Articles | Volume 16, issue 12
https://doi.org/10.5194/essd-16-5643-2024
© Author(s) 2024. 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-16-5643-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Dec 2024
Data description paper |
| 10 Dec 2024
| 10 Dec 2024
The PAZ polarimetric radio occultation research dataset for scientific applications
Institut de Ciències de l'Espai, Consejo Superior de Investigaciones Científicas (ICE-CSIC), c/Can Margans, S/N, Campus UAB, 08193 Bellaterra, Spain
Institut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain
Estel Cardellach
Institut de Ciències de l'Espai, Consejo Superior de Investigaciones Científicas (ICE-CSIC), c/Can Margans, S/N, Campus UAB, 08193 Bellaterra, Spain
Institut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain
Antía Paz
Institut de Ciències de l'Espai, Consejo Superior de Investigaciones Científicas (ICE-CSIC), c/Can Margans, S/N, Campus UAB, 08193 Bellaterra, Spain
Institut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain
Santi Oliveras
Institut de Ciències de l'Espai, Consejo Superior de Investigaciones Científicas (ICE-CSIC), c/Can Margans, S/N, Campus UAB, 08193 Bellaterra, Spain
Institut d'Estudis Espacials de Catalunya (IEEC), Barcelona, Spain
Douglas C. Hunt
University Corporation for Atmospheric Research (UCAR), Boulder, CO, USA
Sergey Sokolovskiy
University Corporation for Atmospheric Research (UCAR), Boulder, CO, USA
Jan-Peter Weiss
University Corporation for Atmospheric Research (UCAR), Boulder, CO, USA
Kuo-Nung Wang
Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, USA
F. Joe Turk
Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, USA
Chi O. Ao
Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, USA
Manuel de la Torre Juárez
Jet Propulsion Laboratory (JPL), California Institute of Technology, Pasadena, CA, USA
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Atmos. Meas. Tech., 18, 953–970, https://doi.org/10.5194/amt-18-953-2025, https://doi.org/10.5194/amt-18-953-2025, 2025
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Polarimetric radio occultations (PROs) use polarized radio signals from satellites to detect moisture and precipitation in Earth's atmosphere. By applying nonlinear regression and k-means cluster analysis to over 2 years of PRO and non-PRO data, this study shows how deviations from a refractivity model relate to vertical profiles of water vapor pressure (moisture) and that differences between components of PRO signals correlate directly with vertical profiles of water path (precipitation).
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This study used Polarimetric radio occultation (PRO) observations to evaluate simulations of cloud hydrometeors with five microphysics schemes for three typhoons from 2019 and 2021. The simulated cloud hydrometeors distributions varied significantly depending on model initial conditions, typhoon structures, and microphysics schemes. Results in this study demonstrate the potential for using PRO observation to evaluate the performance of different microphysics schemes in numerical models.
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Polarimetric radio occultations (PROs) use polarized radio signals from satellites to detect moisture and precipitation in Earth's atmosphere. By applying nonlinear regression and k-means cluster analysis to over 2 years of PRO and non-PRO data, this study shows how deviations from a refractivity model relate to vertical profiles of water vapor pressure (moisture) and that differences between components of PRO signals correlate directly with vertical profiles of water path (precipitation).
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Uncertainty estimation for GNSS radio occultation (RO) soundings in the planetary boundary layer (PBL) depends on the algorithms used to process the RO data. We compare the refractivity retrievals from three RO processing centers — each with their own retrieval algorithm — in the PBL, finding a strong underestimation of refractivity in regions with the strongest refractivity gradients, especially in JPL processing, as well as areas of weak overestimation of refractivity near the Poles.
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The GPS signal going through the atmosphere will experience refraction or bending, which can be precisely measured and used to infer the atmospheric properties. This paper demonstrates that high-quality atmospheric measurement with less than ~ 0.4 K is achievable from a GPS recording system with a simple antenna mounted on top of an aircraft cruising at ~ 13 km. Such a simple airborne GPS system can be implemented on commercial aircraft to provide valuable data for weather models in the future.
Kuo-Nung Wang, Manuel de la Torre Juárez, Chi O. Ao, and Feiqin Xie
Atmos. Meas. Tech., 10, 4761–4776, https://doi.org/10.5194/amt-10-4761-2017, https://doi.org/10.5194/amt-10-4761-2017, 2017
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Refractivity retrievals from GNSS radio occultation (RO) are known to be negatively biased within the planetary boundary layer (PBL). We propose an optimization-based reconstruction method in this paper to correct the negative bias with external measurements of precipitable water (PW). Our results show that the proposed method can greatly reduce the bias and better characterize the PBL.
Esayas Shume and Chi Ao
Atmos. Meas. Tech., 9, 3175–3182, https://doi.org/10.5194/amt-9-3175-2016, https://doi.org/10.5194/amt-9-3175-2016, 2016
Z. Zeng, S. Sokolovskiy, W. Schreiner, D. Hunt, J. Lin, and Y.-H. Kuo
Atmos. Meas. Tech., 9, 335–346, https://doi.org/10.5194/amt-9-335-2016, https://doi.org/10.5194/amt-9-335-2016, 2016
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This study discusses the ionospheric correction problem of RO data in the troposphere and investigates an optimal transition height (OTH) for replacing the standard linear combination method by the L1–L2 bending angle extrapolated from above. By comparing the RO ionosphere-free bending angles with the ECMWF global forecast ones, the OTH is found when it gives the minimal standard deviations. The results show the OTH depends mainly on the L2 signal and is ~20 km for COSMIC L2P and ~10 km for L2C.
R. Padullés, E. Cardellach, M. de la Torre Juárez, S. Tomás, F. J. Turk, S. Oliveras, C. O. Ao, and A. Rius
Atmos. Chem. Phys., 16, 635–649, https://doi.org/10.5194/acp-16-635-2016, https://doi.org/10.5194/acp-16-635-2016, 2016
Short summary
Short summary
The ROHP-PAZ mission will collect, for the first time, GPS radio occultations at two polarizations with the aim of characterizing rain. Prior to the mission's launch (2016), a field campaign has been conducted to identify and understand the measurements. In this study we present the set-up and the results of such a campaign: the main finding is the confirmation of sensitivity to heavy rain and, unexpectedly, to other frozen hydrometeors. This is key information for the spaceborne experiment.
P. Vergados, A. J. Mannucci, C. O. Ao, J. H. Jiang, and H. Su
Atmos. Meas. Tech., 8, 1789–1797, https://doi.org/10.5194/amt-8-1789-2015, https://doi.org/10.5194/amt-8-1789-2015, 2015
A. K. Steiner, D. Hunt, S.-P. Ho, G. Kirchengast, A. J. Mannucci, B. Scherllin-Pirscher, H. Gleisner, A. von Engeln, T. Schmidt, C. Ao, S. S. Leroy, E. R. Kursinski, U. Foelsche, M. Gorbunov, S. Heise, Y.-H. Kuo, K. B. Lauritsen, C. Marquardt, C. Rocken, W. Schreiner, S. Sokolovskiy, S. Syndergaard, and J. Wickert
Atmos. Chem. Phys., 13, 1469–1484, https://doi.org/10.5194/acp-13-1469-2013, https://doi.org/10.5194/acp-13-1469-2013, 2013
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It is hard to access long-time series and high-resolution meteorological data for past years. In this paper, we propose the Geopotential-guided Attention Network (GeoAN) for downscaling which can produce high-resolution data using given low-resolution data. Quantitative and visual comparisons reveal our GeoAN produces better results with regard to most metrics. Using GeoAN, a historical meteorological dataset called MDG625 has been produced daily for the period since 1940.
Susana Barbosa, Nuno Dias, Carlos Almeida, Guilherme Amaral, António Ferreira, António Camilo, and Eduardo Silva
Earth Syst. Sci. Data, 17, 1393–1405, https://doi.org/10.5194/essd-17-1393-2025, https://doi.org/10.5194/essd-17-1393-2025, 2025
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The electric field in the Earth's atmosphere reflects global planetary conditions. It is influenced by both atmospheric processes (such as thunderstorms, pollution, and aerosols) and space weather. Marine measurements of the electric field are rare. Here, we present a unique dataset of atmospheric electric field measurements taken over the Atlantic Ocean. This dataset is valuable not only for atmospheric electricity studies but also for research on climate and space–Earth interactions.
Qi Zhang, Chiyuan Miao, Jiajia Su, Jiaojiao Gou, Jinlong Hu, Xi Zhao, and Ye Xu
Earth Syst. Sci. Data, 17, 837–853, https://doi.org/10.5194/essd-17-837-2025, https://doi.org/10.5194/essd-17-837-2025, 2025
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Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland China, offering detailed insights from 1961 to 2022 at a spatial resolution of 0.1°. This dataset incorporates six key drought indices, including multi-scale versions, facilitating early detection and monitoring of droughts. Through the provision of consistent and reliable data, CHM_Drought enhances our understanding of drought patterns, aiding in effective water management and agricultural planning.
Matilde García-Valdecasas Ojeda, Feliciano Solano-Farias, David Donaire-Montaño, Emilio Romero-Jiménez, Juan José Rosa-Cánovas, Yolanda Castro-Díez, Sonia Raquel Gámiz-Fortis, and María Jesús Esteban-Parra
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-522, https://doi.org/10.5194/essd-2024-522, 2024
Revised manuscript accepted for ESSD
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This work aims to present a series of climate datasets for Sierra Nevada, a region especially vulnerable to climate change in Southern Spain. The database consists of primary climate variables such as precipitation, temperature, radiation, wind speed, pressure, and atmospheric humidity, but also bioclimatic variables and extreme indices, both useful information for assessing the impact of climate change in this region. These datasets are only available on https://doi.org/10.5281/zenodo.14364865
Frédéric Laly, Patrick Chazette, Julien Totems, Jérémy Lagarrigue, Laurent Forges, and Cyrille Flamant
Earth Syst. Sci. Data, 16, 5579–5602, https://doi.org/10.5194/essd-16-5579-2024, https://doi.org/10.5194/essd-16-5579-2024, 2024
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We present a dataset of water vapor mixing ratio profiles acquired during the Water Vapor Lidar Network Assimilation campaign in fall and winter 2022 and summer 2023, using three lidar systems deployed on the western Mediterranean coastline. This innovative campaign provides access to lower-tropospheric water vapor variability to constrain meteorological forecasting models. The scientific objective is to improve forecasting of heavy-precipation events that lead to flash floods and landslides.
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024, https://doi.org/10.5194/essd-16-5243-2024, 2024
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The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.
David Bromwich, Sheng-Hung Wang, Xun Zou, and Alexandra Ensign
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-353, https://doi.org/10.5194/essd-2024-353, 2024
Revised manuscript accepted for ESSD
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Antarctica is a major player in Earth’s climate with the most direct influence arising from its potential to raise global sea level by a meter or more in the coming decades. Near-surface air temperature is the primary variable used to monitor the climate of this remote but important region. Continent-wide direct but sparse measurements that started around 1958 are used to construct a monthly air temperature data set for all of Antarctica spanning 1958–2022.
Xinyan Liu, Tao He, Qingxin Wang, Xiongxin Xiao, Yichuan Ma, Yanyan Wang, Shanjun Luo, Lei Du, and Zhaocong Wu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-458, https://doi.org/10.5194/essd-2024-458, 2024
Revised manuscript accepted for ESSD
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This study addresses the challenge of how clouds affect the Earth's energy balance, which is vital for understanding climate change. We developed a new method to create long-term cloud radiative kernels to improve the accuracy of sunlight reaching the surface, which significantly reduces errors. Findings suggest that prior estimates of cloud cooling effects may have been overstated, emphasizing the need for better strategies to manage climate change impacts in the Arctic.
Thomas Fiolleau and Rémy Roca
Earth Syst. Sci. Data, 16, 4021–4050, https://doi.org/10.5194/essd-16-4021-2024, https://doi.org/10.5194/essd-16-4021-2024, 2024
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This paper presents a database of tropical deep convective systems over the 2012–2020 period, built from a cloud-tracking algorithm called TOOCAN, which has been applied to homogenized infrared observations from a fleet of geostationary satellites. This database aims to analyze the tropical deep convective systems, the evolution of their associated characteristics over their life cycle, their organization, and their importance in the hydrological and energy cycle.
Bing Li, Shunlin Liang, Han Ma, Guanpeng Dong, Xiaobang Liu, Tao He, and Yufang Zhang
Earth Syst. Sci. Data, 16, 3795–3819, https://doi.org/10.5194/essd-16-3795-2024, https://doi.org/10.5194/essd-16-3795-2024, 2024
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This study describes 1 km all-weather instantaneous and daily mean land surface temperature (LST) datasets on the global scale during 2000–2020. It is the first attempt to synergistically estimate all-weather instantaneous and daily mean LST data on a long global-scale time series. The generated datasets were evaluated by the observations from in situ stations and other LST datasets, and the evaluation indicated that the dataset is sufficiently reliable.
Zen Mariani, Sara M. Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie M. Hartten, Jareth Holt, Christopher J. Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data, 16, 3083–3124, https://doi.org/10.5194/essd-16-3083-2024, https://doi.org/10.5194/essd-16-3083-2024, 2024
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During the Year of Polar Prediction (YOPP), we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of datasets that have been collected to facilitate user uptake and model–observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODFs), including a description of the sites, data format, and instruments.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, https://doi.org/10.5194/essd-16-3017-2024, 2024
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Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, Robert M. Beauchamp, and Arturo Umeyama
Earth Syst. Sci. Data, 16, 2701–2715, https://doi.org/10.5194/essd-16-2701-2024, https://doi.org/10.5194/essd-16-2701-2024, 2024
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This paper describes multifrequency radar observations of clouds and precipitation during the EPCAPE campaign. The data sets were obtained from CloudCube, a Ka-, W-, and G-band atmospheric profiling radar, to demonstrate synergies between multifrequency retrievals. This data collection provides a unique opportunity to study hydrometeors with diameters in the millimeter and submillimeter size range that can be used to better understand the drop size distribution within clouds and precipitation.
Francesca Lappin, Gijs de Boer, Petra Klein, Jonathan Hamilton, Michelle Spencer, Radiance Calmer, Antonio R. Segales, Michael Rhodes, Tyler M. Bell, Justin Buchli, Kelsey Britt, Elizabeth Asher, Isaac Medina, Brian Butterworth, Leia Otterstatter, Madison Ritsch, Bryony Puxley, Angelina Miller, Arianna Jordan, Ceu Gomez-Faulk, Elizabeth Smith, Steven Borenstein, Troy Thornberry, Brian Argrow, and Elizabeth Pillar-Little
Earth Syst. Sci. Data, 16, 2525–2541, https://doi.org/10.5194/essd-16-2525-2024, https://doi.org/10.5194/essd-16-2525-2024, 2024
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This article provides an overview of the lower-atmospheric dataset collected by two uncrewed aerial systems near the Gulf of Mexico coastline south of Houston, TX, USA, as part of the TRacking Aerosol Convection interactions ExpeRiment (TRACER) campaign. The data were collected through boundary layer transitions, through sea breeze circulations, and in the pre- and near-storm environment to understand how these processes influence the coastal environment.
Kaixu Bai, Ke Li, Liuqing Shao, Xinran Li, Chaoshun Liu, Zhengqiang Li, Mingliang Ma, Di Han, Yibing Sun, Zhe Zheng, Ruijie Li, Ni-Bin Chang, and Jianping Guo
Earth Syst. Sci. Data, 16, 2425–2448, https://doi.org/10.5194/essd-16-2425-2024, https://doi.org/10.5194/essd-16-2425-2024, 2024
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A global gap-free high-resolution air pollutant dataset (LGHAP v2) was generated to provide spatially contiguous AOD and PM2.5 concentration maps with daily 1 km resolution from 2000 to 2021. This gap-free dataset has good data accuracies compared to ground-based AOD and PM2.5 concentration observations, which is a reliable database to advance aerosol-related studies and trigger multidisciplinary applications for environmental management, health risk assessment, and climate change analysis.
Nele Reyniers, Qianyu Zha, Nans Addor, Timothy J. Osborn, Nicole Forstenhäusler, and Yi He
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-132, https://doi.org/10.5194/essd-2024-132, 2024
Revised manuscript accepted for ESSD
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We present two sets of bias-corrected UK Climate Projections 2018 (UKCP18) regional projections of temperature, precipitation and potential evapotranspiration for 1981–2080. All 12 members of the UKCP18 regional ensemble were bias-corrected using (1) empirical quantile mapping and (2) a change-preserving variant. The two methods were evaluated and compared to guide dataset application. The datasets improve the usability of UKCP18 and serve as a reference for selecting bias correction methods.
Finn Burgemeister, Marco Clemens, and Felix Ament
Earth Syst. Sci. Data, 16, 2317–2332, https://doi.org/10.5194/essd-16-2317-2024, https://doi.org/10.5194/essd-16-2317-2024, 2024
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Knowledge of small-scale rainfall variability is needed for hydro-meteorological applications in urban areas. Therefore, we present an open-access data set covering reanalyzed radar reflectivities and rainfall estimates measured by a weather radar at high spatio-temporal resolution in the urban environment of Hamburg between 2013 and 2021. We describe the data reanalysis, outline the measurement’s performance for long time periods, and discuss open issues and limitations of the data set.
Nicola Bodini, Mike Optis, Stephanie Redfern, David Rosencrans, Alex Rybchuk, Julie K. Lundquist, Vincent Pronk, Simon Castagneri, Avi Purkayastha, Caroline Draxl, Raghavendra Krishnamurthy, Ethan Young, Billy Roberts, Evan Rosenlieb, and Walter Musial
Earth Syst. Sci. Data, 16, 1965–2006, https://doi.org/10.5194/essd-16-1965-2024, https://doi.org/10.5194/essd-16-1965-2024, 2024
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This article presents the 2023 National Offshore Wind data set (NOW-23), an updated resource for offshore wind information in the US. It replaces the Wind Integration National Dataset (WIND) Toolkit, offering improved accuracy through advanced weather prediction models. The data underwent regional tuning and validation and can be accessed at no cost.
Longhu Chen, Qinqin Wang, Guofeng Zhu, Xinrui Lin, Dongdong Qiu, Yinying Jiao, Siyu Lu, Rui Li, Gaojia Meng, and Yuhao Wang
Earth Syst. Sci. Data, 16, 1543–1557, https://doi.org/10.5194/essd-16-1543-2024, https://doi.org/10.5194/essd-16-1543-2024, 2024
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We have compiled data regarding stable precipitation isotopes from 842 sampling points throughout the Eurasian continent since 1961, accumulating a total of 51 753 data records. The collected data have undergone pre-processing and statistical analysis. We also analysed the spatiotemporal distribution of stable precipitation isotopes across the Eurasian continent and their interrelationships with meteorological elements.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
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This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Ling Yuan, Xuelong Chen, Yaoming Ma, Cunbo Han, Binbin Wang, and Weiqiang Ma
Earth Syst. Sci. Data, 16, 775–801, https://doi.org/10.5194/essd-16-775-2024, https://doi.org/10.5194/essd-16-775-2024, 2024
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Accurately monitoring and understanding the spatial–temporal variability of evapotranspiration (ET) components over the Tibetan Plateau (TP) remains difficult. Here, 37 years (1982–2018) of monthly ET component data for the TP was produced, and the data are consistent with measurements. The annual average ET for the TP was about 0.93 (± 0.037) × 103 Gt yr−1. The rate of increase of the ET was around 0.96 mm yr−1. The increase in the ET can be explained by warming and wetting of the climate.
Dominik Rains, Isabel Trigo, Emanuel Dutra, Sofia Ermida, Darren Ghent, Petra Hulsman, Jose Gómez-Dans, and Diego G. Miralles
Earth Syst. Sci. Data, 16, 567–593, https://doi.org/10.5194/essd-16-567-2024, https://doi.org/10.5194/essd-16-567-2024, 2024
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Land surface temperature and surface net radiation are vital inputs for many land surface and hydrological models. However, current remote sensing datasets of these variables come mostly at coarse resolutions, and the few high-resolution datasets available have large gaps due to cloud cover. Here, we present a continuous daily product for both variables across Europe for 2018–2019 obtained by combining observations from geostationary as well as polar-orbiting satellites.
Hadleigh D. Thompson, Julie M. Thériault, Stephen J. Déry, Ronald E. Stewart, Dominique Boisvert, Lisa Rickard, Nicolas R. Leroux, Matteo Colli, and Vincent Vionnet
Earth Syst. Sci. Data, 15, 5785–5806, https://doi.org/10.5194/essd-15-5785-2023, https://doi.org/10.5194/essd-15-5785-2023, 2023
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The Saint John River experiment on Cold Season Storms was conducted in northwest New Brunswick, Canada, to investigate the types of precipitation that can lead to ice jams and flooding along the river. We deployed meteorological instruments, took precipitation measurements and photographs of snowflakes, and launched weather balloons. These data will help us to better understand the atmospheric conditions that can affect local communities and townships downstream during the spring melt season.
Raghavendra Krishnamurthy, Gabriel García Medina, Brian Gaudet, William I. Gustafson Jr., Evgueni I. Kassianov, Jinliang Liu, Rob K. Newsom, Lindsay M. Sheridan, and Alicia M. Mahon
Earth Syst. Sci. Data, 15, 5667–5699, https://doi.org/10.5194/essd-15-5667-2023, https://doi.org/10.5194/essd-15-5667-2023, 2023
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Our understanding and ability to observe and model air–sea processes has been identified as a principal limitation to our ability to predict future weather. Few observations exist offshore along the coast of California. To improve our understanding of the air–sea transition zone and support the wind energy industry, two buoys with state-of-the-art equipment were deployed for 1 year. In this article, we present details of the post-processing, algorithms, and analyses.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
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The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Giovanni Chellini, Rosa Gierens, Kerstin Ebell, Theresa Kiszler, Pavel Krobot, Alexander Myagkov, Vera Schemann, and Stefan Kneifel
Earth Syst. Sci. Data, 15, 5427–5448, https://doi.org/10.5194/essd-15-5427-2023, https://doi.org/10.5194/essd-15-5427-2023, 2023
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We present a comprehensive quality-controlled dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and substantially warm the surface. However, our understanding of microphysical processes in these clouds is incomplete. This dataset includes a comprehensive set of variables which allow for extensive investigation of such processes in LLMPCs at the site.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
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CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Motoshi Nishimura, Teruo Aoki, Masashi Niwano, Sumito Matoba, Tomonori Tanikawa, Tetsuhide Yamasaki, Satoru Yamaguchi, and Koji Fujita
Earth Syst. Sci. Data, 15, 5207–5226, https://doi.org/10.5194/essd-15-5207-2023, https://doi.org/10.5194/essd-15-5207-2023, 2023
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We presented the method of data quality checks and the dataset for two ground weather observations in northwest Greenland. We found that the warm and clear weather conditions in the 2015, 2019, and 2020 summers caused the snowmelt and the decline in surface reflectance of solar radiation at a low-elevated site (SIGMA-B; 944 m), but those were not seen at the high-elevated site (SIGMA-A; 1490 m). We hope that our data management method and findings will help climate scientists.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
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We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
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Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Karl-Göran Karlsson, Martin Stengel, Jan Fokke Meirink, Aku Riihelä, Jörg Trentmann, Tom Akkermans, Diana Stein, Abhay Devasthale, Salomon Eliasson, Erik Johansson, Nina Håkansson, Irina Solodovnik, Nikos Benas, Nicolas Clerbaux, Nathalie Selbach, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 15, 4901–4926, https://doi.org/10.5194/essd-15-4901-2023, https://doi.org/10.5194/essd-15-4901-2023, 2023
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This paper presents a global climate data record on cloud parameters, radiation at the surface and at the top of atmosphere, and surface albedo. The temporal coverage is 1979–2020 (42 years) and the data record is also continuously updated until present time. Thus, more than four decades of climate parameters are provided. Based on CLARA-A3, studies on distribution of clouds and radiation parameters can be made and, especially, investigations of climate trends and evaluation of climate models.
Boyang Jiao, Yucheng Su, Qingxiang Li, Veronica Manara, and Martin Wild
Earth Syst. Sci. Data, 15, 4519–4535, https://doi.org/10.5194/essd-15-4519-2023, https://doi.org/10.5194/essd-15-4519-2023, 2023
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This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
Lukas Frank, Marius Opsanger Jonassen, Teresa Remes, Florina Roana Schalamon, and Agnes Stenlund
Earth Syst. Sci. Data, 15, 4219–4234, https://doi.org/10.5194/essd-15-4219-2023, https://doi.org/10.5194/essd-15-4219-2023, 2023
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The Isfjorden Weather Information Network (IWIN) provides continuous meteorological near-surface observations from Isfjorden in Svalbard. The network combines permanent automatic weather stations on lighthouses along the coast line with mobile stations on board small tourist cruise ships regularly trafficking the fjord during spring to autumn. All data are available online in near-real time. Besides their scientific value, IWIN data crucially enhance the safety of field activities in the region.
Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo
Earth Syst. Sci. Data, 15, 3147–3161, https://doi.org/10.5194/essd-15-3147-2023, https://doi.org/10.5194/essd-15-3147-2023, 2023
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Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.
Christian Borger, Steffen Beirle, and Thomas Wagner
Earth Syst. Sci. Data, 15, 3023–3049, https://doi.org/10.5194/essd-15-3023-2023, https://doi.org/10.5194/essd-15-3023-2023, 2023
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This study presents a long-term data set of monthly mean total column water vapour (TCWV) based on measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. We describe how the TCWV values are retrieved from UV–Vis satellite spectra and demonstrate that the OMI TCWV data set is in good agreement with various different reference data sets. Moreover, we also show that it fulfills typical stability requirements for climate data records.
Jonathan Demaeyer, Jonas Bhend, Sebastian Lerch, Cristina Primo, Bert Van Schaeybroeck, Aitor Atencia, Zied Ben Bouallègue, Jieyu Chen, Markus Dabernig, Gavin Evans, Jana Faganeli Pucer, Ben Hooper, Nina Horat, David Jobst, Janko Merše, Peter Mlakar, Annette Möller, Olivier Mestre, Maxime Taillardat, and Stéphane Vannitsem
Earth Syst. Sci. Data, 15, 2635–2653, https://doi.org/10.5194/essd-15-2635-2023, https://doi.org/10.5194/essd-15-2635-2023, 2023
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A benchmark dataset is proposed to compare different statistical postprocessing methods used in forecasting centers to properly calibrate ensemble weather forecasts. This dataset is based on ensemble forecasts covering a portion of central Europe and includes the corresponding observations. Examples on how to download and use the data are provided, a set of evaluation methods is proposed, and a first benchmark of several methods for the correction of 2 m temperature forecasts is performed.
Dirk Nikolaus Karger, Stefan Lange, Chantal Hari, Christopher P. O. Reyer, Olaf Conrad, Niklaus E. Zimmermann, and Katja Frieler
Earth Syst. Sci. Data, 15, 2445–2464, https://doi.org/10.5194/essd-15-2445-2023, https://doi.org/10.5194/essd-15-2445-2023, 2023
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We present the first 1 km, daily, global climate dataset for climate impact studies. We show that the high-resolution data have a decreased bias and higher correlation with measurements from meteorological stations than coarser data. The dataset will be of value for a wide range of climate change impact studies both at global and regional level that benefit from using a consistent global dataset.
Elisa Adirosi, Federico Porcù, Mario Montopoli, Luca Baldini, Alessandro Bracci, Vincenzo Capozzi, Clizia Annella, Giorgio Budillon, Edoardo Bucchignani, Alessandra Lucia Zollo, Orietta Cazzuli, Giulio Camisani, Renzo Bechini, Roberto Cremonini, Andrea Antonini, Alberto Ortolani, Samantha Melani, Paolo Valisa, and Simone Scapin
Earth Syst. Sci. Data, 15, 2417–2429, https://doi.org/10.5194/essd-15-2417-2023, https://doi.org/10.5194/essd-15-2417-2023, 2023
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The paper describes the database of 1 min drop size distribution (DSD) of atmospheric precipitation collected by the Italian disdrometer network over the last 10 years. These data are useful for several applications that range from climatological, meteorological and hydrological uses to telecommunications, agriculture and conservation of cultural heritage exposed to precipitation. Descriptions of the processing and of the database organization, along with some examples, are provided.
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
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A collection of regional reanalysis datasets has been produced. However, little attention has been paid to East Asia, and there are no long-term, physically consistent regional reanalysis data available. The East Asia Reanalysis System was developed using the WRF model and GSI data assimilation system. A 39-year (1980–2018) reanalysis dataset is available for the East Asia region, at a high temporal (of 3 h) and spatial resolution (of 12 km), for mesoscale weather and regional climate studies.
John Erik Engström, Lennart Wern, Sverker Hellström, Erik Kjellström, Chunlüe Zhou, Deliang Chen, and Cesar Azorin-Molina
Earth Syst. Sci. Data, 15, 2259–2277, https://doi.org/10.5194/essd-15-2259-2023, https://doi.org/10.5194/essd-15-2259-2023, 2023
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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
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Climate reconstruction from proxy data can help evaluate climate models. We present pollen-based reconstructions of mean July temperature, mean annual temperature, and annual precipitation from 2594 pollen records from the Northern Hemisphere, using three reconstruction methods (WA-PLS, WA-PLS_tailored, and MAT). Since no global or hemispheric synthesis of quantitative precipitation changes are available for the Holocene so far, this dataset will be of great value to the geoscientific community.
Aart Overeem, Else van den Besselaar, Gerard van der Schrier, Jan Fokke Meirink, Emiel van der Plas, and Hidde Leijnse
Earth Syst. Sci. Data, 15, 1441–1464, https://doi.org/10.5194/essd-15-1441-2023, https://doi.org/10.5194/essd-15-1441-2023, 2023
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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
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This article presents the datasets collected between November 2019 and February 2020 in the vicinity of the Belgian research base Princess Elisabeth Antarctica. Five meteorological radars, a multi-angle snowflake camera, three weather stations, and two radiometers have been deployed at five sites, up to a maximum distance of 30 km from the base. Their varied locations allow the study of spatial variability in snowfall and its interaction with the complex terrain in the region.
José Dias Neto, Louise Nuijens, Christine Unal, and Steven Knoop
Earth Syst. Sci. Data, 15, 769–789, https://doi.org/10.5194/essd-15-769-2023, https://doi.org/10.5194/essd-15-769-2023, 2023
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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
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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
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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
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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
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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.
Cited articles
Anthes, R. A.: Exploring Earth's atmosphere with radio occultation: contributions to weather, climate and space weather, Atmos. Meas. Tech., 4, 1077–1103, https://doi.org/10.5194/amt-4-1077-2011, 2011. a
Ao, C. O., Waliser, D. E., Chan, S. K., Li, J. L., Tian, B., Xie, F., and Mannucci, A. J.: Planetary boundary layer heights from GPS radio occultation refractivity and humidity profiles, J. Geophys. Res.-Atmos., 117, 1–18, https://doi.org/10.1029/2012JD017598, 2012. a
Berg, W.: GPM SSMIS on F16 Common Calibrated Brightness Temperatures L1C 1.5 hours 12 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SSMIS/F16/1C/07, 2021a. a
Berg, W.: GPM SSMIS on F17 Common Calibrated Brightness Temperatures L1C 1.5 hours 12 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SSMIS/F17/1C/07, 2021b. a
Berg, W.: GPM SSMIS on F18 Common Calibrated Brightness Temperatures L1C 1.5 hours 12 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SSMIS/F18/1C/07, 2021c. a
Berg, W.: GPM GMI Common Calibrated Brightness Temperatures Collocated L1C 1.5 hours 13 km V07, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/GMI/GPM/1C/07, 2022a. a
Berg, W.: GPM AMSR-2 on GCOM-W1 Common Calibrated Brightness Temperature L1C 1.5 hours 10 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/AMSR2/GCOMW1/1C/07, 2022b. a
Berg, W.: GPM ATMS on SUOMI-NPP Common Calibrated Brightness Temperature L1C 1.5 hours 16 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/ATMS/NPP/1C/07, 2022c. a
Berg, W.: GPM MHS on NOAA-19 Common Calibrated Brightness Temperatures L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/NOAA19/1C/07, 2022d. a
Berg, W.: GPM ATMS on NOAA-20 Common Calibrated Brightness Temperatures L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/ATMS/NOAA20/1C/07, 2022e. a
Berg, W.: GPM MHS on METOP-A Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/METOPA/1C/07, 2022f. a
Berg, W.: GPM MHS on METOP-B Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/METOPB/1C/07, 2022g. a
Berg, W.: GPM MHS on METOP-C Common Calibrated Brightness Temperature L1C 1.5 hours 17 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/MHS/METOPC/1C/07, 2022h. a
Berg, W.: GPM SAPHIR on MT1 Common Calibrated Brightness Temperature L1C 1.5 hours 10 km V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/SAPHIR/MT1/1C/07, 2022i. a
Beyerle, G., Grunwaldt, L., Heise, S., Köhler, W., König, R., Michalak, G., Rothacher, M., Schmidt, T., Wickert, J., Tapley, B. D., and Giesinger, B.: First results from the GPS atmosphere sounding experiment TOR aboard the TerraSAR-X satellite, Atmos. Chem. Phys., 11, 6687–6699, https://doi.org/10.5194/acp-11-6687-2011, 2011. a
Cardellach, E., Tomás, S., Oliveras, S., Padullés, R., Rius, A., de la Torre-Juárez, M., Turk, F. J., Ao, C. O., Kursinski, E. R., Schreiner, W. S., Ector, D., and Cucurull, L.: Sensitivity of PAZ LEO Polarimetric GNSS Radio-Occultation Experiment to Precipitation Events, IEEE T. Geosci. Remote, 53, 190–206, https://doi.org/10.1109/TGRS.2014.2320309, 2014. a, b, c, d
Cardellach, E., Padullés, R., Tomás, S., Turk, F. J., de la Torre-Juárez, M., and Ao, C. O.: Probability of intense precipitation from polarimetric GNSS radio occultation observations, Q. J. Roy. Meteor. Soc., 144, 206–220, https://doi.org/10.1002/qj.3161, 2017. a
Cardellach, E., Oliveras, S., Rius, A., Tomás, S., Ao, C. O., Franklin, G. W., Iijima, B. A., Kuang, D., Meehan, T. K., Padullés, R., de la Torre-Juárez, M., Turk, F. J., Hunt, D. C., Schreiner, W. S., Sokolovskiy, S. V., Van Hove, T., Weiss, J. P., Yoon, Y., Zeng, Z., Clapp, J., Xia-Serafino, W., and Cerezo, F.: Sensing heavy precipitation with GNSS polarimetric radio occultations, Geophys. Res. Lett., 46, 1024–1031, https://doi.org/10.1029/2018GL080412, 2019. a, b, c
Culverwell, I. D., Lewis, H. W., Offiler, D., Marquardt, C., and Burrows, C. P.: The Radio Occultation Processing Package, ROPP, Atmos. Meas. Tech., 8, 1887–1899, https://doi.org/10.5194/amt-8-1887-2015, 2015. a
Gong, J. and Wu, D. L.: Microphysical properties of frozen particles inferred from Global Precipitation Measurement (GPM) Microwave Imager (GMI) polarimetric measurements, Atmos. Chem. Phys., 17, 2741–2757, https://doi.org/10.5194/acp-17-2741-2017, 2017. a, b, c
Hajj, G. A., Kursinski, E. R., Romans, L. J., Bertiger, W. I., and Leroy, S. S.: A Technical Description of Atmospheric Sounding By Gps occultation, J. Atmos. Solar-Terr. Phy., 64, 451–469, https://doi.org/10.1016/S1364-6826(01)00114-6, 2002. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D. P., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R. M., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hogan, R. J., Hólm, E. V., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D. P., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a, b
Huffman, G. J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V05, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/05, 2017. a
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-HH/07, 2023. a
Janowiak, J., Joyce, B., and Xie, P.: NCEP/CPC L3 Half Hourly 4km Global (60S–60N) Merged IR V1, edited by: Savtchenko, A., Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/P4HZB9N27EKU, 2017. a, b
Kidd, C., Huffman, G. J., Maggioni, V., Chambon, P., and Oki, R.: The Global Satellite Precipitation Constellation. Current Status and Future Requirements, B. Am. Meteorol. Soc., 102, 1844–1861, https://doi.org/10.1175/BAMS-D-20-0299.1, 2021. a, b
Kursinski, E. R., Hajj, G. A., Schofield, J. T., Linfield, R. P., and Hardy, K. R.: Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System, J. Geophys. Res., 102, 23429–23465, https://doi.org/10.1029/97JD01569, 1997. a, b
Li, Z., Tang, G., Kirstetter, P. E., Gao, S., Li, J. L., Wen, Y., and Hong, Y.: Evaluation of GPM IMERG and its constellations in extreme events over the conterminous united states, J. Hydrol., 606, 127357, https://doi.org/10.1016/j.jhydrol.2021.127357, 2022. a
Murphy, M. J., Haase, J. S., Padullés, R., Chen, S. H., and Morris, M. A.: The potential for discriminating microphysical processes in numerical weather forecasts using airborne polarimetric radio occultations, Remote Sens., 11, U2048–U2072, https://doi.org/10.3390/rs11192268, 2019. a
Padullés, R., Ao, C. O., Turk, F. J., de la Torre Juárez, M., Iijima, B., Wang, K. N., and Cardellach, E.: Calibration and validation of the Polarimetric Radio Occultation and Heavy Precipitation experiment aboard the PAZ satellite, Atmos. Meas. Tech., 13, 1299–1313, https://doi.org/10.5194/amt-13-1299-2020, 2020. a, b, c, d, e, f, g, h, i, j
Padullés, R., Cardellach, E., Turk, F. J., Ao, C. O., de la Torre-Juárez, M., Gong, J., and Wu, D. L.: Sensing Horizontally Oriented Frozen Particles With Polarimetric Radio Occultations Aboard PAZ: Validation Using GMI Coincident Observations and Cloudsat aPriori Information, IEEE T. Geosci. Remote, 60, 1–13, https://doi.org/10.1109/TGRS.2021.3065119, 2022. a, b, c, d
Padullés, R., Cardellach, E., and Turk, F. J.: On the global relationship between polarimetric radio occultation differential phase shift and ice water content, Atmos. Chem. Phys., 23, 2199–2214, https://doi.org/10.5194/acp-23-2199-2023, 2023. a, b, c
Padullés, R., Cardellach, E., and Oliveras, S.: resPrf, DIGITAL.CSIC [data set], https://doi.org/10.20350/DIGITALCSIC/16137, 2024. a, b
Paz, A., Padullés, R., and Cardellach, E.: Evaluating the Polarimetric Radio Occultation Technique Using NEXRAD Weather Radars, Remote Sens., 16, 1118, https://doi.org/10.3390/rs16071118, 2024. a
Peinó, E., Bech, J., and Udina, M.: Performance Assessment of GPM IMERG Products at Different Time Resolutions, Climatic Areas and Topographic Conditions in Catalonia, Remote Sens., 14, 5085, https://doi.org/10.3390/rs14205085, 2022. a
Ramadhan, R., Yusnaini, H., Marzuki, M., Muharsyah, R., Suryanto, W., Sholihun, S., Vonnisa, M., Harmadi, H., Ningsih, A. P., Battaglia, A., Hashiguchi, H., and Tokay, A.: Evaluation of GPM IMERG Performance Using Gauge Data over Indonesian Maritime Continent at Different Time Scales, Remote Sens., 14, 1–24, https://doi.org/10.3390/rs14051172, 2022. a
Sokolovskiy, S.: Improvements, modifications, and alternative approaches in the processing of GPS RO data, in: ECMWF/EUMETSAT ROM SAF Workshop, 16–18 June 2014, Reading, UK, https://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/Sokolovskiy_newroam.pdf (last access: 4 December 2024), 2014. a
Sokolovskiy, S. V., Kuo, Y.-H., Rocken, C., Schreiner, W. S., Hunt, D. C., and Anthes, R. A.: Monitoring the atmospheric boundary layer by GPS radio occultation signals recorded in the open-loop mode, Geophys. Res. Lett., 33, 12–15, https://doi.org/10.1029/2006GL025955, 2006. a
Turk, F. J., Padullés, R., Ao, C. O., de la Torre-Juárez, M., Wang, K.-N., Franklin, G. W., Lowe, S. T., Hristova-Veleva, S. M., Fetzer, E. J., Cardellach, E., Kuo, Y.-H., and Neelin, J. D.: Benefits of a closely-spaced satellite constellation of atmospheric polarimetric radio occultation measurements, Remote Sens., 11, 2399, https://doi.org/10.3390/rs11202399, 2019. a
Turk, F. J., Padullés, R., Cardellach, E., Ao, C. O., Wang, K.-N., Morabito, D. D., de la Torre-Juárez, M., Oyola, M., Hristova-Veleva, S. M., and Neelin, J. D.: Interpretation of the Precipitation Structure Contained in Polarimetric Radio Occultation Profiles Using Passive Microwave Satellite Observations, J. Atmos. Ocean. Tech., 38, 1727–1745, https://doi.org/10.1175/jtech-d-21-0044.1, 2021. a, b
Turk, F. J., Padullés, R., Morabito, D. D., Emmenegger, T., and Neelin, J. D.: Distinguishing Convective-Transition Moisture-Temperature Relationships with a Constellation of Polarimetric Radio Occultation Observations in and near Convection, Atmosphere, 13, 1–27, https://doi.org/10.3390/atmos13020259, 2022. a
Turk, F. J., Cardellach, E., de la Torre-Juárez, M., Padullés, R., Wang, K.-N., Ao, C. O., Kubar, T. L., Murphy, M. J., Neelin, J. D., Emmenegger, T., Wu, D. L., Nguyen, V., Kursinski, E. R., Masters, D., Kirstetter, P. E., Cucurull, L., and Lonitz, K.: Advances in the Use of Global Navigation Satellite System Polarimetric Radio Occultation Measurements for NWP and Weather Applications, B. Am. Meteorol. Soc., 105, E905–E914, https://doi.org/10.1175/BAMS-D-24-0050.1, 2024. a
UCAR-COSMIC-Program: PAZ RO Data Products, UCAR [data set], https://doi.org/10.5065/k9vg-t494, 2023a. a, b
UCAR-COSMIC-Program: TerraSar-X RO Data Products, UCAR [data set], https://doi.org/10.5065/fv7s-ax27, 2023b. a, b
Unidata: NetCDF data format Version 4, Unidata [data set], https://doi.org/10.5065/D6H70CW6, 2023. a
Wang, K.-N., Ao, C. O., Padullés, R., Turk, F. J., de la Torre-Juárez, M., and Cardellach, E.: The Effects of Heavy Precipitation on Polarimetric Radio Occultation (PRO) Bending Angle Observations, J. Atmos. Ocean. Tech., 39, 161–173, https://doi.org/10.1175/jtech-d-21-0032.1, 2021. a
Watters, D. C., Gatlin, P. N., Bolvin, D. T., Huffman, G. J., Joyce, R., Kirstetter, P., Nelkin, E. J., Ringerud, S., Tan, J., Wang, J., and Wolff, D.: Oceanic Validation of IMERG-GMI Version 6 Precipitation Using the GPM Validation Network, J. Hydrometeorol., 25, 125–142, https://doi.org/10.1175/jhm-d-23-0134.1, 2023. a
Wee, T. K., Anthes, R. A., Hunt, D. C., Schreiner, W. S., and Kuo, Y. H.: Atmospheric GNSS RO 1D-Var in Use at UCAR: Description and Validation, Remote Sens., 14, 5614, https://doi.org/10.3390/rs14215614, 2022. a
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Zeng, X., Skofronick-Jackson, G., Tian, L., Emory, A. E., Olson, W. S., and Kroodsma, R. A.: Analysis of the global microwave polarization data of clouds, J. Climate, 32, 3–13, https://doi.org/10.1175/JCLI-D-18-0293.1, 2019. a, b
Short summary
This dataset provides, for the first time, combined observations of clouds and precipitation with coincident retrievals of atmospheric thermodynamics obtained from the same space-based instrument. Furthermore, it provides the locations of the ray trajectories of the observations along various precipitation-related products interpolated into them with the aim of fostering the use of such dataset in scientific and operational applications.
This dataset provides, for the first time, combined observations of clouds and precipitation...
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