├── .gitignore ├── LICENSE ├── README.md ├── c3sdl ├── C3S_data_loader.py ├── __init__.py └── evaluator.py └── environment.yml /.gitignore: -------------------------------------------------------------------------------- 1 | c3sdl/__pycache__/__init__.cpython-39.pyc 2 | c3sdl/__pycache__/C3S_data_loader.cpython-39.pyc 3 | c3sdl/__pycache__/evaluator.cpython-39.pyc 4 | .ipynb_checkpoints/Untitled-checkpoint.ipynb 5 | .ipynb_checkpoints/Untitled1-checkpoint.ipynb 6 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-clim_bias.png 7 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-crps.png 8 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-lbss.png 9 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-mae.png 10 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-nmae.png 11 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-rmse.png 12 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-rps.png 13 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-rpss.png 14 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-spearman_p0001.png 15 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-spearman.png 16 | data/cmcc-2m_temperature-S1-L2-4-grid_orig-ubss.png 17 | data/cmcc-2m_temperature-S1-L2-4-grid_orig.nc 18 | data/cmcc-2m_temperature-S1-L2-4-grid_orig.solved.nc 19 | data/cmcc-2m_temperature-S2-L2-4-grid_orig.nc 20 | data/cmcc-2m_temperature-S2-L2-4-grid_orig.solved.nc 21 | data/cmcc-2m_temperature-S3-L2-4-grid_orig.nc 22 | data/cmcc-2m_temperature-S3-L2-4-grid_orig.solved.nc 23 | data/cmcc-2m_temperature-S4-L2-4-grid_orig.nc 24 | data/cmcc-2m_temperature-S4-L2-4-grid_orig.solved.nc 25 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-clim_bias.png 26 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-crps.png 27 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-lbss.png 28 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-mae.png 29 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-nmae.png 30 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-rmse.png 31 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-rps.png 32 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-rpss.png 33 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-spearman_p0001.png 34 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-spearman.png 35 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig-ubss.png 36 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig.nc 37 | data/cmcc-10m_wind_speed-S1-L2-4-grid_orig.solved.nc 38 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-clim_bias.png 39 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-crps.png 40 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-lbss.png 41 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-mae.png 42 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-nmae.png 43 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-rmse.png 44 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-rps.png 45 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-rpss.png 46 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-spearman_p0001.png 47 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-spearman.png 48 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig-ubss.png 49 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig.nc 50 | data/cmcc-mean_sea_level_pressure-S1-L2-4-grid_orig.solved.nc 51 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-clim_bias.png 52 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-crps.png 53 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-lbss.png 54 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-mae.png 55 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-nmae.png 56 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-rmse.png 57 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-rps.png 58 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-rpss.png 59 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-spearman_p0001.png 60 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-spearman.png 61 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg-ubss.png 62 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg.nc 63 | data/cmcc-total_precipitation-S1-L2-4-grid_1deg.solved.nc 64 | data/cmcc-total_precipitation-S1-L2-4-grid_orig.nc 65 | data/cmcc-total_precipitation-S1-L2-4-grid_orig.solved.nc 66 | data/cmcc-total_precipitation-S2-L2-4-grid_orig.nc 67 | data/cmcc-total_precipitation-S2-L2-4-grid_orig.solved.nc 68 | data/cmcc-total_precipitation-S3-L2-4-grid_orig.nc 69 | data/cmcc-total_precipitation-S3-L2-4-grid_orig.solved.nc 70 | data/cmcc-total_precipitation-S4-L2-4-grid_orig.nc 71 | data/cmcc-total_precipitation-S4-L2-4-grid_orig.solved.nc 72 | data/dwd-2m_temperature-S1-L2-4-grid_orig-clim_bias.png 73 | data/dwd-2m_temperature-S1-L2-4-grid_orig-crps.png 74 | data/dwd-2m_temperature-S1-L2-4-grid_orig-lbss.png 75 | data/dwd-2m_temperature-S1-L2-4-grid_orig-mae.png 76 | data/dwd-2m_temperature-S1-L2-4-grid_orig-nmae.png 77 | data/dwd-2m_temperature-S1-L2-4-grid_orig-rmse.png 78 | data/dwd-2m_temperature-S1-L2-4-grid_orig-rps.png 79 | 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data/dwd-2m_temperature-S2-L2-4-grid_orig.nc 97 | data/dwd-2m_temperature-S2-L2-4-grid_orig.solved.nc 98 | data/dwd-2m_temperature-S3-L2-4-grid_orig-clim_bias.png 99 | data/dwd-2m_temperature-S3-L2-4-grid_orig-crps.png 100 | data/dwd-2m_temperature-S3-L2-4-grid_orig-lbss.png 101 | data/dwd-2m_temperature-S3-L2-4-grid_orig-mae.png 102 | data/dwd-2m_temperature-S3-L2-4-grid_orig-nmae.png 103 | data/dwd-2m_temperature-S3-L2-4-grid_orig-rmse.png 104 | data/dwd-2m_temperature-S3-L2-4-grid_orig-rps.png 105 | data/dwd-2m_temperature-S3-L2-4-grid_orig-rpss.png 106 | data/dwd-2m_temperature-S3-L2-4-grid_orig-spearman_p0001.png 107 | data/dwd-2m_temperature-S3-L2-4-grid_orig-spearman.png 108 | data/dwd-2m_temperature-S3-L2-4-grid_orig-ubss.png 109 | data/dwd-2m_temperature-S3-L2-4-grid_orig.nc 110 | data/dwd-2m_temperature-S3-L2-4-grid_orig.solved.nc 111 | data/dwd-2m_temperature-S4-L2-4-grid_orig-clim_bias.png 112 | data/dwd-2m_temperature-S4-L2-4-grid_orig-crps.png 113 | data/dwd-2m_temperature-S4-L2-4-grid_orig-lbss.png 114 | data/dwd-2m_temperature-S4-L2-4-grid_orig-mae.png 115 | data/dwd-2m_temperature-S4-L2-4-grid_orig-nmae.png 116 | data/dwd-2m_temperature-S4-L2-4-grid_orig-rmse.png 117 | data/dwd-2m_temperature-S4-L2-4-grid_orig-rps.png 118 | data/dwd-2m_temperature-S4-L2-4-grid_orig-rpss.png 119 | data/dwd-2m_temperature-S4-L2-4-grid_orig-spearman_p0001.png 120 | data/dwd-2m_temperature-S4-L2-4-grid_orig-spearman.png 121 | data/dwd-2m_temperature-S4-L2-4-grid_orig-ubss.png 122 | data/dwd-2m_temperature-S4-L2-4-grid_orig.nc 123 | data/dwd-2m_temperature-S4-L2-4-grid_orig.solved.nc 124 | data/dwd-10m_wind_speed-S1-L2-4-grid_orig-clim_bias.png 125 | data/dwd-10m_wind_speed-S1-L2-4-grid_orig-crps.png 126 | data/dwd-10m_wind_speed-S1-L2-4-grid_orig-lbss.png 127 | data/dwd-10m_wind_speed-S1-L2-4-grid_orig-mae.png 128 | data/dwd-10m_wind_speed-S1-L2-4-grid_orig-nmae.png 129 | data/dwd-10m_wind_speed-S1-L2-4-grid_orig-rmse.png 130 | 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| data/dwd-total_precipitation-S4-L2-4-grid_orig-rmse.png 208 | data/dwd-total_precipitation-S4-L2-4-grid_orig-rps.png 209 | data/dwd-total_precipitation-S4-L2-4-grid_orig-rpss.png 210 | data/dwd-total_precipitation-S4-L2-4-grid_orig-spearman_p0001.png 211 | data/dwd-total_precipitation-S4-L2-4-grid_orig-spearman.png 212 | data/dwd-total_precipitation-S4-L2-4-grid_orig-ubss.png 213 | data/dwd-total_precipitation-S4-L2-4-grid_orig.nc 214 | data/dwd-total_precipitation-S4-L2-4-grid_orig.solved.nc 215 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-clim_bias.png 216 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-crps.png 217 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-lbss.png 218 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-lhr.png 219 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-mae.png 220 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-nmae.png 221 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-rmse.png 222 | data/ecmwf-2m_temperature-S1-L2-4-grid_1deg-rps.png 223 | 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data/ncep-total_precipitation-S9-L2-4-grid_orig-rpss.png 1344 | data/ncep-total_precipitation-S9-L2-4-grid_orig-spearman_p0001.png 1345 | data/ncep-total_precipitation-S9-L2-4-grid_orig-spearman.png 1346 | data/ncep-total_precipitation-S9-L2-4-grid_orig-ubss.png 1347 | data/ncep-total_precipitation-S9-L2-4-grid_orig.nc 1348 | data/ncep-total_precipitation-S9-L2-4-grid_orig.solved.nc 1349 | data/ukmo15-2m_temperature-S1-L2-4-grid_orig.nc 1350 | data/ukmo15-2m_temperature-S1-L2-4-grid_orig.solved.nc 1351 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # A simple way to evaluate C3S seasonal forecasts monthly data 2 | 3 | This repository contains a set of Python functions, contained in the folder `c3sdl`, to: 4 | 5 | 1. Retrieve seasonal monthly data from the Copernicus Climate Data Store (CDS) 6 | 2. Retrieve ERA5 monthly data 7 | 3. Regrid ERA5 to the forecast's grid 8 | 4. Calculate deterministic and probabilistic skills 9 | 5. Save the validation data in a NetCDF 10 | 6. Quickly create plots with the computed metrics 11 | 12 | ## Limitations 13 | This code has been created to implement a quick workflow for the users that want to compute the skills in a quick way. Currently, it lacks the following features: 14 | 15 | - Accessing seasonal and reanalysis data from local files 16 | - Some well-known metrics (e.g. CRPSS) 17 | 18 | However, both the features can be easily implemented and the classes have been designed considering this extension. 19 | 20 | Feel free to extend this code and contribute. 21 | 22 | ## Requirements 23 | This code doesn't run on Windows (because `xesmf` supports only Unix-based systems). 24 | The packages needed to run this code are contained in the `environment.yml` file, if you are an Anaconda user you can type: 25 | ``` 26 | conda env create -f environment.yml 27 | ``` 28 | 29 | The user also needs an account to the [Copernicus Climate Change CDS](https://cds.climate.copernicus.eu/) and the CDS API installed ([see here](https://cds.climate.copernicus.eu/api-how-to)) 30 | 31 | If today we can implement this workflow so easily, we have to thanks the following packages and their developers: 32 | - [cfgrib](https://github.com/ecmwf/cfgrib): the Python interface to GRIB files provided by ECMWF 33 | - [xesmf](https://xesmf.readthedocs.io/en/latest/): to regrid xarray data 34 | - [xskillscore](https://xskillscore.readthedocs.io/en/stable/): for the skill score calculation 35 | - xarray, pandas, numpy: the open projects that we researchers should try [to support](https://numfocus.org/donate) as much as possible 36 | 37 | ## Examples 38 | 39 | Let's see how to calculate the skill of ECMWF C3S seasonal forecast for JAS with a lead-time of one month. The first step is downloading the data: 40 | ``` 41 | import c3sdl 42 | d = c3sdl.C3S_data_loader('ecmwf', '2m_temperature', 6, [2,3,4]) 43 | ``` 44 | Unless we specify another folder in the `config.yml` file, a NetCDF named `ecmwf-2m_temperature-S6-L2-4.nc` will appear in the local directory with this structure: 45 | 46 | ``` 47 | 48 | Dimensions: (lat: 181, lon: 360, number: 25, year: 23) 49 | Coordinates: 50 | * lon (lon) float64 0.0 1.0 2.0 3.0 4.0 ... 355.0 356.0 357.0 358.0 359.0 51 | * lat (lat) float64 90.0 89.0 88.0 87.0 86.0 ... -87.0 -88.0 -89.0 -90.0 52 | * number (number) int32 0 1 2 3 4 5 6 7 8 9 ... 16 17 18 19 20 21 22 23 24 53 | * year (year) int32 1993 1994 1995 1996 1997 ... 2011 2012 2013 2014 2015 54 | surface int32 ... 55 | step timedelta64[ns] ... 56 | Data variables: 57 | obs_var (year, lat, lon) float64 ... 58 | fct_var (year, number, lat, lon) float32 ... 59 | ``` 60 | The two data variables contain respectively the JAS average of reanalysis data and the forecast ensemble (25 members). 61 | We can evaluate the deterministic and probabilistic skills: 62 | ``` 63 | e = c3sdl.evaluator(d) 64 | e.compute_deterministic() 65 | e.compute_probabilistic() 66 | ``` 67 | It takes a couple of minutes and then, as for the retrieval, a NetCDF with the computer metrics will appear in the local directory. The NetCDF and the object returned by `e.get_skill_data()` contain all the computed metrics and the needed information for maps and charts. 68 | However, a quick way to generate the maps is the following: 69 | ``` 70 | e.save_maps() 71 | ``` 72 | Then some PNGs will appear showing the global metrics and skill scores. 73 | 74 | By default, the functions will calculate the metrics on the entire domain for all the hindcast years. However, using `e.get_data()` and `e.set_data()`, it is possible to get the forecast/reanalysis data, subset it (in space and/or time) and then go ahead with the computation. 75 | 76 | ## NOTE 77 | 78 | - In some cases the same variable keyword (e.g. `total_precipitation`) leads to different variables in the seasonal forecasts and ERA5 (`tprate` and `tp` respectively for precipitation). For the precipitation a conversion is automatically performed. 79 | -------------------------------------------------------------------------------- /c3sdl/C3S_data_loader.py: -------------------------------------------------------------------------------- 1 | import cdsapi 2 | import xarray as xr 3 | import numpy as np 4 | import xesmf as xe 5 | import os.path, sys, yaml, warnings, datetime 6 | 7 | class C3S_data_loader: 8 | """ 9 | This class represents a single (single-level) variable from a seasonal forecast downloaded from the C3S Climate Data Store (CDS) 10 | (cds.climate.copernicus.eu/). ERA5 reanalysis is associated to the seasonal data for validation purposes. 11 | Both the fields have the same grid. 12 | """ 13 | def read_config(self): 14 | """ 15 | Read and parse the config file (config.yml). 16 | The following keys are read: 17 | - DATA_PATH: path where processed data will be stored. Default value is the execution dir. 18 | """ 19 | try: 20 | file = open(r'config.yml') 21 | documents = yaml.full_load(file) 22 | # DATA PATH 23 | read_path = documents.get('DATA_PATH') 24 | if read_path is None: 25 | warnings.warn('DATA_PATH not found in Config.yml, using local directory') 26 | self.DATA_DIR = "" 27 | else: 28 | self.DATA_DIR = read_path 29 | 30 | except FileNotFoundError: 31 | warnings.warn('Config.yml not found, using default settings') 32 | self.DATA_DIR = "" 33 | 34 | def _retrieve_cds_and_merge(self, centre:str, variable:str, start_month:int, lead_time:list): 35 | """ 36 | Retrieving and procesing seasonal+reanalysis monthly data from the CDS. The function retrieves 37 | all the available hindcast years using the most recent system. Please, be aware that not all the 38 | starting dates and variables are available for all the systems, check the ECMWF wiki for further information: 39 | - Starting dates: https://confluence.ecmwf.int/display/CKB/Summary+of+available+data 40 | - Variables: https://confluence.ecmwf.int/display/CKB/Detailed+list+of+parameters 41 | 42 | This function download the GRIB files for the seasonal forecasts and ERA5, regrid the latter to the 43 | seasonal's grid, calculate the seasonal averages (i.e. one sample per year) and then save them 44 | into the same data structure 45 | 46 | NOTE: this function does not yet support time-ranges crossing the 31th December, (for example DJF) 47 | 48 | Arguments: 49 | centre (str): Originating centre (one in ['ecmwf', 'meteo_france', 'dwd', 'cmcc'', 'ncep', 'jma']) 50 | variable (str): one of the variable (e.g. '2m_temperature') 51 | start_month (int): starting month 1-12 52 | lead_time (list): list of the lead times that will be averaged to calculate the seasonal average 53 | """ 54 | # Define start/end year according to hindcast period 55 | start_year, end_year = self._MODEL_DIC.get(centre).get('range_years') 56 | # define system 57 | centre_name = self._MODEL_DIC.get(centre).get('centre') 58 | system = self._MODEL_DIC.get(centre).get('system') 59 | # check if the time range cross the year 60 | if any([(start_month+x)>13 for x in lead_time]): 61 | sys.exit('This function does not support yet forecast periods spanning two years') 62 | 63 | # SEASONAL FORECAST ------------------------------------------------------------------ 64 | request_dict = { 65 | 'originating_centre': centre_name, 66 | 'variable':variable, 67 | 'product_type':'monthly_mean', 68 | 'year':[x for x in map(str, range(start_year, end_year))], 69 | 'month': str(start_month), 70 | 'leadtime_month':[str(x) for x in lead_time], 71 | 'format':'grib', 72 | 'system': system 73 | } 74 | if not self.quiet: 75 | print('Retrieving seasonal forecasts...') 76 | c = cdsapi.Client() 77 | try: 78 | print(request_dict) 79 | r = c.retrieve( 80 | 'seasonal-monthly-single-levels', 81 | request_dict) 82 | except: 83 | print(request_dict) 84 | sys.exit('Exception from cdsapi, check the request or the status of CDS') 85 | # Update the request and get the reply 86 | r.update() 87 | reply = r.reply 88 | 89 | if reply.get('state') != 'completed': 90 | print(request_dict) 91 | # TODO replace sys.exit with raise and check climetlab implementation 92 | sys.exit('Request not completed, check the request of the status of CDS') 93 | else: 94 | r.download('fct_temp_out.grib') 95 | 96 | # OBSERVATIONS ---------------------------------------------------------- 97 | if not self.quiet: 98 | print('Retrieving ERA5 data...') 99 | request_dict = { 100 | 'variable':variable, 101 | 'product_type':'monthly_averaged_reanalysis', 102 | 'year':[x for x in map(str, range(start_year, end_year))], 103 | 'month': [x for x in map(str, range(start_month, start_month+lead_time[-1]))], 104 | 'format':'grib', 105 | 'time': '00:00' 106 | } 107 | try: 108 | r = c.retrieve( 109 | 'reanalysis-era5-single-levels-monthly-means', 110 | request_dict) 111 | except: 112 | print(request_dict) 113 | sys.exit('Exception from cdsapi, check the request of the status of CDS') 114 | 115 | # Update the request and get the reply 116 | r.update() 117 | reply = r.reply 118 | if reply.get('state') != 'completed': 119 | print(request_dict) 120 | sys.exit('Request not completed, check the request or the status of CDS') 121 | else: 122 | r.download('obs_temp_out.grib') 123 | 124 | # Reading GRIB files into xarray Datasets and calculate the seasonal averages 125 | if not self.quiet: 126 | print('Processing seasonal forecasts: renaming coordinates and computing annual average') 127 | fct = xr.open_dataset('fct_temp_out.grib', engine='cfgrib', backend_kwargs=dict(time_dims = ('verifying_time',))) 128 | fct_final = fct.rename({'latitude':'lat', 'longitude': 'lon'}).groupby('verifying_time.year').mean() 129 | # Reading ERA5 GRIB file and calculate seasonal average 130 | if not self.quiet: 131 | print('Processing ERA5: renaming coordinates and computing annual average') 132 | obs = xr.open_dataset('obs_temp_out.grib', engine = 'cfgrib') 133 | # This to remove the spurious months for accumulated variables 134 | obs = obs.sel(time=np.isin(obs['valid_time.month'], [x + start_month - 1 for x in lead_time])) 135 | obs_y = obs.groupby('time.year').mean('time').rename({'latitude':'lat', 'longitude': 'lon'}) 136 | 137 | if self.grid == 'original': 138 | # Regrid ERA5 on forecast's grid 139 | if not self.quiet: 140 | print("Regridding ERA5 on forecasts' grid") 141 | 142 | regridder = xe.Regridder(obs_y, fct_final, 'bilinear') 143 | obs_final = regridder(obs_y) 144 | elif self.grid == '1deg': 145 | if not self.quiet: 146 | print("Regridding forecasts and ERA5 to 1-degree regular grid") 147 | ds_out = xr.Dataset({'lat': (['lat'], np.arange(-89, 89, 1)), 148 | 'lon': (['lon'], np.arange(-180, 180, 1))}) 149 | 150 | # Convert coords to -180, 180 before regridding 151 | fct_final = fct_final.assign_coords(lon=(((fct_final.lon + 180) % 360) - 180)) 152 | fct_final = fct_final.sortby(fct_final.lon) 153 | regridder = xe.Regridder(fct_final, ds_out, 'bilinear') 154 | fct_final = regridder(fct_final) 155 | 156 | obs_y = obs_y.assign_coords(lon=(((obs_y.lon + 180) % 360) - 180)) 157 | obs_y = obs_y.sortby(obs_y.lon) 158 | regridder = xe.Regridder(obs_y, ds_out, 'bilinear') 159 | obs_final = regridder(obs_y) 160 | 161 | # Check if the variable names and numbers are consistent 162 | fct_name_var = [x for x in fct_final.data_vars] 163 | obs_name_var = [x for x in obs_final.data_vars] 164 | if len(fct_name_var) > 1 or len(obs_name_var) > 1: 165 | sys.exit('Error in grib files: variables > 1') 166 | if fct_name_var[0] != obs_name_var[0]: 167 | # Manage various cases 168 | if fct_name_var[0] == 'tprate' and obs_name_var[0] == 'tp': 169 | print('Convert total precipitation rate to total precipitation') 170 | fct_final['tprate'] *= 86400 171 | else: 172 | raise Exception(f'Error in grib files: variables names do not match ({fct_name_var[0]=} != {obs_name_var[0]=}') 173 | 174 | # Put the data into the instance structure 175 | self._data = xr.merge([fct_final.rename({fct_name_var[0]: 'fct_var'}), obs_final.rename({obs_name_var[0]: 'obs_var'})]) 176 | self._data.attrs = {'created': datetime.datetime.now().strftime("%d/%m/%Y %H:%M:%S"), 177 | 'hostname': os.uname().nodename, 178 | 'centre': centre, 179 | 'variable': variable 180 | } 181 | 182 | def get_data(self)-> xr.Dataset: 183 | """ 184 | Return the xarray Dataset containing seasonal data and ERA5 185 | """ 186 | if not hasattr(self, '_data'): 187 | return xr.Dataset() 188 | else: 189 | return(self._data) 190 | 191 | def get_basename(self) -> str: 192 | """ 193 | Return the basename used to save the retrieved data 194 | """ 195 | return(self.file_out) 196 | 197 | def __init__(self, centre:str, variable:str, start_month:int, lead_time:list, grid:str = 'original', force_download=False, quiet = False): 198 | """ 199 | Initialise a `C3S_data_loader` object, which represents seasonal+reanalysis monthly data from the CDS. 200 | 201 | The function retrieves the hindcast data using the most recent system. Please, be aware that not all the 202 | starting dates and variables are available for all the systems, check the ECMWF wiki for further information: 203 | - Starting dates: https://confluence.ecmwf.int/display/CKB/Summary+of+available+data 204 | - Variables: https://confluence.ecmwf.int/display/CKB/Detailed+list+of+parameters 205 | 206 | This function download the GRIB files for the seasonal forecasts and ERA5, regrid the latter to the 207 | seasonal's grid, calculate the seasonal averages (i.e. one sample per year) and then save them 208 | into the same data structure. 209 | 210 | A unique file name is chosen according to the passed parameters and it is used to save the processed 211 | data (saved in the DATA_PATH folder defined in config.yml) in NetCDF format. If the file exists, the constructor will load it 212 | avoiding to retrieve and process CDS data. 213 | 214 | NOTE: time-ranges crossing the 31th December, (for example DJF), are not yet supported 215 | 216 | Arguments: 217 | centre (str): Originating centre (one in ['ecmwf', 'meteo_france', 'dwd', 'cmcc', 'ncep', 'jma', 'ukmo']) 218 | variable (str): one of the variable (e.g. '2m_temperature') 219 | start_month (int): starting month 1-12 220 | lead_time (list): list of the lead times that will be averaged to calculate the seasonal average 221 | grid (str): specify `original` (default) if you want to use the seasonal forecast's grid otherwise `1deg` to regrid to 1-degree regular grid 222 | force_download (boolean): force the retrieving of the data even if an existing processed file is found 223 | quiet (boolean): define if the execution should print out some information or not 224 | """ 225 | self._MODEL_DIC = { 226 | 'ecmwf':{'centre': 'ecmwf', 'system': '5', 'range_years':(1993, 2016), 'start_dates':range(1, 13)}, 227 | 'ukmo':{'centre': 'ukmo', 'system': 600, 'range_years':(1993, 2016), 'start_dates':[3,4,5,6]}, 228 | 'ukmo15':{'centre': 'ukmo', 'system': 15, 'range_years':(1993, 2016), 'start_dates':[1, 2, 5,6,7,8,9,10,11,12]}, 229 | 'meteo_france':{'centre': 'meteo_france', 'system': '7', 'range_years':(1993,2016), 'start_dates':range(1, 13)}, 230 | 'dwd':{'centre': 'dwd', 'system': '21', 'range_years':(1993,2016), 'start_dates': [1, 2, 3, 4, 5, 6, 11, 12]}, 231 | 'cmcc':{'centre': 'cmcc', 'system': '35', 'range_years':(1993,2016), 'start_dates': [1, 2, 3, 4, 5, 6, 7, 10, 11, 12]}, 232 | 'ncep':{'centre': 'ncep', 'system': '2', 'range_years':(1993,2016), 'start_dates': range(1, 13)}, 233 | 'jma':{'centre': 'jma', 'system': '2', 'range_years':(1993,2016), 'start_dates': [1, 2, 3, 4, 5, 6, 7, 10, 11, 12]} 234 | } 235 | self.grid = grid 236 | self.quiet = quiet 237 | self.read_config() 238 | # File with SEASONAL + OBS 239 | self.file_out = os.path.join(self.DATA_DIR, f'{centre}-{variable}-S{start_month}-L{lead_time[0]}-{lead_time[-1]}-grid_{grid[0:4]}') 240 | if not self.quiet: 241 | print(f"Target filename {self.file_out}") 242 | 243 | if centre not in ['ecmwf', 'ukmo', 'ukmo15', 'meteo_france', 'dwd', 'cmcc', 'ncep', 'jma']: 244 | print(f'Forecasting system {centre} not recognised') 245 | raise ValueError() 246 | elif start_month not in self._MODEL_DIC.get(centre).get('start_dates'): 247 | print(f'The starting month {start_month} is not available for {centre} system, please check https://confluence.ecmwf.int/display/CKB/Summary+of+available+data') 248 | raise ValueError() 249 | else: 250 | if force_download or not os.path.exists(self.file_out+'.nc'): 251 | self._retrieve_cds_and_merge(centre, variable, start_month, lead_time) 252 | self._data.to_netcdf(self.file_out+'.nc') 253 | self._data = xr.open_dataset(self.file_out+'.nc') 254 | 255 | else: 256 | if not self.quiet: 257 | print(f"Loading existing file {self.file_out+'.nc'}") 258 | self._data = xr.open_dataset(self.file_out+'.nc') 259 | 260 | 261 | 262 | -------------------------------------------------------------------------------- /c3sdl/__init__.py: -------------------------------------------------------------------------------- 1 | from .C3S_data_loader import * 2 | from .evaluator import * 3 | __author__ = 'Matteo De Felice' -------------------------------------------------------------------------------- /c3sdl/evaluator.py: -------------------------------------------------------------------------------- 1 | from .C3S_data_loader import * 2 | import xarray as xr 3 | import xskillscore as xs 4 | import numpy as np 5 | import os.path 6 | import matplotlib.pyplot as plt 7 | import cartopy.crs as ccrs 8 | 9 | class evaluator: 10 | """ 11 | This class is used to evaluate and store the skill data of a seasonal forecast created by 12 | `C3S_data_loader`. This class can compute deterministic and probabilistic scores (provided by the `xskillscore` package). 13 | 14 | The skill information is saved in the same folder of the processed seasonal data with the suffix `.solved` 15 | """ 16 | def __init__(self, dl:C3S_data_loader, quiet = False): 17 | """ 18 | Initialise using an object `C3S_data_loader`. If computed skill data is found then it is loaded. 19 | 20 | Arguments: 21 | dl (C3S_data_loader): the object with the retrieved forecast/reanalysis pair 22 | quite (boolean): define if the execution should print out information 23 | """ 24 | self._basename = dl.get_basename() 25 | # Get the forecast/observation data from `dl` 26 | self._data = dl.get_data() 27 | # Initialise the xarray Dataset with the skill information copying the `data` object 28 | # removing the two fields containing forecasts and observations 29 | self._skill_data = self._data.drop(['fct_var', 'obs_var']) 30 | self.quiet = quiet 31 | # Check if exists "solved" data file 32 | if os.path.exists(dl.get_basename() + '.solved.nc'): 33 | if not self.quiet: 34 | print(f"Loading existing computed skill data {dl.get_basename() + '.solved.nc'}") 35 | self._skill_data = xr.open_dataset(dl.get_basename() + '.solved.nc') 36 | 37 | 38 | def compute_deterministic(self): 39 | """ 40 | Calculate deterministic metrics comparing the ensemble mean with the reanalysis data. 41 | 42 | The following metrics are computed: 43 | - Spearman correlation coefficient (both with the p-value and a masked correlation using p < 0.001) 44 | - Climatologies 45 | - Bias (observation minus forecast) 46 | - RMSE 47 | - MAE 48 | - Normalised MAE (NMAE): ratio between MAE and climatology 49 | 50 | 51 | """ 52 | # DETERMINISTIC 53 | if not self.quiet: 54 | print('Computing ensemble mean') 55 | fct_ens_mean = self._data['fct_var'].mean('number') 56 | if not self.quiet: 57 | print('Computing correlation') 58 | self._skill_data = self._skill_data.assign(spearman = xs.spearman_r(fct_ens_mean, self._data['obs_var'], dim = 'year')) 59 | self._skill_data = self._skill_data.assign(spearman_pvalue = xs.spearman_r_p_value(fct_ens_mean, self._data['obs_var'], dim = 'year')) 60 | self._skill_data = self._skill_data.assign(spearman_p001 = self._skill_data.spearman.where(self._skill_data.spearman_pvalue <= 1e-2)) 61 | if not self.quiet: 62 | print('Computing climatology and bias') 63 | self._skill_data = self._skill_data.assign(clim_obs = self._data.obs_var.mean(dim = 'year')) 64 | self._skill_data = self._skill_data.assign(clim_fct = fct_ens_mean.mean(dim = 'year')) 65 | self._skill_data = self._skill_data.assign(clim_bias = self._skill_data.clim_obs - self._skill_data.clim_fct) 66 | if not self.quiet: 67 | print('Computing RMSE and MAE/NMAE') 68 | self._skill_data = self._skill_data.assign(rmse = xs.rmse(fct_ens_mean, self._data['obs_var'], dim = 'year')) 69 | self._skill_data = self._skill_data.assign(mae = xs.mae(fct_ens_mean, self._data['obs_var'], dim = 'year')) 70 | self._skill_data = self._skill_data.assign(nmae = xs.mae(fct_ens_mean, self._data['obs_var'], dim = 'year') / self._skill_data.clim_obs) 71 | if not self.quiet: 72 | print(f"Save skill data to {self._basename + '.solved.nc'}") 73 | self._skill_data.to_netcdf(self._basename + '.solved.nc') 74 | 75 | def compute_probabilistic(self): 76 | """ 77 | Calculate probabilistic metrics comparing the seasonal forecast with the reanalysis data. 78 | 79 | The following metrics are computed: 80 | - CRPS 81 | - upper/lower Brier Skill Scores (using terciles) 82 | - RPSS (using terciles) 83 | """ 84 | 85 | # check if nan exists 86 | if (np.isnan(self._data['obs_var']).any() or np.isnan(self._data['fct_var']).any()): 87 | nan_are_present = True 88 | else: 89 | nan_are_present = False 90 | # PROBABILISTIC scores ----------------------------------------------------------- 91 | if not self.quiet: 92 | print('Computing CRPS') 93 | self._skill_data = self._skill_data.assign(crps = xs.crps_ensemble(self._data['obs_var'], self._data['fct_var'], member_dim='number', dim = 'year')) 94 | 95 | if not self.quiet: 96 | print('Computing upper BSS') 97 | obs_thres = self._data['obs_var'] > self._data['obs_var'].quantile(2/3, dim = 'year', skipna = nan_are_present) 98 | ubs = xs.brier_score(obs_thres, (self._data['fct_var'] > self._data['fct_var'].quantile(2/3, dim = 'year', skipna = nan_are_present)).mean("number"), dim = 'year') 99 | ubaseline = xs.brier_score(obs_thres, xr.DataArray(1/3), dim = 'year') 100 | self._skill_data = self._skill_data.assign(ubss = 1 - (ubs/ubaseline)) 101 | if not self.quiet: 102 | print('Computing lower BSS') 103 | obs_thres = self._data['obs_var'] < self._data['obs_var'].quantile(1/3, dim = 'year', skipna = nan_are_present) 104 | lbs = xs.brier_score(obs_thres, (self._data['fct_var'] < self._data['fct_var'].quantile(1/3, dim = 'year', skipna = nan_are_present)).mean("number"), dim = 'year') 105 | lbaseline = xs.brier_score(obs_thres, xr.DataArray(1/3), dim = 'year') 106 | self._skill_data = self._skill_data.assign(lbss = 1 - (lbs/lbaseline)) 107 | # Save also scores 108 | self._skill_data = self._skill_data.assign(ubs = ubs) 109 | self._skill_data = self._skill_data.assign(lbs = lbs) 110 | 111 | if not self.quiet: 112 | print('Computing RPSS') 113 | obs_zeromean = self._data['obs_var'] - self._data['obs_var'].mean(dim = 'year') 114 | fct_zeromean = self._data['fct_var'] - self._data['fct_var'].mean(dim = 'year') 115 | cat_edges = obs_zeromean.quantile(q = [1/3, 2/3], dim = 'year', skipna = nan_are_present).rename({'quantile':'category_edge'}) 116 | self._skill_data = self._skill_data.assign(rps = xs.rps(obs_zeromean, fct_zeromean, cat_edges, member_dim='number', dim = 'year')) 117 | rps_cl = xs.rps(obs_zeromean, obs_zeromean.mean(dim = 'year').expand_dims({'number':1}), cat_edges, dim = 'year', member_dim='number') 118 | self._skill_data = self._skill_data.assign(rpss = 1 - (self._skill_data.rps/rps_cl)) 119 | 120 | if not self.quiet: 121 | print(f"Save skill data to {self._basename + '.solved.nc'}") 122 | self._skill_data.to_netcdf(self._basename + '.solved.nc') 123 | 124 | def compute_classification_scores(self): 125 | """ 126 | Calculate classification metrics comparing the seasonal forecast with the reanalysis data. 127 | The prediction from the seasonal forecasts' ensemble is considered True when more than 50% of the members agree. 128 | 129 | The following metrics are computed: 130 | - Hit Rate (Upper and Lower) 131 | - Balanced Accuracy (Upper and Lower) 132 | """ 133 | 134 | # check if nan exists 135 | if (np.isnan(self._data['obs_var']).any() or np.isnan(self._data['fct_var']).any()): 136 | nan_are_present = True 137 | else: 138 | nan_are_present = False 139 | # CLASSIFICATION scores ----------------------------------------------------------- 140 | if not self.quiet: 141 | print('Computing Upper Hit Rate') 142 | 143 | obs_thres = self._data['obs_var'] > self._data['obs_var'].quantile(2/3, dim = 'year', skipna = nan_are_present) 144 | fct_thres = (self._data['fct_var'] > self._data['fct_var'].quantile(2/3, dim = 'year', skipna = False)).mean("number") 145 | 146 | o_category_edges = np.array([0, 1, 2]) 147 | f_category_edges = np.array([0, 0.5, 1]) # It's True when >50% of the members agree 148 | 149 | # Calculate contingency 150 | cc = xs.Contingency(obs_thres, fct_thres, 151 | o_category_edges, f_category_edges, 152 | dim='year') 153 | 154 | self._skill_data = self._skill_data.assign(uhr = cc.hit_rate()) 155 | # BACC 156 | tnr = cc.correct_negatives() / (cc.correct_negatives() + cc.misses()) 157 | bacc = (cc.hit_rate() + tnr)/2 158 | self._skill_data = self._skill_data.assign(ubacc = bacc) 159 | if not self.quiet: 160 | print('Computing Lower Hit Rate') 161 | 162 | obs_thres = self._data['obs_var'] < self._data['obs_var'].quantile(1/3, dim = 'year', skipna = nan_are_present) 163 | fct_thres = (self._data['fct_var'] < self._data['fct_var'].quantile(1/3, dim = 'year', skipna = False)).mean("number") 164 | 165 | o_category_edges = np.array([0, 1, 2]) 166 | f_category_edges = np.array([0, 0.5, 1]) # It's True when >50% of the members agree 167 | 168 | # Calculate contingency 169 | cc = xs.Contingency(obs_thres, fct_thres, 170 | o_category_edges, f_category_edges, 171 | dim='year') 172 | 173 | self._skill_data = self._skill_data.assign(lhr = cc.hit_rate()) 174 | # BACC 175 | tnr = cc.correct_negatives() / (cc.correct_negatives() + cc.misses()) 176 | bacc = (cc.hit_rate() + tnr)/2 177 | self._skill_data = self._skill_data.assign(lbacc = bacc) 178 | 179 | if not self.quiet: 180 | print(f"Save skill data to {self._basename + '.solved.nc'}") 181 | self._skill_data.to_netcdf(self._basename + '.solved.nc') 182 | 183 | def get_data(self) -> xr.Dataset: 184 | """ 185 | Return the xarray Dataset containing seasonal data and ERA5 186 | """ 187 | return(self._data) 188 | def set_data(self, data:xr.Dataset): 189 | """ 190 | Set a new xarray Dataset containing seasonal data and ERA5. 191 | """ 192 | self._data = data 193 | if not self.quiet: 194 | print('Forecast/observation data has changed, please recompute metrics and scores for consistency') 195 | def get_skill_data(self) -> xr.Dataset: 196 | """ 197 | Return the xarray Dataset containing only the skill and metrics 198 | """ 199 | return(self._skill_data) 200 | 201 | def save_maps(self): 202 | """ 203 | Save to disk a set of plots for the computed metrics. 204 | """ 205 | 206 | variables = ['spearman', 'rmse', 'mae', 'crps', 'ubss', 'lbss', 'clim_bias', 'nmae', 'spearman_p001', 'rps', 'rpss', 'uhr', 'lhr'] 207 | labels = ['spearman correlation', 'RMSE', 'MAE', 'CRPS', 'Upper BSS', 'Lower BSS', 'Clim. bias (obs-fct)', 'NMAE', 'spearman corr. (p-value < 1e-2)', 'RPS', 'RPSS', 'Hit Rate (Upper)', 'Hit Rate (Lower)'] 208 | for this_fig in zip(variables, labels): 209 | print(this_fig) 210 | if this_fig[0] in self._skill_data.data_vars.keys(): 211 | plt.figure(figsize=(1200/300, 800/300), dpi=300) 212 | ax = plt.axes(projection=ccrs.Robinson()) 213 | p = self._skill_data[this_fig[0]].plot(ax=ax, transform=ccrs.PlateCarree(), add_colorbar = False) 214 | ax.set_global(); ax.coastlines(linewidth=0.3); 215 | plt.title(os.path.basename(self._basename),fontsize=8) 216 | plt.suptitle(this_fig[1],fontsize=14, y=0.9) 217 | plt.colorbar(p,fraction=0.025, pad=0.04) 218 | plt.savefig(f'{self._basename}-{this_fig[0]}.png', dpi = 300) 219 | plt.close() 220 | else: 221 | if not self.quiet: 222 | print(f"{this_fig[0]} not present") 223 | 224 | -------------------------------------------------------------------------------- /environment.yml: -------------------------------------------------------------------------------- 1 | name: c3sdl 2 | channels: 3 | - conda-forge 4 | dependencies: 5 | - numpy=1.21 6 | - cartopy 7 | - matplotlib 8 | - xesmf 9 | - cdsapi 10 | - xarray 11 | - xskillscore>=0.0.19 12 | - cfgrib 13 | --------------------------------------------------------------------------------