Articles | Volume 11, issue 4
https://doi.org/10.5194/essd-11-1531-2019
© Author(s) 2019. 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-11-1531-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
14 Oct 2019
Data description paper |
| 14 Oct 2019
| 14 Oct 2019
seNorge_2018, daily precipitation, and temperature datasets over Norway
Norwegian Meteorological Institute, Oslo, Norway
Ole Einar Tveito
Norwegian Meteorological Institute, Oslo, Norway
Andreas Dobler
Norwegian Meteorological Institute, Oslo, Norway
Ketil Tunheim
Norwegian Meteorological Institute, Oslo, Norway
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The observational gridded climate datasets are among the primary sources of information for climate analysis and monitoring. The seNorge2 high-resolution dataset of daily total precipitation (1957–2017) constitutes a valuable meteorological input for snow and hydrological simulations which are routinely conducted over Norway for research and to support operational applications for civil protection purposes. The dataset and the seNorge2 software are publicly available for download.
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Stefan Liersch, Julia Tecklenburg, Henning Rust, Andreas Dobler, Madlen Fischer, Tim Kruschke, Hagen Koch, and Fred Fokko Hattermann
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Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, https://doi.org/10.5194/essd-10-235-2018, 2018
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The observational gridded climate datasets are among the primary sources of information for climate analysis and monitoring. The seNorge2 high-resolution dataset of daily total precipitation (1957–2017) constitutes a valuable meteorological input for snow and hydrological simulations which are routinely conducted over Norway for research and to support operational applications for civil protection purposes. The dataset and the seNorge2 software are publicly available for download.
Abdelkader Mezghani, Andreas Dobler, Jan Erik Haugen, Rasmus E. Benestad, Kajsa M. Parding, Mikołaj Piniewski, Ignacy Kardel, and Zbigniew W. Kundzewicz
Earth Syst. Sci. Data, 9, 905–925, https://doi.org/10.5194/essd-9-905-2017, https://doi.org/10.5194/essd-9-905-2017, 2017
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Projected changes estimated from an ensemble of nine model simulations showed that annual means of temperature are expected to increase steadily by 1 °C until 2021–2050 and by 2 °C until 2071–2100 assuming the RCP4.5, which is accelerating assuming the RCP8.5 scenario and can reach up to almost 4 °C by 2071–2100. Similarly to temperature, projected changes in regional annual means of precipitation are expected to increase by 6 to 10 % and by 8 to 16 % for the two future horizons and RCPs.
Andreas Dobler, Jan Erik Haugen, and Rasmus Emil Benestad
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2016-27, https://doi.org/10.5194/esd-2016-27, 2016
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Nat. Hazards Earth Syst. Sci., 14, 2189–2201, https://doi.org/10.5194/nhess-14-2189-2014, https://doi.org/10.5194/nhess-14-2189-2014, 2014
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The Dutch real-time gauge-adjusted radar product provides 5 min precipitation accumulations every 5 min covering the Netherlands and the area around it. It plays a key role in hydrological decision-support systems and as input for short-term weather forecasts. Major changes were implemented on 31 January 2023 and the associated quality improvement is presented. Moreover, the employed radar and rain gauge datasets and the algorithms needed to produce this real-time radar product are described.
Lucie Bakels, Michael Blaschek, Marina Dütsch, Andreas Plach, Vincent Lechner, Georg Brack, Leopold Haimberger, and Andreas Stohl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-26, https://doi.org/10.5194/essd-2025-26, 2025
Revised manuscript accepted for ESSD
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Meteorological reanalyses are crucial datasets. Most reanalyses are Eulerian, providing data at specific, fixed points in space and time. When studying how air moves, it is more convenient to follow air masses through space and time, requiring a Lagrangian reanalysis (LARA). We explain how the LARA dataset is organized, and provide four examples of applications. These include studying the evolution of wind patterns, understanding weather systems, and measuring air mass travel time over land.
Pantelis Georgiades, Theo Economou, Yiannis Proestos, Jose Araya, Jos Lelieveld, and Marco Neira
Earth Syst. Sci. Data, 17, 1153–1171, https://doi.org/10.5194/essd-17-1153-2025, https://doi.org/10.5194/essd-17-1153-2025, 2025
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Climate change is posing increasing challenges in the dairy cattle farming sector, as heat stress adversely affects the animals' health and milk production. To accurately assess these impacts, we developed a machine learning model to downscale daily climate data to hourly Temperature Humidity Index (THI) values. We utilized historical weather data to train our model and applied them to future climate projections, under two climate scenarios.
Yiguo Wang, François Counillon, Lea Svendsen, Ping-Gin Chiu, Noel Keenlyside, Patrick Laloyaux, Mariko Koseki, and Eric de Boisseson
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-127, https://doi.org/10.5194/essd-2025-127, 2025
Revised manuscript accepted for ESSD
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CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to today. By using advanced modeling techniques and incorporating sea surface temperature observations, it provides a consistent picture of long-term climate variability. The dataset captures key ocean, sea ice and atmosphere changes, helping scientists understand past climate changes and variability.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-138, https://doi.org/10.5194/essd-2025-138, 2025
Revised manuscript accepted for ESSD
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This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China’s growing low-altitude economy and accessible at https://doi.org/10.5281/zenodo.14959025.
Qi Zhang, Chiyuan Miao, Jiajia Su, Jiaojiao Gou, Jinlong Hu, Xi Zhao, and Ye Xu
Earth Syst. Sci. Data, 17, 837–853, https://doi.org/10.5194/essd-17-837-2025, https://doi.org/10.5194/essd-17-837-2025, 2025
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Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland China, offering detailed insights from 1961 to 2022 at a spatial resolution of 0.1°. This dataset incorporates six key drought indices, including multi-scale versions, facilitating early detection and monitoring of droughts. Through the provision of consistent and reliable data, CHM_Drought enhances our understanding of drought patterns, aiding in effective water management and agricultural planning.
Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-70, https://doi.org/10.5194/essd-2025-70, 2025
Revised manuscript accepted for ESSD
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This study introduces the update to the C-LSAT 2.1 station data and its gridded dataset (5° × 5°). Based on this, we develop high-resolution (0.5° × 0.5°) LSAT (C-LSAT HRv1) and DTR (C-LDTR HRv1) datasets. The C-LSAT 2.1 station data integrates over 3000 additional global stations, significantly improving spatial coverage. The global and regional variations in C-LSAT HRv1 and C-LDTR HRv1 align well with their 5° × 5° datasets (C-LSAT 2.1 and C-LDTR).
David Brus, Viet Le, Joel Kuula, and Konstantinos Doulgeris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-61, https://doi.org/10.5194/essd-2025-61, 2025
Revised manuscript accepted for ESSD
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This manuscript provides datasets collected during Pallas Cloud Experiment campaign in norther Finland during the autumn of 2022. We provided an overview of the custom-built drone backpack for air quality and atmospheric state variables carried on top of the consumer-grade drone (DJI Mavic 2 pro). We described the flight strategies, and provided an overview of the datasets obtained, including a description of the measurement against the reference for data validation.
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-568, https://doi.org/10.5194/essd-2024-568, 2025
Revised manuscript accepted for ESSD
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GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research, allowing also studies for water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
Jinlong Hu, Chiyuan Miao, Jiajia Su, Qi Zhang, Jiaojiao Gou, and Qiaohong Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-20, https://doi.org/10.5194/essd-2025-20, 2025
Revised manuscript accepted for ESSD
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We developed a high-precision daily precipitation dataset for mainland China called CHM_PRE V2. Using data from 3,476 rain gauges, 11 related precipitation variables and advanced machine learning methods, we created a daily precipitation dataset spanning 1960–2023 with unprecedented accuracy. Compared to existing datasets, it better captures rainfall events while reducing false alarms. This work provides a reliable tool for studying water resources, climate change, and disaster management.
Franco Salerno, Nicolas Guyennon, Nicola Colombo, Maria Teresa Melis, Francesco Gabriele Dessì, Gianpietro Verza, Kaji Bista, Ahmad Sheharyar, and Gianni Tartari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-591, https://doi.org/10.5194/essd-2024-591, 2025
Revised manuscript accepted for ESSD
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Climate change is deeply impacting mountain areas around the globe, especially in Himalaya. Here, we present the Pyramid Meteorological Network, located in Himalaya (Nepal), on the southern slopes of Mt. Everest. The network is composed of 7 meteorological stations located between 2660 and 7986 m a.s.l., which have collected continuous climatic data during the last 30 years (1994–2023). The dataset is available freely accessible from https://zenodo.org/records/14450214 (Salerno et al., 2024).
Zhiqi Xu, Jianping Guo, Guwei Zhang, Yuchen Ye, Haikun Zhao, and Haishan Chen
Earth Syst. Sci. Data, 16, 5753–5766, https://doi.org/10.5194/essd-16-5753-2024, https://doi.org/10.5194/essd-16-5753-2024, 2024
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Tropical cyclones (TCs) are powerful weather systems that can cause extreme disasters. Here we generate a global long-term TC size and intensity reconstruction dataset, covering a time period from 1959 to 2022, with a 3 h temporal resolution, using machine learning models. These can be valuable for filling observational data gaps and advancing our understanding of TC climatology, thereby facilitating risk assessments and defenses against TC-related disasters.
Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan-Peter Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, and Manuel de la Torre Juárez
Earth Syst. Sci. Data, 16, 5643–5663, https://doi.org/10.5194/essd-16-5643-2024, https://doi.org/10.5194/essd-16-5643-2024, 2024
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This dataset provides, for the first time, combined observations of clouds and precipitation with coincident retrievals of atmospheric thermodynamics obtained from the same space-based instrument. Furthermore, it provides the locations of the ray trajectories of the observations along various precipitation-related products interpolated into them with the aim of fostering the use of such dataset in scientific and operational applications.
Frédéric Laly, Patrick Chazette, Julien Totems, Jérémy Lagarrigue, Laurent Forges, and Cyrille Flamant
Earth Syst. Sci. Data, 16, 5579–5602, https://doi.org/10.5194/essd-16-5579-2024, https://doi.org/10.5194/essd-16-5579-2024, 2024
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We present a dataset of water vapor mixing ratio profiles acquired during the Water Vapor Lidar Network Assimilation campaign in fall and winter 2022 and summer 2023, using three lidar systems deployed on the western Mediterranean coastline. This innovative campaign provides access to lower-tropospheric water vapor variability to constrain meteorological forecasting models. The scientific objective is to improve forecasting of heavy-precipation events that lead to flash floods and landslides.
Colin Peter Morice, David I. Berry, Richard C. Cornes, Kathryn Cowtan, Thomas Cropper, Ed Hawkins, John J. Kennedy, Timothy J. Osborn, Nick A. Rayner, Beatriz R. Rivas, Andrew P. Schurer, Michael Taylor, Praveen R. Teleti, Emily J. Wallis, Jonathan Winn, and Elizabeth C. Kent
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-500, https://doi.org/10.5194/essd-2024-500, 2024
Revised manuscript accepted for ESSD
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We present a new data set of global gridded surface air temperature change extending back to the 1780s. This is achieved using marine air temperature observations with newly available estimates of diurnal heating biases together with an updated land station database that includes bias adjustments for early thermometer enclosures. These developments allow the data set to extend further into the past than current data sets that use sea surface temperature rather than marine air temperature data.
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024, https://doi.org/10.5194/essd-16-5243-2024, 2024
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The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.
Adam Jaczewski, Michał Marosz, and Mirosław Miętus
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-433, https://doi.org/10.5194/essd-2024-433, 2024
Revised manuscript accepted for ESSD
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This paper introduces a high-resolution dataset of daily air temperatures in Poland from 1951 to 2020, with a 1 km2 spatial resolution. PL1GD-T dataset was developed using radial basis functions applied to quality-controlled observations from ground weather stations and evaluated using cross-validation methods. This open-access dataset is crucial for climate change impact studies on a smaller scale and can serve a wide range of users, including researchers, administrative bodies, and society.
Camila da Cunha Lopes, Rachel Ifanger Albrecht, Douglas Messias Uba, Thiago Souza Biscaro, and Ivan Saraiva
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-438, https://doi.org/10.5194/essd-2024-438, 2024
Revised manuscript accepted for ESSD
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This study used observations collected during The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment to create a database of storms and thunderstorms characteristics with weather radar and lightning measurements. These storms have different sizes and durations between wet and dry seasons as well as throughout the day, with the most intense ones occurring in the dry-to-wet transition. This database is useful in future studies on Amazonian clouds.
Clare Marie Flynn, Julia Moemken, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-298, https://doi.org/10.5194/essd-2024-298, 2024
Revised manuscript accepted for ESSD
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We created a new, publicly available database of the Top 50 most extreme European winter windstorms from each of four different meteorological input data sets covering the years 1995–2015. We found variability in all aspects of our database, from which storms were included in the Top 50 storms for each input to their spatial variability. We urge users of our database to consider the storms as identified from two or more input sources within our database, where possible.
Thomas Fiolleau and Rémy Roca
Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, https://doi.org/10.5194/essd-16-4021-2024, 2024
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This paper presents a database of tropical deep convective systems over the 2012–2020 period, built from a cloud-tracking algorithm called TOOCAN, which has been applied to homogenized infrared observations from a fleet of geostationary satellites. This database aims to analyze the tropical deep convective systems, the evolution of their associated characteristics over their life cycle, their organization, and their importance in the hydrological and energy cycle.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
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This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
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During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, https://doi.org/10.5194/essd-16-3017-2024, 2024
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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.
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.
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.
Zhenhao Wu, Yunfei Fu, Peng Zhang, Songyan Gu, and Lin Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-532, https://doi.org/10.5194/essd-2023-532, 2024
Revised manuscript accepted for ESSD
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We establish a new rain cell precipitation parameter and visible infrared and microwave signal dataset combining with the multi-instrument observation data on the Tropical Rainfall Measuring Mission (TRMM). The purpose of this dataset is to promote the three-dimensional study of rain cell precipitation system, and reveal the spatial and temporal variations of the scale morphology and intensity of the system.
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.
Cited articles
Bremnes, J. B.: Probabilistic wind power forecasts using local quantile
regression, Wind Energy, 7, 47–54, https://doi.org/10.1002/we.107, 2004. a
Cardinali, C., Pezzulli, S., and Andersson, E.: Influence-matrix diagnostic of
a data assimilation system, Q. J. Roy. Meteorological
Society, 130, 2767–2786, https://doi.org/10.1256/qj.03.205,
available at: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1256/qj.03.205 (last access: 7 October 2019),
2004. a
Casati, B.: New Developments of the Intensity-Scale Technique within the
Spatial Verification Methods Intercomparison Project, Weather
Forecast., 25, 113–143, https://doi.org/10.1175/2009WAF2222257.1, 2010. a
Casati, B., Ross, G., and Stephenson, D. B.: A new intensity-scale approach for
the verification of spatial precipitation forecasts, Meteorol.
Appl., 11, 141–154, https://doi.org/10.1017/S1350482704001239, 2004. a
Crespi, A., Lussana, C., Brunetti, M., Dobler, A., Maugeri, M., and Tveito,
O. E.: High-resolution monthly precipitation climatologies over Norway
(1981–2010): Joining numerical model data sets and in situ observations,
Int. J. Climatol., 39, 2057–2070, https://doi.org/10.1002/joc.5933,
2019. a
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation,
background and analysis-error statistics in observation space, Q.
J. Roy. Meteor. Soc., 131, 3385–3396,
https://doi.org/10.1256/qj.05.108,
2005. a, b
Erdin, R., Frei, C., and Künsch, H. R.: Data transformation and uncertainty
in geostatistical combination of radar and rain gauges, J.
Hydrometeorol., 13, 1332–1346, 2012. a
Frei, C.: Interpolation of temperature in a mountainous region using nonlinear
profiles and non-Euclidean distances, Int. J. Climatol.,
34, 1585–1605, https://doi.org/10.1002/joc.3786,
2014. a
Gandin, L. S. and Hardin, R.: Objective analysis of meteorological fields, vol. 242, Israel program for scientific translations Jerusalem, 1965. a
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. a
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.-Atmos., 113, 1–19, https://doi.org/10.1029/2008JD010100, 2008. a
Horel, J. D. and Dong, X.: An evaluation of the distribution of Remote
Automated Weather Stations (RAWS), J. Appl. Meteorol.
Clim., 49, 1563–1578, 2010. a
Ide, K., Courtier, P., Ghil, M., and Lorenc, A.: Unified notation for data
assimilation: operational, sequential and variational, Practice, 75,
181–189, 1997. a
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability,
Cambridge University Press, 2003. a
Klein Tank, A. M., Wijngaard, J. B., Können, G. P., Böhm, R., Demarée, G., Gocheva, A., Mileta, M., Pashiardis, S., Hejkrlik, L., Kern‐Hansen, C., Heino, R., Bessemoulin, P., Müller‐Westermeier, G., Tzanakou, M., Szalai, S., Pálsdóttir, T., Fitzgerald, D., Rubin, S., Capaldo, M., Maugeri, M., Leitass, A., Bukantis, A., Aberfeld, R., van Engelen, A. F., Forland, E., Mietus, M., Coelho, F., Mares, C., Razuvaev, V., Nieplova, E., Cegnar, T., Antonio López, J., Dahlström, B., Moberg, A., Kirchhofer, W., Ceylan, A., Pachaliuk, O., Alexander, L. V., and Petrovic, P.: Daily dataset of 20th-century surface air temperature and
precipitation series for the European Climate Assessment, Int.
J. Climatol., 22, 1441–1453, 2002. a
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, K.: Observational uncertainty and regional climate
model evaluation: A pan-European perspective, Int. J.
Climatol., 39, 3730–3749, https://doi.org/10.1002/joc.5249,
2017. a
Lanzante, J. R.: Resistant, robust and non-parametric techniques for the
analysis of climate data: theory and examples, including applications to
historical radiosonde station data, Int. J. Climatol., 16,
1197–1226, 1996. a
Lind, P., Lindstedt, D., Kjellström, E., and Jones, C.: Spatial and Temporal
Characteristics of Summer Precipitation over Central Europe in a Suite of
High-Resolution Climate Models, J. Climate, 29, 3501–3518,
https://doi.org/10.1175/JCLI-D-15-0463.1, 2016. a
Lorenc, A. C.: Analysis methods for numerical weather prediction, Q.
J. Roy. Meteor. Soc., 112, 1177–1194,
https://doi.org/10.1002/qj.49711247414,
1986. a
Lussana, C.: BLISS – Bayesian statistical interpolation for spatial analysis,
https://doi.org/10.5281/zenodo.2022479, the Norwegian Meteorological Institute,
2018a. a
Lussana, C.: seNorge_2018 daily total precipitation amount 1957–2017,
https://doi.org/10.5281/zenodo.2082320, the Norwegian Meteorological Institute,
2018b. a, b, c
Lussana, C.: seNorge_2018 daily mean temperature 1957–2017,
https://doi.org/10.5281/zenodo.2023997, the Norwegian Meteorological Institute,
2018c. a, b, c
Lussana, C.: seNorge_2018 daily minimum temperature 1957–2017,
https://doi.org/10.5281/zenodo.2559354, the Norwegian Meteorological Institute,
2018d. a, b
Lussana, C.: seNorge_2018 daily maximum temperature 1957–2017,
https://doi.org/10.5281/zenodo.2559372, the Norwegian Meteorological Institute,
2018e. a, b, c
Lussana, C., Salvati, M., Pellegrini, U., and Uboldi, F.: Efficient
high-resolution 3-D interpolation of meteorological variables for operational
use, Adv. Sci. Res., 3, 105–112, 2009. a
Lussana, C., Saloranta, T., Skaugen, T., Magnusson, J., Tveito, O. E., and Andersen, J.: seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day, Earth Syst. Sci. Data, 10, 235–249, https://doi.org/10.5194/essd-10-235-2018, 2018a. a, b, c
Magnusson, J., Wever, N., Essery, R., Helbig, N., Winstral, A., and Jonas, T.:
Evaluating snow models with varying process representations for hydrological
applications, Water Resour. Res., 51, 2707–2723, 2015. a
Masson, D. and Frei, C.: Spatial analysis of precipitation in a high-mountain region: exploring methods with multi-scale topographic predictors and circulation types, Hydrol. Earth Syst. Sci., 18, 4543–4563, https://doi.org/10.5194/hess-18-4543-2014, 2014. a
Masson, D. and Frei, C.: Long-term variations and trends of mesoscale
precipitation in the Alps: recalculation and update for 1901–2008,
Int. J. Climatology, 36, 492–500, 2016. a
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. a
Mohr, M.: New routines for gridding of temperature and precipitation
observations for “seNorge. no”, Met. no Report, 8, 2008, 2008. a
Mohr, M.: Comparison of versions 1.1 and 1.0 of gridded temperature and
precipitation data for Norway, Norwegian Meteorological Institute, met no
note, 19, 2009. a
Müller, M., Homleid, M., Ivarsson, K., Køltzow, M. A., Lindskog, M., Midtbø, K. H., Andrae, U., Aspelien, T., Berggren, L., Bjørge, D., Dahlgren, P., Kristiansen, J., Randriamampianina, R., Ridal, M., and Vignes, O.: AROME-MetCoOp: a nordic convective-scale operational weather
prediction model, Weather Forecast., 32, 609–627, 2017. a
Norwegian Meteorological Institute: seNorge_2018 on-line archive, available at: http://thredds.met.no/thredds/catalog/senorge/seNorge_2018/Archive/catalog.html?dataset=senorge/seNorge_2018/Archive/seNorge2018_2010.nc,
last access: 7 October 2019b. a
Norwegian Water Resources and Energy Directorate: expert tool for notification and emergency, available at: http://www.xgeo.no,
last access: 7 October 2019. a
Orlanski, I.: A rational subdivision of scales for atmospheric processes,
B. Am. Meteorol. Soc., 56, 527–530, 1975. a
Pielke, R. A.: Further Comments on “The Differentiation between Grid Spacing
and Resolution and Their Application to Numerical Modeling”, B.
Am. Meteorol. Soc., 82, 699–700,
https://doi.org/10.1175/1520-0477(2001)082<0699:FCOTDB>2.3.CO;2,
2001. a
Reistad, M., Breivik, Ø., Haakenstad, H., Aarnes, O. J., Furevik, B. R., and
Bidlot, J.-R.: A high-resolution hindcast of wind and waves for the North
Sea, the Norwegian Sea, and the Barents Sea, J. Geophys. Res.-Oceans, 116, 1–18, https://doi.org/10.1029/2010JC006402, 2011. a
Sakov, P. and Bertino, L.: Relation between two common localisation methods for
the EnKF, Computational Geosciences, 15, 225–237, 2011. a
Saloranta, T. M.: Simulating snow maps for Norway: description and statistical evaluation of the seNorge snow model, The Cryosphere, 6, 1323–1337, https://doi.org/10.5194/tc-6-1323-2012, 2012. a
Saloranta, T. M.: Operational snow mapping with simplified data assimilation
using the seNorge snow model, J. Hydrol., 538, 314–325, 2016. a
Seity, Y., Brousseau, P., Malardel, S., Hello, G., Bénard, P., Bouttier, F.,
Lac, C., and Masson, V.: The AROME-France Convective-Scale Operational Model,
Mon. Weather Rev., 139, 976–991, https://doi.org/10.1175/2010MWR3425.1, 2011. a
Simmons, A. J., Berrisford, P., Dee, D. P., Hersbach, H., Hirahara, S., and
Thépaut, J.-N.: A reassessment of temperature variations and trends from
global reanalyses and monthly surface climatological datasets, Q.
J. Roy. Meteor. Soc., 143, 101–119,
https://doi.org/10.1002/qj.2949,
2017. a
Skaugen, T. and Mengistu, Z.: Estimating catchment-scale groundwater dynamics from recession analysis – enhanced constraining of hydrological models, Hydrol. Earth Syst. Sci., 20, 4963–4981, https://doi.org/10.5194/hess-20-4963-2016, 2016. a
Skaugen, T. and Onof, C.: A rainfall-runoff model parameterized from GIS and
runoff data, Hydrol. Process., 28, 4529–4542, 2014. a
Tian, Y., Huffman, G. J., Adler, R. F., Tang, L., Sapiano, M., Maggioni, V.,
and Wu, H.: Modeling errors in daily precipitation measurements: Additive or
multiplicative?, Geophys. Res. Lett., 40, 2060–2065, 2013. a
Tveito, O. E. and Førland, E. J.: Mapping temperatures in Norway applying
terrain information, geostatistics and GIS, Norsk Geogr.
Tidsskr., 53, 202–212, 1999. a
Walters, M. K.: commentary and analysis, B. Am.
Meteorol. Soc., 81, 2475–2479,
https://doi.org/10.1175/1520-0477(2000)081<2475:CAACOT>2.3.CO;2,
2000. a
Wolff, M. A., Isaksen, K., Petersen-Øverleir, A., Ødemark, K., Reitan, T., and Brækkan, R.: Derivation of a new continuous adjustment function for correcting wind-induced loss of solid precipitation: results of a Norwegian field study, Hydrol. Earth Syst. Sci., 19, 951–967, https://doi.org/10.5194/hess-19-951-2015, 2015. a, b, c, d
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson,
T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in
extremes based on daily temperature and precipitation data, Wiley
Interdisciplinary Reviews: Climate Change, 2, 851–870,
https://doi.org/10.1002/wcc.147,
2011. a
Short summary
seNorge_2018 is a collection of observational gridded datasets for daily total precipitation and daily mean, minimum, and maximum temperature for the Norwegian mainland covering the time period from 1957 to the present day. The fields have 1 km of grid spacing. The data are used for applications in climatology, hydrology, and meteorology. seNorge_2018 provides a "gridded truth", especially in data-dense regions. The uncertainty increases with decreasing data density.
seNorge_2018 is a collection of observational gridded datasets for daily total precipitation and...
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