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Lee Chapman and Simon J. Bell

Abstract

The impacts of weather and climate on infrastructure are numerous: snow and ice on roads, railway buckling, leaves on the line, wind impacts on power cabling, etc. Advances in modeling mean that these impacts can now be predicted at a high resolution so that mitigation activities can be targeted at vulnerable sections of the infrastructure network.

However, while high-resolution models have been in operational use for the last decade, in an environment of increasing litigation, practitioners remain nervous about making mitigation decisions solely based on model output. This means that the verification of forecasts is now needed on a scale previously not required, and it is only with this step that end users will become more open to using risk-based methods (e.g., decision support systems that enable selective salting for winter road maintenance where only the coldest sections of road are treated or localized rail speed restrictions in hot weather as opposed to the blanket restrictions currently used).

However, existing monitoring techniques are simply not capable of producing this information. Traditional in situ measurements are too expensive to install in the numbers required and therefore lack the spatial resolution. Conversely, mobile measurements lack the temporal resolution to provide the full picture. This paper outlines how the emerging Internet of Things is starting to provide the enabling technology to saturate our infrastructure with low-cost sensors. In doing so, it will provide unprecedented monitoring of weather impacts as well as facilitating a new generation of products harnessing the benefits of high-resolution observations.

Open access
David Chapman, Mark A. Cane, Naomi Henderson, Dong Eun Lee, and Chen Chen

Abstract

The authors investigate a sea surface temperature anomaly (SSTA)-only vector autoregressive (VAR) model for prediction of El Niño–Southern Oscillation (ENSO). VAR generalizes the linear inverse method (LIM) framework to incorporate an extended state vector including many months of recent prior SSTA in addition to the present state. An SSTA-only VAR model implicitly captures subsurface forcing observable in the LIM residual as red noise. Optimal skill is achieved using a state vector of order 14–17 months in an exhaustive 120-yr cross-validated hindcast assessment. It is found that VAR outperforms LIM, increasing forecast skill by 3 months, in a 30-yr retrospective forecast experiment.

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Catherine L. Muller, Lee Chapman, C.S.B. Grimmond, Duick T. Young, and Xiao-Ming Cai

With the growing number and significance of urban meteorological networks (UMNs) across the world, it is becoming critical to establish a standard metadata protocol. Indeed, a review of existing UMNs indicate large variations in the quality, quantity, and availability of metadata containing technical information (i.e., equipment, communication methods) and network practices (i.e., quality assurance/quality control and data management procedures). Without such metadata, the utility of UMNs is greatly compromised. There is a need to bring together the currently disparate sets of guidelines to ensure informed and well-documented future deployments. This should significantly improve the quality, and therefore the applicability, of the high-resolution data available from such networks. Here, the first metadata protocol for UMNs is proposed, drawing on current recommendations for urban climate stations and identified best practice in existing networks.

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Emma Ferranti, Lee Chapman, Caroline Lowe, Steve McCulloch, David Jaroszweski, and Andrew Quinn

Abstract

High temperatures and heat waves can cause numerous problems for railway infrastructure, such as track buckling, sagging of overhead lines, and the failure of electrical equipment. Without adaptation, these problems are set to increase in a future warmer climate. This study used industry fault data to examine the temporal and spatial distribution of heat-related incidents in southeast England and produce a unique evidence base of the impact of temperature on the rail network. In particular, the analysis explored the concept of failure harvesting, whereby the infrastructure system becomes increasingly resilient to temperature over the course of the summer season (April–September) as the most vulnerable assets fail with each incremental rise in temperature. The analysis supports the hypothesis and clearly shows that a greater number of heat-related incidents occur in the early/midsummer season before reducing significantly, despite equivalently high temperatures. This failure harvesting and the consequential increased resilience of the railway infrastructure system over the course of the summer season could permit an innovative and dynamic new approach to heat risk management on the railway network. New approaches that would reduce the disruption and delays and improve service are explored here.

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Duick T. Young, Lee Chapman, Catherine L. Muller, Xiao-Ming Cai, and C. S. B. Grimmond

Abstract

A wide range of environmental applications would benefit from a dense network of air temperature observations. However, with limitations of costs, existing siting guidelines, and risk of damage, new methods are required to gain a high-resolution understanding of spatiotemporal patterns of temperature for agricultural and urban meteorological phenomena such as the urban heat island. With the launch of a new generation of low-cost sensors, it is possible to deploy a network to monitor air temperature at finer spatial resolutions. This study investigates the Aginova Sentinel Micro (ASM) sensor with a custom radiation shield (together less than USD$150) that can provide secure near-real-time air temperature data to a server utilizing existing (or user deployed) Wi-Fi networks. This makes it ideally suited for deployment where wireless communications readily exist, notably urban areas. Assessment of the performance of the ASM relative to traceable standards in a water bath and atmospheric chamber show it to have good measurement accuracy with mean errors <±0.22°C between −25° and 30°C, with a time constant in ambient air of 110 ±15 s. Subsequent field tests also showed the ASM (in the custom shield) had excellent performance (RMSE = 0.13°C) over a range of meteorological conditions relative to a traceable operational Met Office platinum resistance thermometer. These results indicate that the ASM and radiation shield are more than fit for purpose for dense network deployment in environmental monitoring applications at relatively low cost compared to existing observation techniques.

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Chen Chen, Mark A. Cane, Naomi Henderson, Dong Eun Lee, David Chapman, Dmitri Kondrashov, and Mickaël D. Chekroun

Abstract

A suite of empirical model experiments under the empirical model reduction framework are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality, and the memory effect in the simulation and prediction of tropical Pacific sea surface temperature (SST) anomalies. The model training and evaluation are carried out using 4000-yr preindustrial control simulation data from the coupled model GFDL CM2.1. The results show that multivariate models with tropical Pacific subsurface information and multilevel models with SST history information both improve the prediction skill dramatically. These two types of models represent the ENSO memory effect based on either the recharge oscillator or the time-delayed oscillator viewpoint. Multilevel SST models are a bit more efficient, requiring fewer model coefficients. Nonlinearity is found necessary to reproduce the ENSO diversity feature for extreme events. The nonlinear models reconstruct the skewed probability density function of SST anomalies and improve the prediction of the skewed amplitude, though the role of nonlinearity may be slightly overestimated given the strong nonlinear ENSO in GFDL CM2.1. The models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. The models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill for more diverse target patterns. In particular, they alleviate the spring/autumn prediction barrier and reduce the tendency for predicted values to lag the target month value.

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Chester W. Newton, Roy Lee, Edwin B. Fawcett, William T. Chapman, Donald E. Martin, Frederick F. Sanders, Robert J. Renard, Robert D. Fletcher, and Maurice E. Pautz
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Rick M. Thomas, A. Rob MacKenzie, S. James Reynolds, Jonathan P. Sadler, Ford Cropley, Simon Bell, Stephen J. Dugdale, Lee Chapman, Andrew Quinn, and Xiaoming Cai

Abstract

The increasing miniaturization of accurate, reliable meteorological sensors and logging systems allows the deployment of sensor packages on lightweight airborne platforms. Here, we demonstrate the safe and humane use of avian species (white-tailed and Spanish imperial eagles) to carry a prototype miniature sensor package to measure temperature with a 5-Hz response and ±0.2°C resolution. This technique could allow sensor deployment above complex urban terrain, where such data are urgently required. Recent meteorological work has been facilitated by using unmanned aerial vehicles (UAVs), but their use within, and adjacent to, urban areas is heavily controlled. The package contains a wind speed sensor, a GPS, a pressure altimeter, and accelerometers. Four flight tests were conducted in a steep valley (glen) at a remote Scottish location that provided contrasting vertical temperature profiles. The glen was instrumented with additional meteorological equipment at the bird launch and landing sites. Vertical temperature profile data from the raptors indicated the success of this approach with absolute temperatures and lapse rates consistent with those measured by the weather stations. Movement and airspeed data aided the interpretation of finescale temperature profiles in complex terrain. As well as the potential for meteorological sensing, this work is of interest to the avian ecology and behavior communities and to aerodynamicists interested in developing airborne robotics to mimic aspects of bird flight. These sensors are being miniaturized further for deployment on other bird species in urban areas for rapid, repeatable, and reliable measurements, with the potential to fulfill a measurement niche above the urban canopy.

Open access
Lee Chapman, Catherine L. Muller, Duick T. Young, Elliott L. Warren, C. S. B. Grimmond, Xiao-Ming Cai, and Emma J. S. Ferranti

Abstract

The Birmingham Urban Climate Laboratory (BUCL) is a near-real-time, high-resolution urban meteorological network (UMN) of automatic weather stations and inexpensive, nonstandard air temperature sensors. The network has recently been implemented with an initial focus on monitoring urban heat, infrastructure, and health applications. A number of UMNs exist worldwide; however, BUCL is novel in its density, the low-cost nature of the sensors, and the use of proprietary Wi-Fi networks. This paper provides an overview of the logistical aspects of implementing a UMN test bed at such a density, including selecting appropriate urban sites; testing and calibrating low-cost, nonstandard equipment; implementing strict quality-assurance/quality-control mechanisms (including metadata); and utilizing preexisting Wi-Fi networks to transmit data. Also included are visualizations of data collected by the network, including data from the July 2013 U.K. heatwave as well as highlighting potential applications. The paper is an open invitation to use the facility as a test bed for evaluating models and/or other nonstandard observation techniques such as those generated via crowdsourcing techniques.

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