Articles | Volume 13, issue 6
https://doi.org/10.5194/essd-13-2801-2021
© Author(s) 2021. 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-13-2801-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
16 Jun 2021
Data description paper |
| 16 Jun 2021
| 16 Jun 2021
A high-resolution gridded dataset of daily temperature and precipitation records (1980–2018) for Trentino-South Tyrol (north-eastern Italian Alps)
Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy
Michael Matiu
Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy
Giacomo Bertoldi
Institute for Alpine Environment, Eurac Research, Bolzano, 39100,
Italy
Marcello Petitta
Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy
SSPT-MET-CLIM, ENEA, Rome, 00196, Italy
Marc Zebisch
Institute for Earth Observation, Eurac Research, Bolzano, 39100, Italy
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Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.
Zijiang Song, Zhixiang Cheng, Yuying Li, Shanshan Yu, Xiaowen Zhang, Lina Yuan, and Min Liu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-137, https://doi.org/10.5194/essd-2024-137, 2024
Revised manuscript accepted for ESSD
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It is hard to access long-time series and high-resolution meteorological data for historical years. In this paper, we proposed a geopotential-guide attention network (GeoAN) for downscaling, which can produce high-resolution data from the given low-resolution data. Quantitative and visual comparisons reveal our GeoAN producing better results in most metrics. Using GeoAN, historical meteorological data called MDG625 has been produced daily since 1940.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
Earth Syst. Sci. Data, 16, 2701–2715, https://doi.org/10.5194/essd-16-2701-2024, https://doi.org/10.5194/essd-16-2701-2024, 2024
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This paper describes multifrequency radar observations of clouds and precipitation during the EPCAPE campaign. The data sets were obtained from CloudCube, a Ka-, W-, and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals. This data collection provides a unique opportunity to study hydrometeors with diameters in the millimeter and submillimeter size range that can be used to better understand the drop size distribution within clouds and precipitation.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
Earth Syst. Sci. Data, 16, 2525–2541, https://doi.org/10.5194/essd-16-2525-2024, https://doi.org/10.5194/essd-16-2525-2024, 2024
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This article provides an overview of the lower-atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as part of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. The data were collected through boundary layer transitions, through sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Zhiwei Yang, Jian Peng, Yanxu Liu, Song Jiang, Xueyan Cheng, Xuebang Liu, Jianquan Dong, Tiantian Hua, and Xiaoyu Yu
Earth Syst. Sci. Data, 16, 2407–2424, https://doi.org/10.5194/essd-16-2407-2024, https://doi.org/10.5194/essd-16-2407-2024, 2024
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We produced a monthly Universal Thermal Climate Index dataset (GloUTCI-M) boasting global coverage and an extensive time series spanning March 2000 to October 2022 with a high spatial resolution of 1 km. This dataset is the product of a comprehensive approach leveraging multiple data sources and advanced machine learning models. GloUTCI-M can enhance our capacity to evaluate thermal stress experienced by the human, offering substantial prospects across a wide array of applications.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
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A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Christopher J. Cox, Janet M. Intrieri, Brian Butterworth, Gijs de Boer, Michael R. Gallagher, Jonathan Hamilton, Erik Hulm, Tilden Meyers, Sara M. Morris, Jackson Osborn, P. Ola G. Persson, Benjamin Schmatz, Matthew D. Shupe, and James M. Wilczak
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-158, https://doi.org/10.5194/essd-2024-158, 2024
Revised manuscript accepted for ESSD
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Snow is an essential water resource in the intermountain western United States and predictions are made using models. We made observations to validate, constrain, and develop the models. The data is from the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in Colorado’s East River Valley, 2021–2023. The measurements include meteorology and variables that quantify energy transfer between the atmosphere and surface. The data are available publicly.
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, and Yi He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-132, https://doi.org/10.5194/essd-2024-132, 2024
Revised manuscript accepted for ESSD
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We present two sets of bias-corrected UK Climate Projections 2018 (UKCP18) regional projections of temperature, precipitation and potential evapotranspiration for 1981–2080. All 12 members of the UKCP18 regional ensemble were bias-corrected using (1) empirical quantile mapping and (2) a change-preserving variant. The two methods were evaluated and compared to guide dataset application. The datasets improve the usability of UKCP18 and serve as a reference for selecting bias correction methods.
Finn Burgemeister, Marco Clemens, and Felix Ament
Earth Syst. Sci. Data, 16, 2317–2332, https://doi.org/10.5194/essd-16-2317-2024, https://doi.org/10.5194/essd-16-2317-2024, 2024
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Knowledge of small-scale rainfall variability is needed for hydro-meteorological applications in urban areas. Therefore, we present an open-access data set covering reanalyzed radar reflectivities and rainfall estimates measured by a weather radar at high spatio-temporal resolution in the urban environment of Hamburg between 2013 and 2021. We describe the data reanalysis, outline the measurement’s performance for long time periods, and discuss open issues and limitations of the data set.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
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This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Longhu Chen, Qinqin Wang, Guofeng Zhu, Xinrui Lin, Dongdong Qiu, Yinying Jiao, Siyu Lu, Rui Li, Gaojia Meng, and Yuhao Wang
Earth Syst. Sci. Data, 16, 1543–1557, https://doi.org/10.5194/essd-16-1543-2024, https://doi.org/10.5194/essd-16-1543-2024, 2024
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We have compiled data regarding stable precipitation isotopes from 842 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51 753 data records. The collected data have undergone pre-processing and statistical analysis. We also analysed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Leah Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data, 16, 1301–1316, https://doi.org/10.5194/essd-16-1301-2024, https://doi.org/10.5194/essd-16-1301-2024, 2024
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The vertical structure of clouds has a major impact on global energy flows, air circulation, and the hydrologic cycle. Two satellite instruments, CloudSat radar and CALIPSO lidar, have taken complementary measurements of cloud vertical structure for over a decade. Here, we present the 3S-GEOPROF-COMB product, a globally gridded satellite data product combining CloudSat and CALIPSO observations of cloud vertical structure.
Jiye Leng, Jing M. Chen, Wenyu Li, Xiangzhong Luo, Mingzhu Xu, Jane Liu, Rong Wang, Cheryl Rogers, Bolun Li, and Yulin Yan
Earth Syst. Sci. Data, 16, 1283–1300, https://doi.org/10.5194/essd-16-1283-2024, https://doi.org/10.5194/essd-16-1283-2024, 2024
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We produced a long-term global two-leaf gross primary productivity (GPP) and evapotranspiration (ET) dataset at the hourly time step by integrating a diagnostic process-based model with dynamic parameterizations. The new dataset provides us with a unique opportunity to study carbon and water fluxes at sub-daily time scales and advance our understanding of ecosystem functions in response to transient environmental changes.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
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This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. 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 two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
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Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
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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 obtained by combining observations from geostationary as well as polar-orbiting satellites.
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.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
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Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
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.
Jinghua Xiong, Abhishek, Li Xu, Hrishikesh A. Chandanpurkar, James S. Famiglietti, Chong Zhang, Gionata Ghiggi, Shenglian Guo, Yun Pan, and Bramha Dutt Vishwakarma
Earth Syst. Sci. Data, 15, 4571–4597, https://doi.org/10.5194/essd-15-4571-2023, https://doi.org/10.5194/essd-15-4571-2023, 2023
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To overcome the shortcomings associated with limited spatiotemporal coverage, input data quality, and model simplifications in prevailing evaporation (ET) estimates, we developed an ensemble of 4669 unique terrestrial ET subsets using an independent mass balance approach. Long-term mean annual ET is within 500–600 mm yr−1 with a unimodal seasonal cycle and several piecewise trends during 2002–2021. The uncertainty-constrained results underpin the notion of increasing ET in a warming climate.
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.
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.
Santiago Beguería, Dhais Peña-Angulo, Víctor Trullenque-Blanco, and Carlos González-Hidalgo
Earth Syst. Sci. Data, 15, 2547–2575, https://doi.org/10.5194/essd-15-2547-2023, https://doi.org/10.5194/essd-15-2547-2023, 2023
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A gridded dataset on monthly precipitation over mainland Spain between spans 1916–2020. The dataset combines ground observations from the Spanish National Climate Data Bank and new data rescued from meteorological yearbooks published prior to 1951, which almost doubled the number of weather stations available during the first decades of the 20th century. Geostatistical techniques were used to interpolate a regular 10 x 10 km grid.
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.
Cited articles
Aadhar, S. and Mishra, V.: High-resolution near real-time drought monitoring
in South Asia, Sci. Data, 4, 170145, https://doi.org/10.1038/sdata.2017.145, 2017.
Adler, R. F., Sapiano, M. R. P., Huffman, G. J., Wang, J.-J., Gu, G., Bolvin,
D., Chiu, L., Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R.,
and Shin, D.-B.: The Global Precipitation Climatology Project (GPCP) Monthly
Analysis (New Version 2.3) and a Review of 2017 Global Precipitation,
Atmosphere, 9, 138, https://doi.org/10.3390/atmos9040138, 2018.
Adler, S., Chimani, B., Drechsel, S., Haslinger, K., Hiebl, J., Meyer, V.,
Resch, G., Rudolph, J., Vergeiner, J., Zingerle, C., Marigo, G., Fischer, A., and Seiser, B.: Das Klima: Von
Tirol-Sudtirol-Belluno, ZAMG, Autonome Provinz Bozen, ARPAV,
available at: http://www.3pclim.eu/images/Das_Klima_von_Tirol-Suedtirol-Belluno.pdf
(last access: 12 October 2020), 2015.
Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R.,
Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P.,
Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre,
O., Moisselin, J., Begert, M., Müller-Westermeier, G., Kveton, V.,
Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar,
T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z., and
Nieplova, E.: HISTALP – Historical instrumental climatological surface time
series of the Greater Alpine Region HISTALP, Int. J. Climatol., 27, 17–46,
https://doi.org/10.1002/joc.1377, 2007.
Beven, K., Cloke, H., Pappenberger, F., Lamb, R., and Hunter, N.:
Hyperresolution information and hyperresolution ignorance in modelling the
hydrology of the land surface, Science China Earth Sciences, 58, 25–35,
https://doi.org/10.1007/s11430-014-5003-4, 2015.
Brugnara, Y., Brunetti, M., Maugeri, M., Nanni, T., and Simolo, C.:
High-resolution analysis of daily precipitation trends in the central Alps
over the last century, Int. J. Climatol., 32, 1406–1422,
https://doi.org/10.1002/joc.2363, 2012.
Brunetti, M., Maugeri, M., Monti, F., and Nanni, T.: Temperature and
precipitation variability in Italy in the last two centuries from
homogenised instrumental time series, Int. J. Climatol., 26, 345–381,
https://doi.org/10.1002/joc.1251, 2006.
Brunetti, M., Lentini, G., Maugeri, M., Nanni, T. and Spinoni, J.:
Projecting North Eastern Italy temperature and precipitation secular records
onto a high-resolution grid, Phys. Chem. Earth, 40–41, 9–22,
https://doi.org/10.1016/j.pce.2009.12.005, 2012.
Brunetti, M., Maugeri, M., Nanni, T., Simolo, C., and Spinoni, J.:
High-resolution temperature climatology for Italy: interpolation method
intercomparison, Int. J. Climatol., 34, 1278–1296, https://doi.org/10.1002/joc.3764,
2014.
Brunsdon, C., McClatchey, J., and Unwin, D.: Spatial variations in the
average rainfall–altitude relationship in Great Britain: an approach using
geographically weighted regression, Int. J. Climatol., 21, 455–466,
https://doi.org/10.1002/joc.614, 2001.
Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., and Lange, M.
A.: Evaluation of interpolation techniques for the creation of gridded daily
precipitation (1×1 km2); Cyprus, 1980–2010, J. Geophys.
Res.-Atmos., 119, 693–712, https://doi.org/10.1002/2013JD020611, 2014.
Chimani, B., Matulla, C., Böhm, R., and Hofstätter, M.: A new high
resolution absolute temperature grid for the Greater Alpine Region back to
1780, Int. J. Climatol., 33, 2129–2141, https://doi.org/10.1002/joc.3574, 2013.
Craddock, J.: Methods of comparing annual rainfall records for climatic
purposes, Weather, 34, 332–346, https://doi.org/10.1002/j.1477-8696.1979.tb03465.x,
1979.
Crespi, A., Brunetti, M., Lentini, G., and Maugeri, M.: 1961–1990
high-resolution monthly precipitation climatologies for Italy, Int. J.
Climatol., 3, 878–895, https://doi.org/10.1002/joc.5217, 2018.
Crespi, A., Matiu, M., Bertoldi, G., Petitta, M., and Zebisch, M.:
High-resolution daily series (1980–2018) and monthly climatologies (1981–1010) of mean temperature and precipitation for Trentino – South Tyrol
(north-eastern Italian Alps), PANGAEA, https://doi.org/10.1594/PANGAEA.924502, 2020.
Crespi, A., Brunetti, M., Ranzi, R., Tomirotti, M., and Maugeri, M.: A
multi-century meteo-hydrological analysis for the Adda river basin (Central
Alps). Part I: Gridded monthly precipitation (1800–2016) records, Int. J.
Climatol., 41, 162–180, https://doi.org/10.1002/joc.6614, 2021.
Dalponte, M., Marzini, S., Solano-Correa, Y. T., Tonon, G., Vescovo, L., and
Gianelle, D.: Mapping forest windthrows using high spatial resolution
multispectral satellite images, Int. J. Appl. Earth Obs. Geoinf., 93,
102206, https://doi.org/10.1016/j.jag.2020.102206, 2020.
Daly, C., Gibson, W. P., Taylor, G. H., Johnson, G. L., and Pasteris, P.: A
knowledge-based approach to the statistical mapping of climate, Clim. Res.,
22, 99–113, https://doi.org/10.3354/cr022099, 2002.
Daly, C., Smith, J. W., Smith, J. I., and McKane, R. B.: High-Resolution
Spatial Modeling of Daily Weather Elements for a Catchment in the Oregon
Cascade Mountains, United States, J. Appl. Meteorol. Clim., 46, 1565–1586,
https://doi.org/10.1175/JAM2548.1, 2007.
Davolio, S., Della Fera, S., Laviola, S., Miglietta, M. M., and Levizzani,
V.: Heavy Precipitation over Italy from the Mediterranean Storm “Vaia” in
October 2018: Assessing the Role of an Atmospheric River, Mon. Weather Rev.,
148, 3571–3588, https://doi.org/10.1175/MWR-D-20-0021.1, 2020.
Di Piazza, A., Conti, F. L., Noto, L., Viola, F., and La Loggia, G.:
Comparative analysis of different techniques for spatial interpolation of
rainfall data to create a serially complete monthly time series of
precipitation for Sicily, Italy, Int. J. Appl. Earth Obs. Geoinf., 13,
396–408, https://doi.org/10.1016/j.jag.2011.01.005, 2011.
Duan, Z., Liu, J. Z., Tuo, Y., Chiogna, G., and Disse, M.: Evaluation of
eight high spatial resolution gridded precipitation products in Adige Basin
(Italy) at multiple temporal and spatial scales, Sci. Total Environ., 573,
1536–1553, https://doi.org/10.1016/j.scitotenv.2016.08.213, 2016.
Durre, I., Menne, M. J., Gleason, B. E., Houston, T. G., and Vose, R. S.:
Comprehensive Automated Quality Assurance of Daily Surface Observations, J.
Appl. Meteorol. Clim., 49, 1615–1633, https://doi.org/10.1175/2010JAMC2375.1, 2010.
Engelhardt, M., Schuler, T. V., and Andreassen, L. M.: Contribution of snow and glacier melt to discharge for highly glacierised catchments in Norway, Hydrol. Earth Syst. Sci., 18, 511–523, https://doi.org/10.5194/hess-18-511-2014, 2014.
Foresti, L., Sideris, I., Panziera, L., Nerini, D., and Germann, U.: A
10-year radar-based analysis of orographic precipitation growth and decay
patterns over the Swiss Alpine region, Q. J. Roy. Meteor. Soc., 144,
2277–2301, https://doi.org/10.1002/qj.3364, 2018.
Frei, C. and Schär, C.: A precipitation climatology of the Alps from
high-resolution rain-gauge observations, Int. J. Climatol., 18, 873–900,
https://doi.org/10.1002/(SICI)1097-0088(19980630)18:8<873::AID-JOC255>3.0.CO;2-9, 1998.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., Hoell, A., and Michaelsen, J.: The
climate hazards group infrared precipitation with stations – A new
environmental record for monitoring extremes, Sci. Data, 2, 150066,
https://doi.org/10.1038/sdata.2015.66, 2015.
Grasso, L. D.: The differentiation between grid spacing and resolution and
their application to numerical modeling, B. Am. Meteorol. Soc., 81,
579–580, https://doi.org/10.1175/1520-0477(2000)081<0579:CAA>2.3.CO;2, 2000.
Grossi, G., Lendvai, A., Peretti, G., and Ranzi, R.: Snow Precipitation
Measured by Gauges: Systematic Error Estimation and Data Series Correction
in the Central Italian Alps, Water, 9, 461, https://doi.org/10.3390/w9070461, 2017.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS
monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7,
109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P.
D., and New, M.: A European daily high-resolution gridded data set of
surface temperature and precipitation for 1950–2006, J. Geophys. Res., 113,
D20119, https://doi.org/10.1029/2008JD010201, 2008.
Hengl, T.: A Practical Guide to Geostatistical Mapping, ISBN
978–90–9024981-0, available at:
https://library.wur.nl/isric/fulltext/isricu_i27272_001.pdf (last access: 14 June 2021), 2009.
Hiebl, J. and Frei, C.: Daily precipitation grids for Austria since
1961 – development and evaluation of a spatial dataset for hydroclimatic
monitoring and modelling, Theor. Appl. Climatol., 132, 327–345,
https://doi.org/10.1007/s00704-017-2093-x, 2018.
Hofstra, N., Haylock, M., New, M., Jones, P., and Frei, C.: Comparison of
six methods for the interpolation of daily European climate data, J.
Geophys. Res., 113, D21110, https://doi.org/10.1029/2008JD010100, 2008.
Hofstra, N., New, M., and McSweeney, C.: The influence of interpolation and
station network density on the distributions and trends of climate variables
in gridded daily data, Clim. Dyn., 35, 841–858.
https://doi.org/10.1007/s00382-009-0698-1, 2010.
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F.,
Gu, G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor
Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8, 38–55,
https://doi.org/10.1175/JHM560.1, 2007.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch,
T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernández, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink,
P. D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter,
T. H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A.
B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie J. E.
M.: Importance and vulnerability of the world's water
towers, Nature, 577, 364–369, https://doi.org/10.1038/s41586-019-1822-y, 2020.
Isotta, F. A., Frei, C., Weilguni, V., Perčec Tadić, M.,
Lassègues, P., Rudolf, B., Pavan, V., Cacciamani, C., Antolini, G.,
Ratto, S. M., Munari, M., Micheletti, S., Bonati, V., Lussana, C., Ronchi,
C., Panettieri, E., Marigo, G., and Vertačnik, G.: The climate of daily
precipitation in the Alps: development and analysis of a high-resolution
grid dataset from pan-Alpine rain-gauge data, Int. J. Climatol., 34,
1657–1675, https://doi.org/10.1002/joc.3794, 2014.
Isotta, F. A., Begert, M., and Frei, C.: Long-term consistent monthly
temperature and precipitation grid data sets for Switzerland over the past
150 years, J. Geophys. Res.-Atmos., 124, 3783–3799,
https://doi.org/10.1029/2018JD029910, 2019.
Kotlarski, S., Szabó, P., Herrera, S., Räty, O., Keuler, K., Soares,
P. M., Cardoso, R. M., Bosshard, T., Pagé, C., Boberg, F.,
Gutiérrez, J. M., Isotta, F. A., Jaczewski, A., Kreienkamp, F., Liniger,
M. A., Lussana, C., and Pianko-Kluczyńska, C.: Observational uncertainty
and regional climate model evaluation: A pan-European perspective, Int. J.
Climatol., 39, 3730–3749, https://doi.org/10.1002/joc.5249, 2019.
Laiti, L., Mallucci, S., Piccolroaz, S., Bellin, A., Zardi, D., Fiori, A.,
Nikulin, G., and Majone, B.: Testing the hydrological coherence of
high-resolution gridded precipitation and temperature data sets, Water
Resour. Res., 54, 1999–2016, https://doi.org/10.1002/2017WR021633, 2018.
Ledesma, J. L. J. and Futter, M. N.: Gridded climate data products are an
alternative to instrumental measurements as inputs to rainfall–runoff
models, Hydrol. Process., 31, 3283–3293, https://doi.org/10.1002/hyp.11269, 2017.
Longman, R. J., Frazier, A. G., Newman, A. J., Giambelluca, T. W.,
Schanzenbach, D., Kagawa-Viviani, A., Needham, H., Arnold, J. R., and Clark,
M. P.: High-Resolution Gridded Daily Rainfall and Temperature for the
Hawaiian Islands (1990–2014), J. Hydrometeorol., 20, 489–508,
https://doi.org/10.1175/JHM-D-18-0112.1, 2019.
Lussana, C., Tveito, O. E., Dobler, A., and Tunheim, K.: seNorge_2018, daily precipitation, and temperature datasets over Norway, Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, 2019.
Ly, S., Charles, C., and Degré, A.: Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium, Hydrol. Earth Syst. Sci., 15, 2259–2274, https://doi.org/10.5194/hess-15-2259-2011, 2011.
Mallucci, S., Majone, B., and Bellin, A.: Detection and attribution of
hydrological changes in a large Alpine river basin,
J. Hydrol., 575, 1214–1229, https://doi.org/10.1016/j.jhydrol.2019.06.020, 2019.
Marcolini, G., Bellin, A., Disse, M., and Chiogna, G.: Variability in snow
depth time series in the Adige catchment, J. Hydrol. Reg. Stud., 13,
240–254, https://doi.org/10.1016/j.ejrh.2017.08.007, 2017.
Matiu, M., Jacob, A., and Notarnicola, C.: Daily MODIS snow cover maps for
the European Alps from 2002 onwards at 250m horizontal resolution along with
a nearly cloud-free version (Version v1.0.2) [Data set], Zenodo,
https://doi.org/10.5281/zenodo.3601891, 2019.
Matiu, M., Jacob, A., and Notarnicola, C.: Daily MODIS Snow Cover Maps for
the European Alps from 2002 onwards at 250 m Horizontal Resolution Along
with a Nearly Cloud-Free Version, Data, 5, 1, https://doi.org/10.3390/data5010001, 2020.
Matiu, M., Crespi, A., Bertoldi, G., Carmagnola, C. M., Marty, C., Morin, S., Schöner, W., Cat Berro, D., Chiogna, G., De Gregorio, L., Kotlarski, S., Majone, B., Resch, G., Terzago, S., Valt, M., Beozzo, W., Cianfarra, P., Gouttevin, I., Marcolini, G., Notarnicola, C., Petitta, M., Scherrer, S. C., Strasser, U., Winkler, M., Zebisch, M., Cicogna, A., Cremonini, R., Debernardi, A., Faletto, M., Gaddo, M., Giovannini, L., Mercalli, L., Soubeyroux, J.-M., Sušnik, A., Trenti, A., Urbani, S., and Weilguni, V.: Observed snow depth trends in the European Alps: 1971 to 2019, The Cryosphere, 15, 1343–1382, https://doi.org/10.5194/tc-15-1343-2021, 2021.
Mei, Y., Anagnostou, E. N., Nikolopoulos, E. I., and Borga, M.: Error
analysis of satellite precipitation products in mountainous basins, J.
Hydrometeorol., 15, 1778–1793, https://doi.org/10.1175/JHM-D-13-0194.1, 2014.
Morán-Tejeda, E., López-Moreno, J. I., and Beniston, M.: The
changing roles of temperature and precipitation on snowpack variability in
Switzerland as a function of altitude, Geophys. Res. Lett., 40, 2131–2136,
https://doi.org/10.1002/grl.50463, 2013.
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, H., and
Ramirez-Villega, J.: High-resolution and bias-corrected CMIP5 projections
for climate change impact assessments, Sci. Data, 7, 7,
https://doi.org/10.1038/s41597-019-0343-8, 2020.
New, M., Todd, M., Hulme, M., and Jones, P.: Precipitation measurements and
trends in the twentieth century, Int. J. Climatol., 21, 1899–1922,
https://doi.org/10.1002/joc.680, 2001.
Notarnicola, C., Duguay, M., Moelg, N., Schellenberger, T., Tetzlaff, A.,
Monsorno, R., Costa, A., Steurer, C., and Zebisch, M.: Snow Cover Maps from
MODIS Images at 250 m Resolution, Part 2: Validation, Remote Sens., 5,
1568–1587, https://doi.org/10.3390/rs5041568, 2013.
Price, F. M.: Alpenatlas – Atlas des Alpes – Atlante delle Alpi – Atlas
Alp – Mapping the Alps: Society – Economy – Environment, Mt. Res. Dev., 29, 292–293,
https://doi.org/10.1659/mrd.mm057, 2009.
Schlögel, R., Kofler, C., Gariano, S. L., Van Campenhout, J., and
Plummer, S.: Changes in climate patterns and their association to natural
hazard distribution in South Tyrol (Eastern Italian Alps), Sci. Rep.,
10, 5022, https://doi.org/10.1038/s41598-020-61615-w, 2020.
Schöner, W., Koch, R., Matulla, C., Marty, C., and Tilg, A-M.:
Spatiotemporal patterns of snow depth within the Swiss-Austrian Alps for the
past half century (1961 to 2012) and linkages to climate change, Int. J.
Climatol., 39, 1589–1603, https://doi.org/10.1002/joc.5902, 2019.
Sekulić, A., Kilibarda, M., Protić, D., Perčec Tadić, M.,
and Bajat, B.: Spatio-temporal regression kriging model of mean daily
temperature for Croatia, Theor. Appl. Climatol., 140, 101–114,
https://doi.org/10.1007/s00704-019-03077-3, 2020.
Sevruk, B., Ondrás, M., and Chvíla, B.: The WMO precipitation
measurement intercomparisons, Atmos. Res., 92, 376–380,
https://doi.org/10.1016/j.atmosres.2009.01.016, 2009.
Stewart, S. B. and Nitschke, C. R.: Improving temperature interpolation
using MODIS LST and local topography: a comparison of methods in south east
Australia, Int. J. Climatol., 37, 3098–3110, https://doi.org/10.1002/joc.4902, 2017.
Short summary
A 250 m gridded dataset of 1980–2018 daily mean temperature and precipitation records for Trentino–South Tyrol (north-eastern Italian Alps) was derived from a quality-controlled and homogenized archive of station observations. The errors associated with the final interpolated fields were assessed and thoroughly discussed. The product will be regularly updated and is meant to support regional climate studies and local monitoring and applications in integration with other fine-resolution data.
A 250 m gridded dataset of 1980–2018 daily mean temperature and precipitation records for...
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