Adinrichs et al.
The different temporal resolutions and periods are:
ss for BNSCatm, monthly and climatological monthly
for 1950-2015
a for BNSChydr, monthly, annual and decadal monthly
for 1873-2015.
Atmospheric BNSC
Sources of Data
The atmospheric part of this climatology (BNSCatm) is produced
with marine in situ observations originating from the Marine
Data Center of the DWD, which maintains an extensive
climatological archive of national and international weather
data gathered by vessels and buoys (see https://www.dwd.de/
EN/ourservices/marine_data_center/maritimesdatenzentrum.
html, last access May 14th 2019). The data collection of this
archive started in the middle of the nineteenth century and is
angoing. Most of these observations come from ships and buoys
and are recorded using a variety of methods, including manual
observations at specific times as well as automated transmission
of measurements that were taken with a high frequency, which
is described in more detail by Schade et al. (2013) and Sadikni
zt al. (2018). All data are checked using the DWD high quality
control (HQC) procedure to ensure the maximum degree of
reliability before they are added to the archive. These procedures
do not only check the individual observation but also implement
checks on a sequence of observations from a specific observation
platform in order to identify data errors of location and their
time series consistency, as is described by Sadikni et al. (2018) in
their section 2.a. Even though parts of the data that are included
in this archive are a subset of ICOADS, checking all data in the
same way with the same method was the main argument to
use it for this study. Only data with flags “C” (climatologically
right), “D” (analysis consistent), “E” (temporally consistent),
“F” (internally consistent), “G” (spatially consistent, or “H”
(manually consistent) are used in this study. This leads to about
31 million quality-controlled sets of meteorological in situ
observations in the BNSC region for the period January 1950 to
December 2015. Due to a relatively small amount of available
data in the years before 1950, the period starts later than for
the hydrographical part. The data used here can be considered
as atmospheric observations near sea level at meteorological
standard heights, as is sea level for air pressure and 2 m for the
air and dew point temperatures.
Data Processing
For the calculation of the climatology, it has to be taken into
account, that the observational data are not equally distributed
in space or time. Therefore, measures have been taken to
homogenize the data. Since the creation of the atmospheric
part of the KNSC and the atmospheric BNSC data product do
not differ substantially, the reader is referred to the detailed
description in Sadikni et al. (2018).
Only observations at standard observation times (0, 6, 12, and
18 UTC) are used, like it was done for the KNSC climatology
in order to reduce possible sampling biases caused by the
high frequency of observations by automated measurements.
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Baltic and North Seas Climatology
The selected quality controlled observations are checked for
duplicates, which is an additional test compared to the
precursor KNSC.
In the data, there are fewer observations for the dew point
temperature than for the other two parameters. The number of
observations was increased by calculating dew point temperature
from air temperature and relative humidity, unless it was directly
measured. This method adds several hundred values to dew point
temperature data.
The further procedure is carried out in nearly the same way
as described by Sadikni et al. (2018). In the first step, the data is
sorted into grid boxes of one degree edge length for each month
of each year, to obtain a time series of monthly means. The 1°
x 1° spatial resolution is coarser than that of the hydrographic
part, but is chosen due to the low data density to get statistically
meaningful results for each grid box.
The air and dew point temperature have distinct diurnal and
annual cycles, and therefore a sampling error can occur due
to an unbalanced temporal data distribution. Corrections for
the hours of the day, and as well the day of a month were
calculated to shift the values to the middle of the day and month,
respectively. As this is done on a monthly basis, still the annual
cycle is kept in the climatology. See Sadikni et al. (2018) for
a detailed description of this process. In the first step, for the
diurnal cycle correction terms, they were calculated for each
month and grid cell as difference between the long-term mean
and the 6h means. A more complex approach was used in the
second step for the annual cycle. For each month, the differences
between the long-term monthly mean and the daily means were
calculated and afterwards, the results for the month were fitted
with a polynomial of second order. The difference between the
result of the polynomial on a day and the long-term-mean of
the month was used as correction term. These corrections were
applied to the observations to reduce errors due to the diurnal
and annual cycles.
Since the air pressure observations do not show a pronounced
mean annual cycle, especially in the northern region, and no
diurnal cycle, a different method is applied: The number of
days within a month without pressure data must not exceed 14
consecutive days to ensure an even data distribution in time. This
criterion differs from the previous version, described by Sadikni
et al. (2018), in which data averages over 6 day windows were
used, where 4 of 5 windows had to be covered with data, which
means a maximum possible data gap of 16 days. In the KNSC,
the averages of the 6 day windows were used for monthly mean
values, resulting in temporally strongly smoothed results, and
in small standard deviations, compared to reanalysis data for
example. This is the reason for this new approach for temporal
correction of air pressure data.
In the next step, it is ensured that, if possible, there is a
sufficient number of values per grid box for averaging. For this
purpose, a threshold for the number of observations per grid
point is set for each parameter. This threshold is 20 observations
for the temperatures and 500 observations for the pressure, which
are the same values as in the KNSC (Sadikni et al., 2018). In the
first step, if the number of observations in the grid box is below
this threshold value, the observations of the eight surrounding
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