Introduction
Climate at a specific location is influenced by large-scale as well as local
factors. For a detailed understanding of the climate system atmospheric
conditions have to be observed over sufficiently long time. The Global
Climate Observing System (GCOS) was introduced to support and ensure
systematic observation at a global scale . GCOS defined a
list of variables to be observed with priority so-called Essential
Climate Variables, ECVs, see and defined so-called Climate
Monitoring Principles. Atmospheric near-surface variables are typically
observed by networks of surface stations operated by national weather
services. Taken together, these form the GCOS surface network (GSN). For the
reliable description of climate, i.e. the statistical features of various
atmospheric variables and the assessment of their long-term variability and
change, high-quality meteorological observations have to be performed over
sufficiently long time and non-climatic influences on these time series have
to be understood. It is well known that over such periods the observation
networks and procedures are affected by various modifications. One important
example is the transition from manual to automatic observation techniques.
The GCOS Climate Monitoring Principles suggest that traditional and new
sensors should be operated with a sufficiently long temporal overlap.
Length of time series, time range in which
station has parallel measurements (for conventional measurements: three times
per day), location and height (in meters) of DWD's climate reference
stations. The time range of parallel measurements refers to the interval when
AMDA stations were in use (see text). The data from these intervals have been
used for the analysis in this study, including data until end of June 2016.
Aachen was relocated and continued as Aachen-Orsbach in 2011.
WMO
Station name
Since
Parallel measurem.
Latitude
Long..
Height
Characteristic
ID
manual and AMDA
of the region
10 015
Helgoland
1881
2006–2013
54.1750
7.8920
4
North Sea, German Bight
10 035
Schleswig
1947
from 2006
54.5275
9.5486
43
Coastal lowland, maritime
10 147
Hamburg-Fuhlsbüttel
1891
2008–2014
53.6332
9.9881
11
Anthrop. influenced, maritime lowland
10 379
Potsdam
1893
from 2008
52.3813
13.0622
81
Anthrop. influenced continental lowland
10 393
Lindenberg
1906
from 2008
52.2085
14.1180
98
Continental lowland
10 453
Brocken
1881
from 2008
51.7986
10.6183
1134
Harz Mountains, marginally anthrop. infl.
10 499
Görlitz
1881
2008–2014
51.1622
14.9506
238
Conti. forelands of low mountain landsc.
10 501
Aachen
1891
2008–2011
50.7827
6.0941
202
Hilly countryside with strong…
10 505
Aachen-Orsbach
2011
2011–2014
50.7982
6.0244
231
…influence of westerlies
10 578
Fichtelberg
1890
2008–2014
50.4283
12.9535
1213
Erzgebirge highlands
10 637
Frankfurt (airport)
1949
from 2008
50.0259
8.5213
100
Anthropogenically influenced lowland
10 929
Konstanz
1941
2007–2012
47.6774
9.1901
443
Lake Constance basin
10 962
Hohenpeißenberg
1781
from 2008
47.8009
11.0109
977
Alpine foreland
Such overlapping (“parallel”) measurements have therefore been performed in
several countries. Especially in case of temperature, interest in an analysis
of these series is also motivated by the aim of better understanding the
uncertainty of global temperature datasets and their trends
. To allow studying systematic biases at a global scale, the
Parallel Observations Science Team (POST) of the International Surface
Temperature Initiative ISTI, see compiles a database
with parallel measurements.
Different settings are used for such parallel measurements, e.g. with focus
on the screens or the sensors. In the Netherlands the same type of sensor was
used in a comparison of nine thermometer screens over 6 years
. With this dataset, transfer functions were
derived to relate the measurements from different thermometer screens to each
other. compared temperature differences at screened and
unscreened sites at Kremsmünster (Austria) in order to assess biases in
earlier measurements in the Greater Alpine Region. use
parallel measurements from Switzerland to propose a correction method for
sub-daily temperature data. They also discuss several former studies that
intercompare different types of ventilation or sensor-shielding.
used a twenty year time series of parallel temperature
measurements from one site to study the impact of the replacement of
liquid-in-glass thermometers to electrical thermometers in the US.
DWD operates a network of so-called “climate reference stations” (CRS).
Currently manual and automatic observations are performed in parallel in
order to allow understanding the impact of changes in the instrumentation in
Germany. Here we provide a summary of the status of this network and
conclusions on the homogeneity of temperature series. For a general summary
of DWD's contribution to climate observation see .
History and status of DWD's climate reference stations
After Germany's reunification, the observation network was successively
modernized and automatized. In a first step, seven “reference stations”
were introduced to allow the comparison of conventional and automatic
measurements. Since 2008, 12 stations are operated as climate reference
stations (Table ). Parallel measurements at these stations
officially started 1 May 2008. At these stations observations are
performed by observers and automatic instruments. The stations are located in
different climatic regions of Germany (Fig. ,
Table ). At these locations observations have already been
performed since several decades, in most cases already since the end of the
19th century.
Conventional and automatic measurements taken at
climate reference stations and instruments used for these measurements.
Observation times for these parameters for traditional observations are: (I):
06:30 UTC, (II): 13:30 UTC and (III): 20:30 UTC.
Parameter
Instrument (traditional)
Instrument (automatic)
Air pressure
Hg station barometer
PTB 220, PTB 330, AIR-DB
Dry temperature 2 m
Mercury-in-glass thermometer (Hg)
Pt 100
Wet temperature 2 m
Mercury-in-glass thermometer (Hg)
–
Maximum temperature 2 m
Mercury-in-glass thermometer (Hg)
Pt 100
Minimum temperature 2 m
Glass thermometer (alcohol)
Pt 100
Minimum temperature 0.05 m
Glass thermometer (alcohol)
Pt 100
Precipitation amount
Hellmann, Pluviograph
PLUVIO, NG200, Joss-Tognini
Sunshine duration
Campell-Stokes
SCAPP, SONIEe
Relative humidity
Thermo-hygrograph
HMP45D, EE33, MP100, MP300
Soil temperature
Mercury-in-glass thermometer (Hg)
Pt 100
Snow depth (total and fresh)
Yardstick
SR50-G1, SHM30
Observing time of temperature extremes, soil
temperature, precipitation and snow height.
Parameter
Observing
time
Maximum and minimum temperature (2 m)
III
Minimum temperature (5 cm)
I
Soil temperature at 0.05, 0.1, 0.2, 0.5 m
I, II, III
Soil temperature at 1 m depth
II
Precipitation amount (Hellmann)
I
Snow depth (total and fresh)
I
Instruments and times for interpretation of recordings.
Instrument
Reading
Parameter
time
Thermo-hygrograph
I, II, III
Relative humidity (%)
at observation times
Pluviograph
II
Hourly sums of previous
day (1/10 mm)
Campell–Stokes
I
Hourly sums (minutes)
of previous day based
on 6 min intervals
Location of DWD's climate reference stations (CRS).
•: manual observations will be performed until 2018, ▪:
manual observations have been performed until at least 2014 (Helgoland:
2013), ×: Konstanz was CRS until 2012, Fichtelberg until 2014.
•+▪: stations will be operated as CRS with parallel
observations of automatic sensors (“type II”) after the end of the manual
observations.
![]()
Currently 10 stations are operated as climate reference stations (Konstanz
was transformed to a standard station in 2012 because of difficulties to
ensure a representative surrounding. Fichtelberg was CRS until end of 2013,
see Fig. ). In the current configuration, the CRSs are manned with
observers around the clock. The automatic measurements are performed
equivalently to other stations in DWD's main observing network. In addition,
manual observations are performed in parallel at the three traditional
observing times (so called “Mannheimer Stunden”): 06:30, 13:30 and
20:30 UTC (in the following: observing times I, II, III). These include
readings of instantaneous and extreme values (see
Table ) and the interpretation of recordings (see
Table ). Observed parameters are: air pressure, air
temperature, humidity, precipitation, sunshine duration, snow height
(Table ) and soil temperatures at different depths
(Table ). The instruments used at these stations are
listed in Table . The manual readings are transferred into the
central database of DWD . The time periods in
Table refer to the interval when so-called AMDA stations
(= Automatische Meteorologische Datenerfassungs-Anlage) were
used . These
synoptic-climatological stations also perform the first step of the data
quality control (QC) procedures. After transfer of the data into the central
database, further QC algorithms are applied.
Five CRSs will be operated in the current mode until 2018 (Schleswig,
Lindenberg, Brocken, Frankfurt, Hohenpeißenberg). Ten years of parallel
measurements will then be available for these stations (“type I” stations).
After that period, they will be converted to automatic CRSs (“type II”).
The five additional existing stations will be operated as automatic CRSs
(“type II”) from now onward. At these automatic stations no manual
observations will be performed. When new automatic instruments are introduced
into DWD's network, the previous and new instruments will be operated in
parallel at the CRSs. The intended duration for such parallel measurements is
2 to 5 years. From 2019 onward, all CRSs will be of type II, i.e. automatic
CRSs.
A first comparison of the manually measured data with the automatically
recorded data was performed by . In that study the data of
the climate reference stations available until end of 2010 were used together
with data from five additional stations where parallel measurements were
already performed in earlier years. This analysis led to the following
conclusions: The change of the observing technique resulted in only small
differences for air pressure and temperature, i.e. no inhomogeneities were
caused. Precipitation is slightly higher for traditional measurements, but
the mean differences are in the range of uncertainty of the manual readings.
For humidity, values > 95 % were measured more often with the
traditional technique (Aßmann-Psychrometer). For sunshine duration the
traditional technique typically resulted in higher values. For some stations
the difference in the annual sunshine duration was greater than 100 h for
selected years. The traditional measurement technique (“Campbell–Stokes”)
is based on a burned trace in a paper card whereas the automatic observations
are based on radiation measurements.
With the introduction of automatic stations, a new procedure for the
calculation of the daily mean temperature has been introduced. Traditionally,
the daily means have been calculated based on three daily state observations
according to the following formula:
Tmean=TI+TII+2⋅TIII4,
where TI is the temperature observed at 06:30 UTC,
TII at 13:30 UTC and TIII at 20:30 UTC. This
formula and giving double weight to the third date was suggested by
(, p. 102) and aims at providing
the best estimate for the daily mean for this combination of observation
times. Since April 2001 daily mean values are calculated based on the hourly
observations. also compared the differences between these
approaches. In average, the results based on hourly values were 0.1 K lower.
The spread of the differences between daily means from hourly and three times
daily measurements was found to be wider than the one that arises from the
comparison of traditional and automatic measurements.
Comparison of parallel temperature measurements
The temperature measurements from DWD's station network are regularly used to
provide information on climate change in Germany e.g..
It is therefore important to understand if there are any artificial breaks in
these time series, e.g. caused by changes in the observing technique. Here we
use the parallel measurements to analyse the impacts of changes of the
sensors, screen types and data processing.
Temperature measurements
Traditionally, temperature was measured with a mercury thermometer. Minimum
temperature was measured with alcohol thermometers. At DWD's automatic
stations, a platinum resistance thermometer (“Pt 100”) is used. The
tolerance class of these resistance thermometers is 13 of Class B
(i.e. ±0.1+0.00167⋅|T| with T in ∘C) according to the
IEC 60751 standard
. Details of the measurement accuracy are described in the
appendix of . The electrical thermometers are calibrated
every 60 months, the liquid thermometers every 120 months.
The traditional thermometers are operated in a Stevenson screen, except for
the station Brocken, were a screen of type “Gießener Hütte” is
used. At standard stations, the automatic thermometers are operated in
lamellar shelters “LAM 630”, a multi-plate screen with artificial
ventilation. At the mountain stations Brocken and Fichtelberg the automatic
thermometers are placed in a different screen type: a Stevenson screen is
used at Fichtelberg, a “Gießener Hütte” at Brocken. A Stevenson
screen was also used for the automatic measurements at airport station
Frankfurt until 22 October 2014. From 22 October 2014 onwards, a LAM 630 was
used at that station for the automatic measurements. Generally, the LAM 630 is
equipped with two identical sensors for temperature and two for humidity. One
of each is used operationally, the second one for quality assurance.
Manual state measurements are performed for the traditional observation
times: 06:30, 13:30 and 20:30 UTC. The electrical thermometers measure
continuously and data are stored in the data base every ten minutes. The
value that is encoded and transferred for a specific observation time (e.g.
20:30 UTC) is taken 10 min before that nominal time and is the average of
an 1 min interval, i.e. in case of 20:30 it is the mean value of 20:19 to
20:20 UTC. In this analysis daily minima and maxima refer to the nominal
interval
from 20:30 UTC of the previous day to 20:30 UTC.
In the following, differences between automatic and manual measurements are
analysed, i.e. positive differences indicate that the automatic measurements
provided higher values. Differences higher than 2 K were excluded from the
analyses, as these are obviously incorrect measurements.
Comparison of air temperature at <inline-formula><mml:math display="inline"><mml:mn mathvariant="normal">2</mml:mn></mml:math></inline-formula> m height
The comparison is based on the three daily state observations
at traditional
observation times. Figure (top) shows the results for
Frankfurt: The mean of the differences is 0.03 K with a standard deviation
of 0.2 K. For most dates, the differences are close to 0 K and only for a
small number of cases (less than 3 %) the differences are larger than ±0.5 K (see histogram in Fig. , top right). Similar to
Frankfurt, the mean of the differences is small for the majority of stations,
as show in the second column of Table . With -0.16 K,
station Schleswig shows the largest mean difference (Fig. ,
bottom). The standard deviation (0.28 K) is also larger than for
Frankfurt. The average of the mean difference for all stations is -0.02 K,
i.e. the automatic measurements are on average slightly lower than the
traditional measurements (Table , column 2).
Comparison of automatic versus traditional
temperature measurements: The black line (left) and the histogram (right)
show the difference of the automatic minus traditional measurements in K
based on all traditional observation times (I, II, III). Obvious outliers
were removed from the analysis and are also not considered in the mean and
standard deviation. The blue line is three times the standard deviation. The
mean difference is shown in red and the moving average in green (based on 150
values). The top figure shows the results for station Frankfurt, the bottom
for Schleswig.
![]()
Summary of results for all climate reference
stations: Second and third column: Mean difference (automatic minus manual)
and standard deviation (SD) based on the three traditional observation times.
Forth to seventh column: Comparison of different approaches for calculating
the daily mean: (4, 5): based on the same formula for three daily automatic
and manual observations, (6, 7): based on the traditional and new formula,
i.e. based on 24 hourly automatic vs. three manual observations. Eighth to
eleventh column: Mean differences for daily extremes (8, 9): maximum, (10,
11): minimum. (12, 13): Mean difference of daily values based on daily
minimum and maximum.
Difference between new and traditional technique/approach
For tradit.
Daily mean values
Daily extreme temperature
Daily mean values
obs. times
Same formula
Diff. formula
Maximum
Minimum
based on min/max
Column: 1
2
3
4
5
6
7
8
9
10
11
12
13
Station
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Aachen
0.03
0.25
0.03
0.11
-0.04
0.49
-0.08
0.3
-0.03
0.2
-0.06
0.19
Aachen-Orsbach
-0.04
0.18
-0.05
0.12
-0.08
0.53
-0.12
0.22
-0.2
0.26
-0.16
0.16
Brocken
-0.1
0.2
-0.1
0.12
-0.12
0.53
-0.15
0.28
-0.08
0.24
-0.12
0.21
Fichtelberg
0.02
0.14
0.03
0.08
0.04
0.49
-0.05
0.23
0.03
0.32
-0.01
0.21
Frankfurt
0.03
0.2
0.03
0.12
-0.17
0.57
-0.07
0.27
-0.07
0.23
-0.07
0.22
Görlitz
0.04
0.19
0.03
0.12
-0.02
0.54
0.06
0.26
0.02
0.41
0.03
0.28
Hamburg-Fuhlsbüttel
0.05
0.23
0.05
0.15
-0.02
0.56
0.02
0.3
-0.08
0.33
-0.04
0.29
Helgoland
-0.03
0.18
-0.04
0.12
-0.06
0.32
0.04
0.25
-0.03
0.3
0
0.21
Hohenpeißenberg
-0.08
0.28
-0.08
0.18
-0.08
0.62
0.08
0.39
-0.21
0.33
-0.07
0.29
Konstanz
-0.12
0.28
-0.12
0.14
-0.24
0.5
-0.18
0.33
-0.13
0.25
-0.16
0.26
Lindenberg
0.02
0.16
0.02
0.1
-0.05
0.5
0.01
0.22
-0.14
0.32
-0.07
0.2
Potsdam
0.1
0.22
0.09
0.13
0.04
0.49
0.13
0.21
0.06
0.25
0.1
0.15
Schleswig
-0.16
0.28
-0.18
0.17
-0.16
0.49
-0.12
0.45
-0.12
0.33
-0.14
0.34
All stations
-0.02
0.23
-0.03
0.16
-0.08
0.52
-0.03
0.32
-0.08
0.31
-0.05
0.26
Comparison of averaging procedures for daily temperature
Breaks in the time series can not only result from changes in the sensors
itself, but also from changes in the data processing. Similar to the
comparison in the previous section, column 4 in Table
shows the differences in the daily mean temperature when automatic
observations are used instead of manual observations, but without changing
the averaging procedure, i.e. for both cases, Eq. () has
been applied. The analysis is therefore based on the same observations as the
one in the previous section (column 2) but daily means are calculated before
comparing the data (i.e. a different averaging procedure is applied to the
same data as in the previous section: double weight is given to the third
observation, see Eq. ). This only leads to small differences
in the results. Figure (left) shows the histogram of the
differences: The mean difference between the daily values for all days and
all stations is -0.03 K with a standard deviation of 0.16 K.
Comparison of different methods for calculating
the daily mean temperature. Left: Automatic and traditional observations are
used to calculate the daily mean temperature based on the three traditional
observation times. The histogram is based on the differences (in K; automatic
minus traditional) of the daily mean values for all stations and all
available days. Right: The histogram is based on the differences of the daily
mean values of the new and the traditional procedure, i.e. the arithmetic
average of all hourly values from the automatic measurements versus the daily
mean values calculated with the traditional formula based on three manual
observations (Eq. ). Outliers are removed.
![]()
The new approach for calculating daily mean temperature is the arithmetic
mean of all automatically taken hourly values. To illustrate the impact of
changing this procedure, column 6 in Table shows the mean
difference between the daily means of the new and the traditional approach.
For that comparison the arithmetic mean has been applied to the hourly
automatic observations and Eq. () has been applied to the
manually observed data. Figure (right) shows the histogram
of the difference between both approaches for all days and stations.
From the histogram and the standard deviation (0.52 K for all stations) it
is obvious that the spread of differences is larger than the spread that is
caused by the change of the sensors alone (standard deviation: 0.16 K). The
mean of the differences for all stations is -0.08 K. The change of the
formula leads to larger differences than changing the observing technique
(-0.03 K). However, the mean differences are still rather small for all
stations. Again, the largest bias occurs for station Schleswig (-0.16 K).
These results also show that the formula of Kämtz (Eq. ) is
a good approach for estimating the daily mean temperature.
For both types of changes (sensors and averaging procedure) the overall mean
bias is slightly negative, i.e. the values based on the new approach are
slightly lower. This is important to note, as the question has been raised if
the introduction of new methodologies could have contributed to the observed
long-term increase in temperature. This analysis shows that there is no
artificial increase in temperature for the CRSs of DWD and that the effects
are rather small compared to the climate change signal. The increase of the
average annual temperature for Germany from 1881 to 2015 is 1.4 K
linear trend, see.
Histograms of differences between automatic and
manually observed daily extremes (in K, left: maximum, right: minimum). All
dates and stations are included. The daily extremes refer to the 24 h
interval from 20:30 to 20:30 UTC. Obvious outliers are
removed.
![]()
Comparison of daily extreme temperatures
Daily extremes of temperature (minimum and maximum) are also measured at the
CRSs with traditional and automatic instruments. The liquid thermometers are
read at 20:30 UTC. This interval is therefore also used to provide the daily
extremes for the automatic thermometers in this analysis.
Figure shows the histograms for the differences in observed
extremes (maximum and minimum) for all stations. On average, the automatic
sensors measure slightly lower values for the maximum (-0.03 K) and the
minimum (-0.08 K). The results for the individual stations are shown in
Table (column 8 to 11).
Differences of daily temperature maximum in K as
time series (left) and histogram (right) for station Potsdam: automatic minus
manual measurements (black line), triple standard deviation (blue line), mean
(red line), moving average (green line; 50 days). Outliers are removed.
![]()
Histogram of differences in daily means,
when daily means are calculated based on daily maxima and minima. The
histogram shows the differences when these means are calculated based on
automatically and manually observed daily extremes. All dates and stations
are included. Obvious outliers are removed.
![]()
Figure shows the time series of the differences in the
observed maximum temperature for Potsdam. A seasonal cycle with positive
differences in summer is visible, i.e. the automatic thermometer measures
higher values. A similar seasonal cycle is visible for several other climate
reference stations, specifically those where a LAM 630 shelter is used. It is
not visible in the time series for Brocken, Fichtelberg and Frankfurt, where
either Stevenson screens or a “Gießener Hütte” is used.
Figures and show the average
annual cycle of the differences based on monthly aggregated results. In
contrast to Potsdam (Fig. ), no distinct cycle is
visible for Fichtelberg (Fig. ). It has already been
noted in other studies that the LAM 630 shelter was warmer than some other
screens in case of high solar radiation and low wind speed e.g.for
desert conditions in Algeria.
Further internal investigations at DWD have led to the conclusion that this
effect is at least partly caused by radiation effects and is influenced by
the positioning of the sensor within the screen. Additional internal
guidelines for the placement of the sensor have been defined
. According to the guidelines, the operational sensor
should be placed at the North-Eastern position within the LAM 630, to reduce
the radiation effect close to sunset. For Potsdam the placement has been
changed in March 2016. Figure shows that the bias does not
occur any more in summer 2016.
For the minimum temperature, a seasonal cycle is only visible for two
stations (Schleswig, Potsdam). An explanation for this has not yet been
found and further investigations are necessary.
Boxplots of the differences of temperature
maxima (automatic minus conventional measurements) for Potsdam for each
month. At Potsdam, the automatic temperature sensors are operated in a LAM
630 shelter. Numbers above each boxplot are the p value of a t test which
was performed to check, if the mean of the automatic time series and the mean
of the conventional time series is equal. For this plot, only data until
14 March 2016 have been included. After that date, the positioning of the
sensors was modified (see text).
![]()
Boxplots of the differences of
temperature maxima (automatic minus conventional measurements) for
Fichtelberg for each month (as Fig. ). At Fichtelberg,
the automatic temperature sensors are operated in a Stevenson screen.
![]()
In some countries the daily mean temperature is calculated based on observed
maximum and minimum temperature: Tmean=(Tmax+Tmin)/ 2 see discussion in. It is therefore also
of interest to see the impact of the changes in the sensors for this
approach. Figure and Table
(columns 12 and 13) show the differences when this approach is applied to the
manually and automatically observed extremes. Consistent to the previous
results, the results show that on average, the automatic system results in
slightly lower values (-0.05 K).