Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4021-2024
© 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-16-4021-2024
© 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
| 
12 Sep 2024
Data description paper |
| 12 Sep 2024
| 12 Sep 2024
A database of deep convective systems derived from the intercalibrated meteorological geostationary satellite fleet and the TOOCAN algorithm (2012–2020)
Université de Toulouse, LEGOS (CNRS/UT3), Toulouse, France
Rémy Roca
Université de Toulouse, LEGOS (CNRS/UT3), Toulouse, France
Related authors
Louis Netz, Thomas Fiolleau, and Rémy Roca
EGUsphere, https://doi.org/10.5194/egusphere-2025-2247, https://doi.org/10.5194/egusphere-2025-2247, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
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Convective systems are the primary drivers of rainfall and climate on Earth, yet the spatial organisation of associated convection remains poorly understood. This study presents a simple approach to describing this organisation. First, the convective field is decomposed into elementary structures. Then, four scores are computed to describe the size, density, spacing scale and departure from randomness of the cores. This method robustly characterises the organisation of convection.
Louis Netz, Thomas Fiolleau, and Rémy Roca
EGUsphere, https://doi.org/10.5194/egusphere-2025-2247, https://doi.org/10.5194/egusphere-2025-2247, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Convective systems are the primary drivers of rainfall and climate on Earth, yet the spatial organisation of associated convection remains poorly understood. This study presents a simple approach to describing this organisation. First, the convective field is decomposed into elementary structures. Then, four scores are computed to describe the size, density, spacing scale and departure from randomness of the cores. This method robustly characterises the organisation of convection.
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-568, https://doi.org/10.5194/essd-2024-568, 2025
Revised manuscript accepted for ESSD
Short summary
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GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research, allowing also studies for water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
Rémy Roca, Lisa V. Alexander, Gerald Potter, Margot Bador, Rômulo Jucá, Steefan Contractor, Michael G. Bosilovich, and Sophie Cloché
Earth Syst. Sci. Data, 11, 1017–1035, https://doi.org/10.5194/essd-11-1017-2019, https://doi.org/10.5194/essd-11-1017-2019, 2019
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This paper presents a database that is a collection of datasets of gridded 1° × 1° daily precipitation estimates from a variety of sources. It includes observations from in situ networks, satellite-based estimations and outputs from atmospheric reanalysis. All the datasets have been formatted in the same way to ease their manipulation. This database aims at facilitating intercomparisons and validation exercises performed by the scientific community.
M. Schröder, R. Roca, L. Picon, A. Kniffka, and H. Brogniez
Atmos. Chem. Phys., 14, 11129–11148, https://doi.org/10.5194/acp-14-11129-2014, https://doi.org/10.5194/acp-14-11129-2014, 2014
B. V. Thampi and R. Roca
Atmos. Chem. Phys., 14, 6739–6758, https://doi.org/10.5194/acp-14-6739-2014, https://doi.org/10.5194/acp-14-6739-2014, 2014
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Domain: ESSD – Atmosphere | Subject: Meteorology
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-168, https://doi.org/10.5194/essd-2025-168, 2025
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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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CLIMADAT-GRid is the first, publicly available, daily air temperature and precipitation gridded climate dataset for Greece at a high resolution of 1 km x 1 km and for the period 1981–2019.The dataset is based on quality-controlled station data while various interpolation techniques were evaluated for generating the daily grids. CLIMADAT-GRid serves as a valuable resource for research and information in climate studies as well as in other areas such as hydrology, agriculture, energy and health.
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The Dutch real-time gauge-adjusted radar product provides 5 min precipitation accumulations every 5 min covering the Netherlands and the area around it. It plays a key role in hydrological decision-support systems and as input for short-term weather forecasts. Major changes were implemented on 31 January 2023 and the associated quality improvement is presented. Moreover, the employed radar and rain gauge datasets and the algorithms needed to produce this real-time radar product are described.
Lucie Bakels, Michael Blaschek, Marina Dütsch, Andreas Plach, Vincent Lechner, Georg Brack, Leopold Haimberger, and Andreas Stohl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-26, https://doi.org/10.5194/essd-2025-26, 2025
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Meteorological reanalyses are crucial datasets. Most reanalyses are Eulerian, providing data at specific, fixed points in space and time. When studying how air moves, it is more convenient to follow air masses through space and time, requiring a Lagrangian reanalysis (LARA). We explain how the LARA dataset is organized, and provide four examples of applications. These include studying the evolution of wind patterns, understanding weather systems, and measuring air mass travel time over land.
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-127, https://doi.org/10.5194/essd-2025-127, 2025
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CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to today. By using advanced modeling techniques and incorporating sea surface temperature observations, it provides a consistent picture of long-term climate variability. The dataset captures key ocean, sea ice and atmosphere changes, helping scientists understand past climate changes and variability.
Deli Meng, Jianping Guo, Juan Chen, Xiaoran Guo, Ning Li, Yuping Sun, Zhen Zhang, Na Tang, Hui Xu, Tianmeng Chen, Rongfang Yang, and Jiajia Hua
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-138, https://doi.org/10.5194/essd-2025-138, 2025
Revised manuscript accepted for ESSD
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This study provides a high-resolution dataset of low-level atmospheric turbulence across China, using radar and weather balloon observations. It reveals regional and seasonal variations in turbulence, with stronger activity in spring and summer. The dataset supports weather forecasting, aviation safety, and low-altitude flight planning, aiding China’s growing low-altitude economy and accessible at https://doi.org/10.5281/zenodo.14959025.
Qi Zhang, Chiyuan Miao, Jiajia Su, Jiaojiao Gou, Jinlong Hu, Xi Zhao, and Ye Xu
Earth Syst. Sci. Data, 17, 837–853, https://doi.org/10.5194/essd-17-837-2025, https://doi.org/10.5194/essd-17-837-2025, 2025
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Our study introduces CHM_Drought, an advanced meteorological drought dataset covering mainland China, offering detailed insights from 1961 to 2022 at a spatial resolution of 0.1°. This dataset incorporates six key drought indices, including multi-scale versions, facilitating early detection and monitoring of droughts. Through the provision of consistent and reliable data, CHM_Drought enhances our understanding of drought patterns, aiding in effective water management and agricultural planning.
Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-70, https://doi.org/10.5194/essd-2025-70, 2025
Revised manuscript accepted for ESSD
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This study introduces the update to the C-LSAT 2.1 station data and its gridded dataset (5° × 5°). Based on this, we develop high-resolution (0.5° × 0.5°) LSAT (C-LSAT HRv1) and DTR (C-LDTR HRv1) datasets. The C-LSAT 2.1 station data integrates over 3000 additional global stations, significantly improving spatial coverage. The global and regional variations in C-LSAT HRv1 and C-LDTR HRv1 align well with their 5° × 5° datasets (C-LSAT 2.1 and C-LDTR).
David Brus, Viet Le, Joel Kuula, and Konstantinos Doulgeris
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-61, https://doi.org/10.5194/essd-2025-61, 2025
Revised manuscript accepted for ESSD
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This manuscript provides datasets collected during Pallas Cloud Experiment campaign in norther Finland during the autumn of 2022. We provided an overview of the custom-built drone backpack for air quality and atmospheric state variables carried on top of the consumer-grade drone (DJI Mavic 2 pro). We described the flight strategies, and provided an overview of the datasets obtained, including a description of the measurement against the reference for data validation.
Hannes Konrad, Rémy Roca, Anja Niedorf, Stephan Finkensieper, Marc Schröder, Sophie Cloché, Giulia Panegrossi, Paolo Sanò, Christopher Kidd, Rômulo Augusto Jucá Oliveira, Karsten Fennig, Thomas Sikorski, Madeleine Lemoine, and Rainer Hollmann
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-568, https://doi.org/10.5194/essd-2024-568, 2025
Revised manuscript accepted for ESSD
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GIRAFE v1 is a global satellite-based precipitation dataset covering 2002 to 2022. It combines high-accuracy microwave and high-resolution infrared observations for retrieving daily precipitation, a respective sampling uncertainty for a more robust analysis, and monthly means. It is intended and suitable for climate monitoring and research, allowing also studies for water management, agriculture, and disaster risk reduction. A continuous extension from 2023 onwards will be implemented in 2025.
Jinlong Hu, Chiyuan Miao, Jiajia Su, Qi Zhang, Jiaojiao Gou, and Qiaohong Sun
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-20, https://doi.org/10.5194/essd-2025-20, 2025
Revised manuscript accepted for ESSD
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We developed a high-precision daily precipitation dataset for mainland China called CHM_PRE V2. Using data from 3,476 rain gauges, 11 related precipitation variables and advanced machine learning methods, we created a daily precipitation dataset spanning 1960–2023 with unprecedented accuracy. Compared to existing datasets, it better captures rainfall events while reducing false alarms. This work provides a reliable tool for studying water resources, climate change, and disaster management.
Franco Salerno, Nicolas Guyennon, Nicola Colombo, Maria Teresa Melis, Francesco Gabriele Dessì, Gianpietro Verza, Kaji Bista, Ahmad Sheharyar, and Gianni Tartari
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-591, https://doi.org/10.5194/essd-2024-591, 2025
Revised manuscript accepted for ESSD
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Climate change is deeply impacting mountain areas around the globe, especially in Himalaya. Here, we present the Pyramid Meteorological Network, located in Himalaya (Nepal), on the southern slopes of Mt. Everest. The network is composed of 7 meteorological stations located between 2660 and 7986 m a.s.l., which have collected continuous climatic data during the last 30 years (1994–2023). The dataset is available freely accessible from https://zenodo.org/records/14450214 (Salerno et al., 2024).
Ramon Padullés, Estel Cardellach, Antía Paz, Santi Oliveras, Douglas C. Hunt, Sergey Sokolovskiy, Jan-Peter Weiss, Kuo-Nung Wang, F. Joe Turk, Chi O. Ao, and Manuel de la Torre Juárez
Earth Syst. Sci. Data, 16, 5643–5663, https://doi.org/10.5194/essd-16-5643-2024, https://doi.org/10.5194/essd-16-5643-2024, 2024
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This dataset provides, for the first time, combined observations of clouds and precipitation with coincident retrievals of atmospheric thermodynamics obtained from the same space-based instrument. Furthermore, it provides the locations of the ray trajectories of the observations along various precipitation-related products interpolated into them with the aim of fostering the use of such dataset in scientific and operational applications.
Frédéric Laly, Patrick Chazette, Julien Totems, Jérémy Lagarrigue, Laurent Forges, and Cyrille Flamant
Earth Syst. Sci. Data, 16, 5579–5602, https://doi.org/10.5194/essd-16-5579-2024, https://doi.org/10.5194/essd-16-5579-2024, 2024
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We present a dataset of water vapor mixing ratio profiles acquired during the Water Vapor Lidar Network Assimilation campaign in fall and winter 2022 and summer 2023, using three lidar systems deployed on the western Mediterranean coastline. This innovative campaign provides access to lower-tropospheric water vapor variability to constrain meteorological forecasting models. The scientific objective is to improve forecasting of heavy-precipation events that lead to flash floods and landslides.
Colin Peter Morice, David I. Berry, Richard C. Cornes, Kathryn Cowtan, Thomas Cropper, Ed Hawkins, John J. Kennedy, Timothy J. Osborn, Nick A. Rayner, Beatriz R. Rivas, Andrew P. Schurer, Michael Taylor, Praveen R. Teleti, Emily J. Wallis, Jonathan Winn, and Elizabeth C. Kent
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-500, https://doi.org/10.5194/essd-2024-500, 2024
Revised manuscript accepted for ESSD
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We present a new data set of global gridded surface air temperature change extending back to the 1780s. This is achieved using marine air temperature observations with newly available estimates of diurnal heating biases together with an updated land station database that includes bias adjustments for early thermometer enclosures. These developments allow the data set to extend further into the past than current data sets that use sea surface temperature rather than marine air temperature data.
Uwe Pfeifroth, Jaqueline Drücke, Steffen Kothe, Jörg Trentmann, Marc Schröder, and Rainer Hollmann
Earth Syst. Sci. Data, 16, 5243–5265, https://doi.org/10.5194/essd-16-5243-2024, https://doi.org/10.5194/essd-16-5243-2024, 2024
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The energy reaching Earth's surface from the Sun is a quantity of great importance for the climate system and for many applications. SARAH-3 is a satellite-based climate data record of surface solar radiation parameters. It is generated and distributed by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF). SARAH-3 covers more than 4 decades and provides a high spatial and temporal resolution, and its validation shows good accuracy and stability.
Adam Jaczewski, Michał Marosz, and Mirosław Miętus
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-433, https://doi.org/10.5194/essd-2024-433, 2024
Revised manuscript accepted for ESSD
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This paper introduces a high-resolution dataset of daily air temperatures in Poland from 1951 to 2020, with a 1 km2 spatial resolution. PL1GD-T dataset was developed using radial basis functions applied to quality-controlled observations from ground weather stations and evaluated using cross-validation methods. This open-access dataset is crucial for climate change impact studies on a smaller scale and can serve a wide range of users, including researchers, administrative bodies, and society.
Clare Marie Flynn, Julia Moemken, Joaquim G. Pinto, and Gabriele Messori
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-298, https://doi.org/10.5194/essd-2024-298, 2024
Revised manuscript accepted for ESSD
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We created a new, publicly available database of the Top 50 most extreme European winter windstorms from each of four different meteorological input data sets covering the years 1995–2015. We found variability in all aspects of our database, from which storms were included in the Top 50 storms for each input to their spatial variability. We urge users of our database to consider the storms as identified from two or more input sources within our database, where possible.
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.
Jianfeng Li, Andrew Geiss, Zhe Feng, L. Ruby Leung, Yun Qian, and Wenjun Cui
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-112, https://doi.org/10.5194/essd-2024-112, 2024
Revised manuscript accepted for ESSD
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We develop a high-resolution (4 km and hourly) observational derecho dataset over the United States east of the Rocky Mountains from 2004 to 2021 by using a mesoscale convective system dataset, bow echo detection based on a machine learning method, hourly gust speed measurements, and physically based identification criteria.
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.
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.
Cited articles
Arkin, P. A.: The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-Scale Array, https://doi.org/10.1175/1520-0493(1979)107<1382:TRBFCO>2.0.CO;2, 1979.
Arnaud, Y., Desbois, M., and Maizi, J.: Automatic Tracking and Characterization of African Convective Systems on Meteosat Pictures, J. Appl. Meteor. Climatol, 31, 443–453, https://doi.org/10.1175/1520-0450(1992)031<0443:ATACOA>2.0.CO;2, 1992.
Autones, F. and Moisselin, J.-M.: Algorithm Theoretical Basis Document for “Rapid Development Thunderstorms” (RDT-PGE11 v3.0), Scientific documentation of SAF/NWC, code SAF/NWC/CDOP2/MFT/SCI/ATBD/11, 2013.
Bellerby, T., Hsu, K., and Sorooshian, S.: LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration, J. Hydrometeorol., 10, 1081–1095, https://doi.org/10.1175/2009jhm1091.1, 2009.
Berthet, S., Roca, R., Duvel, J. P., and Fiolleau, T.: Subseasonal variability of mesoscale convective systems over the tropical northeastern Pacific, Q. J. Roy. Meteor. Soc., 143, 1086–1094, https://doi.org/10.1002/qj.2992, 2017.
Boer, E. R. and Ramanathan, V.: Lagrangian approach for deriving cloud characteristics from satellite observations and its implications to cloud parameterization, J. Geophys. Res.-Atmos., 102, 21383–21399, https://doi.org/10.1029/97JD00930, 1997.
Bouniol, D., Roca, R., Fiolleau, T., and Poan, D. E.: Macrophysical, Microphysical, and Radiative Properties of Tropical Mesoscale Convective Systems over Their Life Cycle, J. Climate, 29, 3353–3371, https://doi.org/10.1175/jcli-d-15-0551.1, 2016.
Bouniol, D., Roca, R., Fiolleau, T., and Raberanto, P.: Life cycle-resolved observation of radiative properties of mesoscale convective systems, J. Appl. Meteorol. Climatol., 60, 1091–1104, https://doi.org/10.1175/JAMC-D-20-0244.1, 2021.
Carvalho, L. M. V. and Jones, C.: A Satellite Method to Identify Structural Properties of Mesoscale Convective Systems Based on the Maximum Spatial Correlation Tracking Technique (MASCOTTE), J. Appl. Meteorol., 40, 1683–1701, https://doi.org/10.1175/1520-0450(2001)040<1683:ASMTIS>2.0.CO;2, 2001.
Dias, J., Tulich, S. N., and Kiladis, G. N.: An Object-Based Approach to Assessing the Organization of Tropical Convection, J. Atmos. Sci., 69, 2488–2504, https://doi.org/10.1175/JAS-D-11-0293.1, 2012.
Elsaesser, G. S., Roca, R., Fiolleau, T., Del Genio, A. D., and Wu, J.: A Simple Model for Tropical Convective Cloud Shield Area Growth and Decay Rates Informed by Geostationary IR, GPM, and Aqua/AIRS Satellite Data, J. Geophys. Res.-Atmos., 127, e2021JD035599, https://doi.org/10.1029/2021JD035599, 2022.
Endlich, R. M. and Wolf, D. E.: Automatic Cloud Tracking Applied to GOES and METEOSAT Observations, J. Appl. Meteorol. Climatol., 20, 309–319, https://doi.org/10.1175/1520-0450(1981)020<0309:ACTATG>2.0.CO;2, 1981.
Esmaili, R. B., Tian, Y., Vila, D. A., and Kim, K. M.: A lagrangian analysis of cold cloud clusters and their life cycles with satellite observations, J. Geophys. Res., 121, 11723–11738, https://doi.org/10.1002/2016JD025653, 2016.
Evans, J. L. and Shemo, R. E.: A Procedure for Automated Satellite-Based Identification and Climatology Development of Various Classes of Organized Convection, J. Appl. Meteorol. Climatol., 35, 638–652, https://doi.org/10.1175/1520-0450(1996)035<0638:APFASB>2.0.CO;2, 1996.
Feng, Z., Leung, L. R., Liu, N., Wang, J., Houze, R. A., Li, J., Hardin, J. C., Chen, D., and Guo, J.: A Global High-Resolution Mesoscale Convective System Database Using Satellite-Derived Cloud Tops, Surface Precipitation, and Tracking, J. Geophys. Res.-Atmos., 126, e2020JD034202, https://doi.org/10.1029/2020JD034202, 2021.
Feng, Z., Hardin, J., Barnes, H. C., Li, J., Leung, L. R., Varble, A., and Zhang, Z.: PyFLEXTRKR: a flexible feature tracking Python software for convective cloud analysis, Geosci. Model Dev., 16, 2753–2776, https://doi.org/10.5194/gmd-16-2753-2023, 2023.
Fiolleau, T.: Animation of convective situation segmented by TOOCAN from HIMAWARI IR data over the Western Pacific region in October 2015, TIB AV Portal [video], https://doi.org/10.5446/68200, 2024.
Fiolleau, T. and Roca, R.: An algorithm for the detection and tracking of tropical mesoscale convective systems using infrared images from geostationary satellite, IEEE T. Geosci. Remote, 51, 4302–4315, https://doi.org/10.1109/TGRS.2012.2227762, 2013a.
Fiolleau, T. and Roca, R.: Composite life cycle of tropical mesoscale convective systems from geostationary and low Earth orbit satellite observations: method and sampling considerations, Q. J. Roy. Meteor. Soc., 139, 941–953, https://doi.org/10.1002/qj.2174, 2013b.
Fiolleau, T. and Roca, R.: TOOCAN Database V2.08 – Tracking Of Organized Convection Algorithm using a 3-dimensional segmentation, IPSL Data Catalog [data set], https://doi.org/10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8, 2023a.
Fiolleau, T. and Roca, R.: CACATOES database V1.04, IPSL Data Catalog [data set], https://doi.org/10.14768/98569eea-d056-412d-9f52-73ea07b9cdca, 2023b.
Fiolleau, T. and Roca, R.: GEOgrid_coldcloud: a 2012–2020 global homogenized infrared dataset from a fleet of geostationary satellites, IPSL Data Catalog [data set], https://doi.org/10.14768/93f138f5-a553-4691-96ed-952fd32d2fc3, 2023c.
Fiolleau, T., Roca, R., Cloche, S., Bouniol, D., and Raberanto, P.: Homogenization of Geostationary Infrared Imager Channels for Cold Cloud Studies Using Megha-Tropiques/ScaRaB, IEEE T. Geosci. Remote, 58, 6609–6622, https://doi.org/10.1109/TGRS.2020.2978171, 2020.
Guilloteau, C. and Foufoula-Georgiou, E.: Life Cycle of Precipitating Cloud Systems from Synergistic Satellite Observations: Evolution of Macrophysical Properties and Precipitation Statistics from Geostationary Cloud tracking and GPM Active and Passive Microwave Measurements, J. Hydrometeorol., https://doi.org/10.1175/jhm-d-23-0185.1, 2024.
Harper, B., Kepert, J., and Ginger, J.: Wind speed time averaging conversions for tropical cyclone conditions, in: Proc. 28th Conference Hurricanes and Tropical Meteorology (AMS, 2008), 27 April–2 May 2008, Palms Foyer, USA, 2008.
Heikenfeld, M., Marinescu, P. J., Christensen, M., Watson-Parris, D., Senf, F., van den Heever, S. C., and Stier, P.: tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets, Geosci. Model Dev., 12, 4551–4570, https://doi.org/10.5194/gmd-12-4551-2019, 2019.
Hennon, C. C., Helms, C. N., Knapp, K. R., and Bowen, A. R.: An objective algorithm for detecting and tracking tropical cloud clusters: Implications for tropical cyclogenesis prediction, J. Atmos. Ocean. Tech., 28, 1007–1018, https://doi.org/10.1175/2010JTECHA1522.1, 2011.
Hennon, C. C., Papin, P. P., Zarzar, C. M., Michael, J. R., Adam Caudill, J., Douglas, C. R., Groetsema, W. C., Lacy, J. H., Maye, Z. D., Reid, J. L., Scales, M. A., Talley, M. D., and Helms, C. N.: Tropical cloud cluster climatology, variability, and genesis productivity, J. Climate, 26, 3046–3066, https://doi.org/10.1175/JCLI-D-12-00387.1, 2013.
Hewison, T. J., Wu, X., Yu, F., Tahara, Y., Hu, X., Kim, D., and Koenig, M.: GSICS inter-calibration of infrared channels of geostationary imagers using metop/IASI, IEEE T. Geosci. Remote, 51, 1160–1170, https://doi.org/10.1109/TGRS.2013.2238544, 2013.
Hodges, K. I.: A General-Method For Tracking Analysis And Its Application To Meteorological Data, Mon. Weather Rev., 122, 2573–2586, https://doi.org/10.1175/1520-0493(1994)122<2573:AGMFTA>2.0.CO;2, 1994.
Houze Jr., R. A.: Mesoscale Convective Systems, Rev. Geophys., 42, 1–43, https://doi.org/10.1029/2004RG000150, 2004.
Houze, R. A.: 100 Years of Research on Mesoscale Convective Systems, Meteorological Monographs, AMSMONOGRAPHS-D-18-0001.1, https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0001.1, 2018.
Huang, X., Hu, C., Huang, X., Chu, Y., Tseng, Y., Zhang, G. J., and Lin, Y.: A long-term tropical mesoscale convective systems dataset based on a novel objective automatic tracking algorithm, Clim. Dynam., 51, 3145–3159, https://doi.org/10.1007/s00382-018-4071-0, 2018.
Jirak, I. L., Cotton, W. R., and Mcanelly, R. L.: Satellite and Radar Survey of Mesoscale Convective System Development, Mon. Weather Rev., 131, 2428–2449, https://doi.org/10.1175/1520-0493(2003)131<2428:SARSOM>2.0.CO;2, 2003.
Jones, W. K., Christensen, M. W., and Stier, P.: A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations, Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, 2023.
Knapp, K. R., Kruk, M. C., Levinson, D. H., Diamond, H. J., and Neumann, C. J.: The international best track archive for climate stewardship (IBTrACS), B. Am. Meteorol. Soc., 91, 363–376, https://doi.org/10.1175/2009BAMS2755.1, 2010.
Knapp, K. R., Ansari, S., Bain, C. L., Bourassa, M. A., Dickinson, M. J., Funk, C., Helms, C. N., Hennon, C. C., Holmes, C. D., Huffman, G. J., Kossin, J. P., Lee, H. T., Loew, A., and Magnusdottir, G.: Globally Gridded Satellite observations for climate studies, B. Am. Meteorol. Soc., 92, 893–907, https://doi.org/10.1175/2011BAMS3039.1, 2011.
Kruk, M. C., Knapp, K. R., and Levinson, D. H.: A technique for combining global tropical cyclone best track data, J. Atmos. Ocean. Tech., 27, 680–692, https://doi.org/10.1175/2009JTECHA1627.1, 2010.
Laing, A. G. and Fritsch, J. M.: The global population of mesoscale convective complexes, Q. J. Roy. Meteor. Soc., 123, 389–405, https://doi.org/10.1002/qj.49712353807, 1997.
Laing, A. G. and Fritsch, J. M.: The Large-Scale Environments of the Global Populations of Mesoscale Convective Complexes, Mon. Weather Rev., 128, 2756–2776, 2000.
Liu, C., Zipser, E. J., and Nesbitt, S. W.: Global Distribution of Tropical Deep Convection: Different Perspectives from TRMM Infrared and Radar Data, J. Climate, 20, 489–503, https://doi.org/10.1175/jcli4023.1, 2007.
Liu, C., Zipser, E. J., Cecil, D. J., Nesbitt, S. W., and Sherwood, S.: A cloud and precipitation feature database from nine years of TRMM observations, J. Appl. Meteorol. Climatol., 47, 2712–2728, https://doi.org/10.1175/2008JAMC1890.1, 2008.
Machado, L. A. T. and Rossow, W. B.: Structural Characteristics and Radiative Properties of Tropical Cloud Clusters, Mon. Weather Rev., 121, 3234–3260, https://doi.org/10.1175/1520-0493(1993)121<3234:SCARPO>2.0.CO;2, 1993.
Machado, L. A. T., Desbois, M., Duvel, J.-P., Machado, L. A. T., Desbois, M., and Duvel, J.-P.: Structural Characteristics of Deep Convective Systems over Tropical Africa and the Atlantic Ocean, Mont. Weather Rev., 120, 392–406, https://doi.org/10.1175/1520-0493(1992)120<0392:SCODCS>2.0.CO;2, 1992.
Machado, L. A. T., Rossow, W. B., Guedes, R. L., and Walker, A. W.: Life Cycle Variations of Mesoscale Convective Systems over the Americas, Mon. Weather Rev., 126, 1630–1654, 1998.
Maddox, R. A.: Mesoscale Convective Complexes, B. Am. Meteorol. Soc., 61, 1374–1387, 1980.
Mapes, B. and Houze, R. A.: Cloud Clusters and Superclusters over the Oceanic Warm Pool, Mon. Weather Rev., 121, 1398–1415, https://doi.org/10.1175/1520-0493(1993)121<1398:CCASOT>2.0.CO;2, 1993.
Mapes, B., Milliff, R., and Morzel, J.: Composite life cycle of maritime tropical mesoscale convective systems in scatterometer and microwave satellite observations, J. Atmos. Sci., 66, 199–208, https://doi.org/10.1175/2008JAS2746.1, 2009.
Mathon, V. and Laurent, H.: Life cycle of Sahelian mesoscale convective cloud systems, Q. J. Roy., Meteor. Soc., 127, 377–406, https://doi.org/10.1002/qj.49712757208, 2001.
Mohr, K. I. and Zipser, E. J.: Mesoscale Convective Systems Defined by Their 85-GHz Ice Scattering Signature: Size and Intensity Comparison over Tropical Oceans and Continents, Mon. Weather Rev., 124, 2417–2437, https://doi.org/10.1175/1520-0493(1996)124<2417:mcsdbt>2.0.co;2, 1996.
Ocasio, K. M. N., Evans, J. L., and Young, G. S.: Tracking mesoscale convective systems that are potential candidates for tropical cyclogenesis, Mon. Weather Rev., 148, 655–669, https://doi.org/10.1175/MWR-D-19-0070.1, 2020.
Ostlund, S.: Computer Software for Rainfall Analyses and Echo Tracking of Digitized Radar Data, NOAA technical memorandum ERL WMPO, 15, 1974.
Poujol, B., Prein, A. F., and Newman, A. J.: Kilometer-scale modeling projects a tripling of Alaskan convective storms in future climate, Clim. Dynam., 55, 3543–3564, https://doi.org/10.1007/s00382-020-05466-1, 2020.
Prein, A. F., Feng, Z., Fiolleau, T., Moon, Z. L., Núñez Ocasio, K. M., Kukulies, J., Roca, R., Varble, A. C., Rehbein, A., Liu, C., Ikeda, K., Mu, Y., and Rasmussen, R. M.: Km-Scale Simulations of Mesoscale Convective Systems Over South America – A Feature Tracker Intercomparison, J. Geophys. Res.-Atmos., 129, e2023JD040254, https://doi.org/10.1029/2023JD040254, 2024.
Rajagopal, M., Russell, J., Skok, G., and Zipser, E.: Tracking Mesoscale Convective Systems in IMERG and Regional Variability of Their Properties in the Tropics, J. Geophys. Res.-Atmos., 128, e2023JD038563, https://doi.org/10.1029/2023JD038563, 2023.
Roca, R. and Fiolleau, T.: Extreme precipitation in the tropics is closely associated with long-lived convective systems, Commun. Earth Environ., 1, 18, https://doi.org/10.1038/s43247-020-00015-4, 2020.
Roca, R. and Ramanathan, V.: Scale dependence of monsoonal convective systems over the Indian Ocean, J. Climate, 13, 1286–1298, https://doi.org/10.1175/1520-0442(2000)013<1286:SDOMCS>2.0.CO;2, 2000.
Roca, R., Berges, J. C., Brogniez, H., Capderou, M., Chambon, P., Chomette, O., Cloche, S., Fiolleau, T., Jobard, I., and Lemond, J.: On the water and energy cycles in the Tropics, Comptes Rendus Geosciences, 342, 390–402, https://doi.org/10.1016/j.crte.2010.01.003, 2010.
Roca, R., Aublanc, J., Chambon, P., Fiolleau, T., and Viltard, N.: Robust observational quantification of the contribution of mesoscale convective systems to rainfall in the tropics, J. Climate, 27, 4952–4958, https://doi.org/10.1175/JCLI-D-13-00628.1, 2014.
Roca, R., Fiolleau, T., and Bouniol, D.: A simple model of the life cycle of mesoscale convective systems cloud shield in the tropics, J. Climate, 30, 4283–4298, https://doi.org/10.1175/JCLI-D-16-0556.1, 2017.
Roca, R., Bouniol, D., and Fiolleau, T.: On the Duration and Life Cycle of Precipitation Systems in the Tropics, in: Satellite Precipitation Measurement: Volume 2, edited by: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., and Turk, F., Springer International Publishing, Cham, 729–744, https://doi.org/10.1007/978-3-030-35798-6_14, 2020.
Roca, R., Fiolleau, T., Viju, J., and Schulz, J.: METEOSAT Long-Term Observations Reveal Changes in Convective organization over Tropical Africa and Atlantic Ocean, Surv. Geophys., in review, 2024.
Schröder, M., König, M., and Schmetz, J.: Deep convection observed by the Spinning Enhanced Visible and Infrared Imager on board Meteosat 8: Spatial distribution and temporal evolution over Africa in summer and winter 2006, J. Geophys. Res.-Atmos., 114, 1–14, https://doi.org/10.1029/2008JD010653, 2009.
Szantai, A., Six, B., Cloché, S., and Sèze, G.: megha-tropiques MTTM Megha-Tropiques Technical Memorandum Quality of geostationary satellite images Megha-Tropiques Quality of geostationary satellite images, https://meghatropiques.ipsl.fr/download/megha-tropiques-technical-memorandum-n3/ (last access: 9 September 2024), 2011.
Tan, J., Jakob, C., Rossow, W. B., and Tselioudis, G.: Increases in tropical rainfall driven by changes in frequency of organized deep convection, Nature, 519, 451–454, https://doi.org/10.1038/nature14339, 2015.
Tsakraklides, G. and Evans, J. L.: Global and regional diurnal variations of organized convection, J. Climate, 16, 1562–1572, https://doi.org/10.1175/1520-0442-16.10.1562, 2003.
Vant-Hull, B., Rossow, W., and Pearl, C.: American Meteorological Society Global Comparisons of Regional Life Cycle Properties and Motion of Multiday Convective Systems: Tropical and Midlatitude Land and Ocean, J. Climate, 29, 5837–5858, https://doi.org/10.1175/JCLI-D-15-0698.1, 2016.
Wilcox, E. M.: Spatial and temporal scales of precipitating tropical cloud systems in satellite imagery and the NCAR CCM3, J. Climate, 16, 3545–3559, https://doi.org/10.1175/1520-0442(2003)016<3545:SATSOP>2.0.CO;2, 2003.
Williams, M. and Houze, R. A.: Satellite-Observed Characteristics of Winter Monsoon Cloud Clusters, Mon. Weather Rev., 115, 505–519, https://doi.org/10.1175/1520-0493(1987)115<0505:SOCOWM>2.0.CO;2, 1987.
WMO: Measurement of surface wind. Guide to Meteorological Instruments and Methods of Observations, 5th edn., World Meteorological Organization Tech. Rep. WMO-8, 1983.
Woodley, W. L., Griffith, C. G., Griffin, J. S., and Stromatt, S. C.: The Inference of GATE Convective Rainfall from SMS-1 Imagery, J. Appl. Meteorol., 19, 388–408, 1980.
Short summary
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.
This paper presents a database of tropical deep convective systems over the 2012–2020 period,...
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