Articles | Volume 10, issue 1
https://doi.org/10.5194/essd-10-235-2018
© Author(s) 2018. 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-10-235-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
seNorge2 daily precipitation, an observational gridded dataset over Norway from 1957 to the present day
Norwegian Meteorological Institute, Oslo, Norway
Tuomo Saloranta
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Thomas Skaugen
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Jan Magnusson
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Ole Einar Tveito
Norwegian Meteorological Institute, Oslo, Norway
Jess Andersen
Norwegian Water Resources and Energy Directorate, Oslo, Norway
Related authors
Cristian Lussana, Emma Baietti, Line Båserud, Thomas Nils Nipen, and Ivar Ambjørn Seierstad
Adv. Sci. Res., 20, 35–48, https://doi.org/10.5194/asr-20-35-2023, https://doi.org/10.5194/asr-20-35-2023, 2023
Short summary
Short summary
We have compared hourly precipitation totals measured by rain gauges installed and maintained by citizens against professional weather stations managed by the national weather services of Finland, Norway and Sweden. The manufacturer of the citizen rain gauges is Netatmo. Despite the heterogeneity of citizens' measurements, our results show that the two data sources are comparable with each other, though with some limitations. The results also show how to improve the accuracy of citizens' data.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
Short summary
Short summary
An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Line Båserud, Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, Louise Oram, and Trygve Aspelien
Adv. Sci. Res., 17, 153–163, https://doi.org/10.5194/asr-17-153-2020, https://doi.org/10.5194/asr-17-153-2020, 2020
Short summary
Short summary
We present the open source project Titan for automatic quality control of meteorological in-situ observations. The quality control strategy adopted is a sequence of tests, where several of them utilize the expected spatial consistency between nearby observations.
Titan serves real-time operational applications that process massive amounts of observations measured by networks of automatic weather stations. Further developments include transforming Titan into a more flexible library of functions.
Cristian Lussana, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim
Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, https://doi.org/10.5194/essd-11-1531-2019, 2019
Short summary
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.
F. Uboldi, A. N. Sulis, C. Lussana, M. Cislaghi, and M. Russo
Hydrol. Earth Syst. Sci., 18, 981–995, https://doi.org/10.5194/hess-18-981-2014, https://doi.org/10.5194/hess-18-981-2014, 2014
C. Lussana
Adv. Sci. Res., 10, 59–64, https://doi.org/10.5194/asr-10-59-2013, https://doi.org/10.5194/asr-10-59-2013, 2013
Cristian Lussana, Emma Baietti, Line Båserud, Thomas Nils Nipen, and Ivar Ambjørn Seierstad
Adv. Sci. Res., 20, 35–48, https://doi.org/10.5194/asr-20-35-2023, https://doi.org/10.5194/asr-20-35-2023, 2023
Short summary
Short summary
We have compared hourly precipitation totals measured by rain gauges installed and maintained by citizens against professional weather stations managed by the national weather services of Finland, Norway and Sweden. The manufacturer of the citizen rain gauges is Netatmo. Despite the heterogeneity of citizens' measurements, our results show that the two data sources are comparable with each other, though with some limitations. The results also show how to improve the accuracy of citizens' data.
Elinah Khasandi Kuya, Herdis Motrøen Gjelten, and Ole Einar Tveito
Adv. Sci. Res., 19, 73–80, https://doi.org/10.5194/asr-19-73-2022, https://doi.org/10.5194/asr-19-73-2022, 2022
Short summary
Short summary
Climate normals are used as reference for climate monitoring. When calculating precipitation normals for the period 1991–2020 a homogeneous reference dataset is needed. Norway's observation network has changed drastically in the last 30 years and may have introduced non-climatic changes to the time series. Homogeneity analysis of monthly precipitation series for the period 1961–2018 was performed which produced a homogenous dataset that was used to calculate the new climate normals in Norway.
Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, and Christoffer A. Elo
Nonlin. Processes Geophys., 28, 61–91, https://doi.org/10.5194/npg-28-61-2021, https://doi.org/10.5194/npg-28-61-2021, 2021
Short summary
Short summary
An unprecedented amount of rainfall data is available nowadays, such as ensemble model output, weather radar estimates, and in situ observations from networks of both traditional and opportunistic sensors. Nevertheless, the exact amount of precipitation, to some extent, eludes our knowledge. The objective of our study is precipitation reconstruction through the combination of numerical model outputs with observations from multiple data sources.
Line Båserud, Cristian Lussana, Thomas N. Nipen, Ivar A. Seierstad, Louise Oram, and Trygve Aspelien
Adv. Sci. Res., 17, 153–163, https://doi.org/10.5194/asr-17-153-2020, https://doi.org/10.5194/asr-17-153-2020, 2020
Short summary
Short summary
We present the open source project Titan for automatic quality control of meteorological in-situ observations. The quality control strategy adopted is a sequence of tests, where several of them utilize the expected spatial consistency between nearby observations.
Titan serves real-time operational applications that process massive amounts of observations measured by networks of automatic weather stations. Further developments include transforming Titan into a more flexible library of functions.
Cristian Lussana, Ole Einar Tveito, Andreas Dobler, and Ketil Tunheim
Earth Syst. Sci. Data, 11, 1531–1551, https://doi.org/10.5194/essd-11-1531-2019, https://doi.org/10.5194/essd-11-1531-2019, 2019
Short summary
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.
Valeriya Filipova, Deborah Lawrence, and Thomas Skaugen
Nat. Hazards Earth Syst. Sci., 19, 1–18, https://doi.org/10.5194/nhess-19-1-2019, https://doi.org/10.5194/nhess-19-1-2019, 2019
Short summary
Short summary
This paper presents a stochastic event-based method for analysis of extreme floods, which uses a Monte Carlo procedure to sample initial conditions, snowmelt and rainfall. A study of 20 catchments in Norway shows that this method gives flood estimates that are closer to those obtained using statistical flood frequency analysis than a deterministic event-based model based on a single design storm.
Martin Beniston, Daniel Farinotti, Markus Stoffel, Liss M. Andreassen, Erika Coppola, Nicolas Eckert, Adriano Fantini, Florie Giacona, Christian Hauck, Matthias Huss, Hendrik Huwald, Michael Lehning, Juan-Ignacio López-Moreno, Jan Magnusson, Christoph Marty, Enrique Morán-Tejéda, Samuel Morin, Mohamed Naaim, Antonello Provenzale, Antoine Rabatel, Delphine Six, Johann Stötter, Ulrich Strasser, Silvia Terzago, and Christian Vincent
The Cryosphere, 12, 759–794, https://doi.org/10.5194/tc-12-759-2018, https://doi.org/10.5194/tc-12-759-2018, 2018
Short summary
Short summary
This paper makes a rather exhaustive overview of current knowledge of past, current, and future aspects of cryospheric issues in continental Europe and makes a number of reflections of areas of uncertainty requiring more attention in both scientific and policy terms. The review paper is completed by a bibliography containing 350 recent references that will certainly be of value to scholars engaged in the fields of glacier, snow, and permafrost research.
Emmy E. Stigter, Niko Wanders, Tuomo M. Saloranta, Joseph M. Shea, Marc F. P. Bierkens, and Walter W. Immerzeel
The Cryosphere, 11, 1647–1664, https://doi.org/10.5194/tc-11-1647-2017, https://doi.org/10.5194/tc-11-1647-2017, 2017
Thomas Skaugen and Zelalem Mengistu
Hydrol. Earth Syst. Sci., 20, 4963–4981, https://doi.org/10.5194/hess-20-4963-2016, https://doi.org/10.5194/hess-20-4963-2016, 2016
Short summary
Short summary
This paper introduces a new formulation of hydrological subsurface dynamics for hydrological models. The frequency distribution of the fluctuations of the catchment-scale subsurface storage is estimated from observed recessions and the mean annual runoff. The new formulation of the subsurface has been tested for 73 Norwegian catchments and is found to perform as well as the previous calibrated subsurface formulation. Recessions are better simulated using the new formulation.
Thomas Skaugen and Ingunn H. Weltzien
The Cryosphere, 10, 1947–1963, https://doi.org/10.5194/tc-10-1947-2016, https://doi.org/10.5194/tc-10-1947-2016, 2016
Short summary
Short summary
In hydrological models it is important to properly simulate the spatial distribution of snow water equivalent (SWE) for the timing of spring melt floods and the accounting of energy fluxes. This paper describes a method for the spatial distribution of SWE which is parameterised from observed spatial variability of precipitation and has hence no calibration parameters. Results show improved simulation of SWE and the evolution of snow-free areas when compared with the standard method.
F. Uboldi, A. N. Sulis, C. Lussana, M. Cislaghi, and M. Russo
Hydrol. Earth Syst. Sci., 18, 981–995, https://doi.org/10.5194/hess-18-981-2014, https://doi.org/10.5194/hess-18-981-2014, 2014
C. Lussana
Adv. Sci. Res., 10, 59–64, https://doi.org/10.5194/asr-10-59-2013, https://doi.org/10.5194/asr-10-59-2013, 2013
Related subject area
Meteorology
Atmospheric and surface observations during the Saint John River Experiment on Cold Season Storms (SAJESS)
Year-long buoy-based observations of the air–sea transition zone off the US west coast
The historical Greenland Climate Network (GC-Net) curated and augmented level-1 dataset
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: a comprehensive long-term dataset of remote sensing observations
Global high-resolution drought indices for 1981–2022
CHESS-SCAPE: high-resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled United Kingdom Climate Projections 2018 regional climate model output
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
A dataset of energy, water vapor, and carbon exchange observations in oasis–desert areas from 2012 to 2021 in a typical endorheic basin
Derivation and compilation of lower-atmospheric properties relating to temperature, wind, stability, moisture, and surface radiation budget over the central Arctic sea ice during MOSAiC
CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023
ET-WB: water-balance-based estimations of terrestrial evaporation over global land and major global basins
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
IWIN: the Isfjorden Weather Information Network
A 7-year record of vertical profiles of radar measurements and precipitation estimates at Dumont d’Urville, Adélie Land, East Antarctica
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A Global Gridded Dataset for Cloud Vertical Structure from Combined CloudSat and CALIPSO Observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
The EUPPBench postprocessing benchmark dataset v1.0
MOPREDAScentury: a long-term monthly precipitation grid for the Spanish mainland
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
Data rescue of historical wind observations in Sweden since the 1920s
LegacyClimate 1.0: a dataset of pollen-based climate reconstructions from 2594 Northern Hemisphere sites covering the last 30 kyr and beyond
EURADCLIM: the European climatological high-resolution gauge-adjusted radar precipitation dataset
Radar and ground-level measurements of clouds and precipitation collected during the POPE 2020 campaign at Princess Elisabeth Antarctica
Combined wind lidar and cloud radar for high-resolution wind profiling
An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020
TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations
The AntAWS dataset: a compilation of Antarctic automatic weather station observations
HiTIC-Monthly: a monthly high spatial resolution (1 km) human thermal index collection over China during 2003–2020
A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
GSDM-WBT: global station-based daily maximum wet-bulb temperature data for 1981–2020
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
The PANDA automatic weather station network between the coast and Dome A, East Antarctica
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
High-resolution all-sky land surface temperature and net radiation over Europe
Combined high-resolution rainfall and wind data collected for 3 months on a wind farm 110 km southeast of Paris (France)
Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany
A monthly 0.01° terrestrial evapotranspiration product (1982–2018) for the Tibetan Plateau
Observational data from uncrewed systems over Southern Great Plains
EMO-5: a high-resolution multi-variable gridded meteorological dataset for Europe
GPRChinaTemp1km: a high-resolution monthly air temperature data set for China (1951–2020) based on machine learning
STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) for China
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
Historical and future weather data for dynamic building simulations in Belgium using the regional climate model MAR: typical and extreme meteorological year and heatwaves
Hourly historical and near-future weather and climate variables for energy system modelling
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-301, https://doi.org/10.5194/essd-2023-301, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica are presented in the present paper. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with 2 climate models as an application example of the dataset.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
Short summary
Short summary
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.
William Bertrand, Jennifer E. Kay, John Haynes, and Gijs de Boer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-265, https://doi.org/10.5194/essd-2023-265, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
The vertical structure of clouds has a profound effect 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
Jinfang Yin, Xudong Liang, Yanxin Xie, Feng Li, Kaixi Hu, Lijuan Cao, Feng Chen, Haibo Zou, Feng Zhu, Xin Sun, Jianjun Xu, Geli Wang, Ying Zhao, and Juanjuan Liu
Earth Syst. Sci. Data, 15, 2329–2346, https://doi.org/10.5194/essd-15-2329-2023, https://doi.org/10.5194/essd-15-2329-2023, 2023
Short summary
Short summary
A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
Short summary
Short summary
Newly digitized wind speed observations provide data from the time period from around 1920 to the present, enveloping one full century of wind measurements. The results of this work enable the investigation of the historical variability and trends in surface wind speed in Sweden for
the last century.
Ulrike Herzschuh, Thomas Böhmer, Chenzhi Li, Manuel Chevalier, Raphaël Hébert, Anne Dallmeyer, Xianyong Cao, Nancy H. Bigelow, Larisa Nazarova, Elena Y. Novenko, Jungjae Park, Odile Peyron, Natalia A. Rudaya, Frank Schlütz, Lyudmila S. Shumilovskikh, Pavel E. Tarasov, Yongbo Wang, Ruilin Wen, Qinghai Xu, and Zhuo Zheng
Earth Syst. Sci. Data, 15, 2235–2258, https://doi.org/10.5194/essd-15-2235-2023, https://doi.org/10.5194/essd-15-2235-2023, 2023
Short summary
Short summary
Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
Short summary
Short summary
EURADCLIM is a new precipitation dataset covering a large part of Europe. It is based on weather radar data to provide local precipitation information every hour and combined with rain gauge data to obtain good precipitation estimates. EURADCLIM provides a much better reference for validation of weather model output and satellite precipitation datasets. It also allows for climate monitoring and better evaluation of extreme precipitation events and their impact (landslides, flooding).
Alfonso Ferrone and Alexis Berne
Earth Syst. Sci. Data, 15, 1115–1132, https://doi.org/10.5194/essd-15-1115-2023, https://doi.org/10.5194/essd-15-1115-2023, 2023
Short summary
Short summary
This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
Short summary
Short summary
This paper describes a dataset from a novel experimental setup to retrieve wind speed and direction profiles, combining cloud radars and wind lidar. This setup allows retrieving profiles from near the surface to the top of clouds. The field campaign occurred in Cabauw, the Netherlands, between September 13th and October 3rd 2021. This paper also provides examples of applications of this dataset (e.g. studying atmospheric turbulence, validating numerical atmospheric models).
Peng Yuan, Geoffrey Blewitt, Corné Kreemer, William C. Hammond, Donald Argus, Xungang Yin, Roeland Van Malderen, Michael Mayer, Weiping Jiang, Joseph Awange, and Hansjörg Kutterer
Earth Syst. Sci. Data, 15, 723–743, https://doi.org/10.5194/essd-15-723-2023, https://doi.org/10.5194/essd-15-723-2023, 2023
Short summary
Short summary
We developed a 5 min global integrated water vapour (IWV) product from 12 552 ground-based GPS stations in 2020. It contains more than 1 billion IWV estimates. The dataset is an enhanced version of the existing operational GPS IWV dataset from the Nevada Geodetic Laboratory. The enhancement is reached by using accurate meteorological information from ERA5 for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. The dataset is recommended for high-accuracy applications.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
Short summary
Short summary
Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yetang Wang, Xueying Zhang, Wentao Ning, Matthew A. Lazzara, Minghu Ding, Carleen H. Reijmer, Paul C. J. P. Smeets, Paolo Grigioni, Petra Heil, Elizabeth R. Thomas, David Mikolajczyk, Lee J. Welhouse, Linda M. Keller, Zhaosheng Zhai, Yuqi Sun, and Shugui Hou
Earth Syst. Sci. Data, 15, 411–429, https://doi.org/10.5194/essd-15-411-2023, https://doi.org/10.5194/essd-15-411-2023, 2023
Short summary
Short summary
Here we construct a new database of Antarctic automatic weather station (AWS) meteorological records, which is quality-controlled by restrictive criteria. This dataset compiled all available Antarctic AWS observations, and its resolutions are 3-hourly, daily and monthly, which is very useful for quantifying spatiotemporal variability in weather conditions. Furthermore, this compilation will be used to estimate the performance of the regional climate models or meteorological reanalysis products.
Hui Zhang, Ming Luo, Yongquan Zhao, Lijie Lin, Erjia Ge, Yuanjian Yang, Guicai Ning, Jing Cong, Zhaoliang Zeng, Ke Gui, Jing Li, Ting On Chan, Xiang Li, Sijia Wu, Peng Wang, and Xiaoyu Wang
Earth Syst. Sci. Data, 15, 359–381, https://doi.org/10.5194/essd-15-359-2023, https://doi.org/10.5194/essd-15-359-2023, 2023
Short summary
Short summary
We generate the first monthly high-resolution (1 km) human thermal index collection (HiTIC-Monthly) in China over 2003–2020, in which 12 human-perceived temperature indices are generated by LightGBM. The HiTIC-Monthly dataset has a high accuracy (R2 = 0.996, RMSE = 0.693 °C, MAE = 0.512 °C) and describes explicit spatial variations for fine-scale studies. It is freely available at https://zenodo.org/record/6895533 and https://data.tpdc.ac.cn/disallow/036e67b7-7a3a-4229-956f-40b8cd11871d.
Jun Qin, Weihao Pan, Min He, Ning Lu, Ling Yao, Hou Jiang, and Chenghu Zhou
Earth Syst. Sci. Data, 15, 331–344, https://doi.org/10.5194/essd-15-331-2023, https://doi.org/10.5194/essd-15-331-2023, 2023
Short summary
Short summary
To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble learning model is constructed to estimate monthly SATs from satellite land surface temperatures at a spatial resolution of 1 km, and long-term glacial SATs from 1961 to 2020 are reconstructed using a Bayesian linear regression. This product reveals the overall warming trend and the spatial heterogeneity of warming on TP glaciers and helps to monitor glacier warming, analyze glacier evolution, etc.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
Short summary
Short summary
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Jianquan Dong, Stefan Brönnimann, Tao Hu, Yanxu Liu, and Jian Peng
Earth Syst. Sci. Data, 14, 5651–5664, https://doi.org/10.5194/essd-14-5651-2022, https://doi.org/10.5194/essd-14-5651-2022, 2022
Short summary
Short summary
We produced a new dataset of global station-based daily maximum wet-bulb temperature (GSDM-WBT) through the calculation of wet-bulb temperature, data quality control, infilling missing values, and homogenization. The GSDM-WBT covers the complete daily series of 1834 stations from 1981 to 2020. The GSDM-WBT dataset handles stations with many missing values and possible inhomogeneities, which could better support the studies on global and regional humid heat events.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
Short summary
Short summary
It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Minghu Ding, Xiaowei Zou, Qizhen Sun, Diyi Yang, Wenqian Zhang, Lingen Bian, Changgui Lu, Ian Allison, Petra Heil, and Cunde Xiao
Earth Syst. Sci. Data, 14, 5019–5035, https://doi.org/10.5194/essd-14-5019-2022, https://doi.org/10.5194/essd-14-5019-2022, 2022
Short summary
Short summary
The PANDA automatic weather station (AWS) network consists of 11 stations deployed along a transect from the coast (Zhongshan Station) to the summit of the East Antarctic Ice Sheet (Dome A). It covers the different climatic and topographic units of East Antarctica. All stations record hourly air temperature, relative humidity, air pressure, wind speed and direction at two or three heights. The PANDA AWS dataset commences from 1989 and is planned to be publicly available into the future.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
Short summary
Short summary
Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
Eva Beele, Maarten Reyniers, Raf Aerts, and Ben Somers
Earth Syst. Sci. Data, 14, 4681–4717, https://doi.org/10.5194/essd-14-4681-2022, https://doi.org/10.5194/essd-14-4681-2022, 2022
Short summary
Short summary
This paper presents crowdsourced data from the Leuven.cool network, a citizen science network of around 100 low-cost weather stations distributed across Leuven, Belgium. The temperature data have undergone a quality control (QC) and correction procedure. The procedure consists of three levels that remove implausible measurements while also correcting for between-station and station-specific temperature biases.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego Gonzales Miralles
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-302, https://doi.org/10.5194/essd-2022-302, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Land Surface Temperature and Surface Net Radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions and the few high-resolution datasets available have large gaps due to cloud-cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 by combining observations from geostationary as well as polar-orbiting satellites.
Auguste Gires, Jerry Jose, Ioulia Tchiguirinskaia, and Daniel Schertzer
Earth Syst. Sci. Data, 14, 3807–3819, https://doi.org/10.5194/essd-14-3807-2022, https://doi.org/10.5194/essd-14-3807-2022, 2022
Short summary
Short summary
The Hydrology Meteorology and Complexity laboratory of École des Ponts ParisTech (https://hmco.enpc.fr) has made a data set of high-resolution atmospheric measurements (rainfall, wind, temperature, pressure, and humidity) available. It comes from a campaign carried out on a meteorological mast located on a wind farm in the framework of the Rainfall Wind Turbine or Turbulence project (RW-Turb; supported by the French National Research Agency – ANR-19-CE05-0022).
Bastian Kirsch, Cathy Hohenegger, Daniel Klocke, Rainer Senke, Michael Offermann, and Felix Ament
Earth Syst. Sci. Data, 14, 3531–3548, https://doi.org/10.5194/essd-14-3531-2022, https://doi.org/10.5194/essd-14-3531-2022, 2022
Short summary
Short summary
Conventional observation networks are too coarse to resolve the horizontal structure of kilometer-scale atmospheric processes. We present the FESST@HH field experiment that took place in Hamburg (Germany) during summer 2020 and featured a dense network of 103 custom-built, low-cost weather stations. The data set is capable of providing new insights into the structure of convective cold pools and the nocturnal urban heat island and variations of local temperature fluctuations.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-195, https://doi.org/10.5194/essd-2022-195, 2022
Revised manuscript accepted for ESSD
Short summary
Short summary
Accurately monitoring and understanding the spatial and temporal variability of the ET components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP were produced, and is very consistent with the measurements .The annual average ET for the entire TP was about 0.93 ± 0.037 Gt/year. The rate of increase of the ET was around 0.96 mm/year. The increase in the ET can be explained by warming and wetting of the climate.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
Short summary
Short summary
This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Vera Thiemig, Goncalo N. Gomes, Jon O. Skøien, Markus Ziese, Armin Rauthe-Schöch, Elke Rustemeier, Kira Rehfeldt, Jakub P. Walawender, Christine Kolbe, Damien Pichon, Christoph Schweim, and Peter Salamon
Earth Syst. Sci. Data, 14, 3249–3272, https://doi.org/10.5194/essd-14-3249-2022, https://doi.org/10.5194/essd-14-3249-2022, 2022
Short summary
Short summary
EMO-5 is a free and open European high-resolution (5 km), sub-daily, multi-variable (precipitation, temperatures, wind speed, solar radiation, vapour pressure), multi-decadal meteorological dataset based on quality-controlled observations coming from almost 30 000 stations across Europe, and is produced in near real-time. EMO-5 (v1) covers the time period from 1990 to 2019. In this paper, we have provided insight into the source data, the applied methods, and the quality assessment of EMO-5.
Qian He, Ming Wang, Kai Liu, Kaiwen Li, and Ziyu Jiang
Earth Syst. Sci. Data, 14, 3273–3292, https://doi.org/10.5194/essd-14-3273-2022, https://doi.org/10.5194/essd-14-3273-2022, 2022
Short summary
Short summary
We used three machine learning models and determined that Gaussian process regression (GPR) is best suited to the interpolation of air temperature data for China. The GPR-derived results were compared with that of traditional interpolation techniques and existing data sets and it was found that the accuracy of the GPR-derived data was better. Finally, we generated a gridded monthly air temperature data set with 1 km resolution and high accuracy for China (1951–2020) using the GPR model.
Yinghong Jing, Xinghua Li, and Huanfeng Shen
Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, https://doi.org/10.5194/essd-14-3137-2022, 2022
Short summary
Short summary
Snow variation is a vital factor in global climate change. Satellite-based approaches are effective for large-scale environmental monitoring. Nevertheless, the high cloud fraction seriously impedes the remote-sensed investigation. Therefore, a recent 20-year cloud-free snow cover collection in China is generated for the first time. This collection can serve as a basic dataset for hydrological and climatic modeling to explore various critical environmental issues.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
Short summary
Short summary
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Sébastien Doutreloup, Xavier Fettweis, Ramin Rahif, Essam Elnagar, Mohsen S. Pourkiaei, Deepak Amaripadath, and Shady Attia
Earth Syst. Sci. Data, 14, 3039–3051, https://doi.org/10.5194/essd-14-3039-2022, https://doi.org/10.5194/essd-14-3039-2022, 2022
Short summary
Short summary
This data set provides historical (1980–2014) and future (2015–2100) weather data for 12 cities in Belgium. This data set is intended for architects or building or energy designers. In particular, it makes available to all users hourly open-access weather data according to certain standards to recreate a Typical and an Extreme Meteorological Year. In addition, it provides hourly data on heatwaves from 1980 to 2100. Weather data were produced from the outputs of the MAR model simulations.
Hannah C. Bloomfield, David J. Brayshaw, Matthew Deakin, and David Greenwood
Earth Syst. Sci. Data, 14, 2749–2766, https://doi.org/10.5194/essd-14-2749-2022, https://doi.org/10.5194/essd-14-2749-2022, 2022
Short summary
Short summary
There is a global increase in renewable generation to meet carbon targets and reduce the impacts of climate change. Renewable generation and electricity demand depend on the weather. This means there is a need for high-quality weather data for energy system modelling. We present a new European-level, 70-year dataset which has been specifically designed to support the energy sector. We provide hourly, sub-national climate outputs and include the impacts of near-term climate change.
Cited articles
Aalto, J., Pirinen, P., and Jylhä, K.: New gridded daily climatology of Finland: Permutation-based uncertainty estimates and temporal trends in climate, J. Geophys. Res.-Atmos., 121, 3807–3823, 2016.
Barnes, S. L.: A technique for maximizing details in numerical map analysis, J. Appl. Meteorol., 3, 395–409, 1964.
Berg, P., Donnelly, C., and Rosberg, J.: HYPE EURO4M Evaluation Report, Tech. rep., UERRA Report 4.6, 2014.
Bergthörsson, P. and Döös, B. R.: Numerical weather map analysis, Tellus, 7, 329–340, 1955.
Bratseth, A.: Statistical Interpolation by means of successive corrections, Tellus A, 38, 439–447, 1986.
Crespi, A., Brunetti, M., Lentini, G., and Maugeri, M.: 1961–1990 high-resolution monthly precipitation climatologies for Italy, Int. J. Climatol., https://doi.org/10.1002/joc.5217, 2016.
Daley, R.: Atmospheric Data Analysis, Cambridge University Press, Cambridge, UK, 1991.
Eliassen, A.: Provisional report on calculation of spatial covariance and autocorrelation of the pressure field, Tech. Rep., Inst. Weather and Clim. Res., Acad. Sci., Oslo, 5, 1954.
Engeset, R., Tveito, O. E., Udnæs, H.-C., Alfnes, E., Mengistu, Z., Isaksen, K., and Førland, E. J.: Snow map validation for Norway, in: Proceedings XXIII Nordic Hydrological Conference 2004, 8–12 August 2004, Tallinn, Estonia, 8–12, 2004.
Erdin, R.: Combining rain gauge and radar measurements of a heavy precipitation event over Switzerland: comparison of geostatistical methods and investigation of important influencing factors, PhD thesis, Bundesamt für Meteorologie und Klimatologie, MeteoSchweiz, Zurich, Switzerland, 2009.
Førland, E. and Tveito, O.: Temperatur og snødata for flomberegning, DNMI Report, The Norwegian Meteorological Institute, Oslo, Norway, 28, 51, 1997.
Frei, C., Christensen, J. H., Déqué, M., Jacob, D., Jones, R. G., and Vidale, P. L.: Daily precipitation statistics in regional climate models: Evaluation and intercomparison for the European Alps, J. Geophys. Res.-Atmos., 108, 4124, https://doi.org/10.1029/2002JD002287, 2003.
Gandin, L. S. and Hardin, R.: Objective analysis of meteorological fields, vol. 242, Israel program for scientific translations Jerusalem, 1965.
Gisnås, K., Etzelmüller, B., Lussana, C., Hjort, J., Sannel, A. B. K., Isaksen, K., Westermann, S., Kuhry, P., Christiansen, H. H., Frampton, A., and Åkerman, J.: Permafrost map for Norway, Sweden and Finland, Permafrost Periglac., 28, 359–378, 2017.
Haylock, M., Hofstra, N., Klein Tank, A., Klok, E., Jones, P., and New, M.: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008JD010201, 2008.
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, 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. Dynam., 35, 841–858, 2010.
Ide, K., Courtier, P., Ghil, M., and Lorenc, A.: Unified notation for data assimilation: operational, sequential and variational, Practice, 75, 181–189, 1997.
Jazwinski, A. H.: Stochastic Processes and Filtering Theory, Courier Dover Publications, Mineola, New York, USA, 2007.
Jolliffe, I. T. and Stephenson, D. B.: Forecast Verification: A Practitioner's Guide in Atmospheric Science, John Wiley and Sons, : Chichester, UK, 2003.
Kalnay, E.: Atmospheric Modeling, Data Assimilation and Predictability, Cambridge University Press, Cambridge, UK, 2003.
Klein Tank, A., Wijngaard, J., Können, G., 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. V., 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.
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, 2012.
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., https://doi.org/10.1002/joc.5249, 2017.
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.
Lorenc, A.: Analysis methods for numerical weather prediction, Q. J. Roy. Meteor. Soc., 112, 1177–1194, 1986.
Lussana, C., Salvati, M. R., Pellegrini, U., and Uboldi, F.: Efficient high-resolution 3-D interpolation of meteorological variables for operational use, Adv. Sci. Res., 3, 105–112, https://doi.org/10.5194/asr-3-105-2009, 2009.
Lussana, C., Uboldi, F., and Salvati, M. R.: A spatial consistency test for surface observations from mesoscale meteorological networks, Q. J. Roy. Meteor. Soc., 136, 1075–1088, 2010.
Lussana, C., Tveito, O. E., and Uboldi, F.: seNorge v2.0: an observational gridded dataset of temperature for Norway, MET report 14-2016, publisher: the Norwegian meteorological Institute, Oslo, Norway, 2016.
Lussana, C., Tveito, O., and Uboldi, F.: Three-dimensional spatial interpolation of two-meter temperature over Norway, Q. J. Roy. Meteor. Soc., https://doi.org/10.1002/qj.3208, 2018.
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.
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.
Masson, D. and Frei, C.: Long-term variations and trends of mesoscale precipitation in the Alps: recalculation and update for 1901–2008, Int. J. Climatol., 36, 492–500, 2016.
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.
Mohr, M.: New routines for gridding of temperature and precipitation observations for “seNorge. no”, Met. no Report, 8, the Norwegian meteorological Institute, Oslo, Norway, 2008.
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, the Norwegian meteorological Institute, Oslo, Norway, 2009.
Mu, Q., Zhao, M., and Running, S. W.: Improvements to a MODIS global terrestrial evapotranspiration algorithm, Remote Sens. Environ., 115, 1781–1800, 2011.
Rocke, D. M. and Durbin, B.: Approximate variance-stabilizing transformations for gene-expression microarray data, Bioinformatics, 19, 966–972, 2003.
Salomonson, V. and Appel, I.: Estimating fractional snow cover from MODIS using the normalized difference snow index, Remote Sens. Environ., 89, 351–360, 2004.
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.
Saloranta, T.: New version (v. 1.1. 1) of the seNorge snow model and snow maps for Norway, Norwegian Water Resources and Energy Directorate, Norwegian Water Resources and Energy Directorate, Oslo, Norway, Rapport 6-2014, 2014a.
Saloranta, T. M.: Simulating more accurate snow maps for Norway with MCMC parameter estimation method, The Cryosphere Discuss., https://doi.org/10.5194/tcd-8-1973-2014, 2014b.
Saloranta, T. M.: Operational snow mapping with simplified data assimilation using the seNorge snow model, J. Hydrol., 538, 314–325, 2016.
Schiemann, R., Liniger, M. A., and Frei, C.: Reduced space optimal interpolation of daily rain gauge precipitation in Switzerland, J. Geophys. Res.-Atmos., 115, D14109 https://doi.org/10.1029/2009JD013047, 2010.
Simmons, A., Berrisford, P., Dee, D., 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, 2016.
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.
Skaugen, T. and Onof, C.: A rainfall-runoff model parameterized from GIS and runoff data, Hydrol. Process., 28, 4529–4542, 2014.
Solberg, R., Amlien, J., and Koren, H.: A review of optical snow cover algorithms, Tech. rep., Norwegian Computing Center Note, no, SAMBA/40/06, 2006.
Thompson, P. D.: Numerical Weather Analysis and Prediction, Macmillan, Oslo, Norway, 1961.
Thunis, P. and Bornstein, R.: Hierarchy of mesoscale flow assumptions and equations, J. Atmos. Sci., 53, 380–397, 1996.
Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements, Hydrol. Earth Syst. Sci., 5, 187-199, https://doi.org/10.5194/hess-5-187-2001, 2001.
Tveito, O., Førland, E., Dahlström, B., Elomaa, E., Frich, P., Hanssen-Bauer, I., Jónsson, T., Madsen, H., Perälä, J., Rissanen, P., and Vedin, H.: Nordic precipitation maps, DNMI Report, the Norwegian Meteorological Institute, Oslo, Norway, 22, 22, 1997.
Tveito, O. E., Udnæs, H.-C., Mengistu, Z., Engeset, R., and Førland, E. J.: New snow maps for Norway, in: Proceedings XXII Nordic Hydrological Conference 2002, Røros, Norway, 4–7 August 2002, 527–532, 2002.
Uboldi, F. and Buzzi, A.: Successive-correction methods applied to mesoscale meteorological analysis, Il Nuovo Cimento C, 17, 745–761, 1994.
Uboldi, F., Lussana, C., and Salvati, M.: Three-dimensional spatial interpolation of surface meteorological observations from high-resolution local networks, Meteorol. Appl., 15, 331–345, 2008.
Wackernagel, H.: Multivariate Geostatistics: An Introduction with Applications, Springer Science and Business Media, New York, 2013.
Wahba, G. and Wendelberger, J.: Some new mathematical methods for variational objective analysis using splines and cross validation, Mon. Weather Rev., 108, 1122–1143, 1980.
Short summary
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.
The observational gridded climate datasets are among the primary sources of information for...
Altmetrics
Final-revised paper
Preprint