Scientific Research

Sustained Wind Forecasts from the High-Resolution Rapid Refresh Model: Skill Assessment and Bias Mitigation

2025. Robert G. Fovell, Scott B. Capps

Read Full Article

← Previous Building a Wildfire-Resilient Grid: A Long-Term Planning Approach Next → Improving fire danger forecasting with numerical weather prediction and machine learning (T).
A large wildfire burns on a hillside behind a residential neighborhood with red-roofed houses, sending thick gray smoke into the sky; overlaid text describes an “urban conflagration” where fire spreads into a wildland-urban interface and destroys buildings with high intensity.

Scientific Research

Technosylva’s Urban Conflagration Model Helps Utilities and Fire Agencies Prepare for Fires in Populated Areas

Technosylva’s Urban Conflagration Model addresses that gap by simulating how fires spread through the wildland-urban interface, where structure density, vegetation encroachment, and fuel types create fire behavior that looks fundamentally different from wildland fires.

Scientific Research

Assessing the suppression difficulty of wildland fires for initial attack response

2025 • Adrián Cardil, Adrián Jiménez-Ruano, Santiago Monedero, Phillip SeLegue, Macarena Ortega, Raúl Quilez, Jeff Fuentes, Geoff Marshall, Robert Clark, Tim Chavez, Sergio de-Miguel, Tomás Quiñones, Fernando Bezares, Scott Purdy, Kristen Allison, Tim Metzger, Chris Waters, Midhun Mohan, Joaquín Ramirez

Scientific Research

FIRE-RES Geo-Catch: a mobile application to support reliable fuel mapping at a pan-European scale

2023 • Erico Kutchartt, José R. González-Olabarria, Antoni Trasobares, Sergio de-Miguel, Adrián Cardil, Brigite Botequim, Vassil Vassilev, Palaiologos Palaiologou, Martino Rogai, Francesco Pirotti
Technosylva icon

Reserve your individual session.

Let’s Talk