Skip to main content

Full text: The Baltic and North Seas Climatology (BNSC)\u2014A comprehensive, observation-based data product of atmospheric and hydrographic parameters

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. 
rontiers in Earth Science | www.frontiersin.Orun. 
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 
Alb 2019 1 Valııme 7 1 Article 15%
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.