CERES draws on public model inputs for rainfall (NASA POWER), vegetation (MODIS NDVI), conflict (UCDP GED), food security phase (IPC and Cadre Harmonisé) and market prices (WFP), plus FEWS NET, whose observed classifications grade the forecasts and are not a model input. The cards below cover all of them. All sources are publicly available. No proprietary data is used.
Each data stream feeds into a specific stage of the CERES pipeline. Sub-scores are combined into a composite stress score which drives the probabilistic forecast. See the Methodology page for full mathematical specification.
| Signal | Role in Pipeline | Stage |
|---|---|---|
| NASA POWER rainfall anomaly | Drought stress sub-score | Stress scoring |
| MODIS NDVI anomaly | Vegetation stress sub-score | Stress scoring |
| UCDP GED conflict events | Conflict intensity sub-score | Stress scoring |
| IPC / CH phase classification | Food security phase and food access sub-scores | Stress scoring |
| WFP market prices | Market deviation sub-score | Stress scoring |
| IPC or FEWS NET observed phase | Outcome each forecast is graded against | Grading (T+90d) |
Every CERES forecast is free to download as HXL-tagged CSV or JSON, with no account and no key. Compatible with HDX pipelines, Excel, R, Python, and Tableau. Licensed under CC BY 4.0.
Best for dashboards, situational awareness, and map data integrations.
Download ↓Best for time-series analysis, trend research, and calibration studies.
Download ↓Every prediction graded at T+90, with the observed IPC phase, the grading source and its Brier contribution.
Download ↓HXL format: Row 1 = column headers · Row 2 = HXL hashtags · Row 3+ = data.
The same data the dashboard reads, open to anyone. No key, no sign-up.
Research collaboration: ceres@northflow.no
Every source used by CERES is publicly available and free to access. No proprietary or licensed data is used in the pipeline.
Every prediction stores the data retrieval timestamps for all ingested sources. Full audit trail from raw signal to probability estimate.
Data latency for each source is published here. Known latency constraints are explicitly modelled as limitations in forecast uncertainty.