import cdsapi
client = cdsapi.Client(url='https://cds-beta.climate.copernicus.eu/api', key='6db1903d-4754-4084-a35e-fd1d44e2f457')
Access Temperature variable from ADS/CDS#
Access relative humidity#
# dataset = "reanalysis-era5-pressure-levels"
# request = {
# 'product_type': ['reanalysis'],
# 'variable': ['relative_humidity', 'specific_cloud_liquid_water_content', 'specific_rain_water_content', 'temperature'],
# 'year': ['2020'],
# 'month': ['08'],
# 'day': ['03'],
# 'time': ['14:00'],
# 'data_format': 'netcdf',
# 'download_format': 'unarchived'
# }
# client.retrieve(dataset, request, 'download.nc')
ERA5-Land monthly averaged data from 1950 to present#
https://cds-beta.climate.copernicus.eu/datasets/reanalysis-era5-land-monthly-means?tab=overview
Focus in Madagascar#
'area': [-9.95, 40.23, -26.8, 52.45]
dataset = "reanalysis-era5-land-monthly-means"
request = {
'product_type': ['monthly_averaged_reanalysis'],
'variable': ['2m_temperature', 'skin_temperature', 'soil_temperature_level_1', 'soil_temperature_level_2', 'soil_temperature_level_3', 'soil_temperature_level_4',
'volumetric_soil_water_layer_1', 'volumetric_soil_water_layer_2', 'volumetric_soil_water_layer_3', 'volumetric_soil_water_layer_4',
'potential_evaporation', 'runoff','sub_surface_runoff', 'surface_runoff', 'total_evaporation',
'10m_u_component_of_wind', '10m_v_component_of_wind',
'total_precipitation', 'leaf_area_index_high_vegetation', 'leaf_area_index_low_vegetation'],
'year': [str(x) for x in range(1991, 2024)],
# ['2018', '2019', '2020', '2021', '2022', '2023'],
'month': ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'],
'time': ['00:00'],
'data_format': 'netcdf',
'download_format': 'unarchived',
'area': [-9.95, 40.23, -26.8, 52.45]
}
# client.retrieve(dataset, request, 'era5-land-monthly-means-madagascar-2018-2024.nc')
client.retrieve(dataset, request).download()
2024-09-15 09:55:16,817 INFO Request ID is 84ca07d4-9e86-4b3c-95f5-7f50fef1dcda
2024-09-15 09:55:16,848 INFO status has been updated to accepted
2024-09-15 09:55:18,364 INFO status has been updated to running
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[3], line 19
2 request = {
3 'product_type': ['monthly_averaged_reanalysis'],
4 'variable': ['2m_temperature', 'skin_temperature', 'soil_temperature_level_1', 'soil_temperature_level_2', 'soil_temperature_level_3', 'soil_temperature_level_4',
(...)
15 'area': [-9.95, 40.23, -26.8, 52.45]
16 }
18 # client.retrieve(dataset, request, 'era5-land-monthly-means-madagascar-2018-2024.nc')
---> 19 client.retrieve(dataset, request).download()
File ~/drought-311-venv/lib64/python3.11/site-packages/cads_api_client/legacy_api_client.py:149, in LegacyApiClient.retrieve(self, name, request, target)
146 def retrieve(
147 self, name: str, request: dict[str, Any], target: str | None = None
148 ) -> str | processing.Results:
--> 149 result = self.logging_decorator(self.client.submit_and_wait_on_result)(
150 collection_id=name,
151 retry_options=self.retry_options,
152 **request,
153 )
154 partial_download: Callable[..., str] = functools.partial(
155 result.download,
156 timeout=self.timeout,
157 retry_options=self.retry_options,
158 )
159 result.download = self.logging_decorator(partial_download) # type: ignore[method-assign]
File ~/drought-311-venv/lib64/python3.11/site-packages/cads_api_client/legacy_api_client.py:134, in LegacyApiClient.logging_decorator.<locals>.wrapper(*args, **kwargs)
129 @functools.wraps(func)
130 def wrapper(*args: Any, **kwargs: Any) -> Any:
131 with LoggingContext(
132 logger=processing.logger, quiet=self.quiet, debug=self._debug
133 ):
--> 134 return func(*args, **kwargs)
File ~/drought-311-venv/lib64/python3.11/site-packages/cads_api_client/api_client.py:77, in ApiClient.submit_and_wait_on_result(self, collection_id, retry_options, **request)
74 def submit_and_wait_on_result(
75 self, collection_id: str, retry_options: Dict[str, Any] = {}, **request: Any
76 ) -> processing.Results:
---> 77 return self.retrieve_api.submit_and_wait_on_result(
78 collection_id, retry_options=retry_options, **request
79 )
File ~/drought-311-venv/lib64/python3.11/site-packages/cads_api_client/processing.py:454, in Processing.submit_and_wait_on_result(self, collection_id, retry_options, **request)
450 def submit_and_wait_on_result(
451 self, collection_id: str, retry_options: Dict[str, Any] = {}, **request: Any
452 ) -> Results:
453 remote = self.submit(collection_id, retry_options=retry_options, **request)
--> 454 remote.wait_on_result(retry_options=retry_options)
455 return remote.make_results()
File ~/drought-311-venv/lib64/python3.11/site-packages/cads_api_client/processing.py:248, in Remote.wait_on_result(self, retry_options)
246 raise ProcessingFailedError(f"Unknown API state {status!r}")
247 logger.debug(f"result not ready, waiting for {sleep} seconds")
--> 248 time.sleep(sleep)
KeyboardInterrupt:
!unzip -o ba855dda853e457af1754a3c905632f.zip
Archive: ba855dda853e457af1754a3c905632f.zip
inflating: data_0.nc
inflating: data_1.nc
inflating: data_2.nc
inflating: data_3.nc
import xarray as xr
import os
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import glob
import numpy as np
import dask
plot_params = {
'2m_temperature': {
'short_name': 't2m_c',
'cmap': 'RdBu_r',
'abs_diff_cmap': 'RdBu_r',
'plot_title': '2m Temperature [C]',
'description': 'This parameter is the temperature of air at 2m above the surface of land, sea or in-land waters. \
2m temperature is calculated by interpolating between the lowest model level and the Earth\'s surface, taking account of the atmospheric conditions.'
},
'skin_temperature': {
'short_name': 'skt_c',
'cmap': 'RdBu_r',
'abs_diff_cmap': 'RdBu_r',
'plot_title': 'Skin temperature [C]',
'description': 'The skin temperature is the theoretical temperature that is required to satisfy the surface energy balance. \
It represents the temperature of the uppermost surface layer, which has no heat capacity and so can respond instantaneously to \
changes in surface fluxes. Skin temperature is calculated differently over land and sea.'
},
'soil_temperature_level_1': {
'short_name': 'stl1_c',
'cmap': 'RdBu_r',
'abs_diff_cmap': 'RdBu_r',
'plot_title': 'Soil temperature level 1 (0 - 7cm) [C]',
'description': 'This parameter is the temperature of the soil at level 1 (in the middle of layer 1). Layer 1: 0 - 7cm'
},
'soil_temperature_level_2': {
'short_name': 'stl2_c',
'cmap': 'RdBu_r',
'abs_diff_cmap': 'RdBu_r',
'plot_title': 'Soil temperature level 2 (7 - 28cm) [C]',
'description': 'This parameter is the temperature of the soil at level 2 (in the middle of layer 1). Layer 1: 7 - 28cm'
},
'soil_temperature_level_3': {
'short_name': 'stl3_c',
'cmap': 'RdBu_r',
'abs_diff_cmap': 'RdBu_r',
'plot_title': 'Soil temperature level 3 (28 - 100cm) [C]',
'description': 'This parameter is the temperature of the soil at level 3 (in the middle of layer 1). Layer 1: 28 - 100cm'
},
'soil_temperature_level_4': {
'short_name': 'stl4_c',
'cmap': 'RdBu_r',
'abs_diff_cmap': 'RdBu_r',
'plot_title': 'Soil temperature level 4 (100 - 289cm) [C]',
'description': 'This parameter is the temperature of the soil at level 4 (in the middle of layer 1). Layer 1: 100 - 289cm'
},
'volumetric_soil_water_layer_1': {
'short_name': 'swvl1',
'cmap': 'Blues',
'abs_diff_cmap': 'RdBu',
'plot_title': 'Volumetric soil water layer 1 (0 - 7cm) [m3 m-3]',
'description': 'This parameter is the volume of water in soil layer 1 (0 - 7cm, the surface is at 0cm). \
The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.'
},
'volumetric_soil_water_layer_2': {
'short_name': 'swvl2',
'cmap': 'Blues',
'abs_diff_cmap': 'RdBu',
'plot_title': 'Volumetric soil water layer 2 (7 - 28cm) [m3 m-3]',
'description': 'This parameter is the volume of water in soil layer 1 (7 - 28cm, the surface is at 0cm). \
The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.'
},
'volumetric_soil_water_layer_3': {
'short_name': 'swvl3',
'cmap': 'Blues',
'abs_diff_cmap': 'RdBu',
'plot_title': 'Volumetric soil water layer 3 (28 - 100cm) [m3 m-3]',
'description': 'This parameter is the volume of water in soil layer 1 (28 - 100cm, the surface is at 0cm). \
The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.'
},
'volumetric_soil_water_layer_4': {
'short_name': 'swvl4',
'cmap': 'Blues',
'abs_diff_cmap': 'RdBu',
'plot_title': 'Volumetric soil water layer 4 (100 - 289cm) [m3 m-3]',
'description': 'This parameter is the volume of water in soil layer 1 (100 - 289cm, the surface is at 0cm). \
The volumetric soil water is associated with the soil texture (or classification), soil depth, and the underlying groundwater level.'
},
'potential_evaporation': {
'short_name': 'pev',
'cmap': 'YlOrBr',
'plot_title': 'Potential evaporation [meters]',
'description': 'This parameter is a measure of the extent to which near-surface atmospheric conditions are \
conducive to the process of evaporation. It is usually considered to be the amount of evaporation, \
under existing atmospheric conditions, from a surface of pure water which has the temperature of \
the lowest layer of the atmosphere and gives an indication of the maximum possible evaporation.'
},
'runoff': {
'short_name': 'ro',
'cmap': 'GnBu',
'plot_title': 'Runoff [meters]',
'description': "Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. \
Otherwise, the water drains away, either over the surface (surface runoff), \
or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'."
},
'sub_surface_runoff': {
'short_name': 'ssro',
'cmap': 'GnBu',
'plot_title': 'Sub-Surface Runoff [meters]',
'description': "Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. \
Otherwise, the water drains away, either over the surface (surface runoff), \
or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'."
},
'surface_runoff': {
'short_name': 'sro',
'cmap': 'GnBu',
'plot_title': 'Surface Runoff [meters]',
'description': "Some water from rainfall, melting snow, or deep in the soil, stays stored in the soil. \
Otherwise, the water drains away, either over the surface (surface runoff), \
or under the ground (sub-surface runoff) and the sum of these two is simply called 'runoff'."
},
'total_evaporation': {
'short_name': 'e',
'cmap': 'YlOrBr',
'plot_title': 'Evaporation [m of water equivalent]',
'description': "This parameter is the accumulated amount of water that has evaporated from the Earth's surface, \
including a simplified representation of transpiration (from vegetation), into vapour in the air above."
},
'10m_u_component_of_wind': {
'short_name': 'u10',
'cmap': 'coolwarm',
'plot_title': 'U component of wind [m s-1]',
'description': 'This parameter is the eastward component of the wind. It is the horizontal speed of air moving towards the east, \
in metres per second. A negative sign thus indicates air movement towards the west. \
This parameter can be combined with the V component of wind to give the speed and direction of the horizontal wind.'
},
'10m_v_component_of_wind': {
'short_name': 'v10',
'cmap': 'coolwarm',
'plot_title': 'V component of wind [m s-1]',
'description': 'This parameter is the northward component of the wind. It is the horizontal speed of air moving towards the north, \
in metres per second. A negative sign thus indicates air movement towards the south. \
This parameter can be combined with the U component of wind to give the speed and direction of the horizontal wind.'
},
'total_precipitation': {
'short_name': 'tp',
'cmap': 'PuBuGn',
'abs_diff_cmap': 'RdBu',
'plot_title': 'Total precipitation [meters]',
'description': 'This parameter is the accumulated liquid and frozen water, comprising rain and snow, \
that falls to the Earth\'s surface. It is the sum of large-scale precipitation and convective precipitation.'
},
'leaf_area_index_high_vegetation': {
'short_name': 'lai_hv',
'cmap': 'Greens',
'plot_title': 'Leaf area index, high vegetation [m2 m-2]',
'description': "This parameter is the surface area of one side of all the leaves found over an area of land for \
vegetation classified as 'high'. This parameter has a value of 0 over bare ground or where there are no leaves. \
It can be calculated daily from satellite data. It is important for forecasting, for example, \
how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground."
},
'leaf_area_index_low_vegetation': {
'short_name': 'lai_lv',
'cmap': 'Greens',
'plot_title': 'Leaf area index, low vegetation [m2 m-2]',
'description': "This parameter is the surface area of one side of all the leaves found over an area of land for \
vegetation classified as 'low'. This parameter has a value of 0 over bare ground or where there are no leaves. \
It can be calculated daily from satellite data. It is important for forecasting, for example, \
how much rainwater will be intercepted by the vegetative canopy, rather than falling to the ground."
},
}
ds = xr.open_mfdataset(['data_0.nc', 'data_1.nc', 'data_2.nc', 'data_3.nc'], chunks={'time': "auto"}, concat_dim="valid_time", combine='nested', parallel=False)
# Resample to monthly data (if necessary)
ds_sorted = ds.sortby('valid_time')
ds_monthly = ds_sorted.resample(valid_time='1ME').first()
ds_monthly['t2m_c'] = ds_monthly['t2m'] - 273.15
ds_monthly['skt_c'] = ds_monthly['skt'] - 273.15
ds_monthly['stl1_c'] = ds_monthly['stl1'] - 273.15
ds_monthly['stl2_c'] = ds_monthly['stl2'] - 273.15
ds_monthly['stl3_c'] = ds_monthly['stl3'] - 273.15
ds_monthly['stl4_c'] = ds_monthly['stl4'] - 273.15
/home/george/drought-311-venv/lib64/python3.11/site-packages/dask/array/core.py:4832: PerformanceWarning: Increasing number of chunks by factor of 11
result = blockwise(
/home/george/drought-311-venv/lib64/python3.11/site-packages/dask/array/core.py:4832: PerformanceWarning: Increasing number of chunks by factor of 11
result = blockwise(
/home/george/drought-311-venv/lib64/python3.11/site-packages/dask/array/core.py:4832: PerformanceWarning: Increasing number of chunks by factor of 11
result = blockwise(
/home/george/drought-311-venv/lib64/python3.11/site-packages/dask/array/core.py:4832: PerformanceWarning: Increasing number of chunks by factor of 11
result = blockwise(
/home/george/drought-311-venv/lib64/python3.11/site-packages/dask/array/core.py:4832: PerformanceWarning: Increasing number of chunks by factor of 11
result = blockwise(
/home/george/drought-311-venv/lib64/python3.11/site-packages/dask/array/core.py:4832: PerformanceWarning: Increasing number of chunks by factor of 11
result = blockwise(
/home/george/drought-311-venv/lib64/python3.11/site-packages/xarray/core/indexing.py:1621: PerformanceWarning: Slicing with an out-of-order index is generating 47 times more chunks
return self.array[key]
/home/george/drought-311-venv/lib64/python3.11/site-packages/xarray/core/indexing.py:1621: PerformanceWarning: Slicing with an out-of-order index is generating 72 times more chunks
return self.array[key]
/home/george/drought-311-venv/lib64/python3.11/site-packages/xarray/core/indexing.py:1621: PerformanceWarning: Slicing with an out-of-order index is generating 297 times more chunks
return self.array[key]
ds_monthly
<xarray.Dataset> Size: 856MB
Dimensions: (valid_time: 396, latitude: 169, longitude: 123)
Coordinates:
number int64 8B 0
* latitude (latitude) float64 1kB -10.0 -10.1 -10.2 ... -26.6 -26.7 -26.8
* longitude (longitude) float64 984B 40.23 40.33 40.43 ... 52.23 52.33 52.43
* valid_time (valid_time) datetime64[ns] 3kB 1991-01-31 ... 2023-12-31
Data variables: (12/26)
swvl1 (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
swvl2 (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
swvl3 (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
swvl4 (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
stl1 (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
stl2 (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
... ...
t2m_c (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
skt_c (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
stl1_c (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
stl2_c (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
stl3_c (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
stl4_c (valid_time, latitude, longitude) float32 33MB dask.array<chunksize=(1, 22, 16), meta=np.ndarray>
Attributes:
GRIB_centre: ecmf
GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts
GRIB_subCentre: 0
Conventions: CF-1.7
institution: European Centre for Medium-Range Weather Forecastsdef visualise_variable_annually(variable_name, year):
fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(15, 15), subplot_kw={'projection': ccrs.PlateCarree()})
months = ["January", "February", "March", "April", "May", "June","July", "August", "September", "October", "November", "December"]
for i, ax in enumerate(axes.flatten()):
(ds_monthly[plot_params[variable_name]["short_name"]].sel(valid_time=f'{year}-{i + 1:02d}', method='nearest')).plot(ax=ax, transform=ccrs.PlateCarree(),cmap=plot_params[variable_name]['cmap'], cbar_kwargs={'label': plot_params[variable_name]["plot_title"]})
ax.coastlines()
ax.add_feature(cfeature.BORDERS)
ax.set_title(f'{plot_params[variable_name]["plot_title"]} for {months[i]} {year}')
fig.tight_layout()
plt.show()
visualise_variable_annually('2m_temperature', 2000)
Compare to baseline (1991 to 2023)#
def create_monthly_baseline(ds_monthly, ignore_years=[2020]):
ds_baseline = ds_monthly.sel(valid_time=~ds_monthly['valid_time'].dt.year.isin(ignore_years))
monthly_baseline = ds_baseline.groupby('valid_time.month').mean(dim='valid_time')
return monthly_baseline
def make_comparisons(ds_annual, ds_monthly_baseline, variable_name, year, show_diff_plots=True, show_box_plots=True, show_hists=True, show_extremes_plot=True, extreme_perc=5):
ds_annual_variable = ds_annual[plot_params[variable_name]["short_name"]]
ds_monthly_variable = ds_annual_variable.groupby('valid_time.month').mean(dim='valid_time')
ds_monthly_baseline_variable = ds_monthly_baseline[plot_params[variable_name]["short_name"]]
print('Selected variable....')
months = ["January", "February", "March", "April", "May", "June",
"July", "August", "September", "October", "November", "December"]
# Create subplots
if show_diff_plots:
fig_plot, axes_plot = plt.subplots(nrows=4, ncols=3, figsize=(15, 15), subplot_kw={'projection': ccrs.PlateCarree()})
fig_plot.suptitle(f'Comparisons for {variable_name} in {year}', fontsize=16)
# Box Plots in a 3x4 layout
if show_box_plots:
fig_box, axes_box = plt.subplots(nrows=3, ncols=4, figsize=(20, 15))
fig_box.suptitle(f'{variable_name} Box Plot Comparisons for {year}', fontsize=16)
# Histogram Plots in a 3x4 layout
if show_hists:
fig_hist, axes_hist = plt.subplots(nrows=3, ncols=4, figsize=(20, 15))
fig_hist.suptitle(f'{variable_name} Histogram Comparisons for {year}', fontsize=16)
if show_extremes_plot:
# Focus on Extremes in a 3x4 layout
fig_extremes, axes_extremes = plt.subplots(nrows=3, ncols=4, figsize=(20, 15))
fig_extremes.suptitle(f'{variable_name} Focus on Extremes for {year}', fontsize=16)
for i in range(12):
print(f'Processing month {months[i]}')
month = i + 1
# Extract the data for the current month
annual_month = ds_monthly_variable.sel(month=month)
baseline_month = ds_monthly_baseline_variable.sel(month=month)
if show_diff_plots:
# Absolute Difference Map
abs_diff = annual_month - baseline_month
ax_plot = axes_plot.flatten()[i]
abs_diff.plot(ax=ax_plot, transform=ccrs.PlateCarree(), cmap=plot_params[variable_name]["abs_diff_cmap"], center=0, cbar_kwargs={'label': f'Abs Diff {variable_name} ({months[i]})'})
ax_plot.coastlines()
ax_plot.add_feature(cfeature.BORDERS)
ax_plot.set_title(f'Abs Diff {months[i]} {year}')
# Ensure only valid pixels are compared (non-NaN in both datasets)
if show_box_plots or show_hists or show_extremes_plot:
annual_month_flat = annual_month.to_numpy().flatten()
baseline_month_flat = baseline_month.to_numpy().flatten()
valid_data = ~np.isnan(annual_month_flat) & ~np.isnan(baseline_month_flat)
annual_month_valid = annual_month_flat[valid_data]
baseline_month_valid = baseline_month_flat[valid_data]
# Focus on Extremes Plot
if show_extremes_plot:
ax_extremes = axes_extremes.flatten()[i]
top_2_percent_threshold = np.percentile(baseline_month_valid, 100-extreme_perc)
bottom_2_percent_threshold = np.percentile(baseline_month_valid, extreme_perc)
# Identify the top and bottom 2% pixels
high_baseline = baseline_month_valid[baseline_month_valid >= top_2_percent_threshold]
high_annual = annual_month_valid[baseline_month_valid >= top_2_percent_threshold]
low_baseline = baseline_month_valid[baseline_month_valid <= bottom_2_percent_threshold]
low_annual = annual_month_valid[baseline_month_valid <= bottom_2_percent_threshold]
# Scatter plot for the high 2%
ax_extremes.scatter(high_baseline, high_annual, color='red', label=f'High {extreme_perc}%')
ax_extremes.plot([min(high_baseline), max(high_baseline)],
[min(high_baseline), max(high_baseline)], 'k--')
# Scatter plot for the low 2%
ax_extremes.scatter(low_baseline, low_annual, color='blue', label=f'Low {extreme_perc}%')
ax_extremes.plot([min(low_baseline), max(low_baseline)],
[min(low_baseline), max(low_baseline)], 'k--')
ax_extremes.set_xlabel('Baseline')
ax_extremes.set_ylabel(f'{year}')
ax_extremes.set_title(f'{months[i]}')
ax_extremes.grid(True)
ax_extremes.legend()
# Box Plot
if show_box_plots:
ax_box = axes_box.flatten()[i]
ax_box.boxplot([baseline_month_valid, annual_month_valid], tick_labels=[f'Baseline', f'{months[i]} {year}'], notch=True)
ax_box.set_title(f'{months[i]}')
ax_box.grid(True)
# Histogram Plot
if show_hists:
ax_hist = axes_hist.flatten()[i]
ax_hist.hist(baseline_month_valid, bins=30, alpha=0.5, label='Baseline', color='blue')
ax_hist.hist(annual_month_valid, bins=30, alpha=0.5, label=months[i], color='red')
ax_hist.set_title(f'{months[i]}')
ax_hist.legend(loc='upper right')
if show_diff_plots:
fig_plot.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
if show_box_plots:
fig_box.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
if show_hists:
fig_hist.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
if show_extremes_plot:
fig_extremes.tight_layout(rect=[0, 0.03, 1, 0.95])
plt.show()
target_year = 2017
monthly_baseline = create_monthly_baseline(ds_monthly, ignore_years=[2017, 2018, 2019, 2020, 2021, 2022, 2023])
ds_target = ds_monthly.sel(valid_time=ds_monthly['valid_time'].dt.year == target_year)
make_comparisons(ds_target, monthly_baseline, '2m_temperature', target_year, show_box_plots=False, show_hists=False, show_extremes_plot=False)
Selected variable....
Processing month January
Processing month February
Processing month March
Processing month April
Processing month May
Processing month June
Processing month July
Processing month August
Processing month September
Processing month October
Processing month November
Processing month December
make_comparisons(ds_target, monthly_baseline, 'total_precipitation', target_year, show_box_plots=False, show_hists=False, show_extremes_plot=False)
Selected variable....
Processing month January
Processing month February
Processing month March
Processing month April
Processing month May
Processing month June
Processing month July
Processing month August
Processing month September
Processing month October
Processing month November
Processing month December
Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations#
dataset = "insitu-gridded-observations-global-and-regional"
request = {
'origin': 'chirps',
'region': 'africa',
'variable': ['precipitation'],
'time_aggregation': 'daily',
'horizontal_aggregation': ['0_25_x_0_25'],
'year': ['2019', '2020', '2021'],
'version': ['v2_0']
}
client = cdsapi.Client()
client.retrieve(dataset, request).download()
---------------------------------------------------------------------------
Exception Traceback (most recent call last)
Cell In[16], line 12
1 dataset = "insitu-gridded-observations-global-and-regional"
2 request = {
3 'origin': 'chirps',
4 'region': 'africa',
(...)
9 'version': ['v2_0']
10 }
---> 12 client = cdsapi.Client()
13 client.retrieve(dataset, request).download()
File ~/drought-311-venv/lib64/python3.11/site-packages/cdsapi/api.py:281, in Client.__new__(cls, url, key, *args, **kwargs)
280 def __new__(cls, url=None, key=None, *args, **kwargs):
--> 281 _, token, _ = get_url_key_verify(url, key, None)
282 if ":" in token:
283 return super().__new__(cls)
File ~/drought-311-venv/lib64/python3.11/site-packages/cdsapi/api.py:69, in get_url_key_verify(url, key, verify)
66 verify = bool(int(config.get("verify", 1)))
68 if url is None or key is None or key is None:
---> 69 raise Exception("Missing/incomplete configuration file: %s" % (dotrc))
71 # If verify is still None, then we set to default value of True
72 if verify is None:
Exception: Missing/incomplete configuration file: /home/george/.cdsapirc
dataset = "insitu-gridded-observations-global-and-regional"
request = {
'origin': 'gpcc',
'region': 'global',
'variable': ['precipitation'],
'time_aggregation': 'monthly',
'horizontal_aggregation': ['1_x_1'],
'year': ['2019', '2020', '2021'],
'version': ['v2020_0']
}
client = cdsapi.Client()
client.retrieve(dataset, request).download()