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
| 21 Oct 2022
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
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
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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
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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
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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
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Year-long buoy-based observations of the air–sea transition zone off the US west coast
The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
IWIN: the Isfjorden Weather Information Network
A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d’Urville, Adélie Land, East Antarctica
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
The EUPPBench postprocessing benchmark dataset v1.0
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
LegacyClimate 1.0: a dataset of pollen-based climate reconstructions from 2594 Northern Hemisphere sites covering the last 30 kyr and beyond
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
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
High-resolution all-sky land surface temperature and net radiation over Europe
Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany
A monthly 0.01° terrestrial evapotranspiration product (1982–2018) for the Tibetan Plateau
Observational data from uncrewed systems over Southern Great Plains
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
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We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
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We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
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The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-301, https://doi.org/10.5194/essd-2023-301, 2023
Revised manuscript accepted for ESSD
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7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica are presented in the present paper. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with 2 climate models as an application example of the dataset.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
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Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
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This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
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The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
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A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
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Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego Gonzales Miralles
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-302, https://doi.org/10.5194/essd-2022-302, 2022
Revised manuscript accepted for ESSD
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Land Surface Temperature and Surface Net Radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions and the few high-resolution datasets available have large gaps due to cloud-cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 by combining observations from geostationary as well as polar-orbiting satellites.
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
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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).
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
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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.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-195, https://doi.org/10.5194/essd-2022-195, 2022
Revised manuscript accepted for ESSD
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Accurately monitoring and understanding the spatial and temporal variability of the ET components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP were produced, and is very consistent with the measurements .The annual average ET for the entire TP was about 0.93 ± 0.037 Gt/year. The rate of increase of the ET was around 0.96 mm/year. The increase in the ET can be explained by warming and wetting of the climate.
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
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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
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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...
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