Abstract: Environmental information combined with satellite driven observations and ground observations to create a predictor for wildfire at high spatial resolution. Temperature and precipitation are bias corrected using modeling and processing techniques driven from reanalysis datasets. Such techniques can be used to provide projections of future climate risk data at high temporal cadence over individual addresses or over large regions using spatial aggregation using polygon processing techniques.

Abstract: A plurality of climate models for a variety of hazards and/or initial conditions to produce a collection of Hazard Exposure (Risk) Maps. The Hazard Exposure Maps are transformed to high resolution (and may be filtered) and convolved with auxiliary data related to one or more hazards. The now super resolution maps are input to a learning engine along with spatiotemporally harmonized historic events and active near real-time events to produce a calibrated model (Asset Level Exposure Risk Estimation Model) that utilizes the super resolution maps and asset location(s) to project risk for each asset. The projected risk may be provided to asset operators/owners, initiate signal alerts and other messages, invoke automated responses to protect/preserve assets. The risk results may also be grouped in risk valuation datasets.