This dataset contains change factors for the 2- to 100-year daily (24-hour) extreme rainfall storms for the Continental United States from publicly available downscaled climate projections, namely BCCAv.2, LOCA, MACA and NA-CORDEX data sets. Change factors were estimated as the ratio between the historical (period between1950-2005) climate simulations of extreme rainfall and the future (period between 2044-2099) climate simulations of rainfall depths corresponding to the average recurrence interval (e.g. 2-, 5-year). These change factors were computed using the Generalized Extreme Value Distribution, which is widely used to describe rainfall extremes.
This data archive was prepared as part of the outputs of the published article Lopez‐Cantu, T., Prein, A. F., & Samaras, C. (2020). Uncertainties in Future U.S. Extreme Precipitation from Downscaled Climate Projections. Geophysical Research Letters. https://doi.org/10.1029/2019GL086797. When using the data in this archive, citation must be given to the original article.

R code and documentation of climate chnage risk data and cluster analysis to identify climate change risk groups for Southeast Asian island communities

Overview This repository contains two datasets that were collected and processed as part of a study on public perception of environmental issues and climate change in Ukraine. The datasets are derived from Ukrainian Telegram news channels and include metadata, raw text, and user reactions to posts related to climate events and environmental topics. These datasets are intended to support academic research on the relationship between public discourse, user sentiment, and climate indicators. The datasets are located in the data folder with respect to their extension: csv and parquet. If you decide to read the climate_text_data_final in CSV format, please set the encoding to utf-16. Datasets climate_text_data_final This dataset contains raw text data from Telegram posts, along with additional metadata. It provides a comprehensive view of the content and context of climate-related discussions. The dataset can be joined with the final_reactions_data based on the channel_name and message_id. Please ensure the encoding is set to utf-16 when reading the CSV format of the dataset. Key Features: Post ID: Unique identifier for each Telegram post. Channel Name: The name of the Telegram channel where the post was published. Text: The raw text of the Telegram post. Metadata: Includes timestamp, number of views, and number of forwards. Purpose: This dataset is designed to support natural language processing (NLP) tasks, such as topic modeling, named entity recognition, and sentiment analysis. It provides a foundation for understanding the themes and narratives surrounding climate change and environmental issues in Ukrainian online information space. final_reactions_data This dataset contains user reactions to Telegram posts, represented as emoji counts. It provides a detailed view of how users engage with climate-related content. Key Features: Post ID: Unique identifier for each Telegram post. Channel Name: The name of the Telegram channel where the post was published. Emoji Reactions: Columns representing counts of various emojis used to react to the post. Is NA: A boolean value showing whether the emoji reaction columns have NaN or at least one non-NA value. Purpose: This dataset enables researchers to analyze user sentiment and engagement with climate-related content. It can be used to identify patterns in public reactions to environmental issues and assess the emotional tone of the discourse. The emojis can be classified into categories to reduce dimensionality and work with a combined representation of emojis. Further, statistics on particular emoji class can be generated. This will lead to a solid understanding of user engagement patterns. Research Context The datasets were collected as part of a study aimed at understanding public attitudes toward environmental issues and exploring the relationship between public perception and climate indicators, especially in the period of the full-scale Russian aggression against Ukraine. The study focused on Telegram channels due to their popularity and influence in Ukraine. The research objectives included: Developing a methodology for automated data collection from Ukrainian Telegram channels on climate-related topics. Conducting a comprehensive analysis of the collected data using natural language processing and statistical methods to identify key topics, trends, and patterns. Investigating the relationship between message characteristics and user reactions to determine factors influencing public perception of environmental issues. The study analyzed content from seven influential Telegram news channels: DW Ukraine, BBC Ukrainian, Ukrayinska Pravda, Voice of America, Radio Liberty, Babel, and ZN.UA. These channels were selected based on their audience size, credibility, and regularity of coverage of environmental issues. The data collection period spanned five years (01.01.2020 - 14.01.2025), allowing for an analysis of trends over time, including the impact of the Russian war in Ukraine on public discourse. Ethical Considerations The datasets do not contain any personally identifiable information (PII). However, we acknowledge that the dataset may contain sensitive content due to the nature of the data. Some records may describe war-related activities, destruction, harm, or other sensitive topics. We have made every effort to remain unbiased in collecting data from the selected channels and have not censored any content. The dataset will undergo ethical clearance at Lviv Polytechnic National University to ensure compliance with ethical standards and guidelines for data collection, processing, and usage. This process aims to address potential concerns related to sensitive content and ensure the responsible use of the dataset in academic research. Recommendations for Ethical Use: Fairness and Bias: Evaluate results with fairness metrics to ensure that analyses are not biased or discriminatory. Transparency: Use tools for interpretability and explainability to ensure...

Town Brook watershed, Catskills, New York. SWATWB was also tested on the Town Brook watershed (Figure 2) in the USA, a 37 km2 sub-catchment of the Cannonsville reservoir basin. The region is typified by steep-to-moderate hillslopes of glacial origins with shallow permeable soils, underlain by a restrictive layer. The climate is humid with an average annual temperature of 8 ¢XC and average annual precipitation of 1123 mm. Elevation in the watershed ranges from 493 to 989 m above mean sea level. The slopes are quite steep with a maximum of 91%, and a mean of 21%. Soils are mainly silt loam or silty clay loam with soil hydrological group C ratings (USDA–NRCS, 2000). Soil depth ranges from less than 50 cm to greater than 1 m and is underlain by a fragipan restricting layer (e.g. coarse-loamy, mixed, active, mesic, to frigid Typic Fragiudepts, Lytic or Typic Dystrudepts common to glacial tills) (Schneiderman et al., 2002). The lowland portion of the watershed is predominantly agricultural, consisting of pasture and row crops (20%) or shrub land (18%), whereas the upper slopes are forested (60%). Water and wetlands comprise 2%. Impervious surfaces occupy

This dataset contains information on the Surface Soil Moisture (SM) state derived from satellite observations in the microwave domain.

The operational (ACTIVE, PASSIVE, COMBINED) ESA CCI SM products are available at https://catalogue.ceda.ac.uk/uuid/c256fcfeef24460ca6eb14bf0fe09572/

Abstract

Understanding whether the soil surface is frozen or thawed is crucial for interpreting satellite-based soil moisture measurements and for many Earth system applications. The physical state of water in the soil strongly affects its dielectric properties, which in turn determine how satellites sense moisture content. Current ESA CCI Soil Moisture products exclude data when the surface is likely frozen, as reliable retrievals are not possible under such conditions. Yet, the freeze/thaw state itself carries valuable environmental information: it reflects the changing energy and water exchange between land and atmosphere, shapes seasonal hydrological cycles, and influences agriculture, ecosystems, and climate feedbacks across much of the Northern Hemisphere.

This dataset provides global estimates of the soil moisture freeze/thaw state for the period from 11-1978 to 12-2024 derived from PASSIVE (radiometer) and ACTIVE (scatterometer) satellite observations within the ESA CCI Soil Moisture framework. These sensors, operating in the K- and C-band frequency range, are sensitive to surface temperature, enabling the detection of frozen versus thawed conditions at daily temporal and ~25 km spatial sampling. Data from L-band missions (e.g., SMAP, SMOS) are not included, resulting in a total number of 12 satellites.

The classification algorithm, described in Van der Vliet et al. (2020), was originally developed to flag frozen conditions in passive soil moisture retrievals and has since evolved into a dedicated data product. It applies a decision-tree approach using multi-frequency satellite measurements to classify the surface state for each sensor. Similarly, Naeimi et al. (2012) have developed an algorithm based on ASCAT backscatter for freeze/thaw classification in C-band scatterometer retrievals. Individual classifications are then merged into a single spatiotemporal record using a conservative unanimity rule—if any contributing satellite detects a frozen surface, the merged product is classified as “frozen.”

While this approach ensures reliability, it may lead to some over-flagging, which could be refined in future versions. The current product achieves an estimated accuracy of 75% against in situ surface temperature observations and 92% compared to ERA5 reanalysis data.

Summary

• Daily binary (true/false) freeze/thaw surface soil moisture state classification dataset (~25 km spatial sampling) for the period November 1978 to December 2024.
• Based on a satellite brightness temperature (K-band) classification algorithm (Van der Vliet et al., 2020) from 12 satellite radiometers and a satellite backscatter (C-band) classification algorithm (Naeimi et al., 2012).
• A pixel is classified as "frozen" if it was classified accordingly for at least one satellite. This can lead to potential over-flagging in the current version.
• Approximately 75% agreement with in situ surface temperature measurements (Dorigo et al., 2021) and 92% with ERA5-Land reanalysis temperature fields (Muñoz-Sabater et al., 2021)

Programmatic (bulk) download

You can use command-line tools such as wget or curl to download (and extract) data for multiple years. The following command will download and extract the complete data set to the local directory ~/Download on Linux or macOS systems.

#!/bin/bash

# Set download directory
DOWNLOAD_DIR=~/Downloads

base_url="https://researchdata.tuwien.at/records/m3g2x-a6958/files"

# Loop through years 1978 to 2024 and download & extract data
for year in {1978..2024}; do
  echo "Downloading $year.zip..."
  wget -q -P "$DOWNLOAD_DIR" "$base_url/$year.zip"
  unzip -o "$DOWNLOAD_DIR/$year.zip" -d $DOWNLOAD_DIR
  rm "$DOWNLOAD_DIR/$year.zip"
done

Data details

Filename template

The dataset provides global daily estimates for the 1978-2024 period at 0.25° (~25 km) horizontal grid resolution. Daily images are grouped by year (YYYY), each subdirectory containing one netCDF image file for a specific day (DD) and month (MM) of that year in a 2-dimensional (longitude, latitude) grid system (CRS: WGS84). The file name follows the convention:

ESACCI-SOILMOISTURE-L3S-FT-YYYYMMDD000000-fv09.2.nc

Data Variables

Each netCDF file contains 3 coordinate variables

• lon: longitude (WGS84), [-180,180] degree W/E
• lat: latitude (WGS84), [-90,90] degree N/S
• time: datetime, encoded as "number of days since 1970-01-01 00:00:00 UTC"

and the following data variables

• ft: (int) Soil moisture freeze-thaw state binary indicator (0=not frozen, 1=frozen, -1=missing data)
• ft_agreement (float): Classification agreement between available sensors. 1 means that the frozen/unfrozen classification was the same for all merged sensors. The number decreases as the classification results between available satellites contradict.
• sensor_count (int): Total number of merged sensors/overpasses
• sensor_count_frozen (int): Total number of measuring sensors/overpasses that detected frozen soils
• mode: (int) Indicator for satellite orbit(s) used in the retrieval (1=ascending, 2=descending, 3=both, 0=missing data)
• sensor: (int) Indicator for satellite sensor(s) used in the retrieval. For more details, see netcdf attributes.

Additional information for each variable is given in the netCDF attributes.

Version Changelog

Changes in v9.2 (first released version):

• This version applies the classification algorithms described by Van der Vliet et al. (2020) and Naeimi et al. (2012) to 17 sensors and a unanimous merging approach. Covers the period from 11-1978 to 12-2024.

Software to open netCDF files

These data can be read by any software that supports Climate and Forecast (CF) conform metadata standards for netCDF files, such as:

• https://github.com/pydata/xarray" target="_blank" rel="noopener">Xarray (Python)
• https://unidata.github.io/netcdf4-python/" target="_blank" rel="noopener">netCDF4 (Python)
• https://github.com/TUW-GEO/esa_cci_sm" target="_blank" rel="noopener">esa_cci_sm (Python)
• Similar tools exist for other programming languages (Matlab, R, etc.)
• Software packages and GIS tools can open netCDF files, e.g. CDO, NCO, QGIS, ArcGIS
• You can also use the GUI software Panoply to view the contents of each file

Related Records

This record and all related records are part of the ESA CCI Soil Moisture science data records community.

Engineering Climate Datasets encompasses Intensity-Duration-Frequency IDF Files, Canadian Weather Energy and Engineering Datasets CWEEDS , and Canadian Weather Year For Energy Calculation CWEC . IDF tabulates and graphs short-duration rainfall statistics across 563 locations in Canada. CWEEDS is a computer dataset of hourly conditions at specific locations, including data from 1953 until 2005. It also includes long term weather records used in urban planning and green building design, as well as estimates of hourly solar radiation amounts. CWEC datasets are created by combining 12 "Typical Meteorological Months" selected from a database of, usually, 30 years of data. Months are chosen by comparing individual means with long term monthly means for daily global radiation, mean, minimum and maximum DB temperature, mean, minimum and maximum dew point temperature, and mean and maximum wind speed.

QGIS file to visualise and analyse climate change risk data and cluster analysis results for Southeast Asian island communities

The U.S. water supply and carbon sequestration are increasingly threatened by future climate change and air pollution. This study investigates the ecohydrological responses to the individual and combined impacts of climate change and anthropogenic emission changes at two spatial scales by coupling a regional online-coupled meteorology and chemistry model (WRF-Chem) and a water balance model (WaSSI). Combined effects of climate change and anthropogenic emission changes in 2046-2055 relative to 2001-2010 over the US enhance hydrological cycle and carbon sequestration. However, a drying trend occurs in the central and part of the western U.S. Climate change is projected to dominate the ecohydrological changes in most regions. Anthropogenic emission changes under 2001-2010 climate conditions cools down inland water resource regions with 0.01~0.15℃, moisturizes the east and dry the west U.S. More stringent anthropogenic emission control enhances precipitation and ecosystem production in the east and west but has an opposite trend in the central U.S. The ecohydrological modeling in California and North Carolina based on 4-km resolution meteorological data in 2050 and 2005 shows varying changes in magnitudes and spatial patterns compared to results based on 36-km resolution meteorological data. Projected changes in air pollutant emissions may accelerate climatic warming in coastal areas and the state of New Mexico and decrease precipitation, runoff, and carbon sequestration in part of the western U.S. Strategies to address future possible problems such as heatwaves, water stress, and ecosystem productivity should consider the varying interplay between air quality control and climate change at different spatial scales.

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

Description of aridity indices ranges [60].

Radar range: standard. Max rain level: weak rain. Parent item: IDRA weather radar measurements - month 2015-06 Measuring instrument: IDRA atmospheric radar in CESAR observatory, Cabauw IRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at {http://www.cesar-database.nl}. IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.

Observed rainfall data (1952–2004) and predicted rainfall data (2052–2100).

This project followed the PIEVC (Public Infrastructure Engineering Committee Protocol), developed by Engineers Canada. This led to the completion of five steps in a two-year time frame, beginning with the creation of an advisory committee. Following this creation, leadership sessions with Elders were set up to inform the community of the PIEVC process. Climate change data gathering followed with the engagement of the community. A risk assessment of flood risks on infrastructure, severe storm events and droughts took place, followed by an engineering analysis which calculated data sufficiency, the requirements for additional capacity, load calculations and knowledge gaps. Finally, the data will be synthesized into recommendations and conclusions, resulting in a final statement of vulnerability and resiliency for the community. First Nations involved in the project are Membetou First Nation, Wagmatcook First Nation, Eskasoni First Nation, Chapel Island First Nation, and We'kiqma'q First Nation.

A data set of simulated hydrologic fluxes and states from the Variable Infiltration Capacity (VIC) model, gridded to a 1/16 degree (~6km) resolution that spans the entire country of Mexico, the conterminous U.S. (CONUS), and regions of Canada south of 53 degrees N for the period 1950-2013. Because of the consistent gridding methodology, the current product reduces transboundary discontinuities making it suitable for estimating large-scale hydrologic phenomena.

AgriClimateBD is a comprehensive precision agriculture dataset developed for Bangladesh by integrating agricultural production statistics, crop phenological information, and satellite-derived climate variables. The dataset contains 4,607 records and 33 features representing 73 crop types cultivated across 64 districts of Bangladesh.

The dataset was developed by extending the previously published SPAS-Dataset-BD through the incorporation of climate information obtained from the NASA POWER (Prediction of Worldwide Energy Resources) platform. Agricultural production, area, temperature, and humidity information were collected from the Bangladesh Bureau of Statistics (BBS) Statistical Yearbook 2022, while crop lifecycle information, including transplanting, growth, harvesting periods, and seasonal classifications, was obtained through field surveys involving 223 farmers from diverse agroecological regions of Bangladesh.

A Python-based automated data enrichment pipeline was employed to retrieve daily climate observations from the NASA POWER API for all 64 districts. Four climate parameters—precipitation, solar radiation, wind speed, and evapotranspiration—were aggregated into phenologically meaningful temporal windows. These include full-season summaries, pre-transplant month, transplant month, and post-transplant month conditions. The resulting climate features were integrated with agricultural and phenological attributes to create a unified dataset suitable for precision agriculture research.

The dataset includes agricultural variables such as crop name, district, cultivated area, production, season, transplanting period, growth period, and harvest period; meteorological variables including average, maximum, and minimum temperature and humidity; and sixteen NASA POWER-derived climate variables representing rainfall, solar radiation, wind speed, and evapotranspiration across multiple crop-development stages.

AgriClimateBD supports a wide range of applications including crop yield prediction, crop classification, climate-resilient agriculture, agricultural decision support systems, precision irrigation management, machine learning, deep learning, and agricultural policy analysis. The dataset is particularly valuable because it provides phenologically anchored climate information for Bangladesh’s agricultural systems, including numerous underrepresented crop species that are rarely available in public agricultural datasets.

The dataset is provided in CSV format and is intended to facilitate reproducible research in agricultural informatics, climate-smart agriculture, remote sensing, and data-driven farming systems.

Report about climate change in the Southern and Southwestern Flatlands West NRM region of Australia, focused on Albany Port.

This report was created in reference to Albany Port (AUALH), located in the ABC NRM region Southern and Southwestern Flatlands West. The report is composed of Ports Australia data, CSIRO & BoM trend data, measurements from ACORN-SAT stations, CSIRO future data, CMAR future data, and Jane Mullett's personal analysis.

Climate Smart Seaports is an online decision support toolkit designed to help Australian seaports adapting to climate change and improving their resilience to it. The toolkit lets users access data from various datasets such as CSIRO, BoM, ABS, BITRE as well as their own personal data. Climate Smart Seaports then allows writing and publishing reports based on this data and the user analysis.

This data was created as part of the AusEnHealth pilot project, which included a national data audit of Australian environmental health data at both state and national levels. During this process, data custodians, datasets and their related metadata were compiled and the available data were assessed for national coverage, relevance, spatial resolution and accessibility. This audit followed a structured approach, prioritising datasets that were timely, spatially precise, nationally available, and accessible for research use.

This data is related to extreme climates and air pollution which were the environmental health domains prioritised by collaborating government organisations. These were among five key use cases ranked highest in relevance, feasibility, and urgency during the AusEnHealth pilot project. Ethics for data collection and storage were approved as part of a human ethics exempt research application at the Queensland University of Technology (reference HE-Ex2 2022-4825-7319).

Climate Data

Climate data relevant to the extreme climates case study comprised minimum and maximum temperature, sourced from the Bureau of Meteorology (BOM) over a 20-year period. BOM was identified as the primary custodian for national-scale climate data due to its provision of high-resolution, daily gridded datasets with open access and consistent temporal coverage, satisfying all quality audit criteria. These climate data were used to create two indicators related to the impact of temperature on human health, namely excess heat factor (EHF) and excess cold factor (ECF) using existing methodology. EHF and ECF are metrics used by BOM and commonly in literature to determine whether an area is impacted by a heatwave or coldwave, respectively, using short- and long-term historical temperature recordings.

Air Quality Data

Relevant air pollutant data were collected from the Copernicus Atmosphere Monitoring Service (CAMS). While Australian data from the Centre for Air pollution, energy and health Research Data (CARDAT) were identified as high-quality and spatially resolved, they were not available at the time of analysis. CAMS was selected as a secondary source due to its global coverage, daily temporal resolution, and publicly accessible archive of key pollutants including nitrogen dioxide, sulphur dioxide, ozone, carbon monoxide, and particulate matter of various sizes. Air quality was determined by the measurement of air pollutants which are introduced into the air via road traffic, industrial processes and bushfires.

Built Environment Data

Primary built environment data used in this study were acquired from the Australian Bureau of Statistics (ABS), Geoscience Australia’s Digital Earth Australia (DEA), NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) and the Terrestrial Ecosystem Research Network (TERN). These sources were identified in the audit as providing national or near-national coverage with spatial detail appropriate for local analysis. DEA and MODIS were particularly valuable due to their high spatial resolution and ongoing updates of land surface features relevant to human–environment interactions. The variables accessed include the number of hospitals, green space percentages, water surface percentages, the normalised difference vegetation index (NDVI) and canopy cover percentages.

Demographic Data

Demographic data have been utilised in numerous studies to determine vulnerable populations. Twenty relevant demographic variables were obtained from the Australian Census data. In addition, eleven variables related to existing health conditions were sourced from the Public Health Information Development Unit (PHIDU) social health atlas. These were prioritised in the data audit due to their strong alignment with vulnerability frameworks, public availability, and consistent small-area geographic resolution across the country.

Mortality Data

This study utilises publicly available mortality data sourced from the mortality over regions and time (MORT) books compiled by the Australian Institute of Health and Welfare (AIHW). This data set contains numbers of deaths by leading causes of death disaggregated by sex across Australia. The AIHW was identified in the audit as a key national custodian of health outcomes data, and MORT was among the few datasets to provide cause-specific mortality information with consistent SA3-level geography. Leading causes of death have been grouped to produce mortality data related to heat, air quality, or all causes, determined by combining or referring to specific mortality causes.

Data Dictionary

For more information on the specific indicators included in this data collection, please see the project's documented metadata

Radar range: standard. Max rain level: moderate rain. Parent item: IDRA weather radar measurements - month 2013-05 Measuring instrument: IDRA atmospheric radar in CESAR observatory, Cabauw IRCTR has built a high resolution radar system, IDRA (IRCTR Drizzle Radar), aimed at the detailed observation of the spatial and temporal distribution of rainfall and drizzle. The system was placed at the end of August 2007 on top of a 213 m high meteorological tower in the CESAR (Cabauw Experimental Site for Atmospheric Research) observatory in Cabauw, The Netherlands. This location has several advantages: In the first place, an increased sensitivity due to the reduction of the influence of ground clutter. Secondly, it allows direct observation of the horizontal distribution of low level clouds and fog. Finally, the presence of other instruments in the vicinity enhances the understanding of the physical processes in the atmosphere by synergistically combining their measurements. Data from other instruments at CESAR are available at {http://www.cesar-database.nl}. IDRA provides the horizontal distribution of reflectivity, mean Doppler velocity, Doppler spectrum width and polarimetric parameters like differential reflectivity, linear depolarization ratio or specific differential phase. The data collected is freely available to the scientific community.

This project presents a long-term assessment and prediction of PM₂.₅ concentrations over selected Indian urban regions under changing climatic and socioeconomic conditions using the CMIP6 modelling framework and machine learning techniques. The dataset integrates climate model outputs, meteorological variables, satellite-derived observations, and scenario-based projections under SSP245 and SSP585 to understand future air quality trends up to 2100.

The data can be reused for climate–air quality studies, urban pollution assessment, environmental planning, machine learning model development, health-risk evaluation, and policy analysis. Researchers may use the processed climate variables, PM₂.₅ predictions, and validation outputs to compare models, evaluate future pollution pathways, perform regional assessments, or extend analyses to additional cities and scenarios.

This project is unique because it combines CMIP6 climate projections, meteorological variability, satellite-derived PM₂.₅ observations, and machine learning-based prediction within an Indian urban context, enabling long-term assessment of future pollution pathways under multiple socioeconomic scenarios. The study also supports understanding of climate-sensitive air quality changes and sustainable mitigation planning.

The intended audience includes researchers in climate science, air quality, environmental engineering, atmospheric science, urban planning, public health, sustainability, and policymakers working on climate adaptation and pollution mitigation strategies.

A climate projection is the simulated response of the climate system to a scenario of future emission or concentration of greenhouse gases (GHGs) and aerosols, generally derived using climate models. Regional climate projections use Regional Climate Models (RCMs) that dynamically downscale the global projections to provide more regional/local details. This dataset represents a combined CORDEX Regional Climate Wind Model Data from year 2026 to year 2095 as NC file.