The Best AI Tools in Meteorology
- Tretyak
- Apr 18
- 15 min read
Updated: Jun 1
🌦️ AI: Forecasting Our Future Weather
The Best AI Tools in Meteorology are revolutionizing our ability to understand, predict, and respond to Earth's complex weather and climate systems. Meteorology, the science of the atmosphere, plays a critical role in safeguarding lives and livelihoods, influencing agriculture, energy production, transportation, and disaster preparedness. Artificial Intelligence is now infusing this field with unprecedented analytical power, enhancing forecasting accuracy, refining climate models, and deepening our insights into atmospheric phenomena. As these intelligent systems mature, "the script that will save humanity" guides us to leverage AI to provide more accurate and timely warnings for extreme weather, improve our understanding and projections of climate change, and support global efforts to build resilience and adapt to our changing planet.
This post serves as a directory to some of the leading Artificial Intelligence tools, models, and platforms making a significant impact in meteorology. We aim to provide key information including developer/origin, launch context, core features, primary use cases, general accessibility/pricing models, and practical tips.
In this directory, we've categorized tools to help you find what you need:
🔮 AI in Weather Forecasting and Nowcasting
🌍 AI in Climate Modeling and Climate Change Analysis
🛰️ AI for Remote Sensing and Earth Observation Data Analysis
🌊 AI in Specialized Meteorological Applications (Agriculture, Energy, etc.)
📜 "The Humanity Script": Ethical AI for Responsible Weather and Climate Intelligence
1. 🔮 AI in Weather Forecasting and Nowcasting
Artificial Intelligence is dramatically improving the speed and accuracy of weather predictions, especially for short-term nowcasting and the forecasting of extreme events.
✨ Key Feature(s): AI model by Google DeepMind for medium-range global weather forecasting (up to 10 days) with high accuracy and speed, outperforming traditional models on some metrics.
🗓️ Founded/Launched: Google DeepMind; Research published and model details released around 2022-2023.
🎯 Primary Use Case(s): Global weather forecasting, predicting extreme weather events (cyclones, atmospheric rivers).
💰 Pricing Model: Research model: code and pre-trained model made available for non-commercial use.
💡 Tip: Follow its performance in operational tests by weather agencies; its speed offers potential for rapid forecast updates.
✨ Key Feature(s): Google Research models for high-resolution, short-term precipitation forecasting (nowcasting up to 12-24 hours).
🗓️ Founded/Launched: Google Research; MetNet first presented around 2020, with subsequent versions.
🎯 Primary Use Case(s): Precise precipitation nowcasting, severe storm prediction, flood warnings.
💰 Pricing Model: Research models; insights and techniques often shared via publications.
💡 Tip: These models showcase AI's strength in handling complex, localized weather phenomena critical for immediate public safety.
✨ Key Feature(s): NVIDIA's AI weather forecasting model, emphasizing speed and high resolution for global predictions, part of their Earth-2 initiative.
🗓️ Founded/Launched: NVIDIA; Announced around 2021-2022.
🎯 Primary Use Case(s): Global weather forecasting, climate simulation, extreme weather prediction.
💰 Pricing Model: Research model/platform; access often through NVIDIA's initiatives or collaborations.
💡 Tip: Demonstrates the power of GPU acceleration and physics-informed AI in weather modeling.
✨ Key Feature(s): Huawei Cloud AI model for precise global weather forecasting, claiming high accuracy and speed in predicting meteorological elements.
🗓️ Founded/Launched: Huawei Cloud; Announced and detailed around 2023.
🎯 Primary Use Case(s): Global weather prediction, typhoon track forecasting, improving forecast resolution.
💰 Pricing Model: AI model being integrated into services; access details vary.
💡 Tip: Represents a significant contribution from industry to AI weather modeling, showing rapid progress.
Tomorrow.io (Weather and Climate Security Platform)
✨ Key Feature(s): Platform providing weather intelligence and forecasts using AI to analyze traditional and proprietary data sources (including their own planned radar satellites). Offers operational dashboards and APIs.
🗓️ Founded/Launched: Founded 2016 (as ClimaCell).
🎯 Primary Use Case(s): Industry-specific forecasting (aviation, energy, agriculture), business continuity, risk management.
💰 Pricing Model: Commercial, subscription-based for businesses and enterprises.
💡 Tip: Explore its industry-specific solutions for tailored weather insights that can optimize operations and reduce risk.
IBM The Weather Company (Graf, Deep Thunder)
✨ Key Feature(s): Provides weather data and forecasting services, leveraging AI and machine learning (e.g., GRAF model, Deep Thunder for localized predictions) for improved accuracy and hyper-local insights.
🗓️ Founded/Launched: The Weather Company founded 1982, acquired by IBM 2016; AI integration ongoing.
🎯 Primary Use Case(s): Global weather forecasting, industry solutions, media broadcasting, aviation weather.
💰 Pricing Model: Commercial data services, enterprise solutions.
💡 Tip: Utilize their APIs for integrating high-resolution weather data and AI-driven forecasts into your own applications.
AccuWeather (AI-enhanced forecasting)
✨ Key Feature(s): Global weather forecasting provider increasingly using Artificial Intelligence and machine learning to refine its forecasts, improve MinuteCast® predictions, and analyze severe weather threats.
🗓️ Founded/Launched: Founded 1962; AI integration is a continuous process.
🎯 Primary Use Case(s): Public weather forecasts, severe weather warnings, specialized industry forecasts.
💰 Pricing Model: Free public access; premium app features; enterprise data solutions.
💡 Tip: Cross-reference AI-enhanced forecasts from multiple trusted providers for critical decision-making.
✨ Key Feature(s): AI company focused on improving weather forecasting, particularly for severe weather events and renewable energy applications, using advanced machine learning.
🗓️ Founded/Launched: Founded 2020.
🎯 Primary Use Case(s): Severe weather prediction (wildfires, hurricanes), renewable energy forecasting.
💰 Pricing Model: Commercial solutions for enterprises.
💡 Tip: Look into their specialized forecasts if your work involves high sensitivity to specific extreme weather phenomena.
🔑 Key Takeaways for AI in Weather Forecasting and Nowcasting:
AI models like GraphCast and Pangu-Weather are achieving state-of-the-art results in global weather prediction, often with greater speed.
Nowcasting, especially for precipitation, is significantly benefiting from AI.
Commercial weather providers are increasingly integrating AI to enhance their forecast accuracy and product offerings.
These advancements promise more reliable and timely warnings for severe weather events.
2. 🌍 AI in Climate Modeling and Climate Change Analysis
Understanding long-term climate trends, projecting future changes, and assessing impacts are critical. Artificial Intelligence is helping to process complex climate data and refine these crucial models.
✨ Key Feature(s): AI platform providing climate risk forecasting and adaptation insights for agriculture, supply chains, and other climate-sensitive industries.
🗓️ Founded/Launched: Founded 2017.
🎯 Primary Use Case(s): Assessing climate risks to agriculture, food systems, water resources; informing adaptation strategies.
💰 Pricing Model: Enterprise solutions.
💡 Tip: Useful for businesses and researchers needing to understand and mitigate climate change impacts on specific sectors.
✨ Key Feature(s): Provides climate risk analytics (physical risks like flood, heat, fire, wind) for specific assets and portfolios, using AI and scientific modeling.
🗓️ Founded/Launched: Founded 2017.
🎯 Primary Use Case(s): Climate risk assessment for finance, insurance, real estate, and infrastructure.
💰 Pricing Model: Commercial enterprise solutions.
💡 Tip: Leverage their asset-level risk analytics for detailed understanding of physical climate risks to specific locations or investments.
✨ Key Feature(s): AI-powered Climate Intelligence platform that provides asset-level climate risk assessments and adaptation recommendations for businesses and governments.
🗓️ Founded/Launched: Founded 2015.
🎯 Primary Use Case(s): Assessing climate risk for physical assets, supply chains; informing climate adaptation and resilience strategies.
💰 Pricing Model: Commercial platform.
💡 Tip: Utilize its "Climate Goggles" feature to visualize potential climate impacts on your assets under different scenarios.
✨ Key Feature(s): Coalition using Artificial Intelligence and satellite imagery to provide granular, real-time tracking of global greenhouse gas emissions from specific sources.
🗓️ Founded/Launched: Coalition formed around 2020.
🎯 Primary Use Case(s): Monitoring GHG emissions, verifying emissions reduction efforts, providing transparency for climate action.
💰 Pricing Model: Publicly available data.
💡 Tip: An invaluable resource for researchers, policymakers, and activists tracking emissions and holding entities accountable.
AI for Extreme Event Attribution (Research Area)
✨ Key Feature(s): Field where AI techniques are used to analyze the extent to which anthropogenic climate change influenced the likelihood or intensity of specific extreme weather events.
🗓️ Founded/Launched: Ongoing academic research; initiatives like World Weather Attribution.
🎯 Primary Use Case(s): Understanding climate change impacts, informing climate litigation and policy.
💰 Pricing Model: Primarily research outputs, publicly available studies.
💡 Tip: Follow research from leading climate institutions on how AI is helping to quantify the human fingerprint on extreme weather.
✨ Key Feature(s): Platform providing access to petabytes of global environmental data (satellite imagery, weather, climate) and AI tools for analysis.
🗓️ Founded/Launched: Microsoft; Launched around 2020.
🎯 Primary Use Case(s): Environmental science, climate change research, biodiversity monitoring, sustainable land use planning.
💰 Pricing Model: Data and APIs are largely free for sustainability uses; compute may incur costs.
💡 Tip: A powerful resource for researchers needing access to vast environmental datasets and scalable computing for AI-driven climate analysis.
Google Earth Engine (for Climate Applications)
✨ Key Feature(s): Cloud platform for planetary-scale geospatial analysis, including extensive climate datasets and AI/ML capabilities for trend analysis and impact modeling.
🗓️ Founded/Launched: Google; Launched around 2010.
🎯 Primary Use Case(s): Analyzing climate change impacts, deforestation, land use change, water resource management.
💰 Pricing Model: Free for research/education/non-profit.
💡 Tip: Utilize its extensive data catalog and AI algorithms to conduct large-scale analyses of climate change indicators and impacts.
AI in Downscaling Climate Models (Research & Institutional Tools)
✨ Key Feature(s): AI techniques (e.g., super-resolution, statistical downscaling using ML) are used to translate coarse-resolution global climate model (GCM) outputs into higher-resolution, regional climate information.
🗓️ Founded/Launched: Ongoing research in various academic and governmental institutions (e.g., NCAR, ECMWF).
🎯 Primary Use Case(s): Providing more detailed regional climate projections for impact assessment and adaptation planning.
💰 Pricing Model: Research outputs, data often publicly available from climate portals.
💡 Tip: Look for downscaled climate data products from reputable institutions that utilize AI enhancements for your specific region of interest.
🔑 Key Takeaways for AI in Climate Modeling & Analysis:
AI is helping to assess climate risks, model impacts, and track emissions with greater granularity.
Platforms are emerging that provide asset-level climate intelligence for businesses and governments.
Access to vast environmental datasets combined with AI tools is democratizing climate research.
AI contributes to improving the resolution and regional accuracy of climate projections.
3. 🛰️ AI for Remote Sensing and Earth Observation Data Analysis
Meteorology relies heavily on data from satellites, radar, and other Earth Observation (EO) systems. Artificial Intelligence is crucial for processing and interpreting this deluge of information.
Google Earth Engine (Dominant in EO)
✨ Key Feature(s): Access to massive archives of satellite imagery (Landsat, Sentinel, etc.) and AI/ML algorithms for image processing, classification, and time-series analysis.
🗓️ Founded/Launched: Google; Launched ~2010.
🎯 Primary Use Case(s): Land cover mapping, deforestation monitoring, flood mapping, agricultural monitoring, urban growth tracking for meteorological context.
💰 Pricing Model: Free for research/education/non-profit.
💡 Tip: Its JavaScript and Python APIs allow for powerful custom AI analyses on petabytes of EO data directly in the cloud.
Microsoft Planetary Computer (Comprehensive EO Platform)
✨ Key Feature(s): Provides access to key global environmental datasets, intuitive APIs, and AI tools for building EO applications.
🗓️ Founded/Launched: Microsoft; Launched ~2020.
🎯 Primary Use Case(s): Environmental monitoring, biodiversity studies, sustainable land use, processing satellite and weather data with AI.
💰 Pricing Model: Data/APIs largely free; compute may incur costs.
💡 Tip: Explore its data catalog and example applications for AI-driven analysis of weather-related environmental impacts.
Radiant Earth Foundation (MLHub)
✨ Key Feature(s): Non-profit supporting machine learning on Earth observation; MLHub provides open-source training datasets and models for EO applications.
🗓️ Founded/Launched: Founded 2016.
🎯 Primary Use Case(s): Advancing ML applications in EO, providing training data for land cover classification, crop type mapping, etc.
💰 Pricing Model: Open source, free resources.
💡 Tip: An excellent resource for finding open training datasets to build and test your own AI models for meteorological applications using EO data.
✨ Key Feature(s): Geospatial analytics and AI platform that ingests and processes vast amounts of satellite and other sensor data to create global-scale models and insights.
🗓️ Founded/Launched: Founded 2014.
🎯 Primary Use Case(s): Agricultural forecasting, supply chain intelligence, environmental monitoring, climate analysis, with meteorological inputs.
💰 Pricing Model: Commercial, enterprise solutions.
💡 Tip: Useful for large-scale, AI-driven analysis where fusing diverse global sensor data (including weather) is key.
✨ Key Feature(s): Developer platform and marketplace for geospatial data (satellite, aerial, weather, etc.) and AI analytics, allowing users to build custom processing workflows.
🗓️ Founded/Launched: Founded 2019 (by Airbus).
🎯 Primary Use Case(s): Custom EO application development, environmental monitoring, infrastructure monitoring, precision agriculture.
💰 Pricing Model: Pay-as-you-go for data/analytics; subscriptions.
💡 Tip: Build custom workflows combining various EO data sources with AI algorithms for specific meteorological research questions.
ENVI (with AI/Deep Learning Module)
✨ Key Feature(s): Image analysis software with advanced tools for processing remote sensing data, including AI and deep learning for feature extraction, classification, and target detection.
🗓️ Founded/Launched: L3Harris Geospatial; Long-standing product, AI recent.
🎯 Primary Use Case(s): Detailed analysis of satellite/aerial imagery for land cover mapping, atmospheric correction, identifying weather-related damage.
💰 Pricing Model: Commercial licenses.
💡 Tip: Use its deep learning module to train custom models for identifying specific features (e.g., cloud types, flood extents) in meteorological imagery.
✨ Key Feature(s): Open-source library for remote sensing image processing, offering a wide range of algorithms for image filtering, feature extraction, classification (including ML-based). Can be integrated with QGIS.
🗓️ Founded/Launched: Developed by CNES (French Space Agency); first released 2006.
🎯 Primary Use Case(s): Advanced image processing for satellite data, research in remote sensing, developing custom EO applications.
💰 Pricing Model: Open source (free).
💡 Tip: A powerful option for researchers needing a flexible, open-source toolkit for complex image processing and AI algorithm implementation.
✨ Key Feature(s): Open-source Python framework for building deep learning models on satellite, aerial, and other raster imagery.
🗓️ Founded/Launched: Developed by Azavea; open-sourced.
🎯 Primary Use Case(s): Object detection, image segmentation, and change detection in EO imagery for applications like damage assessment after storms or urban heat island mapping.
💰 Pricing Model: Open source (free).
💡 Tip: For developers looking to build and train custom deep learning models specifically for geospatial imagery analysis.
AI capabilities within major weather satellite programs (e.g., GOES-R, Sentinel Missions)
✨ Key Feature(s): Raw satellite data from NOAA, EUMETSAT, ESA, etc., is increasingly processed using AI/ML algorithms (often by these agencies or research partners) to derive higher-level products like cloud properties, atmospheric motion vectors, fire detection, etc.
🗓️ Founded/Launched: Satellites launched over many years; AI processing is an evolving capability.
🎯 Primary Use Case(s): Operational weather forecasting, climate monitoring, atmospheric research.
💰 Pricing Model: Data from these government programs is typically free and open.
💡 Tip: Access derived AI-enhanced products from official satellite data portals for ready-to-use meteorological insights.
🔑 Key Takeaways for AI in Remote Sensing & EO Data Analysis:
AI is essential for extracting actionable information from the massive volumes of Earth observation data.
Cloud platforms provide the infrastructure for planetary-scale AI analysis of satellite imagery.
Open-source tools and frameworks are democratizing access to advanced AI capabilities for EO data.
These tools are critical for monitoring weather phenomena, climate indicators, and environmental changes.
4. 🌊 AI in Specialized Meteorological Applications (Agriculture, Energy, etc.)
Beyond general forecasting, Artificial Intelligence is providing tailored meteorological insights for specific industries and applications, optimizing operations and mitigating risks.
Tomorrow.io (Industry-Specific Solutions)
✨ Key Feature(s): Provides weather intelligence and forecasts tailored for industries like aviation, energy, transportation, construction, and sports, using AI to translate weather data into actionable business insights.
🗓️ Founded/Launched: Founded 2016 (as ClimaCell).
🎯 Primary Use Case(s): Operational decision-making based on weather, risk mitigation, demand forecasting for weather-sensitive industries.
💰 Pricing Model: Commercial, subscription-based.
💡 Tip: Explore their specific industry dashboards and APIs to integrate hyperlocal, AI-driven weather intelligence into your business processes.
✨ Key Feature(s): Provides operational intelligence, including weather forecasts and analytics, tailored for agriculture, energy, aviation, transportation, and other weather-sensitive sectors, often incorporating AI.
🗓️ Founded/Launched: Founded 1984 (as Data Transmission Network).
🎯 Primary Use Case(s): Precision agriculture, energy trading and demand forecasting, flight planning, logistics optimization.
💰 Pricing Model: Commercial subscriptions and enterprise solutions.
💡 Tip: Their detailed agricultural weather insights, for example, can help optimize planting, irrigation, and harvesting decisions.
✨ Key Feature(s): Provides global weather data and forecasts using its own constellation of satellites collecting radio occultation data and other atmospheric measurements, enhanced by AI models.
🗓️ Founded/Launched: Spire Global founded 2012.
🎯 Primary Use Case(s): Maritime route optimization, aviation weather, renewable energy forecasting, general weather prediction in data-sparse regions.
💰 Pricing Model: Commercial data services and solutions.
💡 Tip: Its unique satellite-based data sources, processed with AI, can offer valuable insights for regions with limited ground-based weather stations.
✨ Key Feature(s): Provides agricultural intelligence and agronomic weather data, including AI-driven insights and forecasts, to help farmers optimize yields and manage risks.
🗓️ Founded/Launched: Founded 1999.
🎯 Primary Use Case(s): Precision agriculture, pest and disease modeling, crop yield forecasting, climate adaptation for farming.
💰 Pricing Model: Commercial data services and platform access.
💡 Tip: Useful for agricultural researchers and businesses needing granular, field-level weather data and agronomic insights.
✨ Key Feature(s): AI-powered electricity demand forecasting company, using machine learning and high-resolution weather data to provide accurate forecasts for utilities and energy retailers.
🗓️ Founded/Launched: Founded 2017.
🎯 Primary Use Case(s): Energy load forecasting, grid management, energy trading.
💰 Pricing Model: Commercial solutions for energy sector clients.
💡 Tip: Demonstrates how AI can translate meteorological data into highly specialized forecasts crucial for energy market operations.
✨ Key Feature(s): AI-based solutions for renewable energy forecasting (solar and wind power generation) and grid management.
🗓️ Founded/Launched: Founded 2017.
🎯 Primary Use Case(s): Optimizing renewable energy production, grid stability, energy trading based on weather-dependent generation.
💰 Pricing Model: Commercial solutions.
💡 Tip: Key for renewable energy operators and grid managers needing accurate forecasts of variable energy generation.
AI for Air Quality Forecasting (e.g., within national weather services like EPA's AirNow, Copernicus CAMS)
✨ Key Feature(s): Many national and international meteorological services use AI and chemical transport models to forecast air quality (e.g., ozone, particulate matter) based on weather conditions, emissions data, and atmospheric chemistry.
🗓️ Founded/Launched: Ongoing development within governmental and research institutions.
🎯 Primary Use Case(s): Public health warnings, air pollution mitigation strategies, research into air quality dynamics.
💰 Pricing Model: Data and forecasts often publicly available.
💡 Tip: Check your national or regional meteorological/environmental agency websites for AI-enhanced air quality forecasts.
AI in Avalanche Forecasting (e.g., tools used by regional centers) Cambridge
✨ Key Feature(s): Avalanche forecasting centers are increasingly using machine learning models trained on historical avalanche data, snowpack information, and weather conditions to improve the accuracy of avalanche danger ratings.
🗓️ Founded/Launched: Research and operational implementation ongoing in mountainous regions.
🎯 Primary Use Case(s): Public safety in mountain areas, ski resort operations, transportation safety.
💰 Pricing Model: Forecasts usually public; underlying tools often research-based.
💡 Tip: AI is helping to process complex factors that contribute to avalanche risk, leading to better warnings.
🔑 Key Takeaways for AI in Specialized Meteorological Applications:
AI provides highly tailored weather and climate insights for specific industries like agriculture, energy, aviation, and maritime.
These tools help optimize operations, mitigate weather-related risks, and improve resource management.
AI is crucial for forecasting variable renewable energy generation.
Specialized applications often involve fusing weather data with industry-specific operational data.
5. 📜 "The Humanity Script": Ethical AI for Responsible Weather and Climate Intelligence
The increasing power and pervasiveness of Artificial Intelligence in meteorology necessitates careful consideration of ethical implications to ensure these technologies serve the global good.
Ensuring Equitable Access to Information: Life-saving weather warnings and crucial climate adaptation information derived from AI should be accessible to all communities, regardless of economic status or geographic location. Bridging the "digital divide" in weather and climate services is essential.
Algorithmic Bias in Impact Assessment: AI models predicting the impacts of weather or climate change could inadvertently reflect or amplify existing societal biases if not carefully designed (e.g., underestimating risks for marginalized communities due to data gaps). Fairness and equity must be central.
Transparency and Explainability of Forecasts and Models: While complex, efforts towards making AI-driven weather forecasts and climate projections more understandable (Explainable AI - XAI) can build public trust and allow for better scrutiny by the scientific community.
Data Sovereignty and Global Data Sharing: Meteorological and climate data is often global. Ethical frameworks are needed for international data sharing, respecting national sensitivities while ensuring data is available for research and global good, particularly for developing nations.
Responsibility for AI-Driven Warnings and Predictions: Clear lines of responsibility must be maintained, especially concerning warnings for severe weather or long-term climate impacts. AI is a tool; human expertise and official agencies remain accountable for issuing critical public alerts.
Preventing Misuse of Climate Intelligence: Powerful AI-driven climate risk analytics could potentially be misused (e.g., by financial markets to exploit vulnerable regions). Ethical guidelines are needed to ensure such intelligence is used for resilience and adaptation.
🔑 Key Takeaways for Ethical AI in Meteorology:
Equitable access to AI-driven weather warnings and climate information is a global imperative.
AI models must be audited for biases that could lead to inequitable risk assessments.
Transparency and explainability in AI meteorological models build trust and allow for scrutiny.
Ethical data sharing and respect for data sovereignty are crucial in a global context.
Human accountability for critical warnings and decisions must be maintained, with AI as a supportive tool.
✨ Forecasting a Safer Future: AI's Vital Role in Understanding Our Atmosphere
Artificial Intelligence is undeniably revolutionizing meteorology, offering unprecedented capabilities to forecast weather with greater accuracy, model long-term climate change with more nuance, and derive critical insights from vast streams of Earth observation data. From providing life-saving warnings for extreme events to informing strategies for climate resilience and optimizing weather-sensitive industries, AI is becoming an indispensable tool in our interaction with Earth's dynamic atmosphere.
"The script that will save humanity" in the face of escalating weather extremes and a changing climate hinges on our ability to harness these technological advancements wisely and ethically. By ensuring that Artificial Intelligence in meteorology is developed and deployed with a commitment to open access, scientific rigor, fairness, transparency, and global collaboration, we can empower communities worldwide to better prepare for, adapt to, and mitigate the impacts of atmospheric hazards. The future of weather and climate intelligence, augmented by AI, holds the promise of a safer, more resilient, and more sustainable world for all.
💬 Join the Conversation:
Which application of Artificial Intelligence in meteorology or climate science do you believe will have the most significant positive impact on society?
What are the biggest ethical challenges or risks associated with increasing reliance on AI for weather forecasting and climate projections?
How can the global community ensure that the benefits of AI-driven meteorological advancements are shared equitably, especially with vulnerable nations?
In what ways do you think AI will further change our daily interaction with weather information in the coming decade?
We invite you to share your thoughts in the comments below!
📖 Glossary of Key Terms
🌦️ Meteorology: The scientific study of the Earth's atmosphere, focusing on weather processes and forecasting.
🤖 Artificial Intelligence: The theory and development of computer systems able to perform tasks that normally require human intelligence, such as learning, pattern recognition, prediction, and data analysis.
🔮 Weather Forecasting / Nowcasting: Predicting atmospheric conditions for a specific location and time. Nowcasting refers to very short-term forecasts (e.g., 0-6 hours).
🌍 Climate Modeling: The use of quantitative methods (often complex computer simulations, increasingly AI-enhanced) to simulate the interactions of the important drivers of climate, including atmosphere, oceans, land surface, and ice.
🛰️ Earth Observation (EO) / Remote Sensing: Gathering information about Earth's physical, chemical, and biological systems via remote-sensing technologies (e.g., satellites, radar), with AI used for data processing and analysis.
🧠 Neural Weather Models (NWMs): A new class of weather prediction models based on deep learning (a type of Artificial Intelligence) that learn atmospheric physics directly from data.
📈 Predictive Analytics (Weather/Climate): Using AI and statistical algorithms to analyze historical and current meteorological data to make predictions about future weather events or climate trends.
⚠️ Algorithmic Bias (Climate/Weather Impact): Systematic errors in AI models that could lead to inequitable or inaccurate predictions of weather/climate impacts for different regions or demographic groups.
📊 Ensemble Forecasting: A weather forecasting technique that generates multiple forecasts (an ensemble) using slightly different initial conditions or model versions to provide a range of possible future states and estimate forecast uncertainty. AI can aid in interpreting ensembles.
🌡️ Downscaling (Climate Models): Techniques used to translate coarse-resolution outputs from global climate models into higher-resolution information relevant for regional or local impact studies, increasingly using AI.
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