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Cited by 18

The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.

Assimilation of crowd‐sourced surface observations over Germany in a regional weather prediction system

Sgoff, Christine, Acevedo, Walter, Paschalidi, Zoi, Ulbrich, Sven, Bauernschubert, Elisabeth, Kratzsch, Thomas, Potthast, Roland·2022·Quarterly Journal of the Royal Meteorological Society
DOI 10.1002/qj.4276
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Abstract

Near‐surface temperature and humidity observations over Germany, coming on the one hand from the citizen weather station's network Netatmo and on the other hand from synoptic weather stations, were assimilated into the limited are mode of the Icosahedral Nonhydrostatic Model with 2‐km resolution (ICON‐D2). For that we use the Kilometre‐Scale Ensemble Data Assimilation (KENDA) system and a bias‐correction approach that improves the assimilation of the observations by taking into account the diurnal cycle of temperature and humidity variables. Our results show that the assimilation of bias‐corrected observations from Netatmo stations reduces the forecast error considerably; meanwhile, the assimilation of Netatmo observations without bias correction leads to a strong warm bias with a negative impact on forecast performance. In contrast, for the assimilation of synoptic observations the usage of our bias‐correction approach does not lead to any significant decrease in the forecast error, yet reduces the bias for the diurnal cycle of synoptic stations. Overall, it can be concluded that the forecast quality can gain from assimilating Netatmo data, provided an effective bias‐correction approach is applied.

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Crossref · 10.1002/qj.4276

Cites

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Figures

FIGURE 1 Pie chart of the number of active observations. Here we consider only one day (September 19, 2018) of our experiments, given that the distribution of observations by variable does not change considerably day by day. Notice the ratio of about 14 between Netatmo and synoptic active observations for both 2-m temperature and 2-m relative humidity. The 2-m temperature and 2-m relative humidity are assimilated over Germany, while the other observations are assimilated over the entire ICON-D2 domain [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 1 Pie chart of the number of active observations. Here we consider only one day (September 19, 2018) of our experiments, given that the distribution of observations by variable does not change considerably day by day. Notice the ratio of about 14 between Netatmo and synoptic active observations for both 2-m temperature and 2-m relative humidity. The 2-m temperature and 2-m relative humidity are assimilated over Germany, while the other observations are assimilated over the entire ICON-D2 domain [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 2 Statistical analysis of station height and model orography after quality controls: Histogram (a) of differences between station height and model orography [m] and scatter plot of (b) station height versus model orography [m]. The Netatmo stations correspond to a random selection of 5000 stations within Germany [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 2 Statistical analysis of station height and model orography after quality controls: Histogram (a) of differences between station height and model orography [m] and scatter plot of (b) station height versus model orography [m]. The Netatmo stations correspond to a random selection of 5000 stations within Germany [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 3 Mean diurnal cycle of difference between model and synoptic observations (blue) and Netatmo observations (red) of the September period (September 19-30, 2018) over Germany. Difference between (a) simulated and observed 2-m temperature and (b) simulated and observed 2-m relative humidity. The differences between the model and observations without bias correction (solid line) are larger than the bias-corrected differences (dashed line). In particular, the warm bias of the temperature measured by Netatmo stations is clearly reduced by the bias correction [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 3 Mean diurnal cycle of difference between model and synoptic observations (blue) and Netatmo observations (red) of the September period (September 19-30, 2018) over Germany. Difference between (a) simulated and observed 2-m temperature and (b) simulated and observed 2-m relative humidity. The differences between the model and observations without bias correction (solid line) are larger than the bias-corrected differences (dashed line). In particular, the warm bias of the temperature measured by Netatmo stations is clearly reduced by the bias correction [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 4 Comparison of RMSE of different variables for the different experiments for different lead times, based on temperature and humidity measurements from synoptic weather stations within Germany (GER). Shown are the 2-m relative humidity (RH2M_raw), 2-m temperature (T2M_raw), and 2-m dewpoint temperature (TD2M). The forecasts were started from September 19-30, 2018 at 0000, 0600, 1200, 1800 UTC. In (a), a positive value indicates that SNP has a reduced RMSE compared with REF, a negative value indicates the opposite. The brightness indicates the significance of the results (based on a t -test). (b) shows the same as (a), but for SNP_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 4 Comparison of RMSE of different variables for the different experiments for different lead times, based on temperature and humidity measurements from synoptic weather stations within Germany (GER). Shown are the 2-m relative humidity (RH2M_raw), 2-m temperature (T2M_raw), and 2-m dewpoint temperature (TD2M). The forecasts were started from September 19-30, 2018 at 0000, 0600, 1200, 1800 UTC. In (a), a positive value indicates that SNP has a reduced RMSE compared with REF, a negative value indicates the opposite. The brightness indicates the significance of the results (based on a t -test). (b) shows the same as (a), but for SNP_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
Image 5
CC-BY · From the PDF
FIGURE 5 Like Figure 4 but for (a) NTM versus REF and (b) NTM_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 5 Like Figure 4 but for (a) NTM versus REF and (b) NTM_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
Image 7
CC-BY · From the PDF
FIGURE 7 Like Figure 6, but for the upper-air relative humidity verification [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 7 Like Figure 6, but for the upper-air relative humidity verification [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 8 Upper-air verification above Germany (GER) at different heights, comparing the different experiment results from the 24-hr forecasts with relative humidity (RH) and temperature (T) observations of radiosoundings in the experiment period (September 19, 2018, 0000 UTC-September 30, 2018, 0000 UTC). The 24-hr forecasts are started at 0000, 0600, 1200, and 1800 UTC for the experiments REF, SNP, and SNP_BC, while REF serves as a reference (Table 1). In (a), green indicates that SNP has a reduced RMSE [%] compared with REF and red indicates the opposite. (b) shows the same as (a), but for SNP_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 8 Upper-air verification above Germany (GER) at different heights, comparing the different experiment results from the 24-hr forecasts with relative humidity (RH) and temperature (T) observations of radiosoundings in the experiment period (September 19, 2018, 0000 UTC-September 30, 2018, 0000 UTC). The 24-hr forecasts are started at 0000, 0600, 1200, and 1800 UTC for the experiments REF, SNP, and SNP_BC, while REF serves as a reference (Table 1). In (a), green indicates that SNP has a reduced RMSE [%] compared with REF and red indicates the opposite. (b) shows the same as (a), but for SNP_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 9 Like Figure 8, but for (a) NTM versus REF and (b) NTM_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 9 Like Figure 8, but for (a) NTM versus REF and (b) NTM_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 10 Like Figure 8, but here aircraft (AMDAR and Mode-S) observations are used for the verification [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 10 Like Figure 8, but here aircraft (AMDAR and Mode-S) observations are used for the verification [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 11 Like Figure 10, but for (a) NTM vs REF and (b) NTM_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 11 Like Figure 10, but for (a) NTM vs REF and (b) NTM_BC versus REF [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF
FIGURE 12 Distribution of temperature observations over Germany from September 19-30, 2018. The brighter the colour, the more observations are available. In (a), the coloured boxes indicate the number and location of Netatmo stations; (b) shows the same as (a), but for aircraft measurements between 800 and 1,000 hPa; (c) shows the same as (a), but for synoptic stations; and (d) also shows the same as (a), but for radiosonde measurements [Colour figure can be viewed at wileyonlinelibrary.com]
FIGURE 12 Distribution of temperature observations over Germany from September 19-30, 2018. The brighter the colour, the more observations are available. In (a), the coloured boxes indicate the number and location of Netatmo stations; (b) shows the same as (a), but for aircraft measurements between 800 and 1,000 hPa; (c) shows the same as (a), but for synoptic stations; and (d) also shows the same as (a), but for radiosonde measurements [Colour figure can be viewed at wileyonlinelibrary.com]CC-BY · From the PDF

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Citations

Cites

Research works printed but not linked: 7

  1. 10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2 · doi:10.1175/1520-0493(1999)127
  2. Meier F., 2015, ICUC9 – 9th International Conference on Urban Climate jointly with 12th Symposium on the Urban Environment Challenges
  3. MetOffice, 2021, Weather Observations Website – WOW
  4. Netatmo, 2021, Netatmo Website
  5. Tostrams L.(2017)Spatio‐temporal analysis and error correction of consumer weather station data. PhD thesis Nijmegen Radbound University.
  6. US National Weather Service, 2021, Citizen Weather Observer Program
  7. Zahumenský I., 2017, Guidelines on quality control procedures for data from automatic weather stations, World Meteorological Organization

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Metadata

Authors
Sgoff, C., Acevedo, W., Paschalidi, Z., Ulbrich, S., Bauernschubert, E., Kratzsch, T., & Potthast, R.
Affiliations
Data Assimilation Deutscher Wetterdienst Offenbach Germany
ORCID
Sgoff, Christine
Venue
Quarterly Journal of the Royal Meteorological Society
Publisher
Wiley
Published
2022-04
Volume
148
Issue
745
Pages
1752-1767
ISSN
  • 0035-9009
  • 1477-870X
DOI
10.1002/qj.4276
Cited by
18
References
43
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor, 25 d)
Funders
Bundesministerium für Verkehr und Digitale Infrastruktur (19F2045B, 0350004B)

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