The U.S. Geological Survey, in cooperation with the California Department of Water Resources (DWR), has constructed a new spatially distributed Precipitation-Runoff Modeling System (PRMS) for the Merced River Basin (Koczot and others, 2021), which is a tributary of the San Joaquin River in California. PRMS is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and basin hydrology to various combinations of climate and land use (Markstrom and others, 2015). Although further refinement may be required to apply the Merced PRMS for official streamflow forecast operations, this application of PRMS is calibrated with intention to simulate (and eventually, forecast) year-to-year variations of inflows to Lake McClure during the critical April–July snowmelt season, and may become part of a suite of methods used by DWR for forecasting streamflow in and from the basin. The Merced application of PRMS is a high-resolution model defined spatially by discreet, georeferenced mapping units (i.e., "hydrologic response units"; HRUs). Daily inputs of precipitation, maximum and minimum temperatures are used to force the application. This application is designed to capture the effects of land use and climate change on streamflows and general hydrogeology from subareas of the model domain. As described in detail in Koczot and others (2021), simulations were calibrated against (1) solar radiation, (2) potential evapotranspiration, and (3) at 5 nodes representing locations of measured or reconstructed (at the outlet) streamflows. This application uses the PRMS 4.0.2 executable. Users should review the performance of this model to ensure applicability for their specific purpose. The PRMS application developed for this study can be operated through a customized Object User Interface (OUI; Markstrom and Koczot, 2008) coupled with a version of the Ensemble Streamflow Prediction (ESP; Day, 1985) forecasting tool, parameter-file editor, and data visualization tools. Furthermore, this includes daily-climate distribution preprocessing tools (Draper Climate-Distribution Software; Donovan and Koczot, 2019). Hereafter referred to as Merced OUI, this framework is the platform used to operate the Merced River Basin PRMS and perform streamflow simulations and forecasts.

The global weather visualization software market is booming, projected to reach $166 million by 2033 with a 4.9% CAGR. Discover key market trends, leading companies (Vizrt, AccuWeather, IBM), and growth drivers in this in-depth analysis.

this graphs the maps was created the : https://experience.arcgis.com/experience/b296879cc1984fda833a8acc93e31476/ https://www.ncei.noaa.gov/maps/daily/

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Climate data is a vital resource for understanding and addressing the complexities of climate change. With the advent of digital technology, accessing and utilizing climate datasets has become increasingly important for researchers, policymakers, and the general public alike. In this era of data-driven decision-making, the availability of comprehensive climate datasets empowers stakeholders to analyze trends, assess risks, and develop informed strategies for climate resilience and mitigation.

The Climate Data Online platform serves as a gateway to a wealth of climate datasets, offering users the opportunity to explore, analyze, and extract valuable insights from a diverse array of environmental data sources. By providing access to a wide range of datasets encompassing various climatic variables, geographic regions, and temporal scales, Climate Data Online facilitates interdisciplinary research, fosters collaboration, and supports evidence-based decision-making in climate science and related fields.

One of the key features of Climate Data Online is its user-friendly interface, which allows users to easily navigate through different datasets and access detailed information about each dataset. By clicking on the name of a dataset, users can expand and view comprehensive descriptions, including metadata, data formats, temporal coverage, spatial resolution, and relevant links to related tools and resources. This intuitive interface enhances the usability of the platform, enabling users to quickly find and retrieve the data they need for their specific research or analysis purposes.

Moreover, Climate Data Online offers various download options, including FTP access and downloadable samples, enabling users to obtain the data in the format and resolution that best suits their requirements. Whether users need raw data for advanced analysis or pre-processed data for visualization and modeling purposes, Climate Data Online provides the flexibility and scalability to meet diverse data needs.

One of the strengths of Climate Data Online is its extensive coverage of different climatic variables, ranging from temperature and precipitation to atmospheric pressure and wind speed. By aggregating data from multiple sources, including weather stations, satellites, and climate models, Climate Data Online offers a comprehensive view of the Earth's climate system, enabling users to explore spatial and temporal patterns, identify trends, and detect anomalies.

For example, researchers studying the impact of climate change on agriculture may utilize temperature and precipitation datasets to assess changes in growing season length, drought frequency, and crop yields. Similarly, urban planners may use data on temperature and air quality to evaluate heat island effects, assess health risks, and design resilient infrastructure. By providing access to such diverse datasets, Climate Data Online facilitates interdisciplinary research and supports evidence-based decision-making across various sectors.

In addition to its rich collection of climate datasets, Climate Data Online also serves as a valuable repository of tools and resources for data analysis and visualization. From interactive maps and charting tools to statistical analysis software and programming libraries, Climate Data Online offers a variety of options for exploring and interpreting the data. Moreover, the platform provides documentation, tutorials, and user support to help users navigate the datasets and leverage the available tools effectively.

Furthermore, Climate Data Online encourages collaboration and knowledge sharing among users by facilitating community forums, workshops, and collaborative projects. By connecting researchers, practitioners, and policymakers with shared interests in climate data analysis and interpretation, Climate Data Online fosters a vibrant community of practice, where ideas are exchanged, best practices are shared, and innovative solutions are developed.

Overall, Climate Data Online plays a crucial role in advancing climate science and supporting evidence-based decision-making in response to the challenges of climate change. By providing access to comprehensive climate datasets, user-friendly tools, and a supportive community, Climate Data Online empowers stakeholders to explore, analyze, and ...

The Channel Islands Marine Sanctuary (CINMS) comprises 1,470 square miles surrounding the Northern Channel Islands: Anacapa, Santa Cruz, Santa Rosa, San Miguel, and Santa Barbara, protecting various species and habitats. However, these sensitive habitats are highly susceptible to climate-driven ‘shock’ events which are associated with extreme values of temperature, pH, or ocean nutrient levels. A particularly devastating example was seen in 2014-16, when extreme temperatures and changes in nutrient conditions off the California coast led to large-scale die-offs of marine organisms. Global climate models are the best tool available to predict how these shocks may respond to climate change. To better understand the drivers and statistics of climate-driven ecosystem shocks, a ‘large ensemble’ of simulations run with multiple climate models will be used. The objective of this project is to develop a Python-based web application to visualize ecologically significant climate variables near th..., Data was accessed through AWS and then after subsetted to the point of interest, a netcdf file was downloaded for the purposes of the web application. More information can be found on the GitHub repository here: https://github.com/Channelislanders/toolkit It should be noted that all data found here is just for the purpose for the web application., , # GENERAL INFORMATION

This dataset is the files that accompany the website created for this project. A subsetted version of the CESM 1 dataset was downloaded to instantly update the website.

1. Title of the Project

Constructing Visualization Tools and Training Resources to Assess Climate Impacts on the Channel Islands National Marine Sanctuary

2. Author Information

Graduate Students at the Bren School for Environmental Science & Management in the Masters of Environmental Data Science program 2023-2024.

A. Principal Investigators Contact Information

Names: Olivia Holt, Diana Navarro, and Patty Park

Institution: Bren School at the University of California, Santa Barbara

Address: Bren Hall, 2400 University of California, Santa Barbara, CA 93117

Emails: olholt@bren.ucsb.edu, dmnavarro@bren.ucsb.edu, p_park@bren.ucsb.edu

B. Associate or Co-investigator Contact Informat...

Zonal Statistics calculated for ADM2 boundaries in India (Geoboundaries) for agriculturally relevant, public climate data from University of Delaware, the UK’s National Centre for Atmospheric Science (NCAS) at the University of East Anglia’s Climatic Research Unit (CRU), National Aeronautics and Space Administration (NASA) and National Oceanographic and Atmospheric Administration (NOAA).

Use of the data requires that you attribute any public use of the database, or works produced from the database. You must cite this data as Goodman, S., BenYishay, A., Lv, Z., & Runfola, D. (2019). GeoQuery: Integrating HPC systems and public web-based geospatial data tools. Computers & Geosciences, 122, 103-112.

For any use or redistribution of the database, or works produced from it, you must make clear to others the license of the database and keep intact any notices on the original database. Otherwise, you are free to share it, copy it, distribute it and use the database to make derivative works to modify it, transform it and build upon it. If you have any questions, please read the license to understand the ODC-BY 1.0 license.

Monsoon precipitation demonstrates a wide range of spatial and temporal variability in the U.S. Southwest. A variety of precipitation monitoring networks, including official networks, municipal flood control districts, and citizen science observers, can help improve our characterization and understanding of the monsoon. The data management challenges of integrating these diverse data sources can be formidable. Computer science and data management techniques provide a pathway for the design of forward looking climate services, especially those developed in collaboration with experts in this field. In this paper we present such a collaboration, integrating natural, social and computer science expertise. We document how we identified data networks and their sources and the computer science and data management workflow we employed to integrate and curate these data. We also present the web based data visualization tool and API that we developed as part of this process (monsoon.environment.arizona.edu). We use case study examples from the Tucson, AZ region to demonstrate the visualizer. We also discuss how this type of collaboration could be extended to existing or potential stakeholder collaborations, as we facilitate access to a curated set of data that gives an increasingly granular perspective on monsoon precipitation variability. We also discuss what this collaborative approach integrating natural, social and computer science perspectives can add to the evolution of climate services.

This folder, titled "Data," contains the MATLAB code, final products, tables, and figures used in Parker, L.E., Zhang, N., Abatzoglou, J.T. et al. A variety-specific analysis of climate change effects on California winegrapes. Int J Biometeorol 68, 1559–1571 (2024). https://doi.org/10.1007/s00484-024-02684-8 Data Collection: Climatological data (daily maximum and minimum temperatures, precipitation, and reference evapotranspiration) were obtained from the gridMET dataset for the contemporary period (1991-2020) and from 20 global climate models (GCMs) for the mid-21st century (2040-2069) under RCP 4.5.Phenology Modeling: Variety-specific phenology models were developed using published climatic thresholds to assess chill accumulation, budburst, flowering, veraison, and maturity stages for the six winegrape varieties.Agroclimatic Metrics: Fourteen viticulturally important agroclimatic metrics were calculated, including Growing Degree Days (GDD), Cold Hardiness, Chilling Degree Days (CDD), Frost Damage Days (FDD), and others.Analysis Tools: MATLAB was used for data processing, analysis, and visualization. The MATLAB code provided in this dataset includes scripts for analyzing climate data, running phenology models, and generating visualizations.MATLAB Code: Scripts and functions used for data analysis and modeling.Processed Data: Results from phenology and agroclimatic analyses, including the projected changes in phenological stages and climate metrics for the selected varieties and AVAs.Tables: Detailed results of phenological changes and climate metrics, presented in a clear and structured format.Figures: Visual representations of the data and results, including charts and maps illustrating the impacts of climate change on winegrape development stages and agroclimatic conditions. Research Description: This study investigates the impacts of climate change on the phenology and agroclimatic metrics of six winegrape varieties (Cabernet Sauvignon, Chardonnay, Pinot Noir, Zinfandel, Pinot Gris, Sauvignon Blanc) across multiple California American Viticultural Areas (AVAs). Using climatological data and phenology models, the research quantifies changes in key development stages and viticulturally important climate metrics for the mid-21st century.

Learn about truthclimate's mission to make climate data accessible through open source visualization tools and transparent environmental monitoring.

Climate Data Stress Testing Tools Market Outlook

According to our latest research, the global climate data stress testing tools market size reached USD 1.48 billion in 2024, reflecting a robust trajectory as organizations increasingly prioritize climate risk assessment and regulatory compliance. The market is projected to grow at a CAGR of 19.2% from 2025 to 2033, reaching a forecasted value of USD 6.38 billion by 2033. This remarkable growth is primarily attributed to the rising demand from the financial sector, regulatory mandates, and the urgent need for organizations across industries to integrate climate risk analytics into their strategic decision-making processes.

The primary growth driver for the climate data stress testing tools market is the intensifying regulatory landscape worldwide. Governments and financial regulators are actively mandating climate risk disclosures and scenario analyses, compelling banks, insurers, and asset managers to adopt advanced tools for stress testing their portfolios against a range of climate scenarios. The Task Force on Climate-related Financial Disclosures (TCFD), the European Central Bank’s climate stress tests, and similar initiatives in North America and Asia Pacific have significantly accelerated adoption. These regulatory requirements not only ensure transparency but also push organizations to proactively identify vulnerabilities, thus driving the market for sophisticated climate data stress testing solutions.

Another significant factor fueling market expansion is the increasing frequency and severity of climate-related events, such as floods, wildfires, hurricanes, and droughts. These events have underscored the urgent need for robust risk management frameworks that incorporate climate variables. Organizations in sectors like energy, utilities, transportation, and logistics are leveraging climate data stress testing tools to assess operational resilience, supply chain vulnerabilities, and asset exposures. The integration of artificial intelligence, machine learning, and big data analytics into these tools has further enhanced their predictive capabilities, allowing for more granular and dynamic scenario modeling.

Additionally, the growing investor focus on environmental, social, and governance (ESG) criteria is catalyzing the adoption of climate data stress testing tools beyond the financial sector. Institutional investors and asset managers are increasingly demanding that companies disclose climate risks and demonstrate resilience strategies. This shift is prompting organizations of all sizes, including small and medium enterprises, to implement climate stress testing as part of their broader ESG reporting and risk management frameworks. The proliferation of cloud-based solutions has democratized access to advanced analytics, enabling even resource-constrained organizations to participate in climate risk assessment.

From a regional perspective, North America and Europe are leading the adoption of climate data stress testing tools, driven by stringent regulatory frameworks and a high level of awareness regarding climate risks. Asia Pacific is emerging as a high-growth region, supported by rapid economic development, increasing climate-related vulnerabilities, and evolving regulatory standards. Latin America and the Middle East & Africa are witnessing steady adoption, particularly among multinational corporations and sectors exposed to acute physical climate risks. This global momentum is expected to sustain market growth over the forecast period, with regional dynamics shaping the competitive landscape and innovation trajectory.

Component Analysis

The climate data stress testing tools market is segmented by component into software and services, each playing a critical role in enabling organizations to manage climate risks effectively. Software solutions form the backbone of the market, providing robust platforms for scenario analysis, data visualization, and predictive modeling. These platforms integrate vast datasets from meteorological, geospatial, and financial sources, allowing users to simulate the impacts of various climate scenarios on assets, portfolios, and operations. The increasing sophistication of software offerings, including the integration of AI and machine learning algorithms, is enhancing the accuracy and scalability of climate risk assessments, making them indispensable for both large enterprises and SMEs.

Climate Data Analysis Market Outlook

The global climate data analysis market size was estimated at USD 3.5 billion in 2023 and is projected to reach USD 7.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% during the forecast period. The growth of this market is primarily driven by the increasing awareness and urgency surrounding climate change and environmental sustainability, which have led to a surge in demand for advanced climate data analysis tools and solutions.

One of the primary growth factors in the climate data analysis market is the heightened global focus on environmental issues. Governments, businesses, and individuals are becoming increasingly aware of the impact of climate change and are taking proactive steps to mitigate its effects. This has prompted a surge in demand for accurate and sophisticated climate data analysis tools. These tools are essential for monitoring changes in the environment, predicting future climate scenarios, and formulating effective strategies to combat climate change. Moreover, the availability of advanced technologies such as artificial intelligence and machine learning has greatly enhanced the capabilities of climate data analysis solutions, making them more efficient and accurate.

Another significant growth factor is the increasing number of natural disasters and extreme weather events. The frequency and severity of events such as hurricanes, floods, and wildfires have risen dramatically in recent years. This has created an urgent need for reliable climate data analysis to aid in disaster management and preparedness. By providing accurate predictions and early warnings, climate data analysis tools can help save lives and reduce economic losses. Additionally, the integration of real-time data from various sources, including satellites and weather stations, has improved the accuracy and timeliness of climate analysis, further driving market growth.

The agricultural sector also plays a crucial role in the growth of the climate data analysis market. Climate change poses significant risks to agriculture, affecting crop yields, soil health, and water availability. Farmers and agricultural businesses are increasingly relying on climate data analysis to make informed decisions about planting, irrigation, and pest control. By leveraging climate data, they can optimize their operations, enhance productivity, and reduce risks. The growing adoption of precision agriculture techniques, which heavily depend on accurate climate data, is expected to further boost the market for climate data analysis.

Regionally, the North American market is expected to dominate the climate data analysis market during the forecast period. This is attributed to the advanced technological infrastructure, strong government initiatives, and the presence of major market players in the region. Europe is also anticipated to witness significant growth due to stringent environmental regulations and a strong focus on sustainability. Meanwhile, the Asia Pacific region is expected to emerge as a lucrative market, driven by rapid industrialization, urbanization, and increased awareness of climate change impacts. Emerging economies in Latin America and the Middle East & Africa are also showing growing interest in climate data analysis, although their market size remains relatively smaller compared to other regions.

Component Analysis

In the climate data analysis market, the component segment is divided into software, hardware, and services. Each of these components plays a crucial role in the effective analysis and utilization of climate data. The software segment includes various applications and platforms designed for data collection, processing, and analysis. These software solutions leverage advanced technologies such as artificial intelligence, machine learning, and big data analytics to provide accurate and actionable insights. They offer features such as data visualization, predictive modeling, and scenario analysis, which are essential for understanding complex climate patterns and making informed decisions.

The hardware segment encompasses various devices and equipment used for collecting and transmitting climate data. This includes sensors, weather stations, satellites, and other monitoring devices. These hardware components are essential for gathering real-time data from different sources, ensuring the accuracy and reliability of the climate analysis. Advances in sensor technology and satellite imaging have significantly improved the quality and resolution of climate data, enabling more precise and detailed analysis. Additionall

The NOAA/NASA Pathfinder climate data CD-ROM contains seven data sets: Advanced Very High Resolution Radiometer (AVHRR)Land and Ocean, TIROS Operational Vertical Sounder (TOVS) Paths A, C1, C2, Special Sensor Microwave/Imager (SSM/I) Precipitation and Atmospheric Moisture for the Benchmark Period of April 1987 to December 1988. These data sets can be viewed with a variety of applications including GeoVu, the NCEI multi-platform data browse and visualization software application, National Center for Supercomputing Applications (NCSA) Collage, IMDISP, Spyglass, and Idrisi.

This dataset contains synthetic weather data generated for ten different locations, including New York, Los Angeles, Chicago, Houston, Phoenix, Philadelphia, San Antonio, San Diego, Dallas, and San Jose. The data includes information about temperature, humidity, precipitation, and wind speed, with 1 million data points generated for each parameter.

Features:

• Location: The city where the weather data was simulated.
• Date_Time: The date and time when the weather data was recorded.
• Temperature_C: The temperature in Celsius at the given location and time.
• Humidity_pct: The humidity in percentage at the given location and time.
• Precipitation_mm: The precipitation in millimeters at the given location and time.
• Wind_Speed_kmh: The wind speed in kilometers per hour at the given location and time.

Additional Information:

• Variability and Complexity: The dataset incorporates variability and complexity to simulate realistic weather patterns. For example, adjustments have been made to temperature and precipitation based on seasonal variations observed in certain locations. In New York, higher temperatures and precipitation are simulated during the summer months, while in Phoenix, lower temperatures and increased precipitation are simulated during the winter months.
• Data Generation Method: The dataset was generated using Python's Faker library to create synthetic weather data for each location. Random values within realistic ranges were generated for temperature, humidity, precipitation, and wind speed, with adjustments made to reflect seasonal variations.

Potential Use Cases:

• Weather Prediction Models: Researchers and data scientists can use this dataset to develop and train weather prediction models for various locations.
• Climate Studies: The dataset can be used for climate studies and analysis to understand weather patterns and trends in different regions.
• Educational Purposes: Students and educators can use this dataset to learn about data analysis, visualization, and modeling techniques in the context of weather data.

Acknowledgements:

• This dataset was generated using Python's Faker library.
• Special thanks to the Faker library developers for providing tools to create synthetic data for various purposes.

Image Credits :

Image by Mohamed Hassan from Pixabay

This dataset is extracted from a research paper and its accompanying software, which can be found on the ClimateBC website: https://climatebc.ca/

The data covers the period from 1961 to 2020 and provides seasonal temperature and precipitation data for British Columbia, Canada.

Data Dictionary:

• year_period: Corresponding year period
• Latitude: Latitude
• Longitude: Longitude
• Tave_wt: Winter mean temperature (°C)
• Tave_sp: Spring mean temperature (°C)
• Tave_sm: Summer mean temperature (°C)
• Tave_at: Autumn mean temperature (°C)
• Tmax_wt: Winter mean maximum temperature (°C)
• Tmax_sp: Spring mean maximum temperature (°C)
• Tmax_sm: Summer mean maximum temperature (°C)
• Tmax_at: Autumn mean maximum temperature (°C)
• Tmin_wt: Winter mean minimum temperature (°C)
• Tmin_sp: Spring mean minimum temperature (°C)
• Tmin_sm: Summer mean minimum temperature (°C)
• Tmin_at: Autumn mean minimum temperature (°C)
• PPT_wt: Winter precipitation (mm)
• PPT_sp: Spring precipitation (mm)
• PPT_sm: Summer precipitation (mm)
• PPT_at: Autumn precipitation (mm)
• TD: Elevation (m)

Please note that this dataset is provided for research and informational purposes.

Attribution: - Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. doi:10.1371/journal.pone.0156720

• Mahony, CR, Wang, T; Hamann, A and Cannon, AJ, 2022. A CMIP6 ensemble for downscaled monthly climate normals over North America. International Journal of Climatology 42 (11), 5871-5891 DOI: https://doi.org/10.1002/joc.7566

The NOAA Weather and Climate Toolkit is an application that provides simple visualization and data export of weather and climatological data archived at NCDC. The Toolkit also provides access to weather and climate web services provided from NCDC and other organizations. The Viewer provides tools for displaying custom data overlay, Web Map Services (WMS), animations and basic filters. The export of images and movies is provided in multiple formats. The Data Exporter allows for data export in both vector point/line/polygon and raster grid formats. Current data types supported include: CF-compliant Fridded NetCDF; Generic CF-compliant Irregularly-Spaced/Curvilinear Gridded NetCDF/HDF; GRIB1, GRIB2, GINI, GEMPAK, HDF(CF-compliant) and more gridded formats; GPES Satellite AREA Files; NEXRAD Radar Data(Level-II and Level-III); U.S. Drought Monitor Service from the National Drought Mitigation Center (NDMC); OPeNDAP support for Gridded Datasets

This file geodatabase contains summary data meant to be visualized within the National Coral Reef Monitoring Program's Data Visualization Tool.This file geodatabase and its associated data/dashboards/hub are developed to represent data in both the Atlantic and Pacific basins and all four monitoring themes (Socioeconomic, Benthic, Fish and Climate). Each dashboard presents data at a resolution that is appropriate for the sampling method and effort for each area. Users can filter the data by a number of variables to allow them to refine the graphs and charts. Additionally, users can download the summary data tables for their own analyses. The metadata for the data in this application can be found at https://www.ncei.noaa.gov/data/oceans/coris/library/NOAA/CRCP/monitoring/metadata/The following AGOL items are dependent upon this file geodatabase:NCRMP_Prod_gdb Feature Layer (hosted) NCRMP Data Visualization Tool Hub Site Application NCRMP Data Visualization Tool Hub Initiative Data Download Hub Page NCRMP Atlantic Benthic Dashboard Web Experience NCRMP Pacific Benthic Dashboard Web Experience NCRMP Atlantic Benthic Embed Dashboard NCRMP Pacific Benthic Embed Dashboard NCRMP Atlantic Benthic Map Web Map NCRMP Pacific Benthic Map Web Map NCRMP Climate Dashboard Web Experience NCRMP Climate Embed Dashboard NCRMP Climate Map Web Map NCRMP Atlantic Fish Dashboard Web Experience NCRMP Pacific Fish Dashboard Web Experience NCRMP Atlantic Fish Embed Dashboard NCRMP Pacific Fish Embed Dashboard NCRMP Atlantic Fish Map Web Map NCRMP Pacific Fish Map Web Map NCRMP Socioeconomic Dashboard Web Experience NCRMP Socioeconomic Embed Dashboard NCRMP Socioeconomic Map Web Map NCRMP Data Download Dashboard

Canopy habitats challenge researchers with their intrinsically difficult access. The current scarcity of climatic data from forest canopies limits our understanding of the conditions and environmental variability of these diverse and dynamic habitats. We present 307 days of climate records collected between 2019 and 2020 in the tropical rainforest canopy of the Yasuní National Park, Ecuador. We monitored climate with a 10-minute temporal resolution in the middle crowns of eight canopy trees. The distance between canopy climate stations ranged from 700 m to 10 km. Apart from air temperature, relative humidity, leaf wetness, and photosynthetically active radiation (PAR), measured in each canopy climate station, global radiation, rainfall, and wind speed were measured in different subsets of them. We processed the eight data series to omit erroneous records resulting from sensor failures or lack of the solar-based power supply. In addition to the eight original data series, we present three derived data series, two aggregating canopy climate for valleys or for ridges (from four stations each), and one overall average (from the eight stations). This last derived data series contains 306 days, while the shortest of the original data series covers 22 days and the longest 296 days. In addition to the data, two open-source tools, developed in RStudio, are presented that facilitate data visualization (a dashboard) and data exploration (a filtering app) of the original and aggregated records.

Climate Digital Twin Visualization Market Outlook

According to our latest research, the Global Climate Digital Twin Visualization market size was valued at $1.2 billion in 2024 and is projected to reach $7.8 billion by 2033, expanding at a robust CAGR of 22.7% during 2024–2033. The primary factor driving this remarkable growth is the increasing need for advanced predictive tools to address the escalating impacts of climate change and support data-driven decision-making across industries. The integration of digital twins with climate visualization technologies allows organizations to simulate, analyze, and visualize complex climate phenomena in real-time, enabling proactive strategies for risk mitigation, resource optimization, and sustainability planning. As governments, enterprises, and research institutions prioritize climate resilience, the adoption of climate digital twin visualization solutions is accelerating globally, underpinned by rapid advancements in AI, IoT, and cloud computing.

Regional Outlook

North America currently holds the largest share of the Climate Digital Twin Visualization market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the region’s mature technological landscape, robust investment in climate research, and proactive government policies supporting digital transformation in environmental management. The United States, in particular, is at the forefront due to significant federal funding for climate initiatives, a thriving ecosystem of technology providers, and strong collaboration between academia, government, and private sector stakeholders. Additionally, the presence of leading software and cloud service companies enables rapid integration of digital twin solutions into climate modeling and disaster response frameworks. The region’s focus on urban resilience, smart infrastructure, and sustainable energy further fuels market expansion.

Asia Pacific is projected to be the fastest-growing region, with an anticipated CAGR of 27.1% through 2033. This exceptional growth is driven by increasing investments in smart city projects, heightened vulnerability to extreme weather events, and a surge in government-led climate adaptation programs. Countries such as China, Japan, India, and South Korea are rapidly deploying digital twin visualization platforms to enhance disaster preparedness, optimize urban planning, and support large-scale renewable energy integration. The region’s expanding tech-savvy population and the availability of advanced cloud computing infrastructure are also catalyzing adoption. Furthermore, international collaborations and public-private partnerships are playing a crucial role in accelerating the deployment of climate digital twin solutions across diverse applications, from agriculture to transportation.

Emerging economies in Latin America, the Middle East, and Africa are beginning to embrace Climate Digital Twin Visualization technologies, albeit at a more gradual pace. These regions face unique challenges, including limited access to high-performance computing resources, gaps in digital infrastructure, and lower levels of climate data standardization. However, growing awareness of climate risks, coupled with international funding and technical assistance, is spurring localized demand for digital twin applications in disaster management and environmental monitoring. Policy reforms aimed at fostering innovation and sustainability are gradually creating a more conducive environment for market growth. As these economies continue to invest in digital transformation and capacity building, the adoption of climate digital twin visualization is expected to gain momentum in the coming years.

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