Hazard Standard Deviation extraction for Cordex Datasets#
A workflow from the CLIMAAX Handbook and MULTI_infrastructure GitHub repository.
See our how to use risk workflows page for information on how to run this notebook.
import pandas as pd
import numpy as np
import os
import re
import shutil
import xarray as xr
from IPython.display import display, Markdown
from scipy.spatial import cKDTree
Extreme Temperature Hazards STD#
# Temperature indicators folders for the extreme temperature hazards
temp_daysAbove_folder = '/work/cmcc/dg07124/climax/indicators/cordex2/tempdays/tempDaysAbove/std_ensembles'
temp_percentiles_folder = '/work/cmcc/dg07124/climax/indicators/cordex2/tempPercentiles/std_ensembles'
temp_std_ensembles_folder = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_std_ensembles'
# Load the coordinates of the airports from the Excel file
csv_file = '/users_home/cmcc/dg07124/climax/airports_coordinates.csv'
airports_df = pd.read_csv(csv_file)
airports_df
| Airports | Lat | Lon | |
|---|---|---|---|
| 0 | Milano Malpensa | 45.63 | 8.73 |
| 1 | Bergamo Orio al Serio | 45.67 | 9.71 |
| 2 | Milano Linate | 45.45 | 9.28 |
| 3 | Roma Fiumicino | 41.80 | 12.25 |
| 4 | Roma Ciampino | 41.80 | 12.59 |
| 5 | Napoli Capodichino | 40.88 | 14.29 |
| 6 | Palermo Punta Raisi | 38.18 | 13.10 |
| 7 | Catania Fontanarossa | 37.47 | 15.07 |
| 8 | Cagliari Elmas | 39.25 | 9.06 |
if not os.path.exists(temp_std_ensembles_folder):
os.makedirs(temp_std_ensembles_folder)
# Step 2: Copy files from temp_daysAbove_folder and temp_percentiles_folder to temp_avg_ensembles_folder
def copy_files_to_new_folder(source_folder, destination_folder):
for file_name in os.listdir(source_folder):
source_file = os.path.join(source_folder, file_name)
destination_file = os.path.join(destination_folder, file_name)
if os.path.isfile(source_file):
shutil.copy(source_file, destination_file) # Copy file
# Copy files from both folders
copy_files_to_new_folder(temp_daysAbove_folder, temp_std_ensembles_folder)
copy_files_to_new_folder(temp_percentiles_folder, temp_std_ensembles_folder)
# Define time periods and RCP scenarios
time_periods = ['2021-2050', '2041-2070', '2071-2100']
rcp_scenarios = ['rcp26', 'rcp45', 'rcp85']
# Create the KDTree for fast nearest-neighbor search
def create_kd_tree(latitudes, longitudes):
coords = np.vstack((latitudes, longitudes)).T # Shape (n, 2)
tree = cKDTree(coords)
return tree
# Function to process the netCDF files for each time period and RCP scenario
def process_netCDF_files(netcdf_folder, time_period, rcp_scenario, airports_df, tree):
# Initialize the list to hold the extracted values (one row for each airport)
extracted_values = []
# Define columns for the indicators (fixed set of columns)
columns = ['Airports', 'p95', 'p999', 'tempabove35', 'tempabove40', 'tempabove45']
# Initialize the dictionary to hold values for each airport
for _, row in airports_df.iterrows():
extracted_values_airports = {'Airports': row['Airports']}
for col in columns[1:]:
extracted_values_airports[col] = np.nan # Initialize all indicators to NaN for this airport
# Loop through each netCDF file and check if it belongs to the given time period and RCP scenario
for file in os.listdir(netcdf_folder):
if file.endswith('.nc') and time_period in file and rcp_scenario in file:
# print(f"Processingnetcdf_folderfile: {netcdf_folder}")
# print(f"Processing file: {file}")
# Open the netCDF file using xarray
nc_file_path = os.path.join(netcdf_folder, file)
ds = xr.open_dataset(nc_file_path)
# Extract the lat/lon from the netCDF file
lat = ds['lat'].values
lon = ds['lon'].values
# Flatten the 2D arrays to 1D for KDTree usage
lat_flat = lat.flatten()
lon_flat = lon.flatten()
# Create a 2D array of lat/lon coordinates
coords_flat = np.vstack((lat_flat, lon_flat)).T # Shape: (n, 2)
# Create a KDTree for fast nearest-neighbor search
tree = cKDTree(coords_flat)
# Extract the coordinates for each airport
airport_coords = (row['Lat'], row['Lon'])
# Find the closest coordinates from the netCDF lat/lon grid for the current airport
closest_idx = tree.query([airport_coords], k=1)[1] # Get index of closest point
# Convert the flattened index to (y, x) coordinates
closest_y, closest_x = np.unravel_index(closest_idx, lat.shape)
# Extract the relevant variable value for each indicator
extracted_value = ds['tasmax'].isel(y=closest_y, x=closest_x).values
# print(f'extracted_value: {extracted_value.flatten()[0]}')
# Check the indicator based on the filename and update the corresponding column
if 'stdabove35' in file:
extracted_values_airports['tempabove35'] = extracted_value.flatten()[0]
elif 'stdabove40' in file:
extracted_values_airports['tempabove40'] = extracted_value.flatten()[0]
elif 'stdabove45' in file:
extracted_values_airports['tempabove45'] = extracted_value.flatten()[0]
elif 'std95' in file:
extracted_values_airports['p95'] = extracted_value.flatten()[0]
elif 'std999' in file:
extracted_values_airports['p999'] = extracted_value.flatten()[0]
# Add the extracted values for this airport to the list
extracted_values.append(extracted_values_airports)
# Convert the extracted values to a DataFrame
df = pd.DataFrame(extracted_values, columns=columns)
return df
# Create a KDTree using airport coordinates
tree = create_kd_tree(airports_df['Lat'], airports_df['Lon'])
# Process the files for each time period and RCP scenario and create separate DataFrames
dfs = {} # Dictionary to store DataFrames for each time period + RCP combination
for time_period in time_periods:
for rcp_scenario in rcp_scenarios:
# Process the files for the given time period and RCP scenario
df = process_netCDF_files(temp_std_ensembles_folder, time_period, rcp_scenario, airports_df, tree)
if df is not None:
dfs[f'{rcp_scenario}_{time_period}'] = df
# Display the DataFrames for each time period and RCP scenario
for key, df in dfs.items():
print(f"Data for {key}:")
df = df.set_index('Airports')
# df['average'] = df.mean(axis=1)
print(df)
# save to CSV
df.to_csv(f'/work/cmcc/dg07124/climax/indicators/cordex2/temp_std_ensembles/temp_std_{key}_data.csv', index=True)
Data for rcp26_2021-2050:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.681519 1.173082 3.318777 0.787577
Bergamo Orio al Serio 0.657671 1.149640 3.564539 0.249772
Milano Linate 0.612470 1.268595 4.648652 0.804932
Roma Fiumicino 0.524879 0.825922 4.588714 0.216768
Roma Ciampino 0.584376 0.827377 5.180357 0.228932
Napoli Capodichino 0.424670 0.845480 3.800186 0.465685
Palermo Punta Raisi 0.302887 0.615009 1.390151 0.232121
Catania Fontanarossa 0.351584 0.753102 6.414966 1.084037
Cagliari Elmas 0.476794 0.893550 7.222046 0.619808
tempabove45
Airports
Milano Malpensa 0.001843
Bergamo Orio al Serio 0.000000
Milano Linate 0.053435
Roma Fiumicino 0.000000
Roma Ciampino 0.000000
Napoli Capodichino 0.071570
Palermo Punta Raisi 0.000000
Catania Fontanarossa 0.143384
Cagliari Elmas 0.018426
Data for rcp45_2021-2050:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.446618 1.010088 3.333919 0.809989
Bergamo Orio al Serio 0.441997 0.935308 3.396257 0.533834
Milano Linate 0.423465 0.907300 4.252840 1.307682
Roma Fiumicino 0.387599 0.745171 4.653045 0.345611
Roma Ciampino 0.400369 0.590373 4.664936 0.335586
Napoli Capodichino 0.359800 0.858633 3.943516 0.550707
Palermo Punta Raisi 0.308694 0.471801 2.051202 0.342317
Catania Fontanarossa 0.233058 0.670039 5.359851 1.529307
Cagliari Elmas 0.361362 0.767072 6.100119 1.202726
tempabove45
Airports
Milano Malpensa 0.016583
Bergamo Orio al Serio 0.000000
Milano Linate 0.038050
Roma Fiumicino 0.009213
Roma Ciampino 0.000000
Napoli Capodichino 0.082207
Palermo Punta Raisi 0.000000
Catania Fontanarossa 0.271141
Cagliari Elmas 0.025459
Data for rcp85_2021-2050:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.483706 1.226128 3.741642 1.194269
Bergamo Orio al Serio 0.505754 1.372640 4.033515 0.815977
Milano Linate 0.466678 1.423913 4.642170 1.776248
Roma Fiumicino 0.449555 1.080606 5.783633 0.533596
Roma Ciampino 0.469419 1.009794 5.910766 0.547640
Napoli Capodichino 0.406028 1.018716 4.523608 0.626797
Palermo Punta Raisi 0.271204 0.714727 1.822134 0.343559
Catania Fontanarossa 0.311886 0.899216 7.422970 2.053937
Cagliari Elmas 0.389761 1.210050 7.756357 1.100951
tempabove45
Airports
Milano Malpensa 0.054916
Bergamo Orio al Serio 0.000000
Milano Linate 0.148881
Roma Fiumicino 0.009213
Roma Ciampino 0.000000
Napoli Capodichino 0.119949
Palermo Punta Raisi 0.000000
Catania Fontanarossa 0.253224
Cagliari Elmas 0.045134
Data for rcp26_2041-2070:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.613934 1.173375 2.581564 0.629350
Bergamo Orio al Serio 0.592966 1.119159 2.953666 0.240175
Milano Linate 0.590273 1.140470 3.677688 0.623363
Roma Fiumicino 0.477681 0.889042 3.516063 0.184398
Roma Ciampino 0.548494 0.641125 4.420334 0.238390
Napoli Capodichino 0.408734 0.517252 3.357908 0.284941
Palermo Punta Raisi 0.429262 0.615496 1.501407 0.219839
Catania Fontanarossa 0.384379 0.903022 6.785546 1.309972
Cagliari Elmas 0.504563 0.865971 6.575292 0.785529
tempabove45
Airports
Milano Malpensa 0.001843
Bergamo Orio al Serio 0.000000
Milano Linate 0.035009
Roma Fiumicino 0.000000
Roma Ciampino 0.000000
Napoli Capodichino 0.030912
Palermo Punta Raisi 0.000000
Catania Fontanarossa 0.137948
Cagliari Elmas 0.044790
Data for rcp45_2041-2070:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.617368 0.952694 5.178634 1.453482
Bergamo Orio al Serio 0.618357 0.922267 5.046188 1.029140
Milano Linate 0.601042 0.979410 6.309758 2.049859
Roma Fiumicino 0.459624 0.913802 7.035359 0.612182
Roma Ciampino 0.468233 0.917239 6.412250 0.742404
Napoli Capodichino 0.379854 0.941929 6.335132 0.674627
Palermo Punta Raisi 0.379039 0.766979 2.219101 0.325286
Catania Fontanarossa 0.358439 0.817603 8.728038 1.987495
Cagliari Elmas 0.406472 1.066539 9.642676 1.840480
tempabove45
Airports
Milano Malpensa 0.062202
Bergamo Orio al Serio 0.009213
Milano Linate 0.099087
Roma Fiumicino 0.028734
Roma Ciampino 0.009213
Napoli Capodichino 0.111736
Palermo Punta Raisi 0.000000
Catania Fontanarossa 0.233092
Cagliari Elmas 0.098914
Data for rcp85_2041-2070:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.731262 0.702369 7.608554 2.444121
Bergamo Orio al Serio 0.698533 0.953890 7.838790 1.785591
Milano Linate 0.702486 0.904333 8.972479 3.594999
Roma Fiumicino 0.561790 0.751451 9.882092 1.204068
Roma Ciampino 0.592269 0.673416 10.515683 1.329396
Napoli Capodichino 0.509219 0.927290 8.358044 1.045679
Palermo Punta Raisi 0.259160 0.795024 2.879451 0.715160
Catania Fontanarossa 0.331662 0.709035 12.205804 3.459762
Cagliari Elmas 0.438389 0.798211 12.976156 2.727733
tempabove45
Airports
Milano Malpensa 0.135919
Bergamo Orio al Serio 0.012423
Milano Linate 0.165114
Roma Fiumicino 0.000000
Roma Ciampino 0.012423
Napoli Capodichino 0.194976
Palermo Punta Raisi 0.018426
Catania Fontanarossa 0.526887
Cagliari Elmas 0.186153
Data for rcp26_2071-2100:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.623893 1.057265 2.787464 0.705000
Bergamo Orio al Serio 0.614063 0.844754 2.986865 0.417140
Milano Linate 0.605729 0.893574 3.239592 0.930525
Roma Fiumicino 0.604547 0.889843 3.809839 0.291942
Roma Ciampino 0.686293 0.850379 3.906746 0.303159
Napoli Capodichino 0.567988 1.046285 4.070541 0.310695
Palermo Punta Raisi 0.375270 0.635072 1.273731 0.259678
Catania Fontanarossa 0.338311 0.815669 5.564975 1.238261
Cagliari Elmas 0.463306 0.997664 5.794419 0.796488
tempabove45
Airports
Milano Malpensa 0.036918
Bergamo Orio al Serio 0.009213
Milano Linate 0.046128
Roma Fiumicino 0.000000
Roma Ciampino 0.000000
Napoli Capodichino 0.045851
Palermo Punta Raisi 0.000000
Catania Fontanarossa 0.243473
Cagliari Elmas 0.067268
Data for rcp45_2071-2100:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 0.733141 1.106038 6.808542 2.329334
Bergamo Orio al Serio 0.686460 1.131841 7.188333 1.675326
Milano Linate 0.686166 1.116730 8.451864 3.238049
Roma Fiumicino 0.559556 0.911627 8.887000 1.043347
Roma Ciampino 0.634023 0.996063 9.183876 1.150653
Napoli Capodichino 0.574716 0.996843 8.523521 1.098984
Palermo Punta Raisi 0.513738 0.819404 3.463530 0.741230
Catania Fontanarossa 0.523745 0.922735 12.531390 3.078992
Cagliari Elmas 0.617601 1.385469 13.637557 2.652229
tempabove45
Airports
Milano Malpensa 0.178953
Bergamo Orio al Serio 0.021403
Milano Linate 0.206574
Roma Fiumicino 0.038446
Roma Ciampino 0.032889
Napoli Capodichino 0.190023
Palermo Punta Raisi 0.009531
Catania Fontanarossa 0.349276
Cagliari Elmas 0.133640
Data for rcp85_2071-2100:
p95 p999 tempabove35 tempabove40 \
Airports
Milano Malpensa 1.059890 1.376907 15.382883 6.429328
Bergamo Orio al Serio 1.081600 1.321048 15.864742 5.901763
Milano Linate 1.003152 1.452095 17.512472 8.703780
Roma Fiumicino 0.770016 0.997169 21.143930 3.943505
Roma Ciampino 0.770391 1.041001 19.446971 4.614446
Napoli Capodichino 0.799083 1.102807 20.606998 3.783762
Palermo Punta Raisi 0.571223 1.017339 9.178618 1.855474
Catania Fontanarossa 0.564423 0.871270 18.413071 8.445586
Cagliari Elmas 0.637520 1.222879 20.648634 7.861219
tempabove45
Airports
Milano Malpensa 1.048282
Bergamo Orio al Serio 0.375436
Milano Linate 1.448174
Roma Fiumicino 0.162805
Roma Ciampino 0.174963
Napoli Capodichino 0.416115
Palermo Punta Raisi 0.129440
Catania Fontanarossa 1.193348
Cagliari Elmas 0.643428
Example of the output for the hazard of extrem temperature for the scenario RCP 2.6 and time period 2021-2050#
df = pd.read_csv('/work/cmcc/dg07124/climax/indicators/cordex2/temp_std_ensembles/temp_std_rcp26_2021-2050_data.csv', index_col='Airports')
df
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.681519 | 1.173082 | 3.318777 | 0.787577 | 0.001843 |
| Bergamo Orio al Serio | 0.657671 | 1.149640 | 3.564539 | 0.249772 | 0.000000 |
| Milano Linate | 0.612470 | 1.268595 | 4.648652 | 0.804932 | 0.053435 |
| Roma Fiumicino | 0.524879 | 0.825922 | 4.588714 | 0.216768 | 0.000000 |
| Roma Ciampino | 0.584376 | 0.827377 | 5.180357 | 0.228932 | 0.000000 |
| Napoli Capodichino | 0.424670 | 0.845480 | 3.800186 | 0.465685 | 0.071570 |
| Palermo Punta Raisi | 0.302887 | 0.615009 | 1.390151 | 0.232121 | 0.000000 |
| Catania Fontanarossa | 0.351584 | 0.753102 | 6.414966 | 1.084037 | 0.143384 |
| Cagliari Elmas | 0.476794 | 0.893550 | 7.222046 | 0.619808 | 0.018426 |
# Define the folder path where your CSV files are stored
csv_folder_path = '/work/cmcc/dg07124/climax/indicators/cordex2/temp_std_ensembles'
# List all the CSV files in the folder
csv_files = [f for f in os.listdir(csv_folder_path) if f.endswith('.csv')]
csv_files
['temp_std_rcp85_2041-2070_data.csv',
'temp_std_rcp45_2041-2070_data.csv',
'temp_std_rcp45_2071-2100_data.csv',
'temp_std_rcp26_2071-2100_data.csv',
'temp_std_rcp45_2021-2050_data.csv',
'temp_std_rcp26_2021-2050_data.csv',
'temp_std_rcp85_2021-2050_data.csv',
'temp_std_rcp85_2071-2100_data.csv',
'temp_std_rcp26_2041-2070_data.csv']
# Open each CSV (Standard Deviation for the Hazarsd for Extreme Temperature) as a DataFrame
for csv_file in csv_files:
file_path = os.path.join(csv_folder_path, csv_file)
df = pd.read_csv(file_path, index_col='Airports')
print(f"\n\033[1mStandard Deviation Hazard for: {csv_file}\033[0m\n")
display(df)
Standard Deviation Hazard for: temp_std_rcp85_2041-2070_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.731262 | 0.702369 | 7.608554 | 2.444121 | 0.135919 |
| Bergamo Orio al Serio | 0.698533 | 0.953890 | 7.838790 | 1.785591 | 0.012423 |
| Milano Linate | 0.702486 | 0.904333 | 8.972479 | 3.594999 | 0.165114 |
| Roma Fiumicino | 0.561790 | 0.751451 | 9.882092 | 1.204068 | 0.000000 |
| Roma Ciampino | 0.592269 | 0.673416 | 10.515683 | 1.329396 | 0.012423 |
| Napoli Capodichino | 0.509219 | 0.927290 | 8.358044 | 1.045679 | 0.194976 |
| Palermo Punta Raisi | 0.259160 | 0.795024 | 2.879451 | 0.715160 | 0.018426 |
| Catania Fontanarossa | 0.331662 | 0.709035 | 12.205804 | 3.459762 | 0.526887 |
| Cagliari Elmas | 0.438389 | 0.798211 | 12.976156 | 2.727733 | 0.186153 |
Standard Deviation Hazard for: temp_std_rcp45_2041-2070_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.617368 | 0.952694 | 5.178634 | 1.453482 | 0.062202 |
| Bergamo Orio al Serio | 0.618357 | 0.922267 | 5.046188 | 1.029140 | 0.009213 |
| Milano Linate | 0.601042 | 0.979410 | 6.309758 | 2.049859 | 0.099087 |
| Roma Fiumicino | 0.459624 | 0.913802 | 7.035359 | 0.612182 | 0.028734 |
| Roma Ciampino | 0.468233 | 0.917239 | 6.412250 | 0.742404 | 0.009213 |
| Napoli Capodichino | 0.379854 | 0.941929 | 6.335132 | 0.674627 | 0.111736 |
| Palermo Punta Raisi | 0.379039 | 0.766979 | 2.219101 | 0.325286 | 0.000000 |
| Catania Fontanarossa | 0.358439 | 0.817603 | 8.728038 | 1.987495 | 0.233092 |
| Cagliari Elmas | 0.406472 | 1.066539 | 9.642676 | 1.840480 | 0.098914 |
Standard Deviation Hazard for: temp_std_rcp45_2071-2100_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.733141 | 1.106038 | 6.808542 | 2.329334 | 0.178953 |
| Bergamo Orio al Serio | 0.686460 | 1.131841 | 7.188333 | 1.675326 | 0.021403 |
| Milano Linate | 0.686166 | 1.116730 | 8.451864 | 3.238049 | 0.206574 |
| Roma Fiumicino | 0.559556 | 0.911627 | 8.887000 | 1.043347 | 0.038446 |
| Roma Ciampino | 0.634023 | 0.996063 | 9.183876 | 1.150653 | 0.032889 |
| Napoli Capodichino | 0.574716 | 0.996843 | 8.523521 | 1.098984 | 0.190023 |
| Palermo Punta Raisi | 0.513738 | 0.819404 | 3.463530 | 0.741230 | 0.009531 |
| Catania Fontanarossa | 0.523745 | 0.922735 | 12.531390 | 3.078992 | 0.349276 |
| Cagliari Elmas | 0.617601 | 1.385469 | 13.637557 | 2.652229 | 0.133640 |
Standard Deviation Hazard for: temp_std_rcp26_2071-2100_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.623893 | 1.057265 | 2.787464 | 0.705000 | 0.036918 |
| Bergamo Orio al Serio | 0.614063 | 0.844754 | 2.986865 | 0.417140 | 0.009213 |
| Milano Linate | 0.605729 | 0.893574 | 3.239592 | 0.930525 | 0.046128 |
| Roma Fiumicino | 0.604547 | 0.889843 | 3.809839 | 0.291942 | 0.000000 |
| Roma Ciampino | 0.686293 | 0.850379 | 3.906746 | 0.303159 | 0.000000 |
| Napoli Capodichino | 0.567988 | 1.046285 | 4.070541 | 0.310695 | 0.045851 |
| Palermo Punta Raisi | 0.375270 | 0.635072 | 1.273731 | 0.259678 | 0.000000 |
| Catania Fontanarossa | 0.338311 | 0.815669 | 5.564975 | 1.238261 | 0.243473 |
| Cagliari Elmas | 0.463306 | 0.997664 | 5.794419 | 0.796488 | 0.067268 |
Standard Deviation Hazard for: temp_std_rcp45_2021-2050_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.446618 | 1.010088 | 3.333919 | 0.809989 | 0.016583 |
| Bergamo Orio al Serio | 0.441997 | 0.935308 | 3.396257 | 0.533834 | 0.000000 |
| Milano Linate | 0.423465 | 0.907300 | 4.252840 | 1.307682 | 0.038050 |
| Roma Fiumicino | 0.387599 | 0.745171 | 4.653045 | 0.345611 | 0.009213 |
| Roma Ciampino | 0.400369 | 0.590373 | 4.664936 | 0.335586 | 0.000000 |
| Napoli Capodichino | 0.359800 | 0.858633 | 3.943516 | 0.550707 | 0.082207 |
| Palermo Punta Raisi | 0.308694 | 0.471801 | 2.051202 | 0.342317 | 0.000000 |
| Catania Fontanarossa | 0.233058 | 0.670039 | 5.359851 | 1.529307 | 0.271141 |
| Cagliari Elmas | 0.361362 | 0.767072 | 6.100119 | 1.202726 | 0.025459 |
Standard Deviation Hazard for: temp_std_rcp26_2021-2050_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.681519 | 1.173082 | 3.318777 | 0.787577 | 0.001843 |
| Bergamo Orio al Serio | 0.657671 | 1.149640 | 3.564539 | 0.249772 | 0.000000 |
| Milano Linate | 0.612470 | 1.268595 | 4.648652 | 0.804932 | 0.053435 |
| Roma Fiumicino | 0.524879 | 0.825922 | 4.588714 | 0.216768 | 0.000000 |
| Roma Ciampino | 0.584376 | 0.827377 | 5.180357 | 0.228932 | 0.000000 |
| Napoli Capodichino | 0.424670 | 0.845480 | 3.800186 | 0.465685 | 0.071570 |
| Palermo Punta Raisi | 0.302887 | 0.615009 | 1.390151 | 0.232121 | 0.000000 |
| Catania Fontanarossa | 0.351584 | 0.753102 | 6.414966 | 1.084037 | 0.143384 |
| Cagliari Elmas | 0.476794 | 0.893550 | 7.222046 | 0.619808 | 0.018426 |
Standard Deviation Hazard for: temp_std_rcp85_2021-2050_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.483706 | 1.226128 | 3.741642 | 1.194269 | 0.054916 |
| Bergamo Orio al Serio | 0.505754 | 1.372640 | 4.033515 | 0.815977 | 0.000000 |
| Milano Linate | 0.466678 | 1.423913 | 4.642170 | 1.776248 | 0.148881 |
| Roma Fiumicino | 0.449555 | 1.080606 | 5.783633 | 0.533596 | 0.009213 |
| Roma Ciampino | 0.469419 | 1.009794 | 5.910766 | 0.547640 | 0.000000 |
| Napoli Capodichino | 0.406028 | 1.018716 | 4.523608 | 0.626797 | 0.119949 |
| Palermo Punta Raisi | 0.271204 | 0.714727 | 1.822134 | 0.343559 | 0.000000 |
| Catania Fontanarossa | 0.311886 | 0.899216 | 7.422970 | 2.053937 | 0.253224 |
| Cagliari Elmas | 0.389761 | 1.210050 | 7.756357 | 1.100951 | 0.045134 |
Standard Deviation Hazard for: temp_std_rcp85_2071-2100_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 1.059890 | 1.376907 | 15.382883 | 6.429328 | 1.048282 |
| Bergamo Orio al Serio | 1.081600 | 1.321048 | 15.864742 | 5.901763 | 0.375436 |
| Milano Linate | 1.003152 | 1.452095 | 17.512472 | 8.703780 | 1.448174 |
| Roma Fiumicino | 0.770016 | 0.997169 | 21.143930 | 3.943505 | 0.162805 |
| Roma Ciampino | 0.770391 | 1.041001 | 19.446971 | 4.614446 | 0.174963 |
| Napoli Capodichino | 0.799083 | 1.102807 | 20.606998 | 3.783762 | 0.416115 |
| Palermo Punta Raisi | 0.571223 | 1.017339 | 9.178618 | 1.855474 | 0.129440 |
| Catania Fontanarossa | 0.564423 | 0.871270 | 18.413071 | 8.445586 | 1.193348 |
| Cagliari Elmas | 0.637520 | 1.222879 | 20.648634 | 7.861219 | 0.643428 |
Standard Deviation Hazard for: temp_std_rcp26_2041-2070_data.csv
| p95 | p999 | tempabove35 | tempabove40 | tempabove45 | |
|---|---|---|---|---|---|
| Airports | |||||
| Milano Malpensa | 0.613934 | 1.173375 | 2.581564 | 0.629350 | 0.001843 |
| Bergamo Orio al Serio | 0.592966 | 1.119159 | 2.953666 | 0.240175 | 0.000000 |
| Milano Linate | 0.590273 | 1.140470 | 3.677688 | 0.623363 | 0.035009 |
| Roma Fiumicino | 0.477681 | 0.889042 | 3.516063 | 0.184398 | 0.000000 |
| Roma Ciampino | 0.548494 | 0.641125 | 4.420334 | 0.238390 | 0.000000 |
| Napoli Capodichino | 0.408734 | 0.517252 | 3.357908 | 0.284941 | 0.030912 |
| Palermo Punta Raisi | 0.429262 | 0.615496 | 1.501407 | 0.219839 | 0.000000 |
| Catania Fontanarossa | 0.384379 | 0.903022 | 6.785546 | 1.309972 | 0.137948 |
| Cagliari Elmas | 0.504563 | 0.865971 | 6.575292 | 0.785529 | 0.044790 |