How Fire Danger Systems Use Meteorological Data

Forecasting forest fire spread is important for providing early warnings to the stakeholders. For the first time team led by Dr Manish Kale from CADC, Pune, has developed an operational Forest fire spread forecasting system, with funding from the Ministry of Electronics and Information Technology of India (https://www.meity.gov.in/). It uses the open-source WRF-SFIRE model to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)--based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25-degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation-specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution. VIIRS satellite sensor-based fire alert was used as an ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. Ministry of Electronics and Information Technology Indian Institute of Remote Sensing (IIRS), Indian Space Research Organization (ISRO) ISRO - Indian Space Research Organization,

This study focuses on a deep learning-based Fire Weather Index (FWI) forecasting system, with Shihab Shahriar, a Ph.D. student in UH Choi’s AQF and machine learning group, as the first author. Wildfires in the United States have risen significantly in recent decades due to climate change, shifting weather patterns, and increased flammable materials. To address this, the study analyzed FWI trends across the Continental United States from 2014 to 2023, leveraging gridMET meteorological data and key variables like temperature, humidity, wind speed, and precipitation to pinpoint wildfire-prone areas. A hybrid forecasting framework was developed, integrating Graph Neural Networks (GNNs) with Temporal Convolutional Neural Networks (TCNNs), Long Short-Term Memory (LSTM), and Deep Autoregressive Networks (DeepAR), and evaluated using performance metrics such as the Index of Agreement (IOA) and root mean squared error (RMSE). The GNN-TCNN model demonstrated superior performance, achieving an IOA of 0.95 and an RMSE of 1.21 on Day 1, making it an effective and scalable solution for proactive wildfire management across both short- and long-term forecasting horizons. https://lnkd.in/gts_AD7X https://lnkd.in/gJpJmkw9

December 2024: Ambee’s wildfire forecast flagged high-risk zones in LA January 2025: Wildfires erupted (right where the data predicted) This four-week warning is not a retrospective analysis. For years, businesses have reacted after wildfires started. But wildfires don’t appear out of nowhere. It follows patterns driven by temperature trends, precipitation forecasts, and historical seasonality. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻? 𝗣𝗿𝗲𝗱𝗶𝗰𝘁, 𝗱𝗼𝗻’𝘁 𝗿𝗲𝗮𝗰𝘁. Ambee’s Wildfire Forecast API delivers fire danger intelligence up to 4 weeks in advance, helping - Construction & Infra: Schedule outdoor work during lower-risk windows - Healthcare Providers: Prepare for air quality impacts in high-risk zones - Emergency Management: Evacuate & deploy resources in advance - Insurance: Adjust policies dynamically with forecasted risk insights - Logistics: Reroute shipments weeks ahead to avoid disruption - Energy: Secure infrastructure before peak fire conditions 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝘁𝗵𝗶𝘀 𝗔𝗣𝗜 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁? • NA-Focused Data: Use regionally validated wildfire risk insights • 500m Grid Precision: Get hyperlocal forecasts for risk assessment • Comprehensive Risk Metrics: FWI, FFMC, temp & precipitation data • Easy API Integration: Implement with developer-friendly documentation • Weekly Updates: Stay ahead with dynamic, categorized risk assessments • 4-Week Advanced Warning: Predict wildfire risk before conditions escalate 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 𝘃𝘀. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Tracking active fires is important. But forecasting fire risk before it happens allows businesses to act early. Wildfire forecasting = long-term risk planning Real-time wildfire monitoring = immediate situational awareness Used together, they provide both immediate insights and long-term risk mitigation. 𝗔𝗺𝗯𝗲𝗲’𝘀 𝗪𝗶𝗹𝗱𝗳𝗶𝗿𝗲 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗔𝗣𝗜 𝗶𝘀 𝗹𝗶𝘃𝗲. We already know where wildfires have been. We already know where wildfires are happening now. Now, we know where wildfires will be next. The data exists. The forecasts are live. If you’d like to start predicting fire risks in under 15 minutes, check the first comment below!