# 1 Introduction

The impacts of weather and climate-related disasters are on the rise, due to human development patterns, environmental degradation, climate change and their dynamic interactions (World Bank 2013). While more than 3,500 disasters resulting from hydrometeorological hazards were observed between 2001 and 2010, only 750 were recorded in the period from 1971 to 1980 (WMO et al. 2014). These events cause not only immediate impacts through losses of life and livelihoods, they also hamper economic growth and exacerbate poverty. While future trends remain somewhat uncertain, it is clear that climate-related impacts will continue to grow due to development and climate drivers, and the impacts will be felt most acutely by the poor (IPCC 2012; World Bank 2013).

Recognized as a crucial component of disaster preparedness, weather/climate forecasting and subsequent early warning contribute to public and private risk management decision-making, thereby helping to save lives, avoid disaster costs and preserve livelihoods. However, low-income countries’ (LICs) National Meteorological and Hydrological Services (NMHSs) often face considerable constraints in delivering the weather and climate information needed to effectively inform early warning and climate adaptation. It is estimated that improving NMHS capacities in all low- and middle-income countries could annually save up to 23,000 lives and prevent up to US$ 2 billion in financial losses due to disasters (Hallegatte 2012).

## 1.1 Numerical weather prediction

Modern weather forecasting relies greatly on the guidance provided by numerical weather prediction (NWP), utilizing advanced computer modelling techniques to analyze observations and predict future conditions based on the complex physics of the atmosphere and its interaction with the ocean and land. As the atmosphere is a chaotic system, very small changes in the initial state used for NWP can lead to large differences in the modeled future. This implies that because every detail of the atmosphere’s initial state cannot be observed, production of a “perfect” NWP is impossible. Forecasters therefore utilize their experience and local knowledge (“skill”) to interpret NWP outputs to produce the forecasts provided to the public and other users.

To further understand the probability of future weather scenarios, NWP are often run multiple times from slightly different starting conditions, referred to as an ensemble prediction system (EPS). The ensemble forecasts can give the forecaster a much better idea of what weather events may occur at a particular time, and by comparing these different forecasts the forecaster can assess how likely a particular weather event will be. If the ensemble forecasts show great variation, the forecaster knows that there is significant uncertainty about the future weather evolution. On the other hand, if the ensemble forecasts are all very similar, they will allow more confidence in predicting a particular event (Met Office 2016). Multi-model ensembles utilize multiple NWPs to further achieve a better understanding of the range of possible future weather, as NWPs tend to utilize different approaches and assumptions to simulate the physics and dynamics of the atmosphere, resulting in occasionally different realizations across the pool of NWP models (each of which may have particular strengths and weaknesses for different scenarios and regions).

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