Articles | Volume 14, issue 10
https://doi.org/10.5194/essd-14-4681-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-4681-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Oct 2022
Data description paper |
| 21 Oct 2022
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
Eva Beele
CORRESPONDING AUTHOR
Division Forest, Nature and Landscape, University of Leuven (KU
Leuven), Celestijnenlaan 200E-2411, 3001 Leuven, Belgium
Maarten Reyniers
Royal Meteorological Institute of Belgium, Ringlaan 3, 1180
Brussels, Belgium
Raf Aerts
Risk and Health Impact Assessment, Sciensano (Belgian Institute of
Health), Juliette Wytsmanstraat 14,1050 Brussels, Belgium
Division Ecology, Evolution and Biodiversity Conservation, University
of Leuven (KU Leuven), Kasteelpark Arenberg 31-2435, 3001 Leuven, Belgium
Ben Somers
Division Forest, Nature and Landscape, University of Leuven (KU
Leuven), Celestijnenlaan 200E-2411, 3001 Leuven, Belgium
KU Leuven Urban Studies Institute, University of Leuven (KU Leuven),
Parkstraat 45-3609, 3000 Leuven, Belgium
Related authors
No articles found.
Vincent Smets, Charlotte Wirion, Willy Bauwens, Martin Hermy, Ben Somers, and Boud Verbeiren
Hydrol. Earth Syst. Sci., 23, 3865–3884, https://doi.org/10.5194/hess-23-3865-2019, https://doi.org/10.5194/hess-23-3865-2019, 2019
Short summary
Short summary
The impact of city trees for intercepting rainfall is quantified using measurements and modeling tools. The measurements show that an important amount of rainfall is intercepted, limiting the amount of water reaching the ground. Models are used to extrapolate the measurement results. The performance of two specialized interception models and one water balance model is evaluated. Our results show that the performance of the water balance model is similar to the specialized interception models.
L. Foresti, M. Reyniers, A. Seed, and L. Delobbe
Hydrol. Earth Syst. Sci., 20, 505–527, https://doi.org/10.5194/hess-20-505-2016, https://doi.org/10.5194/hess-20-505-2016, 2016
Short summary
Short summary
The Short-Term Ensemble Prediction System (STEPS) is implemented in real time at the Royal Meteorological Institute of Belgium (STEPS-BE). The idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic extrapolation of radar images. In this paper we present the deterministic, probabilistic and ensemble verification of STEPS-BE forecasts using four precipitation cases that caused sewer system overflow in the cities of Leuven and Ghent.
Related subject area
Domain: ESSD – Atmosphere | Subject: Meteorology
Combined wind lidar and cloud radar for high-resolution wind profiling
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
The AntAWS dataset: a compilation of Antarctic automatic weather station observations
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany
Observational data from uncrewed systems over Southern Great Plains
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
Short summary
Short summary
This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
Short summary
Short summary
We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
Short summary
Short summary
Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
Short summary
Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
Short summary
Short summary
We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
Short summary
Short summary
To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
Short summary
Short summary
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
Short summary
Short summary
It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
Short summary
Short summary
Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
Short summary
Short summary
The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-295, https://doi.org/10.5194/essd-2022-295, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
Bastian Kirsch, Cathy Hohenegger, Daniel Klocke, Rainer Senke, Michael Offermann, and Felix Ament
Earth Syst. Sci. Data, 14, 3531–3548, https://doi.org/10.5194/essd-14-3531-2022, https://doi.org/10.5194/essd-14-3531-2022, 2022
Short summary
Short summary
Conventional observation networks are too coarse to resolve the horizontal structure of kilometer-scale atmospheric processes. We present the FESST@HH field experiment that took place in Hamburg (Germany) during summer 2020 and featured a dense network of 103 custom-built, low-cost weather stations. The data set is capable of providing new insights into the structure of convective cold pools and the nocturnal urban heat island and variations of local temperature fluctuations.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
Short summary
Short summary
This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
Short summary
Short summary
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Cited articles
Agentschap Digitaal Vlaanderen: 3D GRB, Geopunt [data set], https://www.geopunt.be/catalogus/datasetfolder/42ac31a7-afe6-44c4-a534-243814fe5b58,
last access: 1 March 2022.
Agentschap voor Natuur en Bos: Groenkaart Vlaanderen 2018, Geopunt [data set], https://www.geopunt.be/catalogus/datasetfolder/2c64ca0c-5053-4a66-afac-24d69b1a09e7, last access: 1 March 2022.
Aigang, L., Tianming, W., Shichang, K., and Deqian, P.: On the
Relationship between Latitude and Altitude Temperature Effects, in: 2009
International Conference on Environmental Science and Information
Application Technology, 55–58, https://doi.org/10.1109/ESIAT.2009.335,
2009.
Ahrens, C. D.: Meteorology Today: An Introduction to Weather, Climate, and
the Environment, 9th Edn., Brooks/Cole, 549 pp., 2009.
Arnfield, A. J.: Two decades of urban climate research: A review of
turbulence, exchanges of energy and water, and the urban heat island, Int.
J. Climatol., 23, 1–26, https://doi.org/10.1002/joc.859, 2003.
Båserud, L., Lussana, C., Nipen, T. N., Seierstad, I. A., Oram, L., and Aspelien, T.:
TITAN automatic spatial quality control of meteorological in-situ observations, Adv. Sci. Res., 17, 153–163, https://doi.org/10.5194/asr-17-153-2020, 2020.
Bassani, F., Garbero, V., Poggi, D., Ridolfi, L., Von Hardenberg, J., and
Milelli, M.: Urban Climate An innovative approach to select urban-rural
sites for Urban Heat Island analysis: the case of Turin (Italy), Urban
Clim., 42, 101099, https://doi.org/10.1016/j.uclim.2022.101099, 2022.
Beele, E., Reyniers, M., Aerts, R., and Somers, B.: Replication Data for:
Quality control and correction method for air temperature data from a
citizen science weather station network in Leuven, Belgium, KU Leuven RDR [data set],
https://doi.org/10.48804/SSRN3F, 2022.
Bell, S., Cornford, D., and Bastin, L.: How good are citizen weather
stations? Addressing a biased opinion, Weather, 70, 75–84,
https://doi.org/10.1002/wea.2316, 2015.
Brans, K. I., Tüzün, N., Sentis, A., De Meester, L., and Stoks, R.:
Cryptic eco-evolutionary feedback in the city: Urban evolution of prey
dampens the effect of urban evolution of the predator, J. Anim. Ecol., 91,
514–526, https://doi.org/10.1111/1365-2656.13601, 2022.
Castell, N., Dauge, F. R., Schneider, P., Vogt, M., Lerner, U., Fishbain,
B., Broday, D., and Bartonova, A.: Can commercial low-cost sensor platforms
contribute to air quality monitoring and exposure estimates?, Environ.
Int., 99, 293–302, https://doi.org/10.1016/j.envint.2016.12.007, 2017.
Chapman, L., Bell, C., and Bell, S.: Can the crowdsourcing data paradigm
take atmospheric science to a new level? A case study of the urban heat
island of London quantified using Netatmo weather stations, Int. J.
Climatol., 37, 3597–3605, https://doi.org/10.1002/joc.4940, 2017.
Chen, J., Saunders, K., and Whan, K.: Quality control and bias adjustment of
crowdsourced wind speed observations, Q. J. Roy. Meteor. Soc., 147,
3647–3664, https://doi.org/10.1002/qj.4146, 2021.
Cornes, R. C., Dirksen, M., and Sluiter, R.: Correcting citizen-science air
temperature measurements across the Netherlands for short wave radiation
bias, Meteorol. Appl., 27, 1–16, https://doi.org/10.1002/met.1814, 2020.
Demografie: https://leuven.incijfers.be/dashboard/dashboard/demografie, last
access: 15 December 2021.
Demoury, C., Aerts, R., Vandeninden, B., Van Schaeybroeck, B., and De
Clercq, E. M.: Impact of Short-Term Exposure to Extreme Temperatures on
Mortality: A Multi-City Study in Belgium, Int. J. Env. Res. Pub.
He., 19, 3763,
https://doi.org/10.3390/ijerph19073763, 2022.
Demuzere, M., Hankey, S., Mills, G., Zhang, W., Lu, T., and Bechtel, B.:
Combining expert and crowd-sourced training data to map urban form and
functions for the continental US, Sci. Data, 7, 1–13,
https://doi.org/10.1038/s41597-020-00605-z, 2020.
De Ridder, K., Lauwaet, D., and Maiheu, B.: UrbClim – A fast urban boundary
layer climate model, Urban Clim., 12, 21–48,
https://doi.org/10.1016/j.uclim.2015.01.001, 2015.
De Troeyer, K., Bauwelinck, M., Aerts, R., Profer, D., Berckmans, J.,
Delcloo, A., Hamdi, R., Van Schaeybroeck, B., Hooyberghs, H., Lauwaet, D.,
Demoury, C., and Van Nieuwenhuyse, A.: Heat related mortality in the two
largest Belgian urban areas: A time series analysis, Environ. Res., 188,
109848, https://doi.org/10.1016/j.envres.2020.109848, 2020.
de Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam, Hydrol. Earth Syst. Sci., 21, 765–777, https://doi.org/10.5194/hess-21-765-2017, 2017.
de Vos, L., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Quality Control
for Crowdsourced Personal Weather Stations to Enable Operational Rainfall
Monitoring, Geophys. Res. Lett., 46, 8820–8829,
https://doi.org/10.1029/2019GL083731, 2019.
de Vos, L., Droste, A. M., Zander, M. J., Overeem, A., Leijnse, H., Heusinkveld,
B. G., Steeneveld, G. J., and Uijlenhoet, R.: Hydrometeorological monitoring
using opportunistic sensing networks in the Amsterdam metropolitan area,
B. Am. Meteorol. Soc., 101, E167–E185,
https://doi.org/10.1175/BAMS-D-19-0091.1, 2020.
EEA: Assessing air quality through citizen science, Copenkagen, 63 pp.,
https://doi.org/10.2800/619, 2019.
ESRI World Topographic Map:
http://www.arcgis.com/home/item.html?id=30e5fe3149c34df1ba922e6f5bbf808f,
last access: 1 March 2022.
Feichtinger, M., Wit, R. De, Goldenits, G., Kolejka, T., and Hollósi,
B.: Urban Climate Case-study of neighborhood-scale summertime urban air
temperature for the City of Vienna using crowd-sourced data, Urban Clim.,
32, 1–12, https://doi.org/10.1016/j.uclim.2020.100597, 2020.
Fenner, D., Meier, F., Bechtel, B., Otto, M., and Scherer, D.: Intra and
inter “local climate zone” variability of air temperature as observed by
crowdsourced citizen weather stations in Berlin, Germany, Meteorol.
Z., 26, 525–547, https://doi.org/10.1127/metz/2017/0861, 2017.
Fenner, D., Bechtel, B., Demuzere, M., Kittner, J., and Meier, F.: CrowdQC
+ – A Quality-Control for Crowdsourced Air-Temperature Observations
Enabling World-Wide Urban Climate Applications, Front. Environ. Sci., 9,
1–21, https://doi.org/10.3389/fenvs.2021.720747, 2021.
Hammerberg, K., Brousse, O., Martilli, A., and Mahdavi, A.: Implications of
employing detailed urban canopy parameters for mesoscale climate modelling:
a comparison between WUDAPT and GIS databases over Vienna, Austria, Int. J.
Climatol., 38, e1241–e1257, https://doi.org/10.1002/joc.5447, 2018.
Heaviside, C., Macintyre, H., and Vardoulakis, S.: The Urban Heat Island:
Implications for Health in a Changing Environment, Curr. Environ. Heal.
Rep., 4, 296–305, https://doi.org/10.1007/s40572-017-0150-3, 2017.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, in press,
https://doi.org/10.1017/9781009157896, 2021.
Jenkins, G.: A comparison between two types of widely used weather stations,
Weather, 69, 105–110, https://doi.org/10.1002/wea.2158, 2014.
Kidder, S. Q. and Essenwanger, O. M.: The Effect of Clouds and Wind on the
Difference in Nocturnal Cooling Rates between Urban and Rural Areas, J.
Appl. Meteorol., 34, 2440–2448,
https://doi.org/10.1175/1520-0450(1995)034<2440:TEOCAW>2.0.CO;2, 1995.
Kirk, P. J., Clark, M. R., and Creed, E.: Weather Observations Website,
Weather, 76, 47–49, https://doi.org/10.1002/wea.3856, 2020.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of
the Köppen-Geiger climate classification updated, Meteorol. Z.,
15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Kousis, I., Pigliautile, I., and Pisello, A. L.: Intra-urban microclimate
investigation in urban heat island through a novel mobile monitoring system,
Sci. Rep., 11, 1–17, https://doi.org/10.1038/s41598-021-88344-y, 2021.
Lefebvre, W., Vercauteren, J., Schrooten, L., Janssen, S., Degraeuwe, B.,
Maenhaut, W., de Vlieger, I., Vankerkom, J., Cosemans, G., Mensink, C.,
Veldeman, N., Deutsch, F., Van Looy, S., Peelaerts, W., and Lefebre, F.:
Validation of the MIMOSA-AURORA-IFDM model chain for policy support:
Modeling concentrations of elemental carbon in Flanders, Atmos. Environ.,
45, 6705–6713, https://doi.org/10.1016/j.atmosenv.2011.08.033, 2011.
Leuven.cool: https://www.leuven.cool/, last access: 15 December 2020.
Longman, R. J., Giambelluca, T. W., Nullet, M. A., Frazier, A. G., Kodama,
K., Crausbay, S. D., Krushelnycky, P. D., Cordell, S., Clark, M. P., Newman,
A. J., and Arnold, J. R.: Compilation of climate data from heterogeneous
networks across the Hawaiian Islands, Sci. Data, 5, 180012,
https://doi.org/10.1038/sdata.2018.12, 2018.
Mandement, M. and Caumont, O.: Contribution of personal weather stations to the observation of deep-convection features near the ground, Nat. Hazards Earth Syst. Sci., 20, 299–322, https://doi.org/10.5194/nhess-20-299-2020, 2020.
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013.
Meier, F., Fenner, D., Grassmann, T., Otto, M., and Scherer, D.:
Crowdsourcing air temperature from citizen weather stations for urban
climate research, Urban Clim., 19, 170–191,
https://doi.org/10.1016/j.uclim.2017.01.006, 2017.
Muller, C. L., Chapman, L., Johnston, S., Kidd, C., Illingworth, S., Foody,
G., Overeem, A., and Leigh, R. R.: Crowdsourcing for climate and atmospheric
sciences: Current status and future potential, Int. J. Climatol., 35,
3185–3203, https://doi.org/10.1002/joc.4210, 2015.
Napoly, A., Grassmann, T., Meier, F., and Fenner, D.: Development and
Application of a Statistically-Based Quality Control for Crowdsourced Air
Temperature Data, Front. Earth Sci., 6, 1–16,
https://doi.org/10.3389/feart.2018.00118, 2018.
Nipen, T. N., Seierstad, I. A., Lussana, C., Kristiansen, J., and Hov,
Ø.: Adopting citizen observations in operational weather prediction,
B. Am. Meteorol. Soc., 101, E43–E57,
https://doi.org/10.1175/BAMS-D-18-0237.1, 2020.
Oke, T. R.: City size and the urban heat island, Atmos. Environ., 7, 769–779, 1973.
Qian, Y., Zhou, W., Hu, X., and Fu, F.: The Heterogeneity of Air Temperature
in Urban Residential Neighborhoods and Its Relationship with the Surrounding
Greenspace, Remote Sens., 10, 965, https://doi.org/10.3390/rs10060965, 2018.
Rizwan, A. M., Dennis, L. Y. C., and Lia, C.: A review on the generation,
determination and mitigation of Urban Heat Island, J. Environ. Sci., 20,
120–128, https://doi.org/10.1016/S1001-0742(08)60019-4, 2008.
RMI: Klimaatstatistieken van de Belgische gemeenten: Leuven, Royal Meteorological Institute of Belgium, Leuven, 5 pp., https://www.meteo.be/resources/climatology/climateCity/pdf/climate_INS24062_9120_nl.pdf (last access: 14 October 2022), 2020.
Sgoff, C., Acevedo, W., Paschalidi, Z., Ulbrich, S., Bauernschubert, E.,
Kratzsch, T., and Potthast, R.: Assimilation of crowd-sourced surface
observations over Germany in a regional weather prediction system, Q. J. Roy.
Meteor. Soc., 148, 1752–1767, https://doi.org/10.1002/qj.4276, 2022.
Sotelino, L. G., De Coster, N., Beirinckx, P., and Peeters, P.: Intercomparison of Shelters in the RMI AWS Network, WMO-CIMO, P1_26, Geneva, Switzerland, https://www.wmocimo.net/wp-content/uploads/P1_26_Sotelino_teco_2018_lgs.pdf (last access: 14 October 2022), 2018.
Stewart, I. D.: A systematic review and scientific critique of methodology
in modern urban heat island literature, Int. J. Climatol., 31, 200–217,
https://doi.org/10.1002/joc.2141, 2011.
Stewart, I. D. and Oke, T. R.: Local climate zones for urban temperature
studies, B. Am. Meteorol. Soc., 93, 1879–1900,
https://doi.org/10.1175/BAMS-D-11-00019.1, 2012.
UN, Population Division: World Urbanization Prospects: The 2018 Revision (ST/ESA/SER.A/420), New York, United Nations, 126 pp., https://population.un.org/wup/publications/Files/WUP2018-Report.pdf (last access: 14 October 2022), 2019
Venter, Z. S., Chakraborty, T., and Lee, X.: Crowdsourced air temperatures
contrast satellite measures of the urban heat island and its mechanisms,
Sci. Adv., 7, 1–9,
https://doi.org/10.1126/sciadv.abb9569, 2021.
Verdonck, M. L., Demuzere, M., Hooyberghs, H., Beck, C., Cyrys, J.,
Schneider, A., Dewulf, R., and Van Coillie, F.: The potential of local
climate zones maps as a heat stress assessment tool, supported by simulated
air temperature data, Landscape Urban Plan., 178, 183–197,
https://doi.org/10.1016/j.landurbplan.2018.06.004, 2018.
Vlaamse Overheid – Departement Omgeving – Afdeling Vlaams Planbureau voor
Omgeving: Landgebruik – Vlaanderen – toestand 2019:
https://www.geopunt.be/catalogus/datasetfolder/fe979929-a2b5-4353-94c5-608c4b109dc6,
last access: 1 March 2022.
Weather Observations Website – Belgium: https://wow.meteo.be/nl/, last access: 1 March 2022.
WMO: Guide to meteorological instruments and methods of observation, Volume
I – Measurement of Meteorological Variables, Geneva, 573 pp., https://library.wmo.int/doc_num.php?explnum_id=10616 (last access: 14 October 2022), 2018.
Yang, Q., Huang, X., Yang, J., and Liu, Y.: The relationship between land
surface temperature and artificial impervious surface fraction in 682 global
cities: Spatiotemporal variations and drivers, Environ. Res. Lett., 16, 024032,
https://doi.org/10.1088/1748-9326/abdaed, 2021.
Short summary
This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data have undergone a quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for between-station and station-specific temperature biases.
This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of...
