monthly_precipitation_variation_wuerzburg

a7a6e0e0 · Eric Duminil · 6c7b5e4b · a7a6e0e0 · a7a6e0e0 · 6c7b5e4b
Commit a7a6e0e0 authored 2 years ago by Eric Duminil
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--- a/python_scripts/get_dwd_precipitation/Parse Niederschlag.ipynb
+++ b/python_scripts/get_dwd_precipitation/Parse Niederschlag.ipynb
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       "Dec     60.5  "
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 %% Cell type:markdown id:d52465eb tags:
  
 # Niederschlag from DWD
  
 [stundenwerte_RR_05705_hist.zip](https://www.dwd.de/DE/leistungen/_config/leistungsteckbriefPublication.zip?view=nasPublication&nn=16102&imageFilePath=675349470684952943382999480164406894835507349402593906628786280354581421008571928705011438849092751258299962952312473511744392814375916459681379337607540679777573246835730326122056459559982440454207144420355774317891799102452512290249737221071767178890331035105331501334382788925&download=true)
  
 from [Klimadaten Deutschland - Stundenwerte (Archiv)](https://www.dwd.de/DE/leistungen/klimadatendeutschland/klarchivstunden.html) "Würzburg, Niederschlag, Historisch"
  
 %% Cell type:code id:18d75296 tags:
  
 ``` python
 import pandas as pd
  
 from datetime import datetime
 dateparse = lambda x: datetime.strptime(x, '%Y%m%d%H')
  
 df = pd.read_csv('produkt_rr_stunde_19950901_20211231_05705.txt',
                 sep=';',
                 skipinitialspace=True,
                 usecols = [1, 3],
                 names = ['datetime', 'precipitation'],
                 skiprows = 1,
                 parse_dates = ['datetime'],
                 date_parser=dateparse,
                 index_col = 'datetime'
                )
 df
 ```
  
 %% Output
  
                         precipitation
    datetime
    1995-09-01 00:00:00            0.0
    1995-09-01 01:00:00            0.2
    1995-09-01 02:00:00            0.2
    1995-09-01 03:00:00            0.1
    1995-09-01 04:00:00            0.0
    ...                            ...
    2021-12-31 19:00:00            0.0
    2021-12-31 20:00:00            0.0
    2021-12-31 21:00:00            0.0
    2021-12-31 22:00:00            0.0
    2021-12-31 23:00:00            0.0
    
    [230227 rows x 1 columns]
  
 %% Cell type:code id:52f73e31 tags:
  
 ``` python
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (20, 10)
 ```
  
 %% Cell type:code id:672300ca tags:
  
 ``` python
 df2 = df[df.precipitation >= 0]
 ```
  
 %% Cell type:code id:0fdf4a6f tags:
  
 ``` python
 df2.resample('d').sum().plot(title='Niederschlag in Würzburg [mm / Tag]');
 ```
  
 %% Output
  

  
 %% Cell type:code id:9a68733c tags:
  
 ``` python
 df2.resample('M').sum().plot(title='Niederschlag in Würzburg [mm / Monat]', ylim=(0, None));
 ```
  
 %% Output
  

  
 %% Cell type:code id:929bacb6 tags:
  
 ``` python
 yearly_values = df2[df2.index.year >= 1996].resample('Y').sum()
 ```
  
 %% Cell type:code id:c4dd7535 tags:
  
 ``` python
 yearly_values.describe()
 ```
  
 %% Output
  
           precipitation
    count      26.000000
    mean      568.538462
    std        96.303059
    min       410.500000
    25%       516.575000
    50%       548.900000
    75%       614.400000
    max       807.100000
  
 %% Cell type:code id:0a1cc458 tags:
  
 ``` python
 yearly_values.plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));
 ```
  
 %% Output
  

  
 %% Cell type:code id:c74631eb tags:
  
 ``` python
 yearly_values.boxplot()
 plt.title("Niederschlag in Würzburg, zwischen 1996 & 2021 [mm / Jahr]")
 plt.savefig('wurzburg_yearly_precipitations_1996_2021.png', facecolor='w', bbox_inches='tight')
 ```
  
 %% Output
  

  
 %% Cell type:code id:faf431eb tags:
  
 ``` python
 df3 = df2[df2.index.year == 2021]
 ```
  
 %% Cell type:code id:2509790f tags:
  
 ``` python
 rain2021 = pd.pivot_table(df3, values='precipitation', index=df3.index.time, columns=df3.index.dayofyear)
 rain2021
 ```
  
 %% Output
  
    datetime  1    2    3    4    5    6    7    8    9    10   ...  356  357  \
    00:00:00  0.0  0.0  0.0  0.1  0.0  0.0  0.0  0.2  0.0  0.0  ...  0.0  0.0
    01:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.2  0.0  0.0  ...  0.0  0.0
    02:00:00  0.0  0.0  0.0  0.0  0.0  0.1  0.0  0.1  0.0  0.0  ...  0.0  0.0
    03:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    04:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    05:00:00  0.0  0.0  0.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    06:00:00  0.0  0.0  0.2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    07:00:00  0.0  0.0  0.3  0.0  0.0  0.0  0.0  0.1  0.0  0.0  ...  0.0  0.0
    08:00:00  0.0  0.0  0.3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    09:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    10:00:00  0.0  0.0  0.2  0.0  0.0  0.0  0.0  0.1  0.0  0.0  ...  0.0  0.0
    11:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.2  0.0  0.0  ...  0.0  0.0
    12:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    13:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    14:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    15:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    16:00:00  0.0  0.0  0.0  0.0  0.6  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    17:00:00  0.0  0.0  0.0  0.0  0.4  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    18:00:00  0.0  0.0  0.0  0.0  0.2  0.0  0.0  0.1  0.0  0.0  ...  0.0  0.0
    19:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    20:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    21:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    22:00:00  0.0  0.0  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.1
    23:00:00  0.0  0.0  0.3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0
    
    datetime  358  359  360  361  362  363  364  365
    00:00:00  0.0  0.0  0.0  0.0  0.2  1.5  0.0  0.0
    01:00:00  0.0  0.0  0.0  1.1  0.0  1.2  0.1  0.0
    02:00:00  0.3  0.0  0.0  0.1  0.0  0.3  0.0  0.0
    03:00:00  0.1  0.0  0.0  0.1  0.0  0.3  0.0  0.0
    04:00:00  0.1  0.0  0.0  0.3  0.0  0.0  0.0  0.0
    05:00:00  0.0  0.7  0.0  0.1  0.0  0.0  0.0  0.0
    06:00:00  0.0  2.4  0.0  0.0  0.0  0.0  0.0  0.0
    07:00:00  0.0  2.7  0.0  0.0  1.1  0.0  0.0  0.0
    08:00:00  0.0  0.8  0.0  0.0  2.8  0.0  1.1  0.0
    09:00:00  0.0  0.4  0.0  0.0  1.5  0.0  0.1  0.0
    10:00:00  0.0  0.3  0.0  0.0  0.6  0.0  0.0  0.0
    11:00:00  0.0  0.2  0.0  0.0  0.7  0.1  0.0  0.0
    12:00:00  0.0  0.0  0.0  0.0  0.2  0.0  0.0  0.0
    13:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    14:00:00  0.0  0.2  0.1  0.0  0.0  0.0  0.0  0.0
    15:00:00  0.1  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    16:00:00  0.1  0.0  0.0  0.0  1.1  0.0  0.0  0.0
    17:00:00  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0
    18:00:00  0.0  0.0  0.0  0.0  0.0  0.4  0.0  0.0
    19:00:00  0.0  0.0  0.0  0.1  0.0  1.1  0.0  0.0
    20:00:00  0.0  0.0  0.0  0.3  0.4  0.7  0.0  0.0
    21:00:00  0.0  0.0  0.1  0.2  1.0  0.2  0.0  0.0
    22:00:00  0.0  0.0  0.0  0.1  1.1  0.1  0.0  0.0
    23:00:00  0.0  0.0  0.1  0.6  1.1  0.0  0.0  0.0
    
    [24 rows x 365 columns]
  
 %% Cell type:code id:42ddd82d tags:
  
 ``` python
 import seaborn as sns
 ```
  
 %% Cell type:code id:f1b17ef2 tags:
  
 ``` python
 sns.heatmap(rain2021, annot=False, cmap='viridis', vmax=5)
 plt.title('mm/h Regen in Würzburg, 2021')
 plt.show()
 ```
  
 %% Output
  

  
 %% Cell type:code id:29be9114 tags:
  
 ``` python
 df4 = df2[df2.index.year >= 2010].resample('M').sum()
 df4
 ```
  
 %% Output
  
                precipitation
    datetime
    2010-01-31           35.9
    2010-02-28           28.5
    2010-03-31           29.7
    2010-04-30           23.7
    2010-05-31           68.4
    ...                   ...
    2021-08-31           82.0
    2021-09-30            6.0
    2021-10-31           42.5
    2021-11-30           26.3
    2021-12-31           60.5
    
    [144 rows x 1 columns]
  
-%% Cell type:code id:7f474ad0 tags:
+%% Cell type:code id:283cd890 tags:
  
 ``` python
 import calendar
 df4['Year'] = df4.index.year
 df4['Month'] = df4.index.month
 df4['Month'] = df4['Month'].apply(lambda x: calendar.month_abbr[x])
 df4
 ```
  
 %% Output
  
                precipitation  Year Month
    datetime
    2010-01-31           35.9  2010   Jan
    2010-02-28           28.5  2010   Feb
    2010-03-31           29.7  2010   Mar
    2010-04-30           23.7  2010   Apr
    2010-05-31           68.4  2010   May
    ...                   ...   ...   ...
    2021-08-31           82.0  2021   Aug
    2021-09-30            6.0  2021   Sep
    2021-10-31           42.5  2021   Oct
    2021-11-30           26.3  2021   Nov
    2021-12-31           60.5  2021   Dec
    
    [144 rows x 3 columns]
  
-%% Cell type:code id:a1f53851 tags:
+%% Cell type:code id:28a0fd61 tags:
  
 ``` python
 df4 = df4.set_index(['Year', 'Month'])
 df4
 ```
  
 %% Output
  
                precipitation
    Year Month
    2010 Jan             35.9
         Feb             28.5
         Mar             29.7
         Apr             23.7
         May             68.4
    ...                   ...
    2021 Aug             82.0
         Sep              6.0
         Oct             42.5
         Nov             26.3
         Dec             60.5
    
    [144 rows x 1 columns]
  
-%% Cell type:code id:3c9b3b93 tags:
+%% Cell type:code id:8e3bd7d1 tags:
  
 ``` python
 df_precipitation = df4.reset_index().pivot_table(columns='Year',index='Month',values='precipitation', sort=False)
 df_precipitation
 ```
  
 %% Output
  
    Year    2010   2011  2012   2013  2014  2015  2016   2017  2018  2019   2020  \
    Month
    Jan     35.9   38.6  66.6   29.8  31.1  53.3  62.2   14.7  56.1  37.2   23.2
    Feb     28.5   31.4  12.2   35.1  39.6   8.4  60.1   21.2  12.4  10.6  106.5
    Mar     29.7    6.2   5.7   26.1   7.7  43.3  36.5   41.6  46.0  44.8   27.0
    Apr     23.7   14.2  11.8   42.1  40.7  17.3  49.6   20.9  34.5  27.8   10.9
    May     68.4    6.6  34.7   99.7  73.7  22.4  66.9  118.4  48.4  72.0   41.2
    Jun     21.5  110.0  67.2   44.7  16.7  39.3  53.4   49.3  27.3  44.6   75.6
    Jul     75.2   94.6  66.8   36.3  77.5  26.7  47.5   85.0  54.4  25.3   15.5
    Aug    185.1   43.4  28.0  108.9  95.1  67.6  30.6   61.1  22.5  39.2   61.9
    Sep     55.3   28.3  44.8   88.9  31.2  26.2  32.5   68.3  24.1  31.2   31.9
    Oct     27.6   36.1  41.9   56.5  39.2  28.5  38.1   36.1   8.8  65.9   41.6
    Nov     87.0    0.6  79.7   60.2  35.3  87.8  66.0   62.4  11.4  37.2    9.6
    Dec    106.5  109.5  78.5   30.2  41.3  27.6   7.4   49.9  86.2  54.2   48.3
    
    Year    2021
    Month
    Jan     54.6
    Feb     48.6
    Mar     35.8
    Apr     17.6
    May     69.8
    Jun    100.9
    Jul    138.7
    Aug     82.0
    Sep      6.0
    Oct     42.5
    Nov     26.3
    Dec     60.5
  
-%% Cell type:code id:a2c4799e tags:
+%% Cell type:code id:fa12c1b9 tags:
  
 ``` python
 ax=sns.heatmap(df_precipitation, cmap='viridis_r')
 ax.invert_yaxis()
 plt.title("Precipitation in Würzburg [mm / month]");
 ```
  
 %% Output
  

  
-%% Cell type:code id:06014eb4 tags:
+%% Cell type:code id:470cf56e tags:
  
 ``` python
 df_precipitation.sum().plot.bar(title='Niederschlag in Würzburg [mm / Jahr]', ylim=(0, None));
 ```
  
 %% Output
  

  
-%% Cell type:code id:bf62c2f5 tags:
+%% Cell type:code id:40727a96 tags:
  
 ``` python
 df_precipitation.mean(axis=1).plot.bar(title='Mittlerer Niederschlag in Würzburg\n1966 -> 2021\n[mm / Monat]',
                                       ylim=(0, None));
 ```
  
 %% Output
  

  
-%% Cell type:code id:13fe43a8 tags:
+%% Cell type:code id:0a195120 tags:
  
 ``` python
 df_precipitation.transpose().plot.box(showfliers=False, ylim=(0, None))
 duration = "2010 -> 2021"
 plt.title(f"Variation of monthly precipitation in Würzburg, {duration}")
 plt.ylabel("[mm / month]");
 plt.savefig('monthly_precipitation_variation_wuerzburg.png', facecolor="w", bbox_inches = 'tight')
 ```
  
 %% Output
  

  
-%% Cell type:code id:c49bdeb5 tags:
+%% Cell type:code id:e5d682d5 tags:
  
 ``` python
 # TODO: Compare 1995 -> 2021 with 2015 -> 2021
 # TODO: Get duration from min max index
 # TODO: Ridgeline for each month?
 # TODO: apply to Feuerbach
 # TODO: Push and document
 # TODO: Show on map
 ```
  
-%% Cell type:code id:1542246a tags:
+%% Cell type:code id:803223b4 tags:
  
 ``` python
 ```

--- a/python_scripts/get_dwd_precipitation/monthly_precipitation_variation_wuerzburg.png
+++ b/python_scripts/get_dwd_precipitation/monthly_precipitation_variation_wuerzburg.png
--- a/python_scripts/get_dwd_precipitation/wurzburg_yearly_precipitations_1996_2021.png
+++ b/python_scripts/get_dwd_precipitation/wurzburg_yearly_precipitations_1996_2021.png