Dar es Salaam and coastal/eastern Tanzania, long-rains (El Nino-enhanced). Urban drainage and low-lying communities exposed. 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 Dar es Salaam 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? Dar es Salaam 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 Dar es Salaam, Tanzania? Which mechanism fits Dar es Salaam — 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? Dar es Salaam and coastal/eastern Tanzania, long-rains (El Nino-enhanced). Urban drainage and low-lying communities exposed. 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. Dar es Salaam's main flood hotspot is the Msimbazi valley, whose river drains a small, steep, heavily built-up catchment through the city centre. What does this geography imply for how quickly flood risk materialises after intense rain? Catchment size and urbanisation set the response time.
Flooding builds gradually over a week, leaving ample time to react after rain starts The Msimbazi can flash-flood within hours of an intense storm, because the small urbanised catchment converts rain to channel flow almost immediately The valley only floods when Kilimanjaro snow melts The catchment is too small to ever flood the city BN purpose: Why forecast-based (pre-onset) action is essential when the catchment response time is shorter than any reactive warning chain.
Q10. Beyond the rain itself, which coastal factor can make the same storm produce deeper, longer-lasting flooding in low-lying Dar es Salaam during the April long rains? The Msimbazi discharges through an estuary into the Indian Ocean.
Spring high tides backing river water up at the estuarine outlet, so the river cannot drain freely Cold sea-surface temperatures offshore Strong offshore winds pushing water out to sea Sea-level fall during El Nino years BN purpose: How a compounding, independently knowable factor (tidal state) modifies the impact of the same rainfall evidence.
Q11. Your current risk estimate — before the model runs? Dar es Salaam 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.