{
"cells": [
{
"cell_type": "markdown",
"id": "4d88ed3b",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"logo"
]
},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "3d236c61",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"id": "fd5dbf9e",
"metadata": {},
"source": [
"# Seasonal Forecast Verification"
]
},
{
"cell_type": "markdown",
"id": "88d5a2a8",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"run"
]
},
"source": [
"**This notebook can be run on free online platforms, such as Binder, Kaggle and Colab, or they can be accessed from GitHub. The links to run this notebook in these environments are provided here, but please note they are not supported by ECMWF.**\n",
"\n",
"[](https://mybinder.org/v2/gh/ecmwf-training/c3s-training-submodule-seasonal-forecast/main?labpath=sf-verification.ipynb)\n",
"[](https://kaggle.com/kernels/welcome?src=https://github.com/ecmwf-training/c3s-training-submodule-seasonal-forecast/blob/main/sf-verification.ipynb)\n",
"[](https://colab.research.google.com/github/ecmwf-training/c3s-training-submodule-seasonal-forecast/blob/main/sf-verification.ipynb)\n",
"[](https://github.com/ecmwf-training/c3s-training-submodule-seasonal-forecast/blob/main/sf-verification.ipynb)"
]
},
{
"cell_type": "markdown",
"id": "ddb2c373",
"metadata": {
"editable": true,
"slideshow": {
"slide_type": ""
},
"tags": [
"objectives"
]
},
"source": [
"## Learning Objectives\n",
"\n",
"This notebook provides a practical introduction on how to produce some verification metrics and scores for seasonal forecasts with data from the Copernicus Climate Change Service (C3S).\n",
"\n",
"The tutorial will demonstrate how to access retrospective forecast (hindcast) data of 2-metre temperature initialized in the period 1993-2016, with a forecast start date in the 1st of March. All these forecasts are 6 months long (from March to August). More details about the role of the hindcasts can be found in [this Copernicus Knowledge Base article](https://confluence.ecmwf.int/display/CKB/Seasonal+forecasts+and+the+Copernicus+Climate+Change+Service). Observation data (ERA5 reanalysis) for the same reference period, 1993 to 2016, and the same months will also be obtained from the CDS. The tutorial will then show how to compute some deterministic products (anomalies) and some probabilistic products (probabilities for tercile categories). In addition to the 1-month average data retrieved from the CDS, 3-months aggregations will be also produced. Finally, verification metrics (correlation, area under the ROC curve, and RPS) will be calculated and visualised in a set of plots."
]
},
{
"cell_type": "markdown",
"id": "372e33e2",
"metadata": {},
"source": [
"
xarray/cfgrib
keywords for the real start date of each member (time
) and for the nominal start date (indexing_time
) which is the one we would need to use for those systems initializing their members with a lagged start date approach.\n",
" The following line of code will take care of that as long as we include the value config['isLagged']=True
in the config dictionary as defined in section 1.\n",
"number
in the hindcast xr.Dataset
object will indicate we have a full ensemble (or an ensemble mean).