Skip to content

Latest commit

 

History

History
238 lines (198 loc) · 7.91 KB

05-position.md

File metadata and controls

238 lines (198 loc) · 7.91 KB

Retrieve a time series for selected variables at a position

This DAPA endpoint returns a time series with daily observation values at the selected location (parameter coord or coordRef) in the selected time interval or at the selected time instant (parameter datetime).

The time series contains values for each selected variable (parameter variables) for which a value can be interpolated at the location.

Sample requests

Using coordinates for the location, requesting data for an instant

Percipitation and maximum/minimum temperatures on August 9, 2019, in Lieser, Germany.

http://t16.ldproxy.net/ghcnd/collections/observation/dapa/position?
coord=POINT(7.0218 49.9174)&
datetime=2019-08-09&
variables=TMAX,TMIN,PRCP

Request GeoJSON

Request CSV

Using coordinates for the location, requesting data for an interval (time series)

Percipitation and maximum/minimum temperatures on 5 days in August 2019, in Lieser, Germany.

http://t16.ldproxy.net/ghcnd/collections/observation/dapa/position?
coord=POINT(7.0218 49.9174)&
datetime=2019-08-07/2019-08-11&
variables=TMAX,TMIN,PRCP

Request GeoJSON

Request CSV

Using a reference for the location, requesting data for an instant

Percipitation and maximum/minimum temperatures on August 9, 2019, at a cadastral parcel in Bonn, Germany.

http://t16.ldproxy.net/ghcnd/collections/observation/dapa/position?
coordRef=https%3A%2F%2Fwww.ldproxy.nrw.de%2Fkataster%2Fcollections%2Fflurstueck%2Fitems%2FDENW36AL10005X65FL&
datetime=2019-08-09&
variables=TMAX,TMIN,PRCP

Request GeoJSON

Request CSV

Using a reference for the location, requesting data for an interval (time series)

Percipitation and maximum/minimum temperatures in August 2019 in Washington, DC, USA.

http://t16.ldproxy.net/ghcnd/collections/observation/dapa/position?
coordRef=https%3A%2F%2Fnominatim.openstreetmap.org%2Fsearch%3Fq%3DWashington%26format%3Dgeojson%26limit%3D1&
datetime=2019-08-01/2019-08-31&
variables=TMAX,TMIN,PRCP

Request GeoJSON

Request CSV

Sample responses

CSV

At an instant:

phenomenonTime,TMAX,TMIN,PRCP
2019-08-09,261.97275,148.28244,175.72902

For an interval, a time series:

phenomenonTime,TMAX,TMIN,PRCP
2019-08-31,326.22485,186.5174,1.8554863E-5
2019-08-30,299.9434,164.96535,0.2183162
2019-08-29,290.19263,181.47484,10.744431
2019-08-28,275.73703,190.04454,0.39175823
2019-08-27,249.96811,179.19333,1.7285985E-5
2019-08-26,254.65437,169.74426,3.914662E-9
2019-08-25,266.80197,169.72357,0.6220044
2019-08-24,252.26584,169.91318,60.580723
2019-08-23,309.53717,216.67346,46.07018
2019-08-22,348.21063,229.43196,19.694708
2019-08-21,347.32162,218.79291,98.5237
2019-08-20,357.6917,230.24718,47.368004
2019-08-19,356.72153,237.50298,0.0032744503
2019-08-18,342.5798,236.04352,3.8299541
2019-08-17,321.73395,232.62738,0.0013890546
2019-08-16,310.34668,224.90352,12.868282
2019-08-15,308.08978,237.34647,4.08197
2019-08-14,320.06943,233.05789,35.683617
2019-08-13,319.316,233.87689,2.407364
2019-08-12,310.0867,189.36937,1.0333144E-4
2019-08-11,293.19348,183.55244,0.0
2019-08-10,316.1553,179.15393,0.0
2019-08-09,332.17712,229.48021,0.0049290378
2019-08-08,343.6015,212.32523,186.5498
2019-08-07,339.2283,223.82373,145.55368
2019-08-06,325.06027,210.4592,0.006794223
2019-08-05,323.88538,218.16284,6.199424E-5
2019-08-04,331.6571,234.15137,2.254841
2019-08-03,318.93088,222.72612,2.552398E-4
2019-08-02,332.15082,219.27402,0.0
2019-08-01,319.9032,211.88776,0.012168143

GeoJSON

At an instant:

{
  "type" : "FeatureCollection",
  "features" : [ {
    "type" : "Feature",
    "geometry" : {
      "type" : "Point",
      "coordinates" : [ 7.0218, 49.9174 ]
    },
    "properties" : {
      "phenomenonTime" : "2019-08-09",
      "TMAX" : 261.97275,
      "TMIN" : 148.28244,
      "PRCP" : 175.72902
    }
  } ]
}

For an interval, a time series:

{
  "type" : "FeatureCollection",
  "features" : [ {
    "type" : "Feature",
    "geometry" : {
      "type" : "Point",
      "coordinates" : [ 7.085770560342503, 50.71640207544834 ]
    },
    "properties" : {
      "phenomenonTime" : "2019-08-11",
      "TMAX" : 231.23917,
      "TMIN" : 132.63652,
      "PRCP" : 0.050290037
    }
  }, {
    "type" : "Feature",
    "geometry" : {
      "type" : "Point",
      "coordinates" : [ 7.085770560342503, 50.71640207544834 ]
    },
    "properties" : {
      "phenomenonTime" : "2019-08-10",
      "TMAX" : 259.5085,
      "TMIN" : 159.90451,
      "PRCP" : 0.0731554
    }
  }, {
    "type" : "Feature",
    "geometry" : {
      "type" : "Point",
      "coordinates" : [ 7.085770560342503, 50.71640207544834 ]
    },
    "properties" : {
      "phenomenonTime" : "2019-08-09",
      "TMAX" : 275.02377,
      "TMIN" : 149.30214,
      "PRCP" : 108.23
    }
  }, {
    "type" : "Feature",
    "geometry" : {
      "type" : "Point",
      "coordinates" : [ 7.085770560342503, 50.71640207544834 ]
    },
    "properties" : {
      "phenomenonTime" : "2019-08-08",
      "TMIN" : 134.923,
      "PRCP" : 0.31049046,
      "TMAX" : 237.01088
    }
  }, {
    "type" : "Feature",
    "geometry" : {
      "type" : "Point",
      "coordinates" : [ 7.085770560342503, 50.71640207544834 ]
    },
    "properties" : {
      "phenomenonTime" : "2019-08-07",
      "TMAX" : 246.96074,
      "TMIN" : 148.34018,
      "PRCP" : 35.399906
    }
  } ]
}

Example use in python

Below is a simple plot created from the time series data for August 2019 for a location using python.

import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt

baseUrl = 'http://t16.ldproxy.net/ghcnd/collections/observation/dapa'
lon = '7.0218'
lat = '49.9174'
datetime = '2019-07-01/2019-08-31'
variables = 'TMIN,TMAX,PRCP'
# either read as GeoJSON into GeoPandas or as CSV into Pandas
# ghcnd = gpd.read_file(baseUrl+'/position?coord=POINT('+lon+'%20'+lat+')&datetime='+datetime+'&variables='+variables+'&f=json')
ghcnd = pd.read_csv(baseUrl+'/position?coord=POINT('+lon+'%20'+lat+')&datetime='+datetime+'&variables='+variables+'&f=csv')
pos = ghcnd
pos.index = pos.phenomenonTime
fig, ax = plt.subplots(figsize=(16, 8))
pos.TMAX.plot(label='max. Temperature [0.1°C]', legend='best').invert_xaxis()
pos.TMIN.plot(label='min. Temperature [0.1°C]', legend='best').invert_xaxis()
pos.PRCP.plot(kind='bar', label='Percipitation [0.1mm]', legend='best').invert_xaxis()
plt.show()

position-time-series

BACK