2.1 dev

README.md

@@ -1,8 +1,6 @@
 # noaaplotter
 A python package to create fancy plots with NOAA weather data.
 
-
-
 ## Install
 #### Recommended conda install
 I recommend to use a fresh conda environment
@@ -22,20 +20,29 @@ I recommend to use a fresh conda environment
   - requests
   - joblib
   - tqdm
+  - geemap
+
 
 ## Examples
 ### Download data
-#### Option 1: Download via script
+#### Option 1 NOAA Daily Summaries: Download via script
 Download daily summaries (temperature + precipitation) from Kotzebue (or other station) from 1970-01-01 until 2021-12-31
 * NOAA API Token is required: https://www.ncdc.noaa.gov/cdo-web/token
 
 `python download_data.py -o ./data/kotzebue.csv -sid GHCND:USW00026616 -start 1970-01-01 -end 2021-12-31 -t <NOAA API Token>`
 
- #### Option 2: Download via browser
+ #### Option 2 NOAA Daily Summaries: Download via browser
  CSV files of "daily summaries"
 ("https://www.ncdc.noaa.gov/cdo-web/search")
 * Values: metric
 * File types: csv
+
+ #### Option 3 ERA5 Daily: Download via script
+Download daily summaries (temperature + precipitation) from Potsdam (13.05°E, 52.4°N) from 1980-01-01 until 2021-12-31
+* Google Earthengine account is required
+* Caution: full dataset may take a few minutes
+
+`python download_data_ERA5.py -o ./data/potsdam_ERA5.csv -start 1980-01-01 -end 2021-12-31 -lat 52.4 -lon 13.05`
 
 ### Daily Mean Temperature and Precipitation values vs. Climate
 #### Entire year 1 January until 31 December (e.g. 1992)

download_data_ERA5.py

@@ -0,0 +1,98 @@
+#!/usr/bin/python
+# -*- coding: utf-8 -*-
+# Imports
+import argparse
+import csv
+from datetime import datetime
+import numpy as np
+import os
+import pandas as pd
+import tqdm
+from joblib import delayed, Parallel
+from noaaplotter.utils import dl_noaa_api
+import ee
+import geemap
+
+
+def main():
+    """
+    Main Function
+    :return:
+    """
+    ##### Parse arguments #####
+    parser = argparse.ArgumentParser(description='Parse arguments.')
+
+    parser.add_argument('-o', dest='output_file', type=str, required=True,
+                        default='data/data.csv',
+                        help='csv file to save results')
+
+    parser.add_argument('-lat', dest='lat', type=float, required=True,
+                        help='Latitude of selected location')
+
+    parser.add_argument('-lon', dest='lon', type=float, required=True,
+                        help='Longitude of selected location')
+
+    parser.add_argument('-loc', dest='loc_name', type=str, required=False,
+                        default='',
+                        help='Location name')
+
+    parser.add_argument('-dt', dest='datatypes', type=list, required=False, default=['TMIN', 'TMAX', 'PRCP', 'SNOW'])
+
+    parser.add_argument('-start', dest='start_date', type=str, required=True,
+                        help='start date of plot ("yyyy-mm-dd")')
+
+    parser.add_argument('-end', dest='end_date', type=str, required=True,
+                        help='end date of plot ("yyyy-mm-dd")')
+
+    args = parser.parse_args()
+
+    # remove file if exists
+    if os.path.exists(args.output_file):
+        os.remove(args.output_file)
+
+    ee.Initialize()
+
+    EE_LAYER = 'ECMWF/ERA5/DAILY'
+
+    location = ee.Geometry.Point([args.lon, args.lat])
+
+    # load ImageCollection
+    col = ee.ImageCollection(EE_LAYER).filterBounds(location).filterDate(args.start_date, args.end_date)
+
+    # Download data
+    print("Start downloading daily ERA5 data.")
+    print("Download may take a while.\n1yr: ~5 seconds\n10yrs: ~35 seconds\n50yrs: ~8 min")
+    result = geemap.extract_pixel_values(col, region=location)
+    out_dict = result.getInfo()
+
+    df_gee = pd.DataFrame(data=[out_dict.keys(), out_dict.values()]).T
+
+    # parse dates and values
+    df_gee['time'] = df_gee[0].apply(lambda x: f'{x[:4]}-{x[4:6]}-{x[6:8]}')
+    df_gee['feature'] = df_gee[0].apply(lambda x: x[9:])
+    df_gee['value'] = df_gee[1]
+
+    df = df_gee.pivot_table(values='value', columns=['feature'], index='time')#.reset_index(drop=False)
+
+    # #### recalculate values 
+    df_new = pd.DataFrame(index=df.index)
+
+    temperature_cols = ['mean_2m_air_temperature', 'minimum_2m_air_temperature', 'maximum_2m_air_temperature', 'dewpoint_2m_temperature']
+    precipitation_cols = ['total_precipitation']
+    df_joined = df_new.join(df[temperature_cols] - 273.15).join(df[precipitation_cols] *1e3).reset_index(drop=False)
+
+    # Create Output
+    rename_dict = {'time':'DATE', 'total_precipitation':'PRCP', 'mean_2m_air_temperature':'TAVG', 'maximum_2m_air_temperature':'TMAX', 'minimum_2m_air_temperature':'TMIN'}
+    df_renamed = df_joined.rename(columns=rename_dict)
+    df_renamed['NAME'] = ''
+    df_renamed['STATION'] = ''
+    df_renamed['SNWD'] = ''
+
+    output_cols = ["STATION","NAME","DATE","PRCP","SNWD","TAVG","TMAX","TMIN"]
+    df_save = df_renamed[output_cols].astype(str)
+
+    df_save.to_csv(args.output_file, index=False)
+
+
+if __name__ == "__main__":
+    main()

environment.yml

@@ -11,3 +11,4 @@ dependencies:
   - requests
   - joblib
   - tqdm
+  - geemap

noaaplotter/noaaplotter.py

@@ -10,6 +10,7 @@
 import numpy as np
 ########################
 from matplotlib import pyplot as plt, dates
+import matplotlib.dates as mdates
 
 from .dataset import NOAAPlotterDailyClimateDataset as DS_daily
 from .dataset import NOAAPlotterDailySummariesDataset as Dataset
@@ -80,7 +81,8 @@ def plot_weather_series(self, start_date, end_date,
                             plot_pmax='auto', plot_snowmax='auto',
                             plot_extrema=True, show_plot=True,
                             show_snow_accumulation=True, save_path=False,
-                            figsize=(9, 6), legend_fontsize='x-small', dpi=300):
+                            figsize=(9, 6), legend_fontsize='x-small', dpi=300,
+                            title=None):
         """
         Plotting Function to show observed vs climate temperatures and snowfall
         :param dpi:
@@ -203,6 +205,8 @@ def plot_weather_series(self, start_date, end_date,
             ax_t.set_ylim(plot_tmin, plot_tmax)
         ax_t.set_ylabel('Temperature in °C')
         ax_t.set_xlabel('Date')
+        if title:
+            ax_t.set_title(title)
 
         # add legend
         legend_handle_t = [fb, cm, cm_hi, fill_r, fill_b]
@@ -282,6 +286,11 @@ def plot_weather_series(self, start_date, end_date,
                     bbox_to_anchor=(0.5, -0.2))
         ax_p.legend(legend_handle_p, legend_text_p, loc='upper left', fontsize=legend_fontsize)
 
+        # set locator to monthly
+        locator = dates.MonthLocator()
+        ax_t.xaxis.set_major_locator(locator)
+        ax_p.xaxis.set_major_locator(locator)
+
         fig.tight_layout()
 
         # Save Figure

plot_daily.py

@@ -70,6 +70,10 @@ def main():
                         default=[9, 6],
                         help='figure size in inches width x height. 15 10 recommended for 1 year, 30 10 for 2 years ...')
 
+    parser.add_argument('-title', dest='title', type=str, required=False,
+                        default=None,
+                        help='Plot title')
+
     args = parser.parse_args()
 
     ##### Download from NOAA #####
@@ -91,7 +95,8 @@ def main():
                           plot_pmax=args.p_range,
                           plot_snowmax=args.s_range,
                           dpi=args.dpi,
-                          figsize=args.figsize)
+                          figsize=args.figsize,
+                          title=args.title)
 
 if __name__ == "__main__":
     main()