Eritrea's central highlands (Maekel), summer kiremti rains. Steep catchments and flash-flood-prone wadis. You are the DOC duty analyst as the forecast window opens.
Act I quiz — build the evidence inventory Every answer becomes an input to the risk model: evidence → classification → confidence → belief update → decision. Not scored.
Q1. The rainfall outlook for Maekel (Central Highlands) comes from the ECMWF ensemble (many parallel model runs, updated daily). First, the basics: what is a DETERMINISTIC forecast? A single model run giving one outcome — no range of possibilities Many model runs combined into probabilities A forecast that is always correct An observation of what has already happened BN purpose: Forecast literacy — why a single number is not enough for risk decisions.
Q2. And what is an ENSEMBLE prediction system (EPS)? The same model run many times from slightly different starting conditions, giving a spread of outcomes Several forecasters voting on the most likely outcome One model run at a higher resolution An average of past observed seasons BN purpose: Why forecasts are SOFT evidence in the BN — the spread is the confidence.
Q3. What is the strongest evidence currently available? On your desk this round: 7-day antecedent rainfall (IMERG) · Ensemble-mean exceedance prob (ECMWF P_heavy)
Forecast Observation Community report Historical analogue BN purpose: Determines which BN node receives the highest weight.
Q4. Classify that evidence. Check the tags on the evidence cards: 7-day antecedent rainfall (IMERG) · Ensemble-mean exceedance prob (ECMWF P_heavy)
Hard Soft Virtual BN purpose: Determines confidence weighting — hard = observation (one-hot), soft = probability vector, virtual = likelihood message.
Q5. How reliable is this evidence? Maekel (Central Highlands) is a TAIL-risk case — the ensemble mean can look benign while a few members carry the danger; weigh the tail, not the average. Reliability sets how hard the BN leans on it.
Very Low Low Moderate High Very High BN purpose: Evidence likelihood weighting.
Q6. What hazard condition does this evidence support in Maekel (Central Highlands), Eritrea? Which mechanism fits Maekel (Central Highlands) — slow river rise, sudden flash runoff, or urban-drainage overload? The mechanism decides which impacts follow.
Heavy rainfall River flooding Flash flooding No hazard BN purpose: Updates the hazard node.
Q7. Which impact pathway is most likely here? Eritrea's central highlands (Maekel), summer kiremti rains. Steep catchments and flash-flood-prone wadis. You are the DOC duty analyst as the forecast window opens.
Population displacement Road disruption Crop loss Water contamination BN purpose: Updates the impact node.
Q8. What evidence would you request next? Still off the table this round: Ensemble-max / 2-yr RP (pixel p95) · Spatial coverage of heavy-rain pixels · Yesterday's posterior carried forward (DBN, alpha=0.6) · Observed rainfall / river response. Which would most reduce your uncertainty before you decide?
Satellite observations Gauge observations Hydrological model Field assessment Exposure information BN purpose: Real DOC operations seek more evidence — watch which cards the Act II rounds reveal.
Q9. It is mid-August in Eritrea's central highlands (Maekel). What seasonal and climate context should the duty analyst keep in mind when reading the rainfall forecast? The highlands have one main wet season, and 2019's Indian Ocean state is unusual.
The kremti (kiremt) summer rains peak in July-August, and a developing positive Indian Ocean Dipole in 2019 is enhancing moisture supply to the Horn August is the heart of the dry season, so any rain signal is almost certainly a model artefact The highlands depend on the October-December short rains, which have not started yet Rainfall here is controlled mainly by Atlantic hurricanes BN purpose: Driver and seasonal-calendar context that raises the prior on heavy-rain evidence during the August window.
Q10. Why do Eritrea's central highlands pose a flash-flood rather than a slow riverine flood problem? Picture rainfall landing on a steep plateau of 1,500-2,500 m drained by wadis.
Steep, short catchments drain the plateau through wadis, so intense convective rain converts to fast, high-energy runoff with very little lead time Large permanent rivers take weeks to rise, giving long warning lead times The terrain is flat and sandy, so water infiltrates before it can flow Flooding only occurs from Red Sea storm surge reaching the highlands BN purpose: Why short-lead tail-risk evidence matters more than the ensemble mean in steep, fast-responding catchments.
Q11. Your current risk estimate — before the model runs? Maekel (Central Highlands) is a TAIL-risk case — the ensemble mean can look benign while a few members carry the danger; weigh the tail, not the average. No model has spoken yet — commit your own read so Act III can compare it to the engine.
Low Moderate High Very High BN purpose: Your pre-BN estimate. Act III compares it against the engine risk indication.
Q12. Recommended DOC status — commit now, before seeing the BN output. The ensemble mean may look benign but the tail signal is rising ahead of onset. What DOC level do you set, and why? A 'Watch + re-check + note the tail uncertainty' is a strong answer.
Monitor Watch Warning Emergency Coordination BN purpose: Your pre-BN decision. Act III compares it against the CRMA state.
Q13. The risk model you are about to work with was built from expert rules, not calibrated against decades of events. How much should you trust it? No right answer — your stance is revisited in Act III against what the model actually did, and what history recorded.
Fully — it computes probabilities Not at all — rules are subjective As a transparent colleague — consistent and inspectable, but encoded judgment BN purpose: Hold this question. Act III returns to it.