SpatialKat

account and Portfolio Analysis

Event based portfolio analysis software capturing correlations in space and time for flood, wind, and storm surge events. Models are currently available for the US and Canada. Additional regions are under development.

Features & Highlights

Event sets consist of 50,000 years of simulation
In the US, tropical cyclone events include wind, storm surge, and precipitation-driven inland flooding
Model hazard data and vulnerability are open and can be customized
Detailed financial modeling capabilities including primary insurance and reinsurance
As with the KatRisk hazard maps, inland flood modeling includes both pluvial and fluvial flooding
Output can be generated from the location to portfolio level by event
Sampled losses at the location/coverage level are propagated all the way up to the portfolio level considering financial terms, resulting in straightforward and transparent financial model calculations and the ability to disaggregate risk to the location, policy, and account level
Model analysis parameters such as number of samples and correlation are flexible and user specified

Deployment Options

Deployed to client site - can typically be installed and running within a few hours
Client hosted on AWS and Azure with access via UI and API
KatRisk hosted on AWS with access via UI and API - no client setup required

Model Development Overview

Components that can be altered to reflect climate change have a (C) beside them. The black boxes are probabilistic components of the model. Dark blue boxes are deterministic models. Light blue boxes are deterministic models requiring significant com… — Components that can be altered to reflect climate change have a (C) beside them. The black boxes are probabilistic components of the model. Dark blue boxes are deterministic models. Light blue boxes are deterministic models requiring significant compute time.

3MonthPrecipitationAnomaliesMaxLagCorrelationwithENSO.jpg — GLOBAL CORRELATIONS WITH SST

Catastrophic events are globally correlated with sea surface temperature (SST) as the main driver. We can establish this through linking teleconnections to large scale droughts and floods.
As an example, the following figure shows the maximum cross correlation of 3 month precipitation anomalies with ENSO. Through this teleconnection pattern between ENSO and precipitation, we drive our stochastic non-tropical cyclone precipitation model using an EOF based stochastic VARMAX model.

DEFINING INLAND FLOOD EVENTS

Modeled downscaled precipitation is used to drive a high resolution land surface and river routing model. The output of this stochastic model together with the high resolution flood maps is then used as input to our probabilistic loss model. The relevant inputs to the loss model are event flood footprints clustered in space and time.

TROPICAL CYCLONE MODELING

A 50,000 year event set has been developed for the Atlantic Basin. Wind, storm surge, and precipitation have been modeled for all events.

Storm surge

Rainfall

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