Method
Behind this package/website is a PostGreSQL-database. This database is build and updated with the same package, as for downloading the data. The only difference is, that the given connection in the secretSettings file needs to have write permissions for the database. Therefor everyone can look into the code to find out exactly how the database creation works. But as an overview this page will give basic explanations of the processes behind it.
The timeseries for Temperature, Evapotranspiration and Precipitation are going through a 3-4 step process.The result of every process is saved and can get downloaded, with the corresponding abbreviation:
downloading the raw data –> “raw”
quality check the data –> “qc”
fillup the data –> “filled”
richter correct the values –> “corr”
In the following chapters the processes will get explained furthermore.
downloading the data
The raw data is downloaded from the DWD-CDC server. The timeseries are downloaded and saved from the 1.1.1994 on. If there are historical datas available for a measurement, they are preferred to recent values, because they are already quality checked a bit. The Temperature (T) and potential Evapotranspiration (ET) is downloaded on daily resolution. Where as the Precipitation (N) is downloaded as 10 minute and daily values, but only the 10 minute values are the basis for the downloads.
Table 1: The downloaded raw data, resolution and their source
parameter |
resolution |
source |
|---|---|---|
Temperature |
daily |
|
pot. Evapotranspiration |
daily |
|
Precipitation |
10 minutes |
|
Precipitation |
daily |
|
For computation improvements the downloaded files and their modification time is saved to the database, to be able to only download the updated data.
quality check
To quality check the data it is very dependend on which parameter is treated. Therefor this chapter is grouped into subchapters.
Although every quality check can get computed for different periods:
on all of the data by using e.g.
station.StationT(3).quality_check()only a specified period, with e.g.
station.StationN(3).quality_check(period=("2010-01-01", "2020-12-31"))the last imported period, with e.g.
station.StationET(3).last_imp_quality_check()
Temperature and Evapotranspiration
For T and ET quality check the data is compared to the 5 nearest neighbors data. The data of every neighboor station is regionalised, based on the DWD grids (see chapter regionalisation), to the examined station. Then the mean value of those 5 values is taken to compare to the station data. If this mean value is too far away from the own value, then the measurement point is deleted.
Table 2: Limits for the quality check of the T and ET measurements, when compared with neighbor stations
parameter |
compare equation |
lower limit |
upper limit |
|---|---|---|---|
Temperature |
\(\Delta T = T_{Stat} - \overline{T}_{neighbors}\) |
\(\Delta T < -5°C\) |
\(\Delta T > 5°C\) |
pot. Evapotranspiration |
\(\delta ET = \dfrac{ET_{Stat}}{\overline{ET}_{neighbors}}\) |
\(\begin{cases}\delta ET< 25\% \\ ET_{Stat}< 2 \frac{mm}{d}\end{cases}\) |
\(\begin{cases}\delta ET> 200\% \\ ET_{Stat}> 3 \frac{mm}{d}\end{cases}\) |
For the evapotranspiration there are two rules that need to be fulfilled to be unplausible. One relative and one nominal. This is because, low measurement values tend to have high relative differences and would then get deleted too often.
Precipitation
The precipitation measurements must pass through multiple quality checks.
daily sum is zero
As some precipitation station (e.g. Feldberg station) have measurements of 0mm in the 10 minutes dataset even though the daily dataset has measurements. This is especially true for the measurement points in 20th and early 21th century. This is probably the result of new measurement equipment to measure the 10 minutes values, but without a good quality control. back then the daily values are often measured with other equipments and have a better quality check they are going through, so that the values are more reliable.
To filter those wrongly measurements of 0 mm out of the data, the data is compared to the daily values from the DWD at the same location. For this reason the daily measurements are first filled up (see next chapter).
The 10 minutes measurements get aggregated to daily values. If this daily sum is 0 mm, even though the daily data from the DWD is not 0, all the 10 minutes measurements of that day are deleted.
This check is the only reason why the daily precipitation values were downloaded in the first place.
consecutive equal values
Sometimes there are several consecutive 10 minutes values that are exactly the same. As the accuracy of the measurement is 0.01 mm, this is very improbable to be a real measurement and is more likely the result of splitting e.g. an hourly value into 6 values.
It is assumed, that filling the measurements up with values from the neighbor stations is more accurate than this dissemination. Therefor 3 consecutive same measurements are deleted, if their “Qualitätsnorm” from the DWD is not 3 (meaning that the measurements didn’t get a good quality control from the DWD).
Filling of hols
To have complete timeseries, the holes in the quality checked timeseries is filled with data from the neighbor stations. This is done by regionalising the neighbors measurements value to the station that is filled. Starting with the nearest neighbor station all available stations are taken until the timeserie is completely filled.
For the reginalisation, the muli-annual values for every station for the climate period of 1991-2020 are computed from the corresponding DWD grid.
Table 3: The raster grids that are the basis for the regionalisation
parameter |
source |
|---|---|
Precipitation |
DWD Climate Data Center (CDC): |
Temperature |
DWD Climate Data Center (CDC): |
potential Evapotranspiration |
DWD Climate Data Center (CDC): |
Then to get a regionalisation factor the multi-annual values of both stations are compared. For T and ET only the yearly mean is taken into account. For the precipitation one winter(october-march) and one summer (april-september) factor is computed. The following equation explain the calculation of the filling values for the different parameters, based on their multi-annual mean(ma).
\(T_{fillup} = T_{neighbor} + (T_{station,ma}-T_{neighbor,ma})\)
\(ET_{fillup} = ET_{neighbor} * \dfrac{ET_{station,ma}}{ET_{neighbor,ma}}\)
\(N_{fillup} = \begin{cases} N_{neighbor} * \dfrac{N_{station,ma,winter}}{N_{neighbor,ma,winter}} \space if\space month\in[4:9]\\ N_{neighbor} * \dfrac{N_{station,ma,summer}}{N_{neighbor,ma,summer}} \space if\space month\notin[4:9] \end{cases}\)
For the precipitation values the 10 minutes values are furthermore adjusted to the daily measurements. Therefor the daily sum is computed. Then the quotient with the daily measurement is calculated and multiplied to every 10 minute measurement. So the difference to the daily measurement is added relatively to the measured value. In the end the filled 10 minutes precipitation values summ up to the same daily values as the daily values from the DWD.
Richter correction
This step is only done for the 10 minutes precipitation values. Here the filled precipitation values, get corrected like defined in Richter (1995).
First of all, the horizon angle (“Horizontabschirmung”) is calculated from a DGM20 (aggregated from DGM5) and if the DGM20 was out of bound also from a DGM8. The DGM80 is bigger than the german border and therefor for stations around the border this is gives better results than the DGM20 which is only for the german territory. Therefore the rasters are sampled for their values on one single line of 75km, starting from the station. Then the angle to every point from the station is calculated. The Point with the biggest angle is taken as horizon angle for this line. This step is repeated for several lines ranging from north to south in 3° steps. Afterwards the Richter horizon angle is computed as:
\(H’=0,15*H_{S-SW} + 0,35*H_{SW-W} +0,35*H_{W-NW} +0, 15*H_{NW-N}\)
With this horizon angle the Richter exposition class is defined for every station.
Afterwards the daily correction is calculated with the following table and equation, based on the filled daily temperature at the station.
\(\Delta N = b * N^E\)
Table 4: The Richter correction coeeficients. (Richter (1995) S. 67)
precipitation typ |
temperature |
E |
b |
b |
b |
b |
|---|---|---|---|---|---|---|
precip_sommer |
>= 3 °C |
0,38 |
0,345 |
0,31 |
0,28 |
0,245 |
precip_winter |
>= 3 °C |
0,46 |
0,34 |
0,28 |
0,24 |
0,19 |
mix |
-0,7 °C < T < 3 °C |
0,55 |
0,535 |
0,39 |
0,305 |
0,185 |
snow |
<= -0,7°C |
0,82 |
0,72 |
0,51 |
0,33 |
0,21 |
The daily correction (\(\Delta N\)) is then distributed to every 10 minute measurement where precipitation was measured. So the daily correction is applied as a block to the 10 minutes values. This results in relatively high corrections, when there was only little precipitation and relatively low corrections when the measured precipitation was high. As the systematic error is mostly due to wind and has therefor more effect, when there is low precipitation, this approach is better than adding it relatively to the measurement.
sources
Richter, D. 1995. Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Meßfehlers des Hellmann-Niederschlagsmessers. Offenbach am Main: Selbstverl. des Dt. Wetterdienstes.
GeoBasis-DE: Bundesamt für Kartographie und Geodäsie. 2016. Digitales Geländemodell Gitterweite 5 m. DGM5. Produktstand: 2016.