How climate data becomes actionable knowledge

🌍One map can save thousands of lives. 🌍 Every flood leaves a footprint. But what if we could predict, visualize, and act before disaster strikes? Using ArcGIS, Google Earth Engine, and Python, I built a flood risk model that transforms raw satellite data into actionable insights. ✅ Methodology: Remote sensing + GeoAI + advanced spatial analysis ✅ Real-World Impact: Helps governments, NGOs, and communities plan, respond, and save lives ✅ Big Picture: Turning data into climate resilience The message is clear: 📢 Data is powerful, but only if it reaches decision-makers in time. This is why geospatial science isn’t just about maps — it’s about solutions that protect people and ecosystems. 💡 I’d love to hear your thoughts: 👉 How else can GeoAI & GIS be used to tackle the world’s toughest environmental challenges? 🔁 If you believe geospatial data can change the world, share this post so more people see the power of location intelligence. #GIS #RemoteSensing #FloodMapping #GeoAI #ClimateAction #Sustainability

A new era for climate forecasting: real-time 3D weather models in your browser 🌍 Earth observations are becoming sharper, faster and more accessible than ever before. Gaia3D Inc., led by SANGHEE SHIN, has launched a web-based 3D weather visualisation system for the Korea Meteorological Administration. It’s already fully operational and was recently used to track Typhoon Cỏ May in real time, directly through a browser. Why does this matter? Because the more precisely we can model the atmosphere, the better we can prepare for the impacts of climate change on people and nature. 🌪️ Extreme weather → faster, more accurate forecasting saves lives 🌱 Biodiversity under stress → visualisation highlights shifting habitats and risks 🌊 Rising seas & floods → instant, actionable data for planners and conservationists Not long ago, this kind of analysis required expensive high-end graphics workstations. Now it’s web-native, scalable and collaborative, making advanced Earth observation available to everyone who needs it. As climate impacts intensify, tools like this will become the backbone of environmental modelling, helping us anticipate change, safeguard ecosystems, and make smarter decisions for the planet. Watch the video below to see the system in action. #NatureTech #ClimateAction #Biodiversity #EarthObservation #FutureOfForecasting

When I first started meeting bureaucrats, policymakers, and politicians while working on air pollution and climate change, I assumed scientific research would naturally lead to better policies. But over time, I kept getting the same response—expressed in different ways. Here, I’m sharing some early experiences that shaped my understanding of this disconnect. 🔹 One of my first experiences was when a very senior officer invited us to discuss solutions. As scientists, we proposed a research-driven approach that would take two to three years. His response? "We have funding that must be spent within a year. We expected practical solutions from you. We can’t wait three years—I might even be transferred before then." 🔹 Another realization came when we proposed analyzing pollution sources. A senior officer responded, "We already know the sources—traffic, industry, construction, waste burning, road dust, cooking fuel, etc. Will your study show anything drastically different?" When we explained that our study would refine insights and reduce uncertainties, his response was: "We don’t care about these nuances right now. That detail matters later, once mitigation efforts are underway. Right now, we need feasible solutions that fit economic, demographic, and practical constraints." Another officer later remarked: "Scientists aren’t here to provide solutions. Their focus is securing funding, publishing papers, and showcasing work to funders." He even cited global reports that had never been downloaded. At that moment, I felt disappointed. But I also realized they weren’t entirely wrong—perhaps even more right than I was. Policymakers work within short funding cycles, shifting priorities, and limited tenures—typically three years for an officer, five for a politician. Their constraints are real, and their approach reflects these realities. 💡 This disconnect between science and policy is a major barrier in sustainability. Scientists seek accuracy, while policymakers need actionable, timely solutions. So, how do we bridge this gap? ✔ Policy-Research Intermediaries – Teams that translate scientific findings into actionable policies. ✔ Adaptive Research Timelines – Delivering short-term, high-impact solutions alongside long-term studies. ✔ Collaborative Working Groups – Scientists, policymakers, and stakeholders aligning research with real-world needs. ✔ Flexible Funding Models – Ensuring funding supports both immediate action and long-term research. 🚀 If we don’t bridge this gap, science remains detached from policy, and policy stays reactive instead of proactive. #AirPollution #ClimateAction #SciencePolicy #Sustainability #Collaboration #ResearchToAction

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

Data isn’t just numbers on a chart. It’s the bridge connecting sectors, sparking collaboration, and driving measurable impact.   When data shifts from answering questions to informing actions, it becomes the foundation for sustainable change.   Some questions that drive behavioural change are:-   1.⁠ ⁠What Happens When the Connection Breaks?   Without Data: Decisions lack direction. Insights are missed, and progress stalls.   Without Collaboration Across Sectors: Silos form, limiting impact and innovation.   Without Impact: Efforts lose focus, wasting time and resources.   2.⁠ ⁠What is The 3Es Framework: Engagement, Education, Enablement   When data, sectors, and impact align, we create actionable, scalable solutions. Leveraging data through the 3Es transforms how we connect insights to actions:   A) Engagement (Conversation & Collaboration)   Healthcare: Real-time data from wearables fosters collaboration between doctors and researchers, enabling early diagnoses and tailored treatments.   Governance & Social Justice: Open data portals empower communities to monitor policies and advocate for equity.   B) Education (Analysis & Interpretation)   Education Systems: Countries like Estonia personalize learning experiences by using data to identify gaps and optimize outcomes.   Economic Development: Platforms like UN Global Pulse interpret data trends to ensure equitable growth and inclusivity.   C) Enablement (Easy Access & Application)   Environment: NASA’s Earth data helps nations monitor carbon emissions, predict weather patterns, and mitigate disasters.   Agriculture: Tools like India’s CropIn provide farmers with actionable insights, reducing waste and increasing productivity.   3.⁠ ⁠How do we amplify Impact Through Actionable Data   In crises, data transforms response strategies:   Disaster Relief: Real-time satellite imagery connected aid organizations to impacted areas during the 2023 Turkey-Syria earthquake, ensuring timely resource allocation.   Technology: AI-driven accessibility tools open opportunities for underserved communities, turning data into meaningful solutions.   4.⁠ ⁠What is The Future of Data   Data must evolve from static reports to dynamic strategies that:   Spark Engagement by fostering conversations and partnerships.   Provide Education through clear, actionable insights.   Drive Enablement with tools that make data accessible and actionable.   5.⁠ ⁠How do we build a Connected Future   Data, sectors, and impact form a network—a system that unites ideas, people, and solutions.   By transforming data into a bridge for informed actions, we don’t just answer questions; we create opportunities, solve problems, and drive global progress.   P.S. How are you using data today to build connections and enable action?  

Thunderstorms are becoming more frequent and intense due to climate change. According to the World Meteorological Organization, the number of severe convective storms has increased by over 12% globally in the last two decades, with rainfall intensity up by as much as 20% in parts of Asia and North America. What do you think about this video? As global temperatures rise, the atmosphere holds more moisture, fueling stronger and more unpredictable storms. But technology is stepping up to meet this challenge. 🔹 AI-Powered Climate Models High-performance computing and AI-driven simulations are now capable of modeling atmospheric changes at kilometer-scale resolution. These systems help meteorologists forecast thunderstorm formation hours in advance — a leap forward from traditional models that relied on much coarser data. 🔹 Next-Gen Satellites and Radar Networks Modern satellites like the European Meteosat Third Generation and advanced radar networks use machine learning to detect lightning and storm cell movement in real time. This allows emergency response systems to issue earlier and more accurate alerts. 🔹 IoT Sensor Networks Ground-level IoT stations measure temperature, pressure, and humidity continuously. When combined with AI analytics, they form localized early warning systems that can alert communities via mobile apps or smart city dashboards. 🔹 Smart Infrastructure and Grid Protection AI-assisted smart grids and adaptive materials help protect infrastructure from lightning damage and power surges. These systems can automatically reroute electricity and reduce blackout risks during severe weather. 🔹 Global Climate Data Collaboration Massive open datasets — from satellites, drones, and weather stations — are fueling a new era of collaboration between governments, startups, and research institutes. This data helps improve resilience planning, insurance modeling, and even renewable energy site optimization. 💡 In short: Technology is transforming how we live with nature’s volatility — turning our approach to thunderstorms from reactive to predictive. #ClimateChange #AI #WeatherTech #Sustainability #Innovation #DataScience video via @aiwithjash by Sora 2 Ai engine.

🌦️ How GIS Supports Climatology Geographic Information Systems (GIS) play a key role in understanding and addressing climate-related challenges. By combining spatial data with environmental models, GIS helps scientists and planners visualize, analyze, and predict climate patterns more accurately. Here’s how GIS contributes to climatology: 1️⃣ Data Integration – combines data from satellites, weather stations, and remote sensors to build complete climate maps. 2️⃣ Trend Analysis – identifies temperature changes, rainfall patterns, and extreme weather over time. 3️⃣ Impact Assessment – evaluates how climate affects land use, water resources, biodiversity, and urban areas. 4️⃣ Risk Mapping – pinpoints areas vulnerable to floods, droughts, heatwaves, or sea-level rise. 5️⃣ Adaptation Planning – supports sustainable urban design, disaster preparedness, and environmental protection strategies. In short, GIS turns raw environmental data into actionable climate intelligence, helping us plan for a safer and more resilient future. 🌍 #GIS #Climatology #ClimateChange #DataScience #SpatialAnalysis #Sustainability #UrbanPlanning #QGIS #EnvironmentalData #Resilience

♟️Sustainability Regulations: Saying the quiet part out loud    Sustainability reporting frameworks are continuing to evolve, as we’ve seen most recently in the EU Commission's omnibus proposal. That takes away the 'easy button' from validating sustainability reporting. (Your auditor suddenly go quiet in board meetings about assurance?)   The uncertainty of these regulatory updates brings a silver lining that forces the value of data surfaced back into the conversation.   In my recent The Wall Street Journal interview, I made a point that's getting lost in the sustainability conversation: Carbon accounting is about generating activity-based carbon information that can be broken down along the lines of P&L, surfacing revenue or removing risk by business line or geography. Read it at https://lnkd.in/efNTRMZE   Of the companies I see turning this moment into a value vs. compliance chess game, they are designing their reporting to reveal the direct relationship between sustainability metrics and business performance. They are integrating financial and sustainability data to: 🔹Surface actionable insights to accelerate cost and carbon reduction along the value chain (like ways to reduce material consumption) 🔹Reveal new revenue streams, opportunities for investment, or margin increases (like green steel) 🔹 Already use AI to generate data and draft reports, knowing that investors will also be using agentic AI   The bottom line: While CSRD came out of the box overly complex (in my personal opinion), the foundations of accurate carbon data based on business activities ('actuals') remains the bedrock of the ability to distribute margin and risk from carbon and climate change. It's not about environmental altruism—it's about reminding the Titans of industry that climate change is a business conversation, because mitigation can diffuse risk and adaptation can be profitable.   Deloitte Jodie Stahly Payette Pete Dabbs #SAPsustainability

I recently had the opportunity to discuss with Marcia Toledo Sotillo, director of #adaptation and #resilience at the UN High-Level Climate Champions, about our experience in democratizing climate risk assessment. The Race to Resilience campaign, led by the High-Level Climate Champions, has set an ambitious goal: enhance the resilience of 4 billion people by 2030 by mobilizing action from the so-called "non-state actors" - local communities, NGOs, companies... Assessing and understanding climate risks is the first step of this transformation. Thanks to five decades of intensive research in climate science and earth system modeling, information about future climate and its impacts is available at unprecedented precision and depth. However, accessing and interpreting this data can be almost impossible for non-state actors, as it requires very specific technical and scientific expertise. Since 2019, Callendar has been bridging this gap by developing tools that transform scientific data into ready-to-use, localized information. Our solutions cater to a wide range of stakeholders, from billion-dollar industrial projects to individuals. In 2024, we delivered climate risk assessments - such as submersion, heatwave or wildfire - to over 230,000 people in France. While a far cry of the 4 billion target, it represents a scalable model that can be replicated globally. I strongly believe that delivering high-quality, actionable climate impact assessments to half of humanity within 5 years is technically feasible. However, there is no one-size-fits-all solution for adaptation. Climate impacts vary greatly from one place to another and solutions must be tailored to local contexts. To be truly effective, our approach requires both global endorsement and local collaboration, ensuring that communities have access to tools and support tailored to their specific needs.

We've been reflecting on the concept of "skillful means" and how it might apply to the challenges of collective existence, particularly in the realm of public problem-solving. How can we translate this into actionable strategies for public issues? #climatechange #heat #india Climate change, with its immediacy and complexity, underscores the need for citizen self-organization. We must develop skillful means for climate citizenship, ensuring our knowledge is suited to the urgency of the crisis. This involves not just precision in data but also the ability to act swiftly and adaptively in the face of uncertainty. One area we're focusing on is the public health challenge posed by heat waves, which is the one manifestation of climate change that pretty much everyone in India will suffer from. Taking Delhi as our first example, we looked at heat wave data from the city. Despite the existence of a heat action plan by the Delhi Disaster Management Authority (DDMA), there's a need to rethink how we gather, document, and utilize knowledge for more effective action. This includes making knowledge more contextual, actionable, adaptive, and robust—tailored not just for policymakers but also for everyday citizens, ensuring it guides real-time decision-making. In this ongoing series, we’ll be discussing how we can design cases for action that blend the thoroughness of a legal brief with the creativity of a design brief, supported by data and models. Our aim is to empower communities to make informed, immediate, and impactful changes in response to complex issues like heat waves. Stay tuned for more insights on how we can make heat action more citizen-friendly and effective. Joint work with Shashi Nagarajan Iyer and illustrations as usual by Srinivas Mangipudi. Link to article below 👇🏾