News 2025

TerraCast Case Study.

40% greater accuracy in 96-hour regional climate forecasts.

Triage Copilots
Images of client and real-world deployments are contractually confidential.
All visuals are AI-generated for illustrative purposes.

Executive Summary

In 2025, EverSphere launched TerraCast, a next-generation regional climate forecasting system that reduces error rates by 40% across 96-hour projections compared to incumbent global models.

By integrating satellite imagery, atmospheric datasets, and IoT sensor networks into a continuously adaptive framework, TerraCast enables governments, grid operators, and farmers to anticipate extreme weather and environmental shifts with unprecedented precision.

Unlike static forecasts, TerraCast recalibrates in real time as conditions evolve, providing both probability and confidence metrics. Every forecast is logged and auditable under EverSphere’s Ethics & Assurance Framework, ensuring equitable access for developed and vulnerable regions alike.

Industries Impacted
  • National Meteorological Agencies
  • Agriculture & Food Security
  • Energy & Utilities (grid balancing, renewables)
  • Disaster Preparedness & Humanitarian Relief

Geographies: Early deployments in Europe, Southeast Asia, and Sub-Saharan Africa pilot regions.

Error Rates

Legacy models miss early signals of floods, storms, and heatwaves

Static Models

Forecasts updated on fixed cycles; poor adaptation to live changes

Access Inequality

Advanced forecasting concentrated in wealthy nations; vulnerable regions underserved

The Problem

Climate volatility is one of the defining challenges of the 21st century. National weather services and global forecasting centres provide valuable outlooks, but:

  • Error margins on short-term (96-hour) forecasts remain high, limiting actionable confidence.
  • Static update cycles prevent forecasts from adapting quickly to shifting local conditions.
  • Access gaps mean the most climate-vulnerable regions lack predictive capabilities available to richer nations.

The result: missed opportunities to mitigate disasters, optimise agriculture, and stabilise renewable grids. Decision-makers need forecasts that are faster, sharper, and globally accessible.

Approach

TerraCast fuses multimodal data sources with EverSphere’s Kai optimisation core and VectorGrid’s geospatial intelligence layer.

Data foundation (live, governed, secure)
  • Satellite: multispectral imagery, radar (SAR), cloud/aerosol data.
  • Atmospheric: ECMWF/NOAA datasets, radiosonde balloons, jet-stream telemetry.
  • Sensor networks: IoT soil probes, weather stations, river gauges, power grid telemetry.
  • Historical priors: 50-year reanalysis archives for regional climate baselining.
Core Models
  • Hybrid GNN + transformer architecture: captures spatial-temporal dependencies across regions.
  • Adaptive re-calibration: integrates new sensor feeds every 15 minutes.
  • Extreme event modelling: tropical storms, flash floods, and heatwaves flagged with lead-time alerts.
  • Uncertainty quantification: confidence bands delivered with every projection.
  • Explainability: reason codes for forecast shifts (e.g. “Rapid jet-stream oscillation, P=0.81, Δtemp forecast +2.4°C”).
System Design
  • Human-in-the-loop: meteorologists validate high-impact alerts before public release.
  • Model Zoo integration:
    • Kai → optimisation engine for recalibration and scenario modelling.
    • VectorGrid → geospatial alignment and infrastructure overlays.
  • APIs: WMO-compliant data export; integration with national weather centres and energy operator dashboards
  • Auditability: bias and equity checks overseen by Dr Abigail Shaw’s Assurance group.
Deployment
  • Roll‑out:
    cloud-hosted inference with regional redundancy (EU-West, AP-Southeast, Africa-South).
  • Integration time:
    8–12 weeks with national agencies; pilots run in parallel with existing models.
  • Governance:
    ethical assurance, fairness audits, and equitable scaling as deployment conditions.

Primary Outcomes

+40%
in 96-hour forecasts compared with incumbent models
+4 days
additional notice for agricultural interventions (planting, irrigation, harvest)
+27%
improvement in renewable grid balancing accuracy during volatile weather
+31%
extreme weather alerts (floods, storms, heatwaves) compared to national baselines

Secondary Outcomes

+22%
improvement in emergency service staging efficiency (equipment/personnel placement)
+18%
increase in water-resource optimisation for drought-prone regions
+15.6%
reduction in missed alerts across pilot meteorological agencies

“Climate stability is the foundation of human stability. TerraCast provides foresight at the speed and precision the world has never had before.”

Marcus Knox

Methodology

  • Parallel pilot runs with national weather centres in EU and SE Asia.
  • Evaluation against ECMWF and NOAA reference baselines.
  • Independent audit of error reduction, bias checks, and equity metrics.
  • Field validation through agricultural cooperatives and energy grid operators.

Milestones

2025

Platform moves to select‑partner roll‑out across energy and health, supported by independent assurance and red‑team coverage.

2024

Decision engine hardened with policy‑constrained planning and full audit trails; restricted trials commence with critical‑infrastructure partners.

2023

Milo and Kai complete an extended closed‑box communication study; ShadowIntel undergoes evaluation in live training and operational scenarios.

Let’s build responsibly at planetary scale.

Tell us about your use‑case. Our team will share reference architectures, safety guidelines, and a pilot plan within 3 working days.

By submitting, you agree to our privacy policy.
  • Address
  • 399-405 Oxford Street,
  • Mayfair
  • London
Follow us on: