The AI-Based Climate Modelling Market was valued at USD 242.0 million in 2023 and is expected to reach USD 1715.2 million by 2032, growing at a CAGR of 34.32% from 2024-2032.
In 2023, the AI-based climate Modelling Market witnessed significant trends in model accuracy enhancement, with deep learning and physics-AI hybrid models improving long-range prediction reliability and spatial resolution. Adoption expanded notably across industries such as energy, agriculture, and insurance, while research institutes remained early adopters for emission forecasting and climate resilience projects. There was also increased integration of AI models with diverse environmental data sources like satellite imagery, sensor networks, and oceanographic feeds, enabling more dynamic and adaptive forecasts. These advancements began directly influencing climate risk assessment frameworks and policy decisions, offering localised, actionable insights for disaster preparedness, urban planning, and corporate ESG reporting. Additionally, the market is seeing new developments in AI-powered decarbonization analytics, explainable AI for climate models, regional climate AI startup ecosystems, and AI's growing role in financial climate risk management and regulatory compliance.
In 2023, the U.S. AI-based climate modelling market was valued at approximately USD 36.3 million. It is projected to reach USD 275.2 million by 2034, growing at a CAGR of 25.25% from 2024 to 2032. Key growth drivers include increasing frequency of extreme weather events, advancements in AI and machine learning technologies, and the growing need for improved disaster response and climate change mitigation strategies.
Driver
The rising frequency of extreme weather events drives the demand for accurate AI-based climate models for better disaster preparedness and response.
Demand for more precise climate models is increasing due to the occurrence of more extreme weather events such as hurricanes, droughts, floods, and wildfires. With the help of AI-driven models, we can get more accurate, localized, and clear predictions, enabling better disaster preparedness and response. Through these models, governments and businesses are in a much better position to anticipate the expected climate impacts, which ultimately makes it easier to develop adaptive measures. The increasing severity and uncertainty of weather phenomena are one of the reasons why the demand for real-time forecasting is increasing, further propelling the growth of the market.
Restraint
The accuracy of AI-based climate models is limited by the availability and quality of consistent environmental data, hindering their effectiveness.
Climate models based on AI make accurate and high-quality data the key to making them reliable. The fact that you have access to consistent environmental data is a major issue. The quality of the model might be affected by the quality of the data, especially in underdeveloped regions or in hard-to-reach areas where data sets might be incomplete or sparse. Moreover, the sharing of data across sectors is often limited by fear of data privacy and regulations. This dependence on varied data sets takes significant infrastructure and makes it increasingly complex to develop & launch climate models.
Opportunity
Governments' increasing focus on sustainability and environmental policies presents opportunities for AI-based climate models to gain funding and expand adoption.
Governments around the world are starting to recognize the role AI can play in climate science, as evident from the growing need for AI in climate initiatives, such as the U.S Green New Deal, or the European Green Deal. These policies create incentives to invest in AI-based climate modeling to improve forecasting of disasters, risk assessment, and carbon reduction strategies. The climate-based AI modeling market experiences positive growth due to augmented government funding and tie-ups with the private sector, which promote innovation, adoption, and enhanced utilization across sectors like energy, agriculture, and insurance.
Challenge
AI-based climate models face challenges in trust and adoption due to their complex, "black box" nature, making them hard to interpret and explain.
While AI-based climate models offer significant advancements in accuracy, their complexity can hinder decision-makers' ability to understand and trust predictions. The "black box" nature of many AI systems, where the decision-making process is not easily transparent, poses a challenge in high-stakes areas like policy-making and disaster response. Ensuring that climate models are interpretable, understandable, and explainable is crucial for widespread adoption. Lack of trust in model outputs may delay decision-making processes, hindering timely climate actions.
By Component
The software segment dominated the AI-based climate modelling market in 2023 and accounted for 82% of revenue share, due to the rising demand for advanced, AI-powered modelling tools capable of providing real-time, high-resolution climate forecasts. Increased adoption by research institutions, environmental agencies, and enterprises for emission tracking, disaster management, and risk assessment boosted growth. The ability of AI software to integrate vast environmental datasets and offer scalable, cloud-based solutions continues to drive its dominance. This segment is expected to maintain its lead through 2032 with consistent advancements in AI and machine learning capabilities.
The services segment is projected to register the fastest CAGR in the AI-based climate modelling market through 2032, driven by the growing need for model customization, data integration, consulting, and system maintenance. As organizations seek to deploy complex AI climate models tailored to their operational needs, demand for professional and managed services has surged. Additionally, rising climate-related regulatory requirements and corporate ESG commitments have increased the need for specialized advisory and analytics services. This trend positions the services segment for robust, long-term growth in both the public and private sectors.
By Deployment
The cloud segment dominated the AI-based climate modelling market in 2023 and accounted for 65% of revenue share, driven by its scalability, accessibility, and ability to process massive environmental datasets in real time. Cloud-based platforms enable faster deployment, integration of multi-source data, and collaborative research, making them ideal for government agencies, research institutions, and climate-tech companies. The growing reliance on cloud infrastructure for global climate simulations and remote analytics continues to support its lead. This segment is expected to sustain steady growth through 2032, supported by advancements in AI, IoT, and satellite data integration.
The on-premises segment is expected to register the fastest CAGR through 2032, as industries with stringent data security, regulatory compliance, and infrastructure control needs increasingly adopt localized AI-based climate modelling solutions. Sectors such as defense, energy, and national research agencies prefer on-premises deployment to maintain data sovereignty and ensure uninterrupted operations in sensitive environments. Additionally, the rise in customized climate analytics for region-specific or high-resolution forecasting is driving demand for dedicated, in-house AI infrastructure, positioning the on-premises segment for strong, targeted growth over the forecast period.
By Technology
The machine learning segment dominated the AI-based climate modelling market in 2023 and accounted for significant revenue share, owing to its capability to analyze complex, multivariate environmental data and improve climate prediction accuracy. ML models are widely adopted for temperature, precipitation, and disaster pattern forecasting, supported by increasing investments from governments and research institutes. Their flexibility to handle structured and unstructured climate data makes them ideal for diverse applications. This segment is expected to maintain its dominance through 2032, driven by advancements in supervised, unsupervised, and reinforcement learning algorithms for climate simulation.
The deep learning segment is anticipated to register the fastest CAGR through 2032, as it excels at managing high-dimensional, non-linear climate data for precise, long-range predictions. Its superior capability in identifying hidden patterns and relationships in satellite images, sensor feeds, and historical weather records is fueling adoption. Deep learning’s application in next-generation climate risk modelling, oceanographic simulations, and emission scenario analysis is expanding rapidly. Increasing computational power and open-source model availability further accelerate growth, positioning deep learning as a transformative technology in the AI-based climate modelling market in the coming years.
By Application
The weather forecasting segment dominated the AI-based climate modelling market in 2023 and accounted for significant revenue share, driven by increasing demand for precise, short- and medium-term forecasts across industries like agriculture, transportation, and energy. AI-powered models enable rapid analysis of vast meteorological data, enhancing accuracy and decision-making for weather-dependent operations. Governments and private agencies are prioritizing advanced forecasting systems to mitigate economic and environmental risks. This segment is expected to maintain its lead through 2032, supported by continuous improvements in AI algorithms, IoT sensor networks, and real-time satellite data integration.
The disaster risk reduction segment is projected to record the fastest CAGR through 2032, fueled by the escalating frequency and severity of natural disasters globally. AI-based climate models are increasingly adopted for early warning systems, impact analysis, and emergency preparedness, helping governments and organizations minimize damage and casualties. Rising regulatory mandates and global climate resilience initiatives further support this segment’s growth. Additionally, AI’s ability to provide hyper-localized, predictive insights for floods, wildfires, hurricanes, and droughts is accelerating its adoption in vulnerable, disaster-prone regions worldwide.
North America dominated the AI-based climate modelling market in 2023 and represented 36% of revenue share, owing to strong government initiatives, substantial R&D investments, and the presence of major AI and climate-tech companies. The U.S. leads with federal climate action plans, funding for AI-driven research, and partnerships between universities, tech firms, and space agencies like NASA and NOAA. The region’s advanced digital infrastructure and adoption of cloud-based climate solutions across sectors such as agriculture, energy, and insurance continue to fuel growth. North America is expected to maintain its leadership position through 2032.
Asia Pacific is anticipated to register the fastest CAGR in the AI-based climate modelling market through 2032, driven by rising climate vulnerabilities and increasing adoption of AI in disaster risk management. Countries like China, India, and Japan are heavily investing in AI-powered forecasting systems to address the growing impact of typhoons, floods, and heat waves. Government-backed smart city and environmental monitoring initiatives are further accelerating demand. Additionally, rapid infrastructure digitization, expanding 5G networks, and increased collaborations with international climate research bodies position the region for robust, sustained market growth.
The major key players, along with their products, are
IBM — IBM Environmental Intelligence Suite
Microsoft — Microsoft Planetary Computer
Google — Google Earth Engine
The Climate Corporation (Bayer) — Climate FieldView
Accenture — Climate Analytics Platform
AWS (Amazon Web Services) — Amazon Sustainability Data Initiative (ASDI)
Tomorrow.io — Tomorrow.io Weather Engine
Oracle — Oracle Climate Change Analytics
Climavision — Climavision Climate Data Services
Planet Labs — PlanetScope
Descartes Labs — Descartes Labs Platform
Cervest — EarthScan
Jupiter Intelligence — ClimateScore Global
One Concern — Domino Climate Platform
ClimateAi — ClimateAi Analytics
January 2025: Secured a $230 million contract to build high-resolution Pelican satellites for an Asia-Pacific partner, marking its largest deal to date and expanding into dedicated satellite services.
January 2024: The Climate Corporation introduced enhanced features in FieldView Plus and launched a new Premium subscription tier, offering personalized reports and improved in-cab experiences.
|
Report Attributes |
Details |
|
Market Size in 2023 |
US$ 242.0 Million |
|
Market Size by 2032 |
US$ 1715.2 Million |
|
CAGR |
CAGR of 34.32 % From 2024 to 2032 |
|
Base Year |
2023 |
|
Forecast Period |
2024-2032 |
|
Historical Data |
2020-2022 |
|
Report Scope & Coverage |
Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
|
Key Segments |
• By Component (Software, Services) |
|
Regional Analysis/Coverage |
North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, Rest of Latin America) |
|
Company Profiles |
IBM, Microsoft, Google, The Climate Corporation (Bayer), Accenture, AWS (Amazon Web Services), Tomorrow.io, Oracle, Climavision, Planet Labs, Descartes Labs, Cervest |
Ans - The AI-Based Climate Modelling Market was valued at USD 242.0 million in 2023 and is expected to reach USD 1715.2 million by 2032
Ans- The CAGR of the AI-Based Climate Modelling Market during the forecast period is 34.32% from 2024-2032.
Ans- Asia-Pacific is expected to register the fastest CAGR during the forecast period.
Ans- The rising frequency of extreme weather events drives the demand for accurate AI-based climate models for better disaster preparedness and response.
Ans- AI-based climate models face challenges in trust and adoption due to their complex, "black box" nature, making them hard to interpret and explain.
Table Of Content
1. Introduction
1.1 Market Definition
1.2 Scope (Inclusion and Exclusions)
1.3 Research Assumptions
2. Executive Summary
2.1 Market Overview
2.2 Regional Synopsis
2.3 Competitive Summary
3. Research Methodology
3.1 Top-Down Approach
3.2 Bottom-up Approach
3.3. Data Validation
3.4 Primary Interviews
4. Market Dynamics Impact Analysis
4.1 Market Driving Factors Analysis
4.1.1 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.1.4 Challenges
4.2 PESTLE Analysis
4.3 Porter’s Five Forces Model
5. Statistical Insights and Trends Reporting
5.1 Model Accuracy Enhancement Trends, 2023
5.2 End-User Adoption, by Industry & Research Institutes, 2023
5.3 Integration with Environmental Data Sources, 2023
5.4 Impact on Climate Risk Assessment & Policy Decision-making, 2023
6. Competitive Landscape
6.1 List of Major Companies By Region
6.2 Market Share Analysis By Region
6.3 Product Benchmarking
6.3.1 Product specifications and features
6.3.2 Pricing
6.4 Strategic Initiatives
6.4.1 Marketing and promotional activities
6.4.2 Distribution and Supply Chain Strategies
6.4.3 Expansion plans and new product launches
6.4.4 Strategic partnerships and collaborations
6.5 Technological Advancements
6.6 Market Positioning and Branding
7. AI-based climate modelling Market Segmentation by Component
7.1 Chapter Overview
7.2 Software
7.2.1 Software Market Trends Analysis (2020-2032)
7.2.2 Software Market Size Estimates and Forecasts to 2032 (USD Million)
7.3 Services
7.3.1 Services Market Trends Analysis (2020-2032)
7.3.2 Services Market Size Estimates and Forecasts to 2032 (USD Million)
8. AI-based climate modelling Market Segmentation By Deployment
8.1 Chapter Overview
8.2 Cloud
8.2.1 Cloud Market Trends Analysis (2020-2032)
8.2.2 Cloud Market Size Estimates and Forecasts to 2032 (USD Million)
8.3 On-premises
8.3.1 On-premises Market Trends Analysis (2020-2032)
8.3.2 On-premises Market Size Estimates and Forecasts to 2032 (USD Million)
9. AI-based climate modelling Market Segmentation by Technology
9.1 Chapter Overview
9.2 Machine learning
9.2.1 Machine learning Market Trends Analysis (2020-2032)
9.2.2 Machine learning Market Size Estimates and Forecasts to 2032 (USD Million)
9.3 Deep learning
9.3.1 Deep learning Market Trends Analysis (2020-2032)
9.3.2 Deep learning Market Size Estimates and Forecasts to 2032 (USD Million)
9.4 Natural Language Processing
9.4.1 Natural Language Processing Market Trends Analysis (2020-2032)
9.4.2 Natural Language Processing Market Size Estimates and Forecasts to 2032 (USD Million)
9.5 Computer vision
9.5.1 Computer vision Market Trends Analysis (2020-2032)
9.5.2 Computer vision Market Size Estimates and Forecasts to 2032 (USD Million)
9.6 Others
9.6.1 Others Market Trends Analysis (2020-2032)
9.6.2 Others Market Size Estimates and Forecasts to 2032 (USD Million)
10. AI-based climate modelling Market Segmentation by Application
10.1 Chapter Overview
10.2 Weather forecasting
10.2.1 Weather forecasting Market Trends Analysis (2020-2032)
10.2.2 Weather forecasting Market Size Estimates and Forecasts to 2032 (USD Million)
10.3 Climate prediction
10.3.1 Climate prediction Market Trend Analysis (2020-2032)
10.3.2 Climate prediction Market Size Estimates and Forecasts to 2032 (USD Million)
10.4 Disaster risk reduction
10.4.1 Disaster risk reduction Market Trends Analysis (2020-2032)
10.4.2 Disaster risk reduction Market Size Estimates and Forecasts to 2032 (USD Million)
10.5 Environmental monitoring
10.5.1 Environmental monitoring Market Trends Analysis (2020-2032)
10.5.2 Environmental monitoring Market Size Estimates and Forecasts to 2032 (USD Million)
10.6 Others
10.6.1 Others Market Trends Analysis (2020-2032)
10.6.2 Others Market Size Estimates and Forecasts to 2032 (USD Million)
11. Regional Analysis
11.1 Chapter Overview
11.2 North America
11.2.1 Trend Analysis
11.2.2 North America AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.2.3 North America AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.2.4 North America AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.2.5 North America AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.2.6 North America AI-based climate modelling Market Estimates and Forecasts by Application(2020-2032) (USD Million)
11.2.7 USA
11.2.7.1 USA AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.2.7.2 USA AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.2.7.3 USA AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.2.7.4 USA AI-based climate modelling Market Estimates and Forecasts by Application(2020-2032) (USD Million)
11.2.8 Canada
11.2.8.1 Canada AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.2.8.2 Canada AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.2.8.3 Canada AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.2.8.4 Canada AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.2.9 Mexico
11.2.9.1 Mexico AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.2.9.2 Mexico AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.2.9.3 Mexico AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.2.9.4 Mexico AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3 Europe
11.3.1 Eastern Europe
11.3.1.1 Trend Analysis
11.3.1.2 Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.3.1.3 Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.1.4 Eastern Europe AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.1.5 Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.1.6 Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Application(2020-2032) (USD Million)
11.3.1.7 Poland
11.3.1.7.1 Poland AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.1.7.2 Poland AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.1.7.3 Poland AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.1.7.4 Poland AI-based climate modelling Market Estimates and Forecasts by Application(2020-2032) (USD Million)
11.3.1.8 Romania
11.3.1.8.1 Romania AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.1.8.2 Romania AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.1.8.3 Romania AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.1.8.4 Romania AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.1.9 Hungary
11.3.1.9.1 Hungary AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.1.9.2 Hungary AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.1.9.3 Hungary AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.1.9.4 Hungary AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.1.10 Turkey
11.3.1.10.1 Turkey AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.1.10.2 Turkey AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.1.10.3 Turkey AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.1.10.4 Turkey AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.1.11 Rest of Eastern Europe
11.3.1.11.1 Rest of Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.1.11.2 Rest of Eastern Europe AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.1.11.3 Rest of Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.1.11.4 Rest of Eastern Europe AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2 Western Europe
11.3.2.1 Trend Analysis
11.3.2.2 Western Europe AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.3.2.3 Western Europe AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.4 Western Europe AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.5 Western Europe AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.6 Western Europe AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.7 Germany
11.3.2.7.1 Germany AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.7.2 Germany AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.7.3 Germany AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.7.4 Germany AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.8 France
11.3.2.8.1 France AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.8.2 France AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.8.3 France AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.8.4 France AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.9 UK
11.3.2.9.1 UK AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.9.2 UK AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.9.3 UK AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.9.4 UK AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.10 Italy
11.3.2.10.1 Italy AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.10.2 Italy AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.10.3 Italy AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.10.4 Italy AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.11 Spain
11.3.2.11.1 Spain AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.11.2 Spain AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.11.3 Spain AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.11.4 Spain AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.12 Netherlands
11.3.2.12.1 Netherlands AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.12.2 Netherlands AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.12.3 Netherlands AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.12.4 Netherlands AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.13 Switzerland
11.3.2.13.1 Switzerland AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.13.2 Switzerland AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.13.3 Switzerland AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.13.4 Switzerland AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.14 Austria
11.3.2.14.1 Austria AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.14.2 Austria AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.14.3 Austria AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.14.4 Austria AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.3.2.15 Rest of Western Europe
11.3.2.15.1 Rest of Western Europe AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.3.2.15.2 Rest of Western Europe AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.3.2.15.3 Rest of Western Europe AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.3.2.15.4 Rest of Western Europe AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4 Asia Pacific
11.4.1 Trend Analysis
11.4.2 Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.4.3 Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.4 Asia Pacific AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.5 Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.6 Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.7 China
11.4.7.1 China AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.7.2 China AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.7.3 China AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.7.4 China AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.8 India
11.4.8.1 India AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.8.2 India AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.8.3 India AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.8.4 India AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.9 Japan
11.4.9.1 Japan AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.9.2 Japan AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.9.3 Japan AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.9.4 Japan AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.10 South Korea
11.4.10.1 South Korea AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.10.2 South Korea AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.10.3 South Korea AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.10.4 South Korea AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.11 Vietnam
11.4.11.1 Vietnam AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.11.2 Vietnam AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.11.3 Vietnam AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.11.4 Vietnam AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.12 Singapore
11.4.12.1 Singapore AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.12.2 Singapore AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.12.3 Singapore AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.12.4 Singapore AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.13 Australia
11.4.13.1 Australia AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.13.2 Australia AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.13.3 Australia AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.13.4 Australia AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.4.14 Rest of Asia Pacific
11.4.14.1 Rest of Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.4.14.2 Rest of Asia Pacific AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.4.14.3 Rest of Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.4.14.4 Rest of Asia Pacific AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5 Middle East and Africa
11.5.1 Middle East
11.5.1.1 Trend Analysis
11.5.1.2 Middle East AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.5.1.3 Middle East AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.1.4 Middle East AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.1.5 Middle East AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.1.6 Middle East AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.1.7 UAE
11.5.1.7.1 UAE AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.1.7.2 UAE AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.1.7.3 UAE AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.1.7.4 UAE AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.1.8 Egypt
11.5.1.8.1 Egypt AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.1.8.2 Egypt AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.1.8.3 Egypt AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.1.8.4 Egypt AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.1.9 Saudi Arabia
11.5.1.9.1 Saudi Arabia AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.1.9.2 Saudi Arabia AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.1.9.3 Saudi Arabia AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.1.9.4 Saudi Arabia AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.1.10 Qatar
11.5.1.10.1 Qatar AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.1.10.2 Qatar AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.1.10.3 Qatar AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.1.10.4 Qatar AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.1.11 Rest of Middle East
11.5.1.11.1 Rest of Middle East AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.1.11.2 Rest of Middle East AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.1.11.3 Rest of Middle East AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.1.11.4 Rest of Middle East AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.2 Africa
11.5.2.1 Trend Analysis
11.5.2.2 Africa AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.5.2.3 Africa AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.2.4 Africa AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.2.5 Africa AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.2.6 Africa AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.2.7 South Africa
11.5.2.7.1 South Africa AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.2.7.2 South Africa AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.2.7.3 South Africa AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.2.7.4 South Africa AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.2.8 Nigeria
11.5.2.8.1 Nigeria AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.2.8.2 Nigeria AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.2.8.3 Nigeria AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.2.8.4 Nigeria AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.5.2.9 Rest of Africa
11.5.2.9.1 Rest of Africa AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.5.2.9.2 Rest of Africa AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.5.2.9.3 Rest of Africa AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.5.2.9.4 Rest of Africa AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.6 Latin America
11.6.1 Trend Analysis
11.6.2 Latin America AI-based climate modelling Market Estimates and Forecasts by Country (2020-2032) (USD Million)
11.6.3 Latin America AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.6.4 Latin America AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.6.5 Latin America AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.6.6 Latin America AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.6.7 Brazil
11.6.7.1 Brazil AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.6.7.2 Brazil AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.6.7.3 Brazil AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.6.7.4 Brazil AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.6.8 Argentina
11.6.8.1 Argentina AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.6.8.2 Argentina AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.6.8.3 Argentina AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.6.8.4 Argentina AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.6.9 Colombia
11.6.9.1 Colombia AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.6.9.2 Colombia AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.6.9.3 Colombia AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.6.9.4 Colombia AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
11.6.10 Rest of Latin America
11.6.10.1 Rest of Latin America AI-based climate modelling Market Estimates and Forecasts by Component (2020-2032) (USD Million)
11.6.10.2 Rest of Latin America AI-based climate modelling Market Estimates and Forecasts By Deployment (2020-2032) (USD Million)
11.6.10.3 Rest of Latin America AI-based climate modelling Market Estimates and Forecasts by Technology (2020-2032) (USD Million)
11.6.10.4 Rest of Latin America AI-based climate modelling Market Estimates and Forecasts by Application (2020-2032) (USD Million)
12. Company Profiles
12.1 IBM
12.1.1 Company Overview
12.1.2 Financial
12.1.3 Products/ Services Offered
12.1.4 SWOT Analysis
12.2 Microsoft
12.2.1 Company Overview
12.2.2 Financial
12.2.3 Products/ Services Offered
12.2.4 SWOT Analysis
12.3 Google
12.3.1 Company Overview
12.3.2 Financial
12.3.3 Products/ Services Offered
12.3.4 SWOT Analysis
12.4 The Climate Corporation
12.4.1 Company Overview
12.4.2 Financial
12.4.3 Products/ Services Offered
12.4.4 SWOT Analysis
12.5 Accenture
12.5.1 Company Overview
12.5.2 Financial
12.5.3 Products/ Services Offered
12.5.4 SWOT Analysis
12.6 AWS (Amazon Web Services)
12.6.1 Company Overview
12.6.2 Financial
12.6.3 Products/ Services Offered
12.6.4 SWOT Analysis
12.7 Tomorrow.io
12.7.1 Company Overview
12.7.2 Financial
12.7.3 Products/ Services Offered
12.7.4 SWOT Analysis
12.8 Oracle
12.8.1 Company Overview
12.8.2 Financial
12.8.3 Products/ Services Offered
12.8.4 SWOT Analysis
12.9 Climavision
12.9.1 Company Overview
12.9.2 Financial
12.9.3 Products/ Services Offered
12.9.4 SWOT Analysis
12.10 Planet Labs
12.10.1 Company Overview
12.10.2 Financial
12.10.3 Products/ Services Offered
12.10.4 SWOT Analysis
13. Use Cases and Best Practices
14. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
Key Segmentation:
By Component
Software
Services
By Deployment
On-premises
Cloud
By Technology
Machine learning
Deep learning
Natural Language Processing (NLP)
Computer vision
Others
By Application
Weather forecasting
Climate prediction
Disaster risk reduction
Environmental monitoring
Others
Request for Segment Customization as per your Business Requirement: Segment Customization Request
Regional Coverage:
North America
US
Canada
Mexico
Europe
Eastern Europe
Poland
Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
Italy
Spain
Netherlands
Switzerland
Austria
Rest of Western Europe
Asia Pacific
China
India
Japan
South Korea
Vietnam
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
Middle East
UAE
Egypt
Saudi Arabia
Qatar
Rest of Middle East
Africa
Nigeria
South Africa
Rest of Africa
Latin America
Brazil
Argentina
Colombia
Rest of Latin America
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Available Customization
With the given market data, SNS Insider offers customization as per the company’s specific needs. The following customization options are available for the report:
Detailed Volume Analysis
Criss-Cross segment analysis (e.g. Product X Application)
Competitive Product Benchmarking
Geographic Analysis
Additional countries in any of the regions
Customized Data Representation
Detailed analysis and profiling of additional market players
Cloud Security Posture Management Market was valued at USD 4.91 billion in 2023 and is expected to reach USD 11.65 billion by 2032, growing at a CAGR of 10.14% from 2024-2032.
Geospatial Imagery Analytics Market size was valued at USD 15.8 Billion in 2023 and will grow to USD 197.4 Billion by 2032 and grow at a CAGR of 32.4% by 2032.
The Gamification Market size was valued at USD 14.3 Billion in 2023 and will grow to USD 113.3 Billion by 2032 and grow at a CAGR of 25.9% by 2032.
The GCC in the Retail and Consumer Goods Market size was USD 19.1 Billion in 2023, Will Reach to USD 76.9 Bn by 2032 & grow at a CAGR of 15.1% by 2024-2032.
Cloud Analytics Market was valued at USD 29.94 billion in 2023 and is expected to reach USD 203.48 billion by 2032, growing at a CAGR of 23.80% by 2032.
The Supply Chain Management Market was valued at USD 26.2 billion in 2023 and is expected to reach USD 65.8 billion by 2032, growing at a CAGR of 10.8% from 2024-2032.
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