Forecast verification for triggers
Hit rates, false alarm rates and lead times are measured for the forecast variable that a trigger uses, before money is attached to it.
GRA · Disaster Risk Finance
Spatial financeWhere capital meets hazard, exposure and transition
Forecasting and time-series analysis for disaster risk finance
Forecasting estimates future hazards and impacts from current observations and models. In disaster risk finance it supplies the forecasts and verification that anticipatory triggers depend on, and the seasonal outlooks that shape preparedness funding.
01Contents
Forecasting and time-series analysis for disaster risk finance in five parts.
02Application
What forecasting and time-series analysis does here.
Forecasting and time-series analysis predict future values of variables from past observations and explanatory information. They support early warning and planning across hazards, markets and health. In disaster and crisis, sovereign and public finance, development finance and insurance and catastrophe, forecasts trigger early action and anticipatory finance before shocks materialise. Probabilistic forecasts allow decisions to account for uncertainty across connected systems. The canonical description is on the STEM Commons.
Seasonal outlooks for drought finance
Seasonal rainfall forecasts with stated skill inform when and where drought preparedness funds are released to agencies.
Impact-based forecasts
Hazard forecasts are combined with exposure and vulnerability to forecast impacts, which better match the losses finance aims to reduce.
03Spatial finance
A drought index against its attachment point.
How a dry season is read against the point where drought cover starts to pay. Synthetic season and design terms.
04Data
What the method consumes.
Datasets and records used by forecasting and time-series analysis on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Ensemble forecasts | Trigger inputs | FORECAST |
| Observation records | Verification | OBSERVED |
| Impact records | Impact verification | REPORTED |
05Monitor
Forecast verification.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Forecast verification
Disaster risk finance · Forecasting and time-series · Run R-3195
Skill above referenceGRA · Disaster Risk Finance
Forecast with range
Method card
- Method
- Forecasting and time-series analysis
- Reference
- climatology
- Lead times
- 1 to 9 steps
- Label
- MODELLED
Verification by lead time
held-out period
| T+2 | 0.26 | 0.11 | 0.09 | Skilful |
| T+4 | 0.33 | 0.15 | 0.16 | Marginal |
| T+6 | 0.21 | 0.09 | 0.30 | Skilful |
| T+8 | 0.22 | 0.15 | 0.36 | Marginal |
06Validation
How results are checked.
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
| Check | How it is done |
|---|---|
| Skill by lead time | Skill is reported for each lead time used |
| Regional verification | Verification covers the region and season of the trigger |
| Probabilistic calibration | Forecast probabilities are checked against observed frequencies |
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
07Loss model
Loss exceedance, average annual loss and probable maximum loss.
The loss a portfolio in the footprint can expect per year and at the 1 in 100 and 1 in 250 levels that insurers and lenders price, from the same three blocks every catastrophe model uses. Teaching model with stated illustrative values, not a prediction. The map on this page shows Pacific Island states; the model itself is not tied to a place.
Controls
- Typical annual hazard intensity (location)
- 1.00 intensity index
- Spread of extremes (scale)
- 0.80 intensity index
- Intensity at which half the value is lost
- 4.00 intensity index
- Dispersion of vulnerability
- 0.55
- Share of value in the hazard footprint
- 40%
- Protection standard (return period)
- 50 years
Readouts
- Average annual loss
- 0.51% of value
- Loss at 1 in 100 years
- 24.5% of value
- Loss at 1 in 250 years
- 28.4% of value
- Intensity at 1 in 100 years
- 4.68 intensity index
Numbers, methods and sources
| Return period (years) | Intensity (intensity index) | Loss (% of value) |
|---|---|---|
| 1 | 0.09 | 0.00 |
| 2 | 1.27 | 0.00 |
| 4 | 1.91 | 0.00 |
| 7 | 2.47 | 0.00 |
| 13 | 3.00 | 0.00 |
| 24 | 3.51 | 0.00 |
| 44 | 4.02 | 0.00 |
| 82 | 4.53 | 23.55 |
| 154 | 5.03 | 26.44 |
| 287 | 5.53 | 28.87 |
| 536 | 6.03 | 30.88 |
| 1,000 | 6.53 | 32.53 |
Methods
- Gumbel extreme-value hazard Gumbel (1958), Statistics of Extremes, Columbia University Press
- Lognormal fragility (vulnerability) FEMA (2003), HAZUS-MH Technical Manual
- Exceedance-probability curve and average annual loss Grossi and Kunreuther (2005), Catastrophe Modeling, Springer
- Levee effect Di Baldassarre and others (2013), Socio-hydrology, HESS 17
- Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
- One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley
Data
The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:
- Rivers Natural Earth
- Urban extents Natural Earth
- Cities Natural Earth
- Earthquakes M4.5+, past week (USGS) U.S. Geological Survey · register usgs.earthquake.summary-feeds
- Rain rate, last half hour (IMERG) NASA GPM IMERG Early · register nasa.gibs.context-live-layers
- River discharge forecast, 30 days with range (GloFAS via Open-Meteo) Open-Meteo.com (CC BY 4.0), Copernicus GloFAS · register open-meteo.public-pages
- Built-up and settlement extent, 2010 (HBASE, Landsat) CIESIN/Columbia, NASA SEDAC HBASE · register sedac.context-urban-layers
Across the six pillars, led by Nexus Consortium
- Experiments Labs
- Built in Disaster Risk Finance in Foundry
- Tested at Disaster Risk Finance in Universe
- Codified in Hazards and Disaster Risk Reduction in Standards
- Published in Disaster Risk Finance in Reports
- Taught in Faculty of Hazards, Disasters and Resilience in Academy
08Connections
Topics that rely on this method.
09Twin and cascade simulator
Trace a shock to the balance sheet.
Assembled from the Nexus Standards area profile Disaster and climate risk finance: the shocks it names, the systems and sectors that carry the loss, the controls that dampen it and the data that observe it. Pick an initiating shock and step through the cascade to see which exposures it reaches and where losses could concentrate. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise, not a forecast, a valuation, a rating or investment advice. To model a place of your own choosing, open the full System Builder.
Hazards
- Tropical cyclone
- Earthquake
- Ice-jam flood
- Storm surge (set off downstream)
- Intense rainfall (set off downstream)
- River flood (set off downstream)
- Flash flood (set off downstream)
- Dangerous surf (set off downstream)
- Widespread power outage (set off downstream)
- Mass displacement (set off downstream)
- Coastal flood (set off downstream)
- Aftershock sequence (set off downstream)
- Fault surface rupture (set off downstream)
- Soil liquefaction (set off downstream)
- Slope slide (set off downstream)
- Rockfall (set off downstream)
- Tsunami (set off downstream)
- Building collapse (set off downstream)
Systems and couplings
Controls
NS-0Constitution and principlesNS-3Risk record UNIs (grammar, legacy crosswalk, allocation authority)NS-9Contextual UNIs (deployments, instances, programme instances)NS-10Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)NS-11Verification and monitoring (evidence, attestations, zero-trust conformance observation)NS-14Conformance and crosswalk to external standardsNRS-F8Parametric and index-based risk transferNRS-F7Disaster risk finance and pre-arranged financeNRS-H113Hazard family: Financial-system fragility (NHS-1 soc.financial-system)NRS-H114Hazard family: Macroeconomic and fiscal shocks (NHS-1 soc.macro-fiscal)NRS-D1Risk-informed development and project screeningNRS-H28Hazard family: Climate-state shifts (NHS-1 eco.climate-state)
Observation
- ERA5 reanalysis (Copernicus Climate Data Store) (listed for reference)
- CHIRPS quasi-global gridded precipitation (listed for reference)
- USGS earthquake GeoJSON summary feeds (listed for reference)
- Total public international support for national disaster risk reduction actions (Sendai indicator F-1)
- Direct economic loss attributed to disasters in relation to global GDP
Example geography
- Western Europe: 152 hexagonal cells
- Rain rate, last half hour (IMERG), NASA GIBS
- Night lights tonight, outages (VIIRS Day/Night Band), NASA GIBS
- Surface water through cloud, 30 m (OPERA DSWx-S1 radar), NASA GIBS
- Surface soil moisture (SMAP), NASA GIBS
10Stewardship
Who maintains the method.
Climate and Hazards stewards forecasting and time-series analysis for the Consortium and reviews how it is applied across platforms.
11Exposure, loss and capital
What it means for exposure, loss and capital.
The exposure, loss, scenario and capital views a risk officer, supervisor or ministry of finance would ask for, with the frameworks they report against. Structures follow supervisory and actuarial practice. Values are illustrative: never prices, ratings or advice.
Financing instruments by layer
How retention and transfer instruments could share losses from forecasting and time-series analysis for disaster risk finance, from frequent to extreme.
| Layer | Who holds the risk | Speed of funds | Main limit |
|---|---|---|---|
| Retention and reserves | Budget, contingency funds | Immediate | Size of the budget |
| Contingent credit | Development banks, ministries of finance | Days to weeks | Debt capacity |
| Parametric cover and risk pools | Regional pools, insurers | Days, on a trigger | Basis risk |
| Reinsurance and catastrophe bonds | Reinsurers, capital markets investors | Weeks to months | Cost of capital |
| Residual loss | Households, firms, humanitarian funding | Uncertain | Unfunded |
NGFS climate scenarios · Network for Greening the Financial SystemIFRS S2 Climate-related Disclosures · ISSBTCFD recommendations · Financial Stability Board TCFDPrinciples for the effective management of climate-related financial risks · Basel Committee on Banking Supervision
12Connected
Where this page leads across the Consortium.
13Questions
Questions and answers.
What are the limits of forecasting and time-series analysis in this field?
Structural breaks and regime changes undermine models trained on the past. Forecast skill typically declines rapidly with lead time.
How are results checked?
Forecasts are evaluated out of sample with proper scoring rules and reliability diagrams. Rolling-origin evaluation tests performance over time.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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