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Journal articleCheng S, Jin Y, Harrison S, et al., 2022,
Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling
, Remote Sensing, Vol: 14, ISSN: 2072-4292Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.
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Journal articleLawrance EL, Thompson R, Newberry Le Vay J, et al., 2022,
The Impact of Climate Change on Mental Health and Emotional Wellbeing: A Narrative Review of Current Evidence, and its Implications
, INTERNATIONAL REVIEW OF PSYCHIATRY, Vol: 34, Pages: 443-498, ISSN: 0954-0261- Cite
- Citations: 222
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Journal articleGangopadhyay A, Seshadri AK, Sparks NJ, et al., 2022,
The role of wind-solar hybrid plants in mitigating renewable energy-droughts
, Renewable Energy, Vol: 194, Pages: 926-937, ISSN: 0960-1481Increasing the share of weather-dependent renewables in the electricity grid is essential to deeply decarbonize the electricity system. Wind and solar “droughts” or low generation days can severely impact grid stability in a renewable-rich grid. This paper analyzes for the first time wind, solar, and hybrid energy-droughts in India using a stochastic weather generator. Available literature analyze the observational data that is of limited duration (30–40 years). Therefore, discussion of low-probability high-impact renewable energy-droughts that have long return periods (in the range of 30 years) is limited in the literature. The present study seeks to address this research gap by exploring the risk of wind, solar, and wind-solar powered energy-droughts based on simulated long time series (5000 years). It is found that the weather generator captures mean, seasonality, and correlation between wind speed and solar irradiance and is therefore used to estimate return periods of extreme wind and solar-droughts. Our analysis shows that wind-droughts are more intense than solar-droughts in India. We examine the role that wind-solar hybridization can play in offsetting low wind energy episodes. The benefits of hybridization are regionally dependent. In South India, hybrid plants have advantages over either wind or solar plants alone. In comparison, for Rajasthan, the benefits of hybridization are limited. When one of the regions (South India or Rajasthan) has a renewable drought, the other region has only a 10% probability of having a similar drought. Our findings highlight the need for having robust inter-regional grid connections to mitigate regional level renewable droughts.
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Journal articleShoari N, Beevers S, Brauer M, et al., 2022,
Towards healthy school neighbourhoods: a baseline analysis in Greater London
, Environment International, Vol: 165, ISSN: 0160-4120Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5-10 minutes walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO2 and PM2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m3 of NO2, 19.3 µg/m3 of PM2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m3 of NO2, 15.1 µg/m3 of PM2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.
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Journal articlePrasow-Emond M, Hlavacek-Larrondo J, Fogarty K, et al., 2022,
The first high-contrast images of X-ray binaries: detection of candidate companions in the gamma cas analog RX J1744.7-2713
, The Astronomical Journal, Vol: 164, Pages: 1-9, ISSN: 0004-6256X-ray binaries provide exceptional laboratories for understanding the physics of matter under the most extreme conditions. Until recently, there were few, if any, observational constraints on the circumbinary environments of X-ray binaries at ∼100–5000 au scales. It remains unclear how the accretion onto the compact objects or the explosions giving rise to the compact objects interact with their immediate surroundings. Here, we present the first high-contrast adaptive optics images of X-ray binaries. These observations target all X-ray binaries within ∼3 kpc accessible with the Keck/NIRC2 vortex coronagraph. This paper focuses on one of the first key results from this campaign; our images reveal the presence of 21 sources potentially associated with the γ Cassiopeiae analog high-mass X-ray binary RX J1744.7−2713. By conducting different analyses—a preliminary proper motion analysis, a color–magnitude diagram, and a probability of chance alignment calculation—we found that three of these 21 sources have a high probability of being bound to the system. If confirmed, they would be in wide orbits (∼450 to 2500 au). While follow-up astrometric observations will be needed in ∼5–10 yr to confirm further the bound nature of these detections, these discoveries emphasize that such observations may provide a major breakthrough in the field. In fact, they would be useful not only for our understanding of stellar multiplicity, but also for our understanding of how planets, brown dwarfs, and stars can form even in the most extreme environments.
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Journal articleTsui EYL, Toumi R, 2022,
Pacific subsurface temperature as a long‐range indicator of El Niño, regional precipitation, and fire
, Quarterly Journal of the Royal Meteorological Society, Vol: 148, Pages: 2102-2117, ISSN: 0035-9009The SubNiño4 index based on the subsurface potential temperature around the thermocline beneath the west Pacific warm pool, the Niño 4 region, is examined as a long-range indicator of the surface El Niño–Southern Oscillation (ENSO) and ENSO-driven atmospheric response. The SubNiño4 index captures the evolution of subsurface ocean heat content between the El Niño and La Niña phases of the ENSO cycle, allowing it to serve as a long-range indicator of surface ENSO and hence also many ENSO-driven atmospheric anomalies. The SubNiño4 index has more temporally stable correlations with Niño 3.4 than the widely used western equatorial Pacific warm-water volume indicator. For a lead time of the order of 12 months, Niño 3.4 correlations afforded by the lead observed SubNiño4 index become similar to and can exceed those produced by typical dynamical ENSO predictions. The value and viability of the SubNiño4 index as a simple statistical long-range indicator of ENSO-driven atmospheric response is shown for regional precipitation anomalies throughout the Tropics and fires in Continental and Maritime Southeast Asia.
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Journal articleBrizzi A, O'Driscoll M, Dorigatti I, 2022,
Refining reproduction number estimates to account for unobserved generations of infection in emerging epidemics
, Clinical Infectious Diseases, Vol: 75, Pages: e114-e121, ISSN: 1058-4838Background:Estimating the transmissibility of infectious diseases is key to inform situational awareness and for response planning. Several methods tend to overestimate the basic (R0) and effective (Rt) reproduction numbers during the initial phases of an epidemic. The reasons driving the observed bias are unknown. In this work we explore the impact of incomplete observations and underreporting of the first generations of infections during the initial epidemic phase.Methods:We propose a debiasing procedure which utilises a linear exponential growth model to infer unobserved initial generations of infections and apply it to EpiEstim. We assess the performance of our adjustment using simulated data, considering different levels of transmissibility and reporting rates. We also apply the proposed correction to SARS-CoV-2 incidence data reported in Italy, Sweden, the United Kingdom and the United States of America.Results:In all simulation scenarios, our adjustment outperforms the original EpiEstim method. The proposed correction reduces the systematic bias and the quantification of uncertainty is more precise, as better coverage of the true R0 values is achieved with tighter credible intervals. When applied to real world data, the proposed adjustment produces basic reproduction number estimates which closely match the estimates obtained in other studies while making use of a minimal amount of data.Conclusions:The proposed adjustment refines the reproduction number estimates obtained with the current EpiEstim implementation by producing improved, more precise estimates earlier than with the original method. This has relevant public health implications.
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Journal articleGryspeerdt E, McCoy DT, Crosbie E, et al., 2022,
The impact of sampling strategy on the cloud droplet number concentration estimated from satellite data
, Atmospheric Measurement Techniques, Vol: 15, Pages: 3875-3892, ISSN: 1867-1381Cloud droplet number concentration (Nd) is of central importance to observation-based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases.A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of in situ measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5–0.8). This is lower in other cloud regimes but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol–cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling.
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Journal articleBager P, Wohlfahrt J, Bhatt S, et al., 2022,
Risk of hospitalisation associated with infection with SARS-CoV-2 omicron variant versus delta variant in Denmark: an observational cohort study
, LANCET INFECTIOUS DISEASES, Vol: 22, Pages: 967-976, ISSN: 1473-3099- Cite
- Citations: 137
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Journal articleXu H, Tian Z, Sun L, et al., 2022,
Compound flood impact of water level and rainfall during tropical cyclone periods in a coastal city: the case of Shanghai
, Natural Hazards and Earth System Sciences, Vol: 22, Pages: 2347-2358, ISSN: 1561-8633Compound flooding is generated when two or more flood drivers occur simultaneously or in close succession. Multiple drivers can amplify each other and lead to greater impacts than when they occur in isolation. A better understanding of the interdependence between flood drivers would facilitate a more accurate assessment of compound flood risk in coastal regions. This study employed the D-Flow Flexible Mesh model to simulate the historical peak coastal water level, consisting of the storm surge, astronomical tide, and relative sea level rise (RSLR), in Shanghai over the period 1961–2018. It then applies a copula-based methodology to calculate the joint probability of peak water level and rainfall during historical tropical cyclones (TCs) and to calculate the marginal contribution of each driver. The results indicate that the astronomical tide is the leading driver of peak water level, followed by the contribution of the storm surge. In the longer term, the RSLR has significantly amplified the peak water level. This study investigates the dependency of compound flood events in Shanghai on multiple drivers, which helps us to better understand compound floods and provides scientific references for flood risk management and for further studies. The framework developed in this study could be applied to other coastal cities that face the same constraint of unavailable water level records.
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