | ||
" xshift = abs(xlim[1] - xlim[0]) * xfactor\n", | ||
" xlim = [x + xshift * sign for x, sign in zip(xlim, (-1, 1))]\n", | ||
" return xlim, ylim\n", | ||
"\n", | ||
"\n", | ||
"# Density plot for each lead time\n", | ||
"for leadtime_month, ds_forecast in datasets[\"forecast\"].groupby(\"leadtime_month\"):\n", | ||
" ds_hindcast = datasets[\"hindcast\"].sel(leadtime_month=leadtime_month)\n", | ||
"\n", | ||
" colors = [(26, 150, 65), (100, 50, 150)]\n", | ||
" values = [\n", | ||
" ds_hindcast[\"t2m\"].values.flatten(),\n", | ||
" ds_forecast[\"t2m\"].values.flatten(),\n", | ||
" ]\n", | ||
" labels = [centre + \" climatology\", centre + \" forecast\"]\n", | ||
" fig = ff.create_distplot(\n", | ||
" values,\n", | ||
" labels,\n", | ||
" show_hist=False,\n", | ||
" show_rug=True,\n", | ||
" colors=[f\"rgb{color}\" for color in colors],\n", | ||
" curve_type=\"kde\",\n", | ||
" )\n", | ||
" for color, data in zip(colors, fig.data):\n", | ||
" # Fill area under distline\n", | ||
" fig.add_scatter(\n", | ||
" x=data.x,\n", | ||
" y=data.y,\n", | ||
" fill=\"tozeroy\",\n", | ||
" mode=\"none\",\n", | ||
" fillcolor=f\"rgba{color + (.4,)}\",\n", | ||
" showlegend=False,\n", | ||
" )\n", | ||
"\n", | ||
" xlim, ylim = get_limits(fig.data[:2], xfactor=0.03, yfactor=1.4)\n", | ||
" quantiles = np.quantile(values[0], [1 / 3, 2 / 3]).tolist()\n", | ||
" scatter_dicts = {\n", | ||
" \"lower tercile\": {\n", | ||
" \"color\": (0, 180, 250, 0.1),\n", | ||
" \"text\": \"COLD\",\n", | ||
" \"mask\": values[1] <= quantiles[0],\n", | ||
" \"xlim\": [xlim[0], quantiles[0]],\n", | ||
" },\n", | ||
" \"middle tercile\": {\n", | ||
" \"color\": (230, 230, 0, 0.1),\n", | ||
" \"text\": \"NEAR AVERAGE\",\n", | ||
" \"mask\": (values[1] > quantiles[0]) & (values[1] <= quantiles[1]),\n", | ||
" \"xlim\": quantiles,\n", | ||
" },\n", | ||
" \"upper tercile\": {\n", | ||
" \"color\": (250, 50, 0, 0.1),\n", | ||
" \"text\": \"WARM\",\n", | ||
" \"mask\": values[1] > quantiles[1],\n", | ||
" \"xlim\": [quantiles[1], xlim[1]],\n", | ||
" },\n", | ||
" }\n", | ||
" for i, (name, scatter_dict) in enumerate(scatter_dicts.items()):\n", | ||
" # Add background color and text\n", | ||
" fig.add_scatter(\n", | ||
" x=scatter_dict[\"xlim\"],\n", | ||
" y=[ylim[1]] * 2,\n", | ||
" fill=\"tozeroy\",\n", | ||
" mode=\"none\",\n", | ||
" fillcolor=f\"rgba{scatter_dict['color']}\",\n", | ||
" name=name,\n", | ||
" )\n", | ||
" percentage = 100 * scatter_dict[\"mask\"].sum() / values[1].size\n", | ||
" text_color = tuple(c // 2 for c in scatter_dict[\"color\"][:-1]) + (0.4,)\n", | ||
" fig.add_scatter(\n", | ||
" x=[sum(scatter_dict[\"xlim\"]) / 2],\n", | ||
" y=[ylim[1] * 0.98],\n", | ||
" mode=\"text\",\n", | ||
" name=\"\",\n", | ||
" text=f\"{scatter_dict['text']}<br>{round(percentage)}%\",\n", | ||
" textfont=dict(size=18, color=f\"rgba{text_color}\"),\n", | ||
" textposition=\"bottom center\",\n", | ||
" showlegend=False,\n", | ||
" )\n", | ||
"\n", | ||
" # Title and labels\n", | ||
" forecast_reference_time = ds_forecast[\"forecast_reference_time\"].dt.date.values\n", | ||
" title = (\n", | ||
" f\"Density plot of {ds_forecast['t2m'].attrs['long_name']}\"\n", | ||
" f\" over {region_name} for {calendar.month_abbr[leadtime_month]} {year_forecast},\"\n", | ||
" f\"<br>from {centre} with start time {forecast_reference_time}.\"\n", | ||
" f\" Hindcast period from {year_start_hindcast} to {year_stop_hindcast}.\"\n", | ||
" )\n", | ||
" fig.update_layout(\n", | ||
" title=dict(text=title, font={\"size\": 22}),\n", | ||
" yaxis_range=ylim,\n", | ||
" xaxis_range=xlim,\n", | ||
" font_size=17,\n", | ||
" autosize=False,\n", | ||
" width=900,\n", | ||
" height=500,\n", | ||
" )\n", | ||
" fig.update_xaxes(\n", | ||
" title_text=(\n", | ||
" f\"Mean {ds_forecast['t2m'].attrs['long_name']}\"\n", | ||
" f\" ({ds_forecast['t2m'].attrs['units']})\"\n", | ||
" )\n", | ||
" )\n", | ||
" fig.show()" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3 (ipykernel)", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.10.11" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |