No abstract

Extreme weather causes substantial adverse socio-economic impacts by damaging and disrupting the infrastructure services that underpin modern society. Globally, $2.5tn a year is spent on infrastructure which is typically designed to last decades, over which period projected changes in the climate will modify infrastructure performance. A systems approach has been developed to assess risks across all infrastructure sectors to guide national policy making and adaptation investment. The method analyses diverse evidence of climate risks and adaptation actions, to assess the urgency and extent of adaptation required. Application to the UK shows that despite recent adaptation efforts, risks to infrastructure outweigh opportunities. Flooding is the greatest risk to all infrastructure sectors: even if the Paris Agreement to limit global warming to 2°C is achieved, the number of users reliant on electricity infrastructure at risk of flooding would double, while a 4°C rise could triple UK flood damage. Other risks are significant, for example 5% and 20% of river catchments would be unable to meet water demand with 2°C and 4°C global warming respectively. Increased interdependence between infrastructure systems, especially from energy and information and communication technology (ICT), are amplifying risks, but adaptation action is limited by lack of clear responsibilities. A programme to build national capability is urgently required to improve infrastructure risk assessment.This article is part of the theme issue ‘Advances in risk assessment for climate change adaptation policy’.

Electrical impedance tomography (EIT) is a noninvasive imaging technique which aims to image the impedance within a body from electrical measurements made on the surface. The reconstruction of impedance images is a ill-posed problem which is both extremely sensitive to noise and highly computationally intensive. The authors define an experimental measurement in EIT and calculate optimal experiments which maximize the distinguishability between the region to be imaged and a best-estimate conductivity distribution. These optimal experiments can be derived from measurements made on the boundary. The analysis clarifies the properties of different voltage measurement schemes. A reconstruction algorithm based on the use of optimal experiments is derived. It is shown to be many times faster than standard Newton-based reconstruction algorithms, and results from synthetic data indicate that the images that it produces are comparable.

[1] Recommendation ITU-R P.530-13 provides an internationally recognized prediction model for the fading due to wet snow on low-elevation, terrestrial microwave links. An important parameter in this model is the altitude difference between the link and the rain height. The top of rain events is usually assumed to be 360 m above the zero-degree isotherm (ZDI). Above this height, hydrometeors are ice with low specific attenuation. Below this level, melting ice particles produce a specific attenuation up to 4 times that of the associated rain rate. A previous paper identified increasing ZDI height trends across northern Europe, North America and central Asia with slopes up to 10 m/yr. This paper examines NOAA National Centers for Environmental Prediction-National Center for Atmospheric Research Reanalysis 1 data to identify global distributions of ZDI height around mean levels that increase linearly over time. The average annual distribution of ZDI heights relative to the annual mean are calculated for each NOAA Reanalysis grid square and skew normal distributions are fitted. These are compared to models in Recommendation ITU-R P.530-13 and Recommendation ITU-R 452-14. The effects of ZDI trends and the calculated skew normal distributions are illustrated using calculated trends in fading due to wet snow for two notional 38 GHz links in Edinburgh. A slow decrease in the incidence of fading due to wet snow is predicted over most of Europe. However, some links could experience increases where warming has increased the wetness of snow.Citation: Paulson, K., and A. Al-Mreri (2011), A rain height model to predict fading due to wet snow on terrestrial links, Radio Sci., 46, RS4010,

[1] This paper develops a downscaling algorithm capable of producing ensembles of rain rate time series, with integration times as short as 10 s, consistent with a time series of rain rates with integration times as long as 6 hours. The algorithm is based on a stochastic multiplicative cascade using beta distributions as the random generator. The statistics of these cascades are developed in the paper. The cascade requires two parameters at each of a geometric progression of scales. These parameters are estimated from 1 gauge year of rain gauge data, with a 10 s integration period, collected in the southern United Kingdom. The statistical moments up to third order, of 9 gauge years of data, are calculated for integration times in the range 10 s to 6 hours. These data are compared with time series derived by accumulating 10 s data to larger integration times and then downscaling using the proposed algorithm. The proposed algorithm and parameters are expected to be applicable to downscaling 5 min or shorter rain rate data from temperate regions. For other regions, and longer initial accumulation times, some parameter estimation based on local data will be required.