Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-331-2023
© Author(s) 2023. 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-15-331-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 Jan 2023
Data description paper |
| 19 Jan 2023
| 19 Jan 2023
A long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
Jun Qin
CORRESPONDING AUTHOR
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Weihao Pan
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
Key Laboratory of Tibetan Environmental Changes and Land Surfaces
Processes, Institute of Tibetan Plateau Research, Chinese Academy of
Sciences, Beijing 100101, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Chenghu Zhou
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Related authors
Wenjun Tang, Jun Qin, Kun Yang, Yaozhi Jiang, and Weihao Pan
Earth Syst. Sci. Data, 14, 2007–2019, https://doi.org/10.5194/essd-14-2007-2022, https://doi.org/10.5194/essd-14-2007-2022, 2022
Short summary
Short summary
Photosynthetically active radiation (PAR) is a fundamental physiological variable for research in the ecological, agricultural, and global change fields. In this study, we produced a 35-year high-resolution global gridded PAR dataset. Compared with the well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES), our PAR product was found to be a more accurate dataset with higher resolution.
Wenjun Tang, Jun Qin, Kun Yang, Yaozhi Jiang, and Weihao Pan
Earth Syst. Sci. Data, 14, 2007–2019, https://doi.org/10.5194/essd-14-2007-2022, https://doi.org/10.5194/essd-14-2007-2022, 2022
Short summary
Short summary
Photosynthetically active radiation (PAR) is a fundamental physiological variable for research in the ecological, agricultural, and global change fields. In this study, we produced a 35-year high-resolution global gridded PAR dataset. Compared with the well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES), our PAR product was found to be a more accurate dataset with higher resolution.
Hou Jiang, Ling Yao, Ning Lu, Jun Qin, Tang Liu, Yujun Liu, and Chenghu Zhou
Earth Syst. Sci. Data, 13, 5389–5401, https://doi.org/10.5194/essd-13-5389-2021, https://doi.org/10.5194/essd-13-5389-2021, 2021
Short summary
Short summary
A multi-resolution (0.8, 0.3, and 0.1 m) photovoltaic (PV) dataset is established using satellite and aerial images. The dataset contains 3716 samples of PVs installed on various land and rooftop types. The dataset can support multi-scale PV segmentation (e.g., concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs) and cross applications between different resolutions (e.g., from satellite to aerial samples and vice versa), as well as other research related to PVs.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Hou Jiang, Ning Lu, Jun Qin, and Ling Yao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-209, https://doi.org/10.5194/essd-2019-209, 2019
Revised manuscript not accepted
Short summary
Short summary
This study produces a 12-year (2007–2018) hourly surface global and diffuse solar radiation dataset with 0.05° grids over China based on geostationary satellite data using deep learning technique. The produced data have much higher accuracy than results of traditional methods and current widely-used products due to integration of spatial pattern through convolutional neural network, enlightening studies involving spatial features, and reveal the long-term regional variations in fine scales.
Jiawei Yi, Yunyan Du, Fuyuan Liang, Tao Pei, Ting Ma, and Chenghu Zhou
Nat. Hazards Earth Syst. Sci., 19, 2169–2182, https://doi.org/10.5194/nhess-19-2169-2019, https://doi.org/10.5194/nhess-19-2169-2019, 2019
Short summary
Short summary
This paper utilized the advantages of smartphone location data to study human responses to rainstorm disasters. Intense rainstorms disrupt city residents' behaviors as reflected in anomalies of location-based service requests. Anomaly identification from fine-scale smartphone location data facilitates the monitoring of social responses to rainstorms. Residents' collective geotagged behaviors in different cities show different sensitivities to rainstorms.
Changchun Huang, Quanliang Jiang, Ling Yao, Hao Yang, Chen Lin, Tao Huang, A-Xing Zhu, and Yimin Zhang
Biogeosciences, 15, 1827–1841, https://doi.org/10.5194/bg-15-1827-2018, https://doi.org/10.5194/bg-15-1827-2018, 2018
Short summary
Short summary
The latitudinal dependency of POC / PON in ocean and inland water is significant, regulated by trophic state and climate, etc. factors. POC / PON significantly increased from coastal water (6.89 ± 2.38) to open ocean (7.59 ± 4.22) with the increasing rate of 0.0024 / km. The re-examination of the global relationship between, and variations in, POC and PON could be important for the global and regional coupling between the carbon and nitrogen cycles in the ocean and freshwater.
J. Yi, Y. Du, Z. He, and C. Zhou
Ocean Sci., 10, 39–48, https://doi.org/10.5194/os-10-39-2014, https://doi.org/10.5194/os-10-39-2014, 2014
J. Yi, Y. Du, X. Wang, Z. He, and C. Zhou
Ocean Sci., 9, 171–182, https://doi.org/10.5194/os-9-171-2013, https://doi.org/10.5194/os-9-171-2013, 2013
Related subject area
Domain: ESSD – Atmosphere | Subject: Meteorology
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 new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range
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
The EUPPBench postprocessing benchmark dataset v1.0
Year-long Buoy-Based Observations of the Air–Sea Transition Zone off the U.S. West Coast
CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies
Database of the Italian disdrometer network
East Asia Reanalysis System (EARS)
A dataset of energy, water vapor and carbon exchange observations in oasis-desert areas from 2012 to 2021 in a typical endorheic basin
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
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
Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, 2012–2020
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
CHESS-SCAPE: High resolution future projections of multiple climate scenarios for the United Kingdom derived from downscaled UKCP18 regional climate model output
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 global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
Quality control and correction method for air temperature data from a citizen science weather station network in Leuven, Belgium
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
Observational data from uncrewed systems over Southern Great Plains
A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis
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.
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.
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.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-141, https://doi.org/10.5194/essd-2023-141, 2023
Revised manuscript accepted for ESSD
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.
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.
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 Discuss., https://doi.org/10.5194/essd-2023-115, https://doi.org/10.5194/essd-2023-115, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
Our understanding, ability to observe and model air-sea processes has been identified as a principal limitation to our ability to predict future climate. 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 along the coast of California. In this article, we present details of the post-processing, algorithms and analysis.
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.
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 Discuss., https://doi.org/10.5194/essd-2023-149, https://doi.org/10.5194/essd-2023-149, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
We present a suite of observational datasets in artificial and natural oases-desert systems, which consist of long-term turbulent flux and auxiliary data involving hydrometeorology, 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 believe that the datasets will support ecological security and sustainable development in oasis-desert areas.
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.
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 Discuss., https://doi.org/10.5194/essd-2023-133, https://doi.org/10.5194/essd-2023-133, 2023
Revised manuscript accepted for ESSD
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.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-116, https://doi.org/10.5194/essd-2023-116, 2023
Preprint under review for ESSD
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 declined the 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.
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.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-430, https://doi.org/10.5194/essd-2022-430, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
CHESS-SCAPE is a suite of high-resolution climate projections for the United Kingdom to 2080, derived from UKCP18 and designed to support climate impacts modelling. It contains four realisations each of four different scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), provided with and without bias-correction to historical data. The variables are available at 1 km resolution and daily timestep, with monthly, seasonal and annual means, plus twenty-year mean-monthly timeslices.
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.
Tao Zhang, Yuyu Zhou, Kaiguang Zhao, Zhengyuan Zhu, Gang Chen, Jia Hu, and Li Wang
Earth Syst. Sci. Data, 14, 5637–5649, https://doi.org/10.5194/essd-14-5637-2022, https://doi.org/10.5194/essd-14-5637-2022, 2022
Short summary
Short summary
We generated a global 1 km daily maximum and minimum near-surface air temperature (Tmax and Tmin) dataset (2003–2020) using a novel statistical model. The average root mean square errors ranged from 1.20 to 2.44 °C for Tmax and 1.69 to 2.39 °C for Tmin. The gridded global air temperature dataset is of great use in a variety of studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting.
Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann
Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022, https://doi.org/10.5194/essd-14-5287-2022, 2022
Short summary
Short summary
In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.
Craig D. Smith, Eva Mekis, Megan Hartwell, and Amber Ross
Earth Syst. Sci. Data, 14, 5253–5265, https://doi.org/10.5194/essd-14-5253-2022, https://doi.org/10.5194/essd-14-5253-2022, 2022
Short summary
Short summary
It is well understood that precipitation gauges underestimate the measurement of solid precipitation (snow) as a result of systematic bias caused by wind. Relationships between the wind speed and gauge catch efficiency of solid precipitation have been previously established and are applied to the hourly precipitation measurements made between 2001 and 2019 in the automated Environment and Climate Change Canada observation network. The adjusted data are available for download and use.
Zen Mariani, Laura Huang, Robert Crawford, Jean-Pierre Blanchet, Shannon Hicks-Jalali, Eva Mekis, Ludovick Pelletier, Peter Rodriguez, and Kevin Strawbridge
Earth Syst. Sci. Data, 14, 4995–5017, https://doi.org/10.5194/essd-14-4995-2022, https://doi.org/10.5194/essd-14-4995-2022, 2022
Short summary
Short summary
Environment and Climate Change Canada (ECCC) commissioned two supersites in Iqaluit (64°N, 69°W) and Whitehorse (61°N, 135°W) to provide new and enhanced automated and continuous altitude-resolved meteorological observations as part of the Canadian Arctic Weather Science (CAWS) project. These observations are being used to test new technologies, provide recommendations to the optimal Arctic observing system, and evaluate and improve the performance of numerical weather forecast systems.
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.
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.
Fan Mei, Mikhail S. Pekour, Darielle Dexheimer, Gijs de Boer, RaeAnn Cook, Jason Tomlinson, Beat Schmid, Lexie A. Goldberger, Rob Newsom, and Jerome D. Fast
Earth Syst. Sci. Data, 14, 3423–3438, https://doi.org/10.5194/essd-14-3423-2022, https://doi.org/10.5194/essd-14-3423-2022, 2022
Short summary
Short summary
This work focuses on an expanding number of data sets observed using ARM TBS (133 flights) and UAS (seven flights) platforms by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility. These data streams provide new perspectives on spatial variability of atmospheric and surface parameters, helping to address critical science questions in Earth system science research, such as the aerosol–cloud interaction in the boundary layer.
Falu Hong, Wenfeng Zhan, Frank-M. Göttsche, Zihan Liu, Pan Dong, Huyan Fu, Fan Huang, and Xiaodong Zhang
Earth Syst. Sci. Data, 14, 3091–3113, https://doi.org/10.5194/essd-14-3091-2022, https://doi.org/10.5194/essd-14-3091-2022, 2022
Short summary
Short summary
Daily mean land surface temperature (LST) acquired from satellite thermal sensors is crucial for various applications such as global and regional climate change analysis. This study proposed a framework to generate global spatiotemporally seamless daily mean LST products (2003–2019). Validations show that the products outperform the traditional method with satisfying accuracy. Our further analysis reveals that the LST-based global land surface warming rate is 0.029 K yr−1 from 2003 to 2019.
Cited articles
Belgiu, M. and Dragut, L.: Random Forest in remote sensing: A review of
applications and future directions, Isprs J. Photogramm., 114, 24-31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016.
Benali, A., Carvalho, A. C., Nunes, J. P., Carvalhais, N., and Santos, A.:
Estimating air surface temperature in Portugal using MODIS LST data, Remote
Sens. Environ., 124, 108–121, https://doi.org/10.1016/j.rse.2012.04.024, 2012.
Bhattacharya, A., Bolch, T., Mukherjee, K., King, O., Menounos, B., Kapitsa,
V., Neckel, N., Yang, W., and Yao, T.: High Mountain Asian glacier response
to climate revealed by multi-temporal satellite observations since the
1960s, Nat. Commun., 12, 4133, https://doi.org/10.1038/s41467-021-24180-y, 2021.
Box, J. E., Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M.,
Parmentier, F.-J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S.,
Romanovsky, V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R. W.,
Meier, W. N., Wouters, B., Mernild, S., Mård, J., Pawlak, J., and Olsen,
M. S.: Key indicators of Arctic climate change: 1971–2017, Environ.
Res. Lett., 14, 045010, https://doi.org/10.1088/1748-9326/aafc1b, 2019.
Brun, F., Berthier, E., Wagnon, P., Kaab, A., and Treichler, D.: A spatially
resolved estimate of High Mountain Asia glacier mass balances from 2000 to
2016, Nat. Geosci., 10, 668, https://doi.org/10.1038/ngeo2999, 2017.
Cao, L., Zhu, Y., Tang, G., Yuan, F., and Yan, Z.: Climatic warming in China
according to a homogenized data set from 2419 stations, Int.
J. Climatol., 36, 4384–4392, 2016.
Chen, Y., Liang, S., Ma, H., Li, B., He, T., and Wang, Q.: An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data, Earth Syst. Sci. Data, 13, 4241–4261, https://doi.org/10.5194/essd-13-4241-2021, 2021.
Farinotti, D., Immerzeel, W. W., de Kok, R. J., Quincey, D. J., and Dehecq,
A.: Manifestations and mechanisms of the Karakoram glacier Anomaly, Nat.
Geosci., 13, 8–16, https://doi.org/10.1038/s41561-019-0513-5, 2020.
Guo, D. L., Sun, J. Q., Yang, K., Pepin, N., and Xu, Y. M.: Revisiting
Recent Elevation-Dependent Warming on the Tibetan Plateau Using
Satellite-Based Data Sets, J. Geophys. Res.-Atmos., 124,
8511–8521, 2019.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H. J.: Updated
high-resolution grids of monthly climatic observations–the CRU TS3. 10
Dataset, Int. J. Climatol., 34, 623–642, 2014.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.
J.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 2020.
Hock, R., Bliss, A., Marzeion, B., Giesen, R. H., Hirabayashi, Y., Huss, M.,
Radić, V., and Slangen, A. B.: GlacierMIP–A model intercomparison of
global-scale glacier mass-balance models and projections, J. Glaciol., 65, 453–467, 2019.
Hooker, J., Duveiller, G., and Cescatti, A.: A global dataset of air
temperature derived from satellite remote sensing and weather stations,
Sci. Data, 5, 180246, https://doi.org/10.1038/sdata.2018.246, 2018.
Huang, M., Piao, S., Ciais, P., Peñuelas, J., Wang, X., Keenan, T. F.,
Peng, S., Berry, J. A., Wang, K., Mao, J., Alkama, R., Cescatti, A., Cuntz,
M., De Deurwaerder, H., Gao, M., He, Y., Liu, Y., Luo, Y., Myneni, R. B.,
Niu, S., Shi, X., Yuan, W., Verbeeck, H., Wang, T., Wu, J., and Janssens, I.
A.: Air temperature optima of vegetation productivity across global biomes,
Nat. Ecol. Evol., 3, 772–779, https://doi.org/10.1038/s41559-019-0838-x, 2019.
Immerzeel, W. W., Lutz, A. F., Andrade, M., Bahl, A., Biemans, H., Bolch,
T., Hyde, S., Brumby, S., Davies, B. J., Elmore, A. C., Emmer, A., Feng, M.,
Fernandez, A., Haritashya, U., Kargel, J. S., Koppes, M., Kraaijenbrink, P.
D. A., Kulkarni, A. V., Mayewski, P. A., Nepal, S., Pacheco, P., Painter, T.
H., Pellicciotti, F., Rajaram, H., Rupper, S., Sinisalo, A., Shrestha, A.
B., Viviroli, D., Wada, Y., Xiao, C., Yao, T., and Baillie, J. E. M.:
Importance and vulnerability of the world's water towers, Nature, 577,
364–369, 2020.
Kang, S., Zhang, Q., Zhang, Y., Guo, W., Ji, Z., Shen, M., Wang, S., Wang,
X., Tripathee, L., Liu, Y., Gao, T., Xu, G., Gao, Y., Kaspari, S., Luo, X.,
and Mayewski, P.: Warming and thawing in the Mt. Everest region: A review of
climate and environmental changes, Earth-Sci. Rev., 225, 103911,
https://doi.org/10.1016/j.earscirev.2021.103911, 2022.
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F., and Immerzeel, W.
W.: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's
glaciers, Nature, 549, 257, https://doi.org/10.1038/nature23878, 2017.
Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K., and Wunderle, S.: Climate change in the High Mountain Asia in CMIP6, Earth Syst. Dynam., 12, 1061–1098, https://doi.org/10.5194/esd-12-1061-2021, 2021.
Li, X. P., Wang, L., Chen, D. L., Yang, K., Xue, B. L., and Sun, L. T.:
Near-surface air temperature lapse rates in the mainland China during
1962–2011, J. Geophys. Res.-Atmos., 118, 7505–7515,
https://doi.org/10.1002/jgrd.50553, 2013.
Miles, E., McCarthy, M., Dehecq, A., Kneib, M., Fugger, S., and
Pellicciotti, F.: Health and sustainability of glaciers in High Mountain
Asia, Nat. Commun., 12, 2868, https://doi.org/10.1038/s41467-021-23073-4, 2021.
Muñoz-Sabater, J., Dutra, E., Agustí-Panareda, A., Albergel, C., Arduini, G., Balsamo, G., Boussetta, S., Choulga, M., Harrigan, S., Hersbach, H., Martens, B., Miralles, D. G., Piles, M., Rodríguez-Fernández, N. J., Zsoter, E., Buontempo, C., and Thépaut, J.-N.: ERA5-Land: a state-of-the-art global reanalysis dataset for land applications, Earth Syst. Sci. Data, 13, 4349–4383, https://doi.org/10.5194/essd-13-4349-2021, 2021.
Nie, Y., Pritchard, H. D., Liu, Q., Hennig, T., Wang, W., Wang, X., Liu, S.,
Nepal, S., Samyn, D., Hewitt, K., and Chen, X.: Glacial change and
hydrological implications in the Himalaya and Karakoram, Nat. Rev.
Earth Environ., 2, 91–106, https://doi.org/10.1038/s43017-020-00124-w, 2021.
Noi, P. T., Degener, J., and Kappas, M.: Comparison of Multiple Linear
Regression, Cubist Regression, and Random Forest Algorithms to Estimate
Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data,
Remote Sens., 9, 398, https://doi.org/10.3390/rs9050398, 2017.
Peng, X., Frauenfeld, O. W., Jin, H., Du, R., Qiao, L., Zhao, Y., Mu, C.,
and Zhang, T.: Assessment of Temperature Changes on the Tibetan Plateau
During 1980–2018, Earth Space Sci., 8, e2020EA001609,
https://doi.org/10.1029/2020EA001609, 2021.
Pfeffer, W. T., Arendt, A. A., Bliss, A., Bolch, T., Cogley, J. G., Gardner,
A. S., Hagen, J.-O., Hock, R., Kaser, G., Kienholz, C., Miles, E. S.,
Moholdt, G., Moelg, N., Paul, F., Radic, V., Rastner, P., Raup, B. H., Rich,
J., Sharp, M. J., Andeassen, L. M., Bajracharya, S., Barrand, N. E., Beedle,
M. J., Berthier, E., Bhambri, R., Brown, I., Burgess, D. O., Burgess, E. W.,
Cawkwell, F., Chinn, T., Copland, L., Cullen, N. J., Davies, B., De Angelis,
H., Fountain, A. G., Frey, H., Giffen, B. A., Glasser, N. F., Gurney, S. D.,
Hagg, W., Hall, D. K., Haritashya, U. K., Hartmann, G., Herreid, S., Howat,
I., Jiskoot, H., Khromova, T. E., Klein, A., Kohler, J., Konig, M., Kriegel,
D., Kutuzov, S., Lavrentiev, I., Le Bris, R., Li, X., Manley, W. F., Mayer,
C., Menounos, B., Mercer, A., Mool, P., Negrete, A., Nosenko, G., Nuth, C.,
Osmonov, A., Pettersson, R., Racoviteanu, A., Ranzi, R., Sarikaya, M. A.,
Schneider, C., Sigurdsson, O., Sirguey, P., Stokes, C. R., Wheate, R.,
Wolken, G. J., Wu, L. Z., Wyatt, F. R., and Randolph, C.: The Randolph
Glacier Inventory: a globally complete inventory of glaciers, J. Glaciol., 60, 537–552, https://doi.org/10.3189/2014JoG13J176, 2014.
Pratap, B., Sharma, P., Patel, L., Singh, A. T., Gaddam, V. K., Oulkar, S.,
and Thamban, M.: Reconciling High Glacier Surface Melting in Summer with Air
Temperature in the Semi-Arid Zone of Western Himalaya, Water, 11, 1561,
https://doi.org/10.3390/w11081561, 2019.
Qin, J.: Monthly average air temperature data of glacier surface in the
Tibetan Plateau (1961–2020), National Tibetan Plateau Data Center [data set],
https://doi.org/10.11888/Atmos.tpdc.272550, 2022.
Qin, J., Yang, K., Liang, S., and Guo, X.: The altitudinal dependence of
recent rapid warming over the Tibetan Plateau, Clim. Change, 97, 321–327,
https://doi.org/10.1007/s10584-009-9733-9, 2009.
Qin, J., Yang, K., Lu, N., Chen, Y., Zhao, L., and Han, M.: Spatial
upscaling of in-situ soil moisture measurements based on MODIS-derived
apparent thermal inertia, Remote Sens. Environ., 138, 1–9,
https://doi.org/10.1016/j.rse.2013.07.003, 2013.
Radić, V., Bliss, A., Beedlow, A. C., Hock, R., Miles, E., and Cogley,
J. G.: Regional and global projections of twenty-first century glacier mass
changes in response to climate scenarios from global climate models, Clim.
Dynam., 42, 37–58, https://doi.org/10.1007/s00382-013-1719-7, 2014.
Rao, Y., Liang, S., Wang, D., Yu, Y., Song, Z., Zhou, Y., Shen, M., and Xu,
B.: Estimating daily average surface air temperature using satellite land
surface temperature and top-of-atmosphere radiation products over the
Tibetan Plateau, Remote Sens. Environ., 234, 111462, https://doi.org/10.1016/j.rse.2019.111462, 2019.
Rasouli, K., Pomeroy, J. W., and Whitfield, P. H.: The sensitivity of snow
hydrology to changes in air temperature and precipitation in three North
American headwater basins, J. Hydrol., 606, 127460,
https://doi.org/10.1016/j.jhydrol.2022.127460, 2022.
Rounce, D. R., Hock, R., and Shean, D. E.: Glacier Mass Change in High
Mountain Asia Through 2100 Using the Open-Source Python Glacier Evolution
Model (PyGEM), Front. Earth Sci., 7, 331, https://doi.org/10.3389/feart.2019.00331,
2020a.
Rounce, D. R., Khurana, T., Short, M. B., Hock, R., Shean, D. E., and
Brinkerhoff, D. J.: Quantifying parameter uncertainty in a large-scale
glacier evolution model using Bayesian inference: application to High
Mountain Asia, J. Glaciol., 66, 175–187, https://doi.org/10.1017/jog.2019.91,
2020b.
Shean, D. E., Bhushan, S., Montesano, P., Rounce, D. R., Arendt, A., and
Osmanoglu, B.: A systematic, regional assessment of high mountain Asia
glacier mass balance, Front. Earth Sci., 7, 363, https://doi.org/10.3389/feart.2019.00363, 2020.
Shen, H., Jiang, Y., Li, T., Cheng, Q., Zeng, C., and Zhang, L.: Deep
learning-based air temperature mapping by fusing remote sensing, station,
simulation and socioeconomic data, Remote Sens. Environ., 240, 111692,
https://doi.org/10.1016/j.rse.2020.111692, 2020.
Tong, S., Wong, N. H., Jusuf, S. K., Tan, C. L., Wong, H. F., Ignatius, M.,
and Tan, E.: Study on correlation between air temperature and urban
morphology parameters in built environment in northern China, Building and
Environment, 127, 239–249, https://doi.org/10.1016/j.buildenv.2017.11.013,
2018.
Trebicki, P.: Climate change and plant virus epidemiology, Virus Res.,
286, 198059, https://doi.org/10.1016/j.virusres.2020.198059, 2020.
Wang, C., Graham, R. M., Wang, K., Gerland, S., and Granskog, M. A.: Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution, The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, 2019.
Xu, B. Q.: Glacier temperature dataset of Xiaodong Kemadi (2012–2015),
National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Glacio.tpdc.270019,
2018.
Xu, Y., Knudby, A., Shen, Y., and Liu, Y.: Mapping Monthly Air Temperature
in the Tibetan Plateau From MODIS Data Based on Machine Learning Methods,
Ieee J. Sel. Top. Appl. Earth Obs., 11, 345–354, https://doi.org/10.1109/jstars.2017.2787191, 2018.
Yang, K., Wu, H., Qin, J., Lin, C., Tang, W., and Chen, Y.: Recent climate
changes over the Tibetan Plateau and their impacts on energy and water
cycle: A review, Global Planet. Change, 112, 79–91,
https://doi.org/10.1016/j.gloplacha.2013.12.001, 2014.
Yang, W.: Data from automatic weather station at the end of glacier in
Qinghai-Tibet Plateau (2019–2020), National Tibetan Plateau Data Center
[data set], https://doi.org/10.11888/Meteoro.tpdc.271394, 2021.
Yao, T. D., Thompson, L., Yang, W., Yu, W. S., Gao, Y., Guo, X. J., Yang, X.
X., Duan, K. Q., Zhao, H. B., Xu, B. Q., Pu, J. C., Lu, A. X., Xiang, Y.,
Kattel, D. B., and Joswiak, D.: Different glacier status with atmospheric
circulations in Tibetan Plateau and surroundings, Nat. Clim. Change, 2,
663–667, https://doi.org/10.1038/nclimate1580, 2012.
Yao, T. D., Xue, Y. K., Chen, D. L., Chen, F. H., Thompson, L., Cui, P.,
Koike, T., Lau, W. K. M., Lettenmaier, D., Mosbrugger, V., Zhang, R. H., Xu,
B. Q., Dozier, J., Gillespie, T., Gu, Y., Kang, S. C., Piao, S. L.,
Sugimoto, S., Ueno, K., Wang, L., Wang, W. C., Zhang, F., Sheng, Y. W., Guo,
W. D., Ailikun, Yang, X. X., Ma, Y. M., Shen, S. S. P., Su, Z. B., Chen, F.,
Liang, S. L., Liu, Y. M., Singh, V. P., Yang, K., Yang, D. Q., Zhao, X. Q.,
Qian, Y., Zhang, Y., and Li, Q.: Recent Third Pole's Rapid Warming
Accompanies Cryospheric Melt and Water Cycle Intensification and
Interactions between Monsoon and Environment: Multidisciplinary Approach
with Observations, Modeling, and Analysis, B. Am.
Meteorol. Soc., 100, 423–444, https://doi.org/10.1175/bams-d-17-0057.1, 2019.
You, Q. L., Kang, S. C., Pepin, N., Flugel, W. A., Yan, Y. P., Behrawan, H.,
and Huang, J.: Relationship between temperature trend magnitude, elevation
and mean temperature in the Tibetan Plateau from homogenized surface
stations and reanalysis data, Global Planet. Change, 71, 124–133,
https://doi.org/10.1016/j.gloplacha.2010.01.020, 2010.
Zeng, L., Hu, Y., Wang, R., Zhang, X., Peng, G., Huang, Z., Zhou, G., Xiang,
D., Meng, R., Wu, W., and Hu, S.: 8-Day and Daily Maximum and Minimum Air
Temperature Estimation via Machine Learning Method on a Climate Zone to
Global Scale, Remote Sens., 13, 2355, https://doi.org/10.3390/rs13122355, 2021.
Zhang, H., Immerzeel, W. W., Zhang, F., de Kok, R. J., Gorrie, S. J., and
Ye, M.: Creating 1 km long-term (1980–2014) daily average air temperatures
over the Tibetan Plateau by integrating eight types of reanalysis and land
data assimilation products downscaled with MODIS-estimated temperature lapse
rates based on machine learning, Int. J. Appl. Earth
Obs., 97, 102295,
https://doi.org/10.1016/j.jag.2021.102295, 2021.
Zhang, H. B., Zhang, F., Ye, M., Che, T., and Zhang, G. Q.: Estimating daily
air temperatures over the Tibetan Plateau by dynamically integrating MODIS
LST data, J. Geophys. Res.-Atmos., 121, 11425–11441,
https://doi.org/10.1002/2016jd025154, 2016.
Zhang, Y. S.: Meteorological observation data of the terminus of Naimona'nyi
Glacier (2011–2017), National Tibetan Plateau Data Center [data set],
https://doi.org/10.11888/Hydro.tpdc.270081, 2018a.
Zhang, Y. S.: Meteorological observation data of Kunsha Glacier (2015–2017),
National Tibetan Plateau Data Center [data set],
https://doi.org/10.11888/Meteoro.tpdc.270086, 2018b.
Zhao, H. B.: Mass balance (2008–2018) on Naimona'nyi Glacier and related
meteorological data (2011–2018), National Tibetan Plateau Data Center
[data set], https://doi.org/10.11888/Meteoro.tpdc.271606, 2021.
Zhou, Q., Chen, D., Hu, Z., and Chen, X.: Decompositions of Taylor diagram
and DISO performance criteria, Int. J. Climatol., 41,
5726–5732, https://doi.org/10.1002/joc.7149, 2021.
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.
To enrich a glacial surface air temperature (SAT) product of a long time series, an ensemble...
Altmetrics
Final-revised paper
Preprint