Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3147-2023
© 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-15-3147-2023
© 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
| 
25 Jul 2023
Data description paper |
| 25 Jul 2023
| 25 Jul 2023
A new daily gridded precipitation dataset for the Chinese mainland based on gauge observations
Jingya Han
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Jiaojiao Gou
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Haiyan Zheng
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Qi Zhang
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Xiaoying Guo
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, Beijing 100875,
China
Related authors
No articles found.
Ting Su, Chiyuan Miao, Qingyun Duan, Jiaojiao Gou, Xiaoying Guo, and Xi Zhao
Hydrol. Earth Syst. Sci., 27, 1477–1492, https://doi.org/10.5194/hess-27-1477-2023, https://doi.org/10.5194/hess-27-1477-2023, 2023
Short summary
Short summary
The Three-River Source Region (TRSR) plays an extremely important role in water resources security and ecological and environmental protection in China and even all of Southeast Asia. This study used the variable infiltration capacity (VIC) land surface hydrologic model linked with the degree-day factor algorithm to simulate the runoff change in the TRSR. These results will help to guide current and future regulation and management of water resources in the TRSR.
Liren Wei, Duoying Ji, Chiyuan Miao, Helene Muri, and John C. Moore
Atmos. Chem. Phys., 18, 16033–16050, https://doi.org/10.5194/acp-18-16033-2018, https://doi.org/10.5194/acp-18-16033-2018, 2018
Short summary
Short summary
We analyzed streamflow and flood frequency under the stratospheric aerosol geoengineering scenario simulated by climate models. Stratospheric aerosol geoengineering appears to reduce flood risk in most regions, but the overall effects are largely determined by the large-scale geographic pattern. Over the Amazon, stratospheric aerosol geoengineering ameliorates the drying trend here under a future warming climate.
W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao
Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, https://doi.org/10.5194/hess-19-2409-2015, 2015
J. Li, Q. Y. Duan, W. Gong, A. Ye, Y. Dai, C. Miao, Z. Di, C. Tong, and Y. Sun
Hydrol. Earth Syst. Sci., 17, 3279–3293, https://doi.org/10.5194/hess-17-3279-2013, https://doi.org/10.5194/hess-17-3279-2013, 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 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 long-term 1 km monthly near-surface air temperature dataset over the Tibetan glaciers by fusion of station and satellite observations
A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)
Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region
The hourly wind-bias-adjusted precipitation data set from the Environment and Climate Change Canada automated surface observation network (2001–2019)
Enhanced automated meteorological observations at the Canadian Arctic Weather Science (CAWS) supersites
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.
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.
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.
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
Ahrens, B.: Distance in spatial interpolation of daily rain gauge data, Hydrol. Earth Syst. Sci., 10, 197–208, https://doi.org/10.5194/hess-10-197-2006, 2006.
Allan, R. P., Barlow, M., Byrne, M. P., Cherchi, A., Douville, H., Fowler,
H. J., Gan, T. Y., Pendergrass, A. G., Rosenfeld, D., Swann, A. L. S.,
Wilcox, L. J., and Zolina, O.: Advances in understanding large-scale
responses of the water cycle to climate change, Ann. NY
Acad. Sci., 1472, 49–75, https://doi.org/10.1111/nyas.14337, 2020.
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 228–232,
https://doi.org/10.1038/nature01092, 2002.
Beck, H. E., Wood, E. F., Pan, M., Fisher, C. K., Miralles, D. G., van Dijk,
A. I. J. M., McVicar, T. R., and Adler, R. F.: MSWEP V2 global 3-hourly
0.1∘ precipitation: methodology and quantitative assessment,
B. Am. Meteorol. Soc., 100, 473–500,
https://doi.org/10.1175/BAMS-D-17-0138.1, 2019.
Beck, H. E., Westra, S., Tan, J., Pappenberger, F., Huffman, G. J., McVicar,
T. R., Gründemann, G. J., Vergopolan, N., Fowler, H. J., Lewis, E.,
Verbist, K., and Wood, E. F.: PPDIST, global 0.1∘ daily and
3-hourly precipitation probability distribution climatologies for
1979–2018, Sci. Data, 7, 1–12,
https://doi.org/10.1038/s41597-020-00631-x, 2020.
Caesar, J., Alexander, L., and Vose, R.: Large-scale changes in observed
daily maximum and minimum temperatures: Creation and analysis of a new
gridded data set, J. Geophys. Res.-Atmos., 111, D05101,
https://doi.org/10.1029/2005JD006280, 2006.
Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., and Lange, M.
A.: Evaluation of interpolation techniques for the creation of gridded daily
precipitation (1×1 km2); Cyprus, 1980–2010, J. Geophys. Res.-Atmos., 119, 693–712,
https://doi.org/10.1002/2013JD020611, 2014.
Chen, M., Xie, P., Janowiak, J. E., and Arkin, P. A.: Global Land
Precipitation: A 50-yr Monthly Analysis Based on Gauge Observations, J. Hydrometeorol., 3, 249–266,
https://doi.org/10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2, 2002.
Daly, C., Neilson, R. P., and Phillips, D. L.: A statistical-topographic
model for mapping climatological precipitation over mountainous terrain,
J. Appl. Meteorol., 33, 140–158,
https://doi.org/10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2, 1994.
Daly, C., Gibson, W., Taylor, G. H., Johnson, G. L., and Pasteris, P. P.: A
knowledge-based approach to the statistical mapping of climate, Clim.
Res., 22, 99–113, https://doi.org/10.3354/cr022099, 2002.
Delaunay, B.: Sur la sphere vide, Izv. Akad. Nauk SSSR,
Otdelenie Matematicheskii i Estestvennyka Nauk, 7, 1–2, 1934 (in French).
Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., and Arnold, J. G.:
Constructing retrospective gridded daily precipitation and temperature
datasets for the conterminous United States, J. Appl. Meteorol.
Climatol., 47, 475–497, https://doi.org/10.1175/2007JAMC1356.1, 2008.
Dunn, R. J. H., Alexander, L. V., Donat, M. G., Zhang, X., Bador, M.,
Herold, N., Lippmann, T., Allan, R., Aguilar, E., Barry, A. A., Brunet, M.,
Caesar, J., Chagnaud, G., Cheng, V., Cinco, T., Durre, I., de Guzman, R.,
Htay, T. M., Wan Ibadullah, W. M., Bin Ibrahim, M. K. I., Khoshkam, M.,
Kruger, A., Kubota, H., Leng, T. W., Lim, G., Li-Sha, L., Marengo, J.,
Mbatha, S., McGree, S., Menne, M., de los Milagros Skansi, M., Ngwenya, S.,
Nkrumah, F., Oonariya, C., Pabon-Caicedo, J. D., Panthou, G., Pham, C.,
Rahimzadeh, F., Ramos, A., Salgado, E., Salinger, J., Sané, Y.,
Sopaheluwakan, A., Srivastava, A., Sun, Y., Timbal, B., Trachow, N., Trewin,
B., van der Schrier, G., Vazquez-Aguirre, J., Vasquez, R., Villarroel, C.,
Vincent, L., Vischel, T., Vose, R., and Bin Hj Yussof, M. N. A.: Development
of an updated global land in situ-based data set of temperature and
precipitation extremes: HadEX3, J. Geophys. Res.-Atmos., 125, e2019JD032263, https://doi.org/10.1029/2019JD032263, 2020.
Efthymiadis, D., Jones, P. D., Briffa, K. R., Auer, I., Böhm, R.,
Schöner, W., Frei, C., and Schmidli, J.: Construction of a
10-min-gridded precipitation data set for the Greater Alpine Region for
1800–2003, J. Geophys. Res.-Atmos., 111, D01105,
https://doi.org/10.1029/2005JD006120, 2006.
Eischeid, J. K., Pasteris, P. A., Diaz, H. F., Plantico, M. S., and Lott, N.
J.: Creating a serially complete, national daily time series of temperature
and precipitation for the western United States, J. Appl. Meteorol., 39, 1580–1591,
https://doi.org/10.1175/1520-0450(2000)039<1580:CASCND>2.0.CO;2, 2000.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S.,
Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf,
D.: The shuttle radar topography mission, Rev. Geophys., 45, RG2004,
https://doi.org/10.1029/2005RG000183, 2007.
Fischer, E. M. and Knutti, R.: Observed heavy precipitation increase
confirms theory and early models, Nat. Clim. Change, 6, 986–991,
https://doi.org/10.1038/nclimate3110, 2016.
Golian, S., Javadian, M., and Behrangi, A.: On the use of satellite, gauge,
and reanalysis precipitation products for drought studies, Environ.
Res. Lett., 14, 075005, https://doi.org/10.1088/1748-9326/ab2203,
2019.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009.
Han, J., Miao, C., Duan, Q., Wu, J., Gou, J., and Zheng, H.: Changes in
Unevenness of Wet-Day Precipitation Over China During 1961–2020, J. Geophys. Res.-Atmos., 126, e2020JD034483,
https://doi.org/10.1029/2020JD034483, 2021.
Han, J. Y. and Miao, C. Y.: A new daily gridded precipitation dataset for the
Chinese mainland based on gauge observations, figshare [data set],
https://doi.org/10.6084/m9.figshare.21432123.v4, 2022.
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU
TS monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020.
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P.
D., and New, M.: A European daily high-resolution gridded data set of
surface temperature and precipitation for 1950–2006, J. Geophys. Res.-Atmos., 113, D20119, https://doi.org/10.1029/2008JD010201,
2008.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first
high-resolution meteorological forcing dataset for land process studies over
China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y,
2020.
Hofstra, N. and New, M.: Spatial variability in correlation decay distance
and influence on angular-distance weighting interpolation of daily
precipitation over Europe, Int. J. Climatol., 29,
1872–1880, https://doi.org/10.1002/joc.1819, 2009.
Hutchinson, M. F.: Interpolating mean rainfall using thin plate smoothing
splines, Int. J. Geogr. Inf. Syst., 9,
385–403, https://doi.org/10.1080/02693799508902045, 1995.
Hutchinson, M. F.: Interpolation of rainfall data with thin plate smoothing
splines – part I: two dimensional smoothing of data with short range
correlation, Journal of Geographic Information and Decision Analysis, 2,
153–167, 1998.
Hutchinson, M. F. and Xu, T.: Anusplin version 4.2 user guide, Centre for
Resource and Environmental Studies, The Australian National University,
Canberra, 5, 2004.
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A.,
Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang,
M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K.,
Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University
Press, Cambridge, UK, 1–195, https://doi.org/10.1017/9781009157896, 2021.
Jones, P. D., Osborn, T. J., and Briffa, K. R.: Estimating sampling errors
in large-scale temperature averages, J. Climate, 10, 2548–2568,
https://doi.org/10.1175/1520-0442(1997)010<2548:ESEILS>2.0.CO;2, 1997.
Kirschbaum, D. B., Huffman, G. J., Adler, R. F., Braun, S., Garrett, K.,
Jones, E., McNally, A., Skofronick-Jackson, G., Stocker, E., Wu, H., and
Zaitchik, B. F.: NASA's remotely sensed precipitation: a reservoir for
applications users, B. Am. Meteorol. Soc., 98,
1169–1184, https://doi.org/10.1175/BAMS-D-15-00296.1, 2017.
Kucera, P. A., Ebert, E. E., Turk, F. J., Levizzani, V., Kirschbaum, D.,
Tapiador, F. J., Loew, A., and Borsche, M.: Precipitation from space:
advancing earth system science, B. Am. Meteorol. Soc., 94, 365–375, https://doi.org/10.1175/BAMS-D-11-00171.1, 2013.
Li, R., Wang, K., and Qi, D.: Validating the integrated multisatellite
retrievals for global precipitation measurement in terms of diurnal
variability with hourly gauge observations collected at 50 000 stations in
China, J. Geophys. Res.-Atmos., 123, 10423–10442,
https://doi.org/10.1029/2018JD028991, 2018.
Liszka, T.: An interpolation method for an irregular net of nodes,
Int. J. Numer. Meth. Eng., 20, 1599–1612,
https://doi.org/10.1002/nme.1620200905, 1984.
Lu, G. Y. and Wong, D. W.: An adaptive inverse-distance weighting spatial
interpolation technique, Comput. Geosci., 34, 1044–1055,
https://doi.org/10.1016/j.cageo.2007.07.010, 2008.
Ly, S., Charles, C., and Degré, A.: Different methods for spatial
interpolation of rainfall data for operational hydrology and hydrological
modeling at watershed scale: a review, Biotechnol. Agron. Soc., 17, 392–406, https://doi.org/10.6084/M9.FIGSHARE.1225842.V1,
2013.
Menne, M. J., Durre, I., Vose, R. S., Gleason, B. E., and Houston, T. G.: An
overview of the global historical climatology network-daily database,
J. Atmos. Ocean. Tech., 29, 897–910,
https://doi.org/10.1175/JTECH-D-11-00103.1, 2012.
Merino, A., García-Ortega, E., Navarro, A., Fernández-González,
S., Tapiador, F. J., and Sánchez, J. L.: Evaluation of gridded
rain-gauge-based precipitation datasets: Impact of station density, spatial
resolution, altitude gradient and climate, Int. J. Climatol., 41, 3027–3043, https://doi.org/10.1002/joc.7003, 2021.
Mitchell, T. D. and Jones, P. D.: An improved method of constructing a
database of monthly climate observations and associated high-resolution
grids, Int. J. Climatol., 25, 693–712,
https://doi.org/10.1002/joc.1181, 2005.
Morrissey, M. L., Maliekal, J. A., Greene, J. S., and Wang, J.: The
uncertainty of simple spatial averages using rain gauge networks, Water
Resour. Res., 31, 2011–2017, https://doi.org/10.1029/95WR01232, 1995.
Myhre, G., Samset, B. H., Hodnebrog, Ø., Andrews, T., Boucher, O.,
Faluvegi, G., Fläschner, D., Forster, P. M., Kasoar, M., Kharin, V.,
Kirkevåg, A., Lamarque, J. F., Olivié, D., Richardson, T. B.,
Shawki, D., Shindell, D., Shine, K. P., Stjern, C. W., Takemura, T., and
Voulgarakis, A.: Sensible heat has significantly affected the global
hydrological cycle over the historical period, Nat. Commun., 9,
1922, https://doi.org/10.1038/s41467-018-04307-4, 2018.
New, M., Hulme, M., and Jones, P.: Representing twentieth-century
space–time climate variability. part II: development of 1901–96 monthly
grids of terrestrial surface climate, J. Climate, 13, 2217–2238,
https://doi.org/10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2, 2000.
Peng, S., Ding, Y., Liu, W., and Li, Z.: 1 km monthly temperature and precipitation dataset for China from 1901 to 2017, Earth Syst. Sci. Data, 11, 1931–1946, https://doi.org/10.5194/essd-11-1931-2019, 2019.
Qin, R., Zhao, Z., Xu, J., Ye, J.-S., Li, F.-M., and Zhang, F.: HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China, Earth Syst. Sci. Data, 14, 4793–4810, https://doi.org/10.5194/essd-14-4793-2022, 2022.
Rodell, M., Famiglietti, J. S., Wiese, D. N., Reager, J. T., Beaudoing, H.
K., Landerer, F. W., and Lo, M. H.: Emerging trends in global freshwater
availability, Nature, 557, 651–659,
https://doi.org/10.1038/s41586-018-0123-1, 2018.
Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U., Schröder, M., and Stender, P.: Global gridded precipitation over land: a description of the new GPCC First Guess Daily product, Earth Syst. Sci. Data, 6, 49–60, https://doi.org/10.5194/essd-6-49-2014, 2014.
Shen, Y. and Xiong, A.: Validation and comparison of a new gauge-based
precipitation analysis over mainland China, Int. J. Climatol., 36, 252–265, https://doi.org/10.1002/joc.4341, 2016.
Shen, Y., Feng, M., Zhang, H., and Gao, F.: Interpolation Methods of China
Daily Precipitation Data, J. Appl. Meteorol.
Sci., 21, 279–286, 2010 (in Chinese).
Shen, Y., Zhao, P., Pan, Y., and Yu, J.: A high spatiotemporal
gauge-satellite merged precipitation analysis over China, J. Geophys. Res.-Atmos., 119, 3063–3075,
https://doi.org/10.1002/2013JD020686, 2014.
Shepard, D.: A two-dimensional interpolation function for irregularly-spaced
data, Proceedings of the 1968 23rd ACM national conference, 27–29 August 1968, New York, United States, 517–524,
https://doi.org/10.1145/800186.810616, 1968.
Shepard, D. S.: Computer Mapping: The SYMAP Interpolation Algorithm, in:
Spatial Statistics and Models, edited by: Gaile, G. L., and Willmott, C. J.,
Springer Netherlands, Dordrecht, 133–145,
https://doi.org/10.1007/978-94-017-3048-8_7, 1984.
Sibson, R.: Locally equiangular triangulations, Comput. J., 21,
243–245, https://doi.org/10.1093/comjnl/21.3.243, 1978.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
review of global precipitation data sets: Data sources, estimation, and
intercomparisons, Rev. Geophys., 56, 79–107,
https://doi.org/10.1002/2017RG000574, 2018.
Tait, A., Henderson, R., Turner, R., and Zheng, X.: Thin plate smoothing
spline interpolation of daily rainfall for New Zealand using a
climatological rainfall surface, Int. J. Climatol., 26,
2097–2115, https://doi.org/10.1002/joc.1350, 2006.
Thiessen, A. H.: Precipitation averages for large areas, Mon. Weather
Rev., 39, 1082–1089, https://doi.org/10.1175/1520-0493(1911)39<1082b:PAFLA>2.0.CO;2, 1911.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The
changing character of precipitation, B. Am. Meteorol. Soc., 84, 1205–1218, https://doi.org/10.1175/BAMS-84-9-1205, 2003.
Vivoni Enrique, R., Ivanov Valeri, Y., Bras Rafael, L., and Entekhabi, D.:
Generation of triangulated irregular networks based on hydrological
similarity, J. Hydrol. Eng., 9, 288–302,
https://doi.org/10.1061/(ASCE)1084-0699(2004)9:4(288), 2004.
Wahba, G. and Wendelberger, J.: Some new mathematical methods for
variational objective analysis using splines and cross validation, Mon.
Weather Rev., 108, 1122–1143,
https://doi.org/10.1175/1520-0493(1980)108<1122:SNMMFV>2.0.CO;2, 1980.
Wu, J. and Gao, X.: A gridded daily observation dataset over China region
and comparison with the other datasets, Chinese J.
Geophys., 56, 1102–1111, https://doi.org/10.6038/cjg20130406, 2013 (in Chinese).
Xie, P., Chen, M., Yang, S., Yatagai, A., Hayasaka, T., Fukushima, Y., and
Liu, C.: A gauge-based analysis of daily precipitation over east Asia,
J. Hydrometeorol., 8, 607–626, https://doi.org/10.1175/JHM583.1,
2007.
Yatagai, A., Kamiguchi, K., Arakawa, O., Hamada, A., Yasutomi, N., and
Kitoh, A.: APHRODITE: constructing a long-term daily gridded precipitation
dataset for Asia based on a dense network of rain gauges, B. Am. Meteorol. Soc., 93, 1401–1415,
https://doi.org/10.1175/BAMS-D-11-00122.1, 2012.
Zhang, Y., Ren, Y., Ren, G., and Wang, G.: Precipitation trends over
mainland China from 1961–2016 after removal of measurement biases, J. Geophys. Res.-Atmos., 125, e2019JD031728,
https://doi.org/10.1029/2019JD031728, 2020.
Zhao, Y., Zhu, J., and Xu, Y.: Establishment and assessment of the grid
precipitation datasets in China for recent 50 years (in Chinese), J.
Meteorol. Sci., 34, 414–420,
https://doi.org/10.3969/2013jms.0008, 2014.
Zou, C., Zhang, H., Yang, Y., Liu, Y., Sun, H., and Gao, X.: Effects of
rainfall characteristics on bromide leaching in a typical tobacco field in
China's Yunnan Province, Soil Sci. Soc. Am. J., 87,
231–245, https://doi.org/10.1002/saj2.20502, 2022.
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.
Constructing a high-quality, long-term daily precipitation dataset is essential to current...
Altmetrics
Final-revised paper
Preprint