diff --git a/GLB.Ts+dSST.csv b/GLB.Ts+dSST.csv
new file mode 100644
index 0000000000000000000000000000000000000000..be76880f5979689b9e7196d5b769f3f4ad305d28
--- /dev/null
+++ b/GLB.Ts+dSST.csv
@@ -0,0 +1,144 @@
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diff --git a/World_TEMP_GISdata_LTAy_GlobalSolarAtlas-v2_GEOTIFF.zip b/World_TEMP_GISdata_LTAy_GlobalSolarAtlas-v2_GEOTIFF.zip
new file mode 100644
index 0000000000000000000000000000000000000000..fafb638cf5a34d8b811e79d16ef25abcf3d703ef
Binary files /dev/null and b/World_TEMP_GISdata_LTAy_GlobalSolarAtlas-v2_GEOTIFF.zip differ
diff --git a/ablauf.txt b/ablauf.txt
index a1e8048059ccbbc39ebdfb122b2890d0db49e11a..b5e1f11f57d9ebedb35ebc9a98b2f6cb2be2c163 100644
--- a/ablauf.txt
+++ b/ablauf.txt
@@ -10,5 +10,3 @@ __Verknüpfen der Klimadaten und Stationen___
Stationsliste iterieren und Wetterdaten herunterladen: [StationsID]_[Startdatum]_[Enddatum].txt
Mittelwert aller Stationen des Landes bilden
-
-
diff --git a/data/DwdAcquisition.py b/data/DwdAcquisition.py
index 3bf0e8b62dc8a35ff324869707a72f7cb81c4ab8..10f198cde452eacb2af59a8b5f33790271021933 100644
--- a/data/DwdAcquisition.py
+++ b/data/DwdAcquisition.py
@@ -23,7 +23,7 @@ def loadDWDGauges():
stationList['lon'] = stationList['lon'].str.strip()
stationList['lat'] = stationList['lat'].str.strip()
- # rename countries to merge with world shape file
+ # rename countries to merge with geopandas world shape file
stationList.loc[stationList['country']=="Korea, Dem. People's Rep.", 'country'] = 'South Korea'
stationList.loc[stationList['country']=="Slovakia (Slovak. Rep.)", 'country'] = 'Slovakia'
stationList.loc[stationList['country']=="Slowenia", 'country'] = 'Slovenia'
@@ -62,10 +62,10 @@ def loadDWDGauges():
dwdFTP.quit()
- # filter climate files list by longest timeseries (multiple timeseries per station)
+ # filter climate files list by longest timeseries
+ # (because: there are multiple timeseries-files per station with same historical values)
longestSeries = pd.DataFrame()
for index, ftpFiles in fileList.groupby("id", axis=0):
- #print(ftpFiles.iloc[-1])
longestSeries = longestSeries.append(ftpFiles.iloc[-1])
fileList.drop(fileList.index, inplace=True)
@@ -77,11 +77,11 @@ def loadDWDGauges():
# with open("stationList.pickle","wb") as pf:
# pickle.dump(stationList, pf)
+
def fillMissingData(annualData):
months = ["Jan", "Feb", "Mrz","Apr","Mai","Jun","Jul","Aug","Sep","Okt","Nov","Dez"]
for y in range(0,len(annualData)):
- year = annualData.iloc[y]
# check month for nan values
for m in range(0,len(months)):
@@ -132,7 +132,7 @@ def loadTemperatureFromDWDGauges():
global annualData
global worldTemperature
- with open("stationList.pickle", "rb") as pickleFile:
+ with open("./pickle/stationList.pickle", "rb") as pickleFile:
stationList = pickle.load(pickleFile)
#stationList = pd.concat([stationList, pd.DataFrame(columns=list(range(1950,2020)))])
@@ -145,26 +145,55 @@ def loadTemperatureFromDWDGauges():
for gid, gurl in zip(gaugeIds, gaugeURLs):
annualData = pd.read_csv(gurl, delimiter=";")
annualData = annualData.set_index("Jahr")
- #annualData["mean"] = annualData.mean(axis=1)
- annualData = fillMissingData(annualData)
- # with open("annualData.pickle", "wb") as pickleFile:
- # pickle.dump(annualData, pickleFile)
+ annualData["mean"] = annualData.mean(axis=1)
+ #annualData = fillMissingData(annualData)
for dataIndex, annualMean in annualData.iterrows():
try:
stationList.at[gid, dataIndex] = annualMean["mean"]
except:
continue
- #for index, gaugeCountry in stationList.groupby("country", axis=0):
- with open("stationList_temps.pickle", "wb") as pickleFile:
+
+ with open("./pickle/stationList_temps_missingData.pickle", "wb") as pickleFile:
pickle.dump(stationList, pickleFile)
-#
+
+def buildAverageTimeseries(fromYear, toYear, name):
+ global stationList
+
+ meanAverage = []
+ for stationID, station in stationList.iterrows():
+ temps = []
+ for i in range(fromYear,toYear):
+ if not np.isnan(station[i]):
+ temps.append(station[i])
+
+ if len(temps) > 5:
+ meanAverage.append(np.mean(temps))
+ else:
+ meanAverage.append(np.NaN)
+
+ stationList[name] = np.round(meanAverage,1)
+
+def cleanAverageTimeseries():
+ # determine gauges that includes both timeseries. If not delete them.
+ global stationList
+
+ for stationID, station in stationList.iterrows():
+ if np.isnan(station['m1961T1990']) or np.isnan(station['m1991T2018']):
+ #station['m1961T1990'] = None
+ #station['m2010T2018'] = None
+ stationList.at[stationID, "m1961T1990"] = None
+ stationList.at[stationID, "m1991T2018"] = None
+
+
+
+
def buildStationListGDP():
global stationList
global stationGPD
- with open("stationList_temps.pickle", "rb") as pickleFile:
+ with open("./pickle/stationList_temps_missingData.pickle", "rb") as pickleFile:
stationList = pickle.load(pickleFile)
del stationList["file"]
@@ -175,33 +204,42 @@ def buildStationListGDP():
stationList["lat"] = stationList["lat"].astype(str).astype(float)
stationList["lon"] = stationList["lon"].astype(str).astype(float)
+ buildAverageTimeseries(1961,1990,"m1961T1990")
+ buildAverageTimeseries(1991,2018,"m1991T2018")
+ cleanAverageTimeseries()
+
stationGPD = gpd.GeoDataFrame(stationList, geometry=gpd.points_from_xy(stationList.lat, stationList.lon)).reset_index()
del stationGPD["lat"]
del stationGPD["lon"]
+
stationGPD.columns = stationGPD.columns.astype(str)
stationGPD = stationGPD.sort_index(axis=1, ascending=False)
- stationGPD.to_file("stationList.shp", "ESRI Shapefile")
+ #stationGPD.to_file("stationList.shp", "ESRI Shapefile")
+
def buildAnnualCountryTemp():
global stationList
global countryTemp
global countryAnnualTemp
- global countryMeanGPD
- global world
- world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
+ world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
countryMeanGPD = gpd.sjoin(world, stationGPD, how="inner", op='intersects')
- print(countryMeanGPD.head())
countryMeanGPD = countryMeanGPD.groupby("name", axis=0).mean().reset_index()
+
+ countryMeanGPD["anomalie"] = countryMeanGPD["m1991T2018"] - countryMeanGPD["m1961T1990"]
+
del countryMeanGPD["pop_est"]
del countryMeanGPD["gdp_md_est"]
+ for i in range(1873,1950):
+ del countryMeanGPD[str(i)]
+
worldGauge = world.set_index("name").join(countryMeanGPD.set_index("name"))
#worldGauge = worldGauge.loc[(worldGauge.continent=="Europe")]
#worldGauge.columns = countryAnnualTemp.columns.map(str)
- worldGauge.to_file("globalAnnualTemp_country.shp", "ESRI Shapefile")
+ worldGauge.to_file("./output/countryAnnualTemperature.shp", "ESRI Shapefile")
#countryAnnualTemp = stationList.groupby("country", axis=0).mean().reset_index()
#countryAnnualTemp.columns = countryAnnualTemp.columns.map(str)
diff --git a/data/globalAnnualTemp_country.cpg b/data/globalAnnualTemp_country.cpg
deleted file mode 100644
index cd89cb9758e84c59b13399a25325d366af87e4f8..0000000000000000000000000000000000000000
--- a/data/globalAnnualTemp_country.cpg
+++ /dev/null
@@ -1 +0,0 @@
-ISO-8859-1
\ No newline at end of file
diff --git a/data/globalAnnualTemp_country.dbf b/data/globalAnnualTemp_country.dbf
deleted file mode 100644
index 28f639e1103a4f7dd2145e37ff3c6463b2142fdb..0000000000000000000000000000000000000000
Binary files a/data/globalAnnualTemp_country.dbf and /dev/null differ
diff --git a/data/globalAnnualTemp_country.prj b/data/globalAnnualTemp_country.prj
deleted file mode 100644
index a30c00a55de19be195abf9e942f6cff93bf0a825..0000000000000000000000000000000000000000
--- a/data/globalAnnualTemp_country.prj
+++ /dev/null
@@ -1 +0,0 @@
-GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137,298.257223563]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/data/globalAnnualTemp_country.shp b/data/globalAnnualTemp_country.shp
deleted file mode 100644
index 9318e45c7015568100ad4d7a271790fbec966876..0000000000000000000000000000000000000000
Binary files a/data/globalAnnualTemp_country.shp and /dev/null differ
diff --git a/data/globalAnnualTemp_country.shx b/data/globalAnnualTemp_country.shx
deleted file mode 100644
index c3728e0dd6d23d5dca5cf848264b023a3b7e99b5..0000000000000000000000000000000000000000
Binary files a/data/globalAnnualTemp_country.shx and /dev/null differ
diff --git a/data/globalAnnualTemp.cpg b/data/output/countryAnnualTemperature.cpg
similarity index 100%
rename from data/globalAnnualTemp.cpg
rename to data/output/countryAnnualTemperature.cpg
diff --git a/data/output/countryAnnualTemperature.dbf b/data/output/countryAnnualTemperature.dbf
new file mode 100644
index 0000000000000000000000000000000000000000..75f03b9456bbadb6ebcc984c3aa6e878c2167f5f
Binary files /dev/null and b/data/output/countryAnnualTemperature.dbf differ
diff --git a/data/globalAnnualTemp.prj b/data/output/countryAnnualTemperature.prj
similarity index 100%
rename from data/globalAnnualTemp.prj
rename to data/output/countryAnnualTemperature.prj
diff --git a/data/globalAnnualTemp.shp b/data/output/countryAnnualTemperature.shp
similarity index 100%
rename from data/globalAnnualTemp.shp
rename to data/output/countryAnnualTemperature.shp
diff --git a/data/globalAnnualTemp.shx b/data/output/countryAnnualTemperature.shx
similarity index 100%
rename from data/globalAnnualTemp.shx
rename to data/output/countryAnnualTemperature.shx
diff --git a/data/globalAnnualTemp.zip b/data/output/countryAnnualTemperature.zip
similarity index 65%
rename from data/globalAnnualTemp.zip
rename to data/output/countryAnnualTemperature.zip
index 9114a78cef25ef28f258ddffe2648e959a96f7fc..83c2f9c577886624b1b16f1ba968d685d999ec52 100644
Binary files a/data/globalAnnualTemp.zip and b/data/output/countryAnnualTemperature.zip differ
diff --git a/data/output/test_shape/Germany.cpg b/data/output/test_shape/Germany.cpg
new file mode 100644
index 0000000000000000000000000000000000000000..3ad133c048f2189041151425a73485649e6c32c0
--- /dev/null
+++ b/data/output/test_shape/Germany.cpg
@@ -0,0 +1 @@
+UTF-8
\ No newline at end of file
diff --git a/data/globalAnnualTemp.dbf b/data/output/test_shape/Germany.dbf
similarity index 53%
rename from data/globalAnnualTemp.dbf
rename to data/output/test_shape/Germany.dbf
index 4e083f9e6a9e98441110561350aa6ef9bc5b2e0a..9e63f90aa9a995df60542c7ce54ee111832bb72c 100644
Binary files a/data/globalAnnualTemp.dbf and b/data/output/test_shape/Germany.dbf differ
diff --git a/data/output/test_shape/Germany.prj b/data/output/test_shape/Germany.prj
new file mode 100644
index 0000000000000000000000000000000000000000..66ea55106ec00c72691deff0c56105905551db5a
--- /dev/null
+++ b/data/output/test_shape/Germany.prj
@@ -0,0 +1 @@
+PROJCS["ETRS_1989_UTM_Zone_33N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",15.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
\ No newline at end of file
diff --git a/data/output/test_shape/Germany.shp b/data/output/test_shape/Germany.shp
new file mode 100644
index 0000000000000000000000000000000000000000..4d621a67a6dfa8183cca145522242994555028a8
Binary files /dev/null and b/data/output/test_shape/Germany.shp differ
diff --git a/data/output/test_shape/Germany.shx b/data/output/test_shape/Germany.shx
new file mode 100644
index 0000000000000000000000000000000000000000..d697da34cf92584b3d8068f8e9ea1d274c30faa9
Binary files /dev/null and b/data/output/test_shape/Germany.shx differ
diff --git a/data/stationList.cpg b/data/output/test_shape/stationList.cpg
similarity index 100%
rename from data/stationList.cpg
rename to data/output/test_shape/stationList.cpg
diff --git a/data/stationList.dbf b/data/output/test_shape/stationList.dbf
similarity index 100%
rename from data/stationList.dbf
rename to data/output/test_shape/stationList.dbf
diff --git a/data/stationList.shp b/data/output/test_shape/stationList.shp
similarity index 100%
rename from data/stationList.shp
rename to data/output/test_shape/stationList.shp
diff --git a/data/stationList.shx b/data/output/test_shape/stationList.shx
similarity index 100%
rename from data/stationList.shx
rename to data/output/test_shape/stationList.shx
diff --git a/data/annualData.pickle b/data/pickle/annualData.pickle
similarity index 100%
rename from data/annualData.pickle
rename to data/pickle/annualData.pickle
diff --git a/data/stationList.pickle b/data/pickle/stationList.pickle
similarity index 100%
rename from data/stationList.pickle
rename to data/pickle/stationList.pickle
diff --git a/data/pickle/stationList_temps_missingData.pickle b/data/pickle/stationList_temps_missingData.pickle
new file mode 100644
index 0000000000000000000000000000000000000000..d96a2e7edc410927951f019b7e13a22920030592
Binary files /dev/null and b/data/pickle/stationList_temps_missingData.pickle differ
diff --git a/data/test_DwdAcquisition.py b/data/test_DwdAcquisition.py
index 1b9c0b7a7ece748041773d9e559c6b4f34e773e7..682feb30b10315c50fb22d9fd5d446f3e54e14f7 100644
--- a/data/test_DwdAcquisition.py
+++ b/data/test_DwdAcquisition.py
@@ -17,7 +17,7 @@ class TestDwdAcquisition(unittest.TestCase):
global annualData
cut.loadTemperatureFromDWDGauges()
- with open("stationList_temps.pickle", "rb") as pickleFile:
+ with open("./pickle/stationList_temps.pickle", "rb") as pickleFile:
annualData = pickle.load(pickleFile)
def _testLoadDWDGauges(self):
@@ -26,7 +26,7 @@ class TestDwdAcquisition(unittest.TestCase):
def _testFillMissingData(self):
- with open("annualData.pickle", "rb") as testFile:
+ with open("./pickle/annualData.pickle", "rb") as testFile:
global annualData
annualData = pickle.load(testFile)
diff --git a/text b/text
new file mode 100644
index 0000000000000000000000000000000000000000..efc6faa8dd70ebf51592b4f3eb34dd0a3c659734
--- /dev/null
+++ b/text
@@ -0,0 +1,89 @@
+-0.7 - 0.4 #b24625
+0.4 - 0.7 #e58b48
+0.7 - 1 #eeb177
+1 - 2.9 #fbe9bd
+
+
+____
+var an = $datapoint.anomalie
+
+var bgColor = When(an<0.4,"#fbe9bd",an>=0.4 && an<0.7, "#eeb177", an>=0.7 && an<1,"#e58b48", an>=1,"#eb5c31", "#ffffff")
+
+return {
+ textColor: '#222222',
+ backgroundColor: bgColor,
+ separatorColor: bgColor,
+ selectionColor: '',
+ selectionTextColor: '#000000',
+ // attributes: {
+ // attribute1: '',
+ // attribute2: ''
+ // }
+}
+____
+<p style="font-size:2px"> </p>
+
+<p><em>{continent} </em>- <strong>{name}</strong>: {anomalie} °C</p>
+
+<p style="font-size:2px"> </p>
+____
+<p><em>Kontinent</em>- <strong>Land</strong>: Anomalie</p>
+___
+Hintergrundfarbe: Anomalieabstufung (siehe Kartenlegende "Anomalie")
+____
+
+Für die Anomalien wurden nur Stationen berücksichtigt, die Werte für die beiden Zeiträume geben.
+
+https://scilogs.spektrum.de/klimalounge/deutschland-ist-schon-2c-waermer-geworden/
+http://www.bom.gov.au/climate/change/index.shtml#tabs=Tracker&tracker=timeseries
+
+https://scilogs.spektrum.de/klimalounge/verwirrspiel-um-die-absolute-globale-mitteltemperatur/
++/- 0.5 Grad
+Daher zielt das Pariser Klimaabkommen auch nicht darauf ab, die globale Temperatur auf (sagen wir) maximal 16 °C zu begrenzen, sondern darauf, die Erwärmung deutlich unter 2 Grad zu halten: Es gibt eine 2-Grad-Grenze und keine 16-Grad-Grenze.
+
+https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/1999RG900002
+
+Basisperioden
+
+
+https://www.umweltbundesamt.de/daten/klima/trends-der-lufttemperatur#mildere-herbste-und-winter
+spannende Excel Tabelle
+
+
+CLINO-Periode von 1961 bis 1990.
+
+
+___________________________________________________________
+Langjähriger globaler Temperaturvergleich der festen Erde (1961-1990 und 1991-2018)
+
+
+
+Das folgende Geo-Dashboard zeigt die Temperaturanomalien zwischen 1961-1990 und 1991-2018 der Landoberfläche dar. Die Basisperiode 1961-1990 ist eine allgemeingebräuchlich Referenzperiode und ist auch bekannt als CLINO-Periode[1].
+
+Das Dashboard zeigt im linken Teil des Boards eine Liste aller Länder mit deren Kontinentzugehörigkeit und deren Temperaturanomalie der beiden Basisperioden (1961-1990 und 1991-2018).
+In der Mitte des Dashboard befinden sich drei Karten (Tab-gesteuert), die erste Karte zeigt die Temperaturanomalie als Chloroplethenkarte, die beiden weiteren Karten das langjährige Temperaturmittel von 1961-1990 und 1991-2018 dar. Mit einem Klick auf ein Land öffnet sich ein Popup, welches unteranderem den Temperaturverlauf des Landes beinhaltet.
+Auf der rechten Seite ist der Statistikteil zu sehen. Die jeweiligen Statistiken beziehen sich dabei auf den Ausschnitt der Karte und fasst alle zusehenden Länder zusammen. Die Länder, welche in die Statistik einfließen, werden in der Liste auf der rechten Seite angezeigt.
+
+
+Daten:
+
+Die jährlichen Mittelwerte der Länder wurden aus den globalen Messstationen durch eigene Berechnungen aus dem opendata hubs des Deutschen Wetterdienstes (DWD) berechnet (siehe [2]). Das Skript zur Berechnung der Jahresmittelwerte der Länder ist ein einfaches Skript welches die Stationsnummern mit den Ländern gruppiert und daraus die Mittelwerte berechnet. Da die Datensätze des DWDs oft Lücken aufweisen, bzw. aus diversen Gründen Stationen weggfallen (zum Beispiel politische Konflikte), können die Daten von den offiziellen Werten abweichen. Dennoch korrilieren die Werte gut mit dem allgemein wissenschaftlichen Trend zusammen (vgl. [3] und [4]). Die Temperaturanomalie (1961-1990 und 1991-2018) entspricht hier für Deutschland einen Anstieg von 0.94 C°, der Wert des Umweltbundesamtes zeigt 1.1, wobei 2019 und 2020 eines der heißesten Jahre waren [4] und kachelmannwetter zeigt einen Anstieg des gleichen Bezugzeitraums von 1.05 auf. Es ist zudem bekannt, dass die Mitteltemperatur 1961-1990
+
+
+[1] https://www.spektrum.de/lexikon/geographie/clino/1403 -
+[2] https://opendata.dwd.de/climate_environment/CDC/observations_global/CLIMAT/monthly/qc/air_temperature_mean/historical/
+[3] https://wetterkanal.kachelmannwetter.com/temperaturentwicklung-in-deutschland-seit-1881/
+[4] https://www.umweltbundesamt.de/daten/klima/trends-der-lufttemperatur#mildere-herbste-und-winter
+
+
+Datenquellen:
+https://opendata.dwd.de/climate_environment/CDC/observations_global/CLIMAT/monthly/qc/air_temperature_mean/historical/
+Deutscher Wetterdienst (DWD)
+Länderpolygone: geopandas - naturalearth_lowres
+
+
+Das Projekt entstand an der Beuth-Hochschule für Technik Berlin im Rahmen des Master-Studiums "Geoinformation" im Fach "Interaktive Geomediengestaltung"
+
+
+Erstellt von: Peter Morstein
+Stand: 02.07.2021