Intense Events Research Articles

Quaternary geomorphological dynamics of colluvial deposits from silicophytoliths and soil micromorphology, Araripe plateau, northeast of Brazil

Quaternary geomorphological dynamics in the lowland tropics of South America can be described from the stratigraphy of hillslope colluvium and alluvial deposits. The analysis of sediments through the identification of depositional microfeatures and biomineralized microfossils, combined with their absolute dating by optically stimulated luminescence (OSL), allow the reconstruction of paleoenvironmental conditions. The objective of this work was to investigate the dynamics of depositional processes leading to colluvial deposition in the Araripe Plateau, Northeastern Brazil, from the analysis of stratigraphic sections, using phytoliths as indicators of paleovegetation and sediment micromorphology in the identification of depositional processes. The correlation of sediments ages, ranging from the OS3 to the OS2 (Last Glacial Maximum) with phytophysiognomy indices indicated the contribution and deposition of phytoliths from an arboreal/shrubby vegetation, replaced on the surface by grass vegetation. Periods of drier climates were correlated with LGM ages, with more open vegetation composed of grasses, in synchronously with less intense geomorphological events. The micromorphological analysis revealed that the characteristics of the deposits reflect more the action of depositional processes than pedogenetic ones. The correlation of phytolithic indices with micromorphology corroborated the environmental and geomorphological dynamics of the periods dated in the stratigraphic sections.

A designathon to co-create HPV screening and vaccination approaches for mothers and daughters in Nigeria: findings from a community-led participatory event

BackgroundOncogenic types of human Papillomavirus (HPV) infection cause substantial morbidity and mortality in Nigeria. Nigeria has low cervical cancer screening and vaccination rates, suggesting the need for community engagement to enhance reach and uptake. We organised a designathon to identify community-led, innovative approaches to promote HPV screening and vaccination for women and girls, respectively, in Nigeria. A designathon is a three-phase participatory process informed by design thinking that includes the preparation phase that includes soliciting innovative ideas from end-users, an intensive collaborative event to co-create intervention components, and follow-up activities.MethodsWe organised a three-phase designathon for women (30-65yrs) and girls (11-26yrs) in Nigeria. First, we launched a national crowdsourcing open call for ideas on community-driven strategies to support HPV screening among women and vaccination among girls. The open call was promoted widely on social media and at in-person gatherings. All eligible entries were graded by judges and 16 exceptional teams (with 4-6members each). All six geo-political zones of Nigeria were invited to join an in-person event held over three days in Lagos to refine their ideas and present them to a panel of expert judges. The ideas from teams were reviewed and scored based on relevance, feasibility, innovation, potential impact, and mother-daughter team dynamics. We present quantitative data on people who submitted and themes from the textual submissions.ResultsWe received a total of 612 submissions to the open call from mother-daughter dyads. Participants submitted ideas via a website designated for the contest (n = 392), in-person (n = 99), email (n = 31), or via an instant messaging application (n = 92). Overall, 470 were eligible for judging after initial screening. The average age of participants for daughters was 19 years and 39 years for mothers. Themes from the top 16 proposals included leveraging local leaders (5/16), faith-based networks (4/16), educational systems (4/16), and other community networks (7/16) to promote awareness of cervical cancer prevention services. After an in-person collaborative event, eight teams were selected to join an innovation training boot camp, for capacity building to implement ideas.ConclusionsInnovative strategies are needed to promote HPV screening for mothers and vaccination for girls in Nigeria. Our designathon was able to facilitate Nigerian mother-daughter teams to develop cervical cancer prevention strategies. Implementation research is needed to assess the effectiveness of these strategies.

Botulinum Toxin-A for the Treatment of Myogenous Temporomandibular Disorders: An Umbrella Review of Systematic Reviews.

Temporomandibular disorders (TMDs) encompass several conditions that cause pain and impair function of the masticatory muscles (M-TMDs) and temporomandibular joints. There is a large interest among clinicians and researchers in the use of botulinum toxin-A (BoNT-A) as a treatment for M-TMD. However, due to the lack of consistent evidence regarding the efficacy as well as adverse events of BoNT-A, clinical decision making is challenging. Therefore, this umbrella review aimed to systematically assess systematic reviews (SRs) evaluating BoNT-A treatment effects on pain intensity, mandibular movements, and adverse events in patients with M-TMDs. An electronic search was undertaken in the databases MEDLINE, EMBASE, CINAHL, Cochrane Central Registry of Controlled Trials (CENTRAL), Web of Science, Epistemonikos, ClinicalTrials.gov, and ICTRP to identify SRs investigating BoNT-A effects on M-TMDs, published from the inception of each database until 6 December 2023. The quality of evidence was rated according to the critical appraisalchecklist developed by the umbrella review methodology working group. Only high-quality SRs were included. In total, 18 SRs were included. BoNT-A was shown to be more effective than placebo to reduce pain intensity, but not compared to standard treatments. Additionally, BoNT-A was not superior to placebo or standard treatments regarding improvement of mandibular movements. BoNT-A was considered to have a higher risk for adverse events on muscle and bony tissue compared with other treatments. The synthesis in this umbrella review provides the highest level of evidence present. Taken together, there are indications of effectiveness of BoNT-A for treatment of M-TMDs, supported by moderate evidence. However, considering the risk of causing serious adverse events, treatment with BoNT-A is recommended to be the last treatment alternative.

The impacts of extreme heat events on non-accidental, cardiovascular, and respiratory mortality: An analysis of 12 Canadian cities from 2000 to 2020.

Extreme heat has significant impacts on mortality. In Canada, past research has analyzed the degree to which non-accidental mortality increases during single extreme heat events; however, few studies have considered multiple causes of death and the impacts of extreme heat events on mortality over longer time periods. Daily death counts attributable to non-accidental, cardiovascular, and respiratory causes were retrieved for the 12 most populous cities in Canada from 2000 to 2020. Generalized additive models were applied to quantify daily mortality risks for people aged younger than 65 years and for those aged 65 years and older in each city and for each cause of death. Model results were used to calculate the change in mortality risks and the number of excess deaths attributable to extreme heat during extreme heat events. Elevated mortality risks were observed during extreme heat events in most cities for non-accidental and respiratory causes. The impacts of extreme heat on non-accidental mortality were typically greater for people aged 65 and older than for those aged younger than 65. Significantly higher non-accidental mortality risks were observed during extreme heat events for people aged 65 and older in Montréal, the city of Québec, Surrey, and Toronto. For cardiovascular and respiratory causes, people aged 65 and older had significantly higher mortality risks during extreme heat events in Montréal, and both Montréal and Toronto, respectively. In the 12 cities, approximately 670 excess non-accidental deaths, 115 excess cardiovascular deaths, and 115 excess respiratory deaths were attributable to extreme heat events during the study period. Mortality risks during extreme heat events were generally higher in cities with larger proportions of renter households and fewer extreme heat events. This study estimates the longer-term impacts of extreme heat events on three mortality outcomes in a set of large Canadian cities. As climate change causes more frequent and intense extreme heat events, and as policy makers aim to reduce the health impacts of heat, it is important to understand how and where extreme heat affects health.

A novel AI-based model for real-time flooding image recognition using super-resolution generative adversarial network

Intensified climate change in recent years has had a global impact, leading to increased precipitation events of short duration and high intensity. This phenomenon poses a severe challenge to urban and underground infrastructure. Accurate detection and location of floodplains and water bodies are essential to ensure informed decision-making and implement proactive measures to minimize risks and losses. Therefore, an automated and efficient flood image detection system is the need of the hour. This study proposes a deep learning–based flood detection system. Images taken by surveillance cameras at intersections are used as input data, making the system well-suited to urban applications. Data augmentation techniques are used to improve the model performance. We demonstrated the practicality of this model by applying it to street surveillance images taken in Taiwan. The developed model quickly and successfully identified the extent and location of flooding with high precision and reliability. The developed model can be used to provide valuable insights for flood management and disaster management agencies. The test findings obtained from this study demonstrate the superior performance of the DeepLabv3 + model compared to the Mask R-convolutional neural network model, which was further enhanced using a super-resolution generative adversarial network. The model achieved remarkable precision with a precision metric score of 84 %, which is also complemented by a recall rate of 91 %. Most notably, the mean Intersection over Union (mIoU) metric reached an impressive accuracy level of 85.8 %. The results of this study highlight the importance of developing advanced flood imagery detection models aiming to considerably reduce the risks and losses incurred by flooding. The application of such a flood image detection system could help increase the ability of a city to cope with flood events caused by climate change.

Bridge decks under cold waves: Implications of concrete’s temperature-dependent CTE

Temperature fluctuations within a bridge deck during a cold wave event can cause severe continuity stresses in structures where movement is restrained. Thermal loads are accounted for using thermal gradients that vary depending on bridge location and cross-sectional geometry. However, the effect of temperature change on material properties, particularly on the coefficient of thermal expansion (CTE) is rarely accounted for in the analysis and design. A review of the literature indicates that the CTE of concrete materials can vary significantly with changes in temperature, especially under freezing conditions, whereas saturated or partly saturated concrete exhibits nonlinear expansion at temperatures below freezing, resulting from effective reversal of the CTE sign. This effect has not been considered in the literature, when investigating thermal actions on bridge decks in cold temperatures. The aim of this study is to analyze, using an advanced nonlinear F.E. platform, the structural response of a prestressed concrete box bridge structure subjected to cold wave events, when considering the effect of temperature on the nonlinearity, the magnitude and the sign of the CTE of concrete. Meteorological data from three historical cold events of different intensity is input in the thermal models to simulate the temperature distribution in the bridge deck; the estimated temperature field was then used in a structural model to determine the resulting thermal stresses. The temperature-dependence and nonlinearity of the CTE is modeled after calibration of the mathematical relationships against published data. The results indicate that this behavior has a significant influence on structural response, highlighting the need for pertinent consideration of its effect in the relevant sections of the bridge codes.

THE ROLE OF BOTTOM MESO-SCALE DYNAMICS IN CONTOURITE FORMATION IN THE ARGENTINE BASIN

The Argentine Basin is a deep-sea basin located in the South Atlantic Ocean that contains sedimentary deposits derived from different provenances. It is characterized by complex ocean dynamics encompassing diverse spatial and temporal dimensions. The northward subantarctic Malvinas Current and southward subtropical Brazil Current converge at the western margin of the Argentine Basin, resulting in the formation of the Brazil-Malvinas Confluence. Bottom currents, particularly currents flowing alongslope and horizontal eddies, are crucial in shaping the seafloor and in the formation of sedimentary features (e.g. contourites). The poorly understood strength and variability of bottom currents leave the processes that control sedimentation in deep environments unclear. High-resolution (1/12°) reanalysis was used to analyze near-bottom flows and bottom dynamics were compared with seafloor sedimentary characteristics obtained from geophysical datasets and sediment cores. High speeds, up to 3.5 m/s at the surfa ce and up to 1.4 m/s at the bottom, reveal the presence of intense flows in this area. The Zapiola Drift, an approx. 1,200 m high sedimentary deposit located in the central part of the Argentine Basin, is bounded by a zone of high bottom eddy kinetic energy (EKE) that resulted in the erosion of the seafloor and in the accumulation of sandy mud. The Malvinas Current is distinguished by strong and constant currents flowing northwards along the continental slope and by minimal EKE at the bottom. The area of the continental slope along which the Malvinas Current flows corresponds to a contourite terrace, a relatively flat surface composed almost entirely of sandy sediments and with abundant erosional features. The regions of highest EKE activity in the bottom layer is the overshoot of the Brazil-Malvinas Confluence and the Abyssal Plain. Our study highlights the impact of bottom current dynamics on contouritic sedimentation. In some regions, sedimentation is influenced by sporadic processes that occur between intense and weak flow events over time, which are considered intermittent processes. While sedimentation in other areas is controlled by constant flows. A better understanding of the strength and variability of bottom currents will improve paleoceanographic reconstructions based on the sedimentary record.

The declining availability of wild mussel seed for aquaculture in a coastal upwelling system

A general decline in foundation species at the rocky intertidal has been observed during the last decades all around the world and primarily related to climate change. In agreement with that trend, the mussel aquaculture sector in Galicia (NW Spain), the main production area in Europe, has warned over the last years about a decline in the availability of wild mussel seed from the rocky coast. Here we compile for the first time, mussel seed collection reports by mussel farmers in Galicia for the period 2006–2021. We employed that dataset as a proxy of mussel recruitment evolution in the rocky shore for the last 16 years. Temporal analysis of our data confirmed the reported decline (-148 t yr-1), particularly pronounced from 2012 onwards. The data base also allowed us to analyze inter-annual variability according to both, climatic variations and management scenarios. Since cultivated mussels conform a meta-population with wild mussels from the rocky shore, alterations on the market preferences towards smaller individuals at harvest, could also contribute to a reduction in reproductive output. Our results show a decrease in life-time egg production under certain scenarios. Nonetheless, coastal upwelling seems to be the largest factor conditioning recruitment abundance, explaining as much as 60% of the variability observed. Decline on recruitment abundance was highly modulated by the observed increment in frequency of intense upwelling events, exceeding 500 m3 km-1 s-1 between July and November. Meridional winds also determined the spatial recruitment patterns, pointing to the large role of wind forcing on mussel larval dispersal. Our results highlights how alterations on upwelling regimes related to climate change can interact with mussel population dynamics and also condition aquaculture sustainability and food security.

Importance of Heat Health Warnings in Heat Management

During intense heat events, the morbidity and mortality of the population increase [...]

Comprehensive analysis of characteristics of dry–wet events and their transitions in Uttar Pradesh, India

Understanding the occurrence and characteristics of dry and wet events is crucial for effective disaster prevention, resource management, and risk reduction in vulnerable regions. This study analyzed the spatiotemporal patterns of dry–wet events and their transition characteristics in Uttar Pradesh, India. The standardized precipitation evapotranspiration index (SPEI) at a monthly timescale was utilized to identify hotspot regions vulnerable to concurrent and frequent dry and wet events and their transitions. The severity, duration, and intensity of dry and wet events were characterized with the run theory over SPEI time series data from 18 synoptic stations in Uttar Pradesh over 48 years (1971–2018), sourced from the Indian Institute of Tropical Meteorology and the India Meteorological Department. Multiple assessment methods were utilized to examine the interaction of these extreme events, considering characteristics such as wet–dry ratio, average transition time, and rapid transition times from wet to dry events and from dry to wet events. Average wet durations ranged from 1.27 to 1.58 months, and average dry durations ranged from 1.29 to 1.82 months. Rapid transition times from dry to wet events ranged from 2.5 to 4.1 months, and those for wet-to-dry events ranged from 2.1 to 5.3 months. The eastern region experienced a significantly high number of dry events, while the western and Bundelkhand regions experienced more intense dry events. In contrast, the eastern region had intense wet events. This research on the occurrence of dry–wet events and their transitions can provide valuable insights for government decision-making and disaster prevention and reduction efforts.

Evaluation of wind turbine blades’ rain-induced leading edge erosion using rainfall measurements at offshore, coastal and onshore locations in the Netherlands

The impingement of rain drops on wind turbine blades determines leading edge erosion (LEE) which is a factor driving high maintenance costs. In order to better quantify rain-induced LEE, we carried out detailed rainfall measurements, by means of disdrometers, in conjunction with wind speed measurements. Measurements were performed at three different Dutch sites, encompassing an offshore, a coastal and an onshore location. Based on rainfall and wind speed measurements, and assuming a virtual 15 MW wind turbine, we estimated the blade’s LEE using a fatigue-based model. Developed by means of different published rotating arm erosion data, our fatigue model relates the measured rainfall characteristics to the LEE incubation period, here assumed as the leading edge protection (LEP) system’s end of life. Assuming a polyurethane LEP system, results indicate that the blades’ incubation period is around 3.9 years at the offshore location, 6.6 years at the coastal location and 8.3 years at the onshore location. These results are connected to the higher wind speeds during rainfalls, and higher occurrences of very intense rainy events which, according to the measurements, progressively occur at the onshore, coastal and offshore locations.

Geostationary aerosol retrievals of extreme biomass burning plumes during the 2019–2020 Australian bushfires

Abstract. Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5.

Colored noise in GRACE total water storage time series: Its impact on trend significance in the Türkiye region and major world river basins

Temporal correlations are prevalent in most geophysical time series data, leading to what is known as colored noise, which become more apparent in the frequency domain rather than time domain. Neglecting this type of noise in modeling can result in underestimated standard errors for the parameters of the model used to analyze the time series. As a result, statistical tests misinterpret the significance of these parameters, such as the overall trend rate, because the “signal to noise” ratio becomes incorrectly larger than it should be. In this study, we investigate the temporal correlations in the time series data from the Gravity Recovery and Climate Experiment (GRACE) mission. GRACE and its successor mission GRACE Follow-On (GRACE-FO) have been successfully employed to monitor global total water storage anomalies over two decades since its initial launch in 2002. We primarily utilize monthly GRACE mascon (mass concentration) solutions provided by NASA Goddard Space Flight Center (GSFC), examining both regional and global scales. The power spectrum density of the residuals, calculated after applying standard harmonic regression to each mascon time series on Earth’s land, reveals an average spectral index of κ = −1. This indicates the predominance of flicker noise, one of the most common forms of colored noise in geodetic time series. Notably, the noise becomes more Brownian (κ < −1) in regions with intense hydrological events, while it tends to be more white noise (κ → 0) in areas with less water circulation compared to other land regions. This observation suggests that unmodelled hydrological events, such as droughts, floods, and ones associated with ice-melting, contribute to the residuals as colored noise in the time series. To account for this noise in our time series modeling, we primarily consider autoregressive (AR) moving average (MA) process, commonly known as ARMA, in addition to the standard variance component estimation for noise amplitudes. We focus on individual mascon blocks in the Türkiye Region and mascon solutions for 24 world’s major river basins. Our findings demonstrate that an ARMA(1,1) model is sufficient for describing the noise in these regions. The results underscore the importance of considering colored noise, as it leads to approximately three times larger standard errors for the overall trend rate. This substantially impacts the significance of the trend rates.

The spatiotemporal characteristics and driving mechanisms of subsurface marine heatwaves in the Xisha Region

Under the background of global climate change, Subsurface Marine Heatwaves (SSMHWs) have become a ‘hot-spot’ research due to their significant impacts on marine ecosystems. Temperature data from the ECCO2 for the years 1992 to 2021 is used to research the spatiotemporal characteristics of SSMHWs in the upper 500 m over the Xisha region (110°E to 113°E, 15°N to 18°N). This study indicates that SSMHWs of high intensity occur in the Xisha area, with the vertical maximum intensity at approximately 100 m. These events exhibit significant seasonal variations, with the highest intensity occurring in May. The intensity of SSMHW in the analysis region varies interannually. SSMHWs have intensified at a rate of 1.53°C·Days/Year and tend to shift to deeper water over the past three decades. A block-based method for SSMHW identification is proposed, in order to take the vertical extent of the event in consideration. By analyzing the eight most intense events based on spatial cumulative intensity, it suggested that warm mesoscale eddies may play an important role on the spatial distribution of the SSMHWs. Statistical analysis shows that the intensity and coverage of the warm mesoscale eddy in the Xisha zone may influence the intensity and evolution of the SSMHWs. Additionally, only 18.27% of SSMHW events occur with apparent surface marine heatwave signals. These findings are vital for uncovering SSMHW dynamics in the Xisha area, and are important for future monitoring, early warnings and marine conservation.

Higher atmospheric aridity-dominated drought stress contributes to aggravating dryland productivity loss under global warming

Dryland ecosystems are highly vulnerable to extreme droughts under climate change. Yet, response of vegetation productivity across global drylands to changes in drought stress in a warming climate remains obscure. Here, we investigated future changes in drought stress, characterized by low soil moisture (SM) and high vapor pressure deficit (VPD), under severe drought conditions and its impact on gross primary productivity (GPP) deviations in drylands, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system model (ESM) simulations. Under both intermediate (SSP2-4.5) and high (SSP5-8.5) emission scenarios, the dryland ecosystems are projected to experience more intense, extensive and frequent severe drought events owing to increasing VPD. The probabilities of high VPD-dominated drought stress in the end of the 21st century would be nearly double (2.1–2.4 times) of the present-day (39%). Excluding the carbon dioxide (CO2) fertilization effect, the annual GPP loss caused by severe drought is projected to further deteriorate over more than half fraction (56.9–70.9%) of global vegetated dryland areas, reaching 2.0 (1.9–2.2) times of the present-day (with an area-weighted total of −21.5 KgC m−2 yr−1) by the end of the 21st century. Such aggravating reduction is predominantly induced by drought stress with higher-than-usual VPD anomaly. The high VPD-dominated drought stress would lead to approximately 100% (95–102%) of annual aggregated dryland GPP loss by the end of 21st century from the present-day 68%. Our results suggest an increasing risk of high atmospheric aridity-dominated drought stress on dryland ecosystems. It is of great urgency to make adaption and mitigation strategies for the natural and cultivated vegetation in drylands.

Data Assimilation of Satellite-Derived Rain Rates Estimated by Neural Network in Convective Environments: A Study over Italy

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model’s precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms.

Regional characteristics of extreme precipitation events over Aotearoa New Zealand

We document 1394 extreme precipitation events (EPEs) over Aotearoa New Zealand’s (ANZ) Regional Councils between March 1996 and December 2021. The characteristics of EPEs are documented using a novel spatio-temporal framework that diagnoses the peak intensity, duration, and accumulation of the EPE using the ERA-5 and MERRA-2 reanalysis products. Properties of EPEs were evaluated according to region across ANZ, and clear regional differences are highlighted. In particular, it is found that the duration of an EPE has a stronger influence than the peak intensity on the total accumulated precipitation across all regions and precipitation event types (large-scale or convective). Since larger precipitation accumulations have greater potential to cause extensive flooding over larger areas, an important implication is the need for numerical weather prediction in ANZ to forecast the duration of an intense precipitation event adequately in order to improve emergency preparedness.

Improving forecasts of precipitation extremes over northern and central Italy using machine learning

AbstractThe accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme.

Transient dataset of household appliances with Intensive switching events

With the development of Non-Intrusive Load Monitoring (NILM), it has become feasible to perform device identification, energy consumption decomposition, and load switching detection using Deep Learning (DL) methods. Similar to other machine learning problems, the research and validation of NILM necessitate substantial data support. Moreover, different regions exhibit distinct characteristics in their electricity environments. Therefore, there is a need to provide open datasets tailored to different regions. In this paper, we introduce the Transient Dataset of Household Appliances with Intensive Switching Events (TDHA25). This dataset comprises switch instantaneous data from 10 typical household appliances in China. The TDHA dataset features a high sampling rate, accurate labelling, and realistic representation of actual appliance start-up waveforms. Additionally, appliance switching is achieved through precise control of relay switches, thus mitigating interference caused by mechanical switches. By furnishing such a dataset, we aim not only to enhance the recognition accuracy of existing NILM algorithms but also to facilitate the application of NILM algorithms in regions sharing similar electricity consumption characteristics to those of China.

The impact of precipitation changes on the safety of railway operations in China under the background of climate change

Global climate change has intensified the water cycle, leading to frequent extreme precipitation events, posing a significant threat to railway infrastructure and safety operations. Based on the analysis of past and future precipitation changes in China, this study investigates the impact of climate change on railway safety operations. The study reveals the following findings: (1) Under the influence of the intensified East Asian summer monsoon and the northward shift of the subtropical high during the 2017–2021 compared to the 2012–2016, precipitation has significantly decreased (120 mm) in the regions south of the Yangtze River and South China, while it has increased (60 mm) in the regions from the eastern of Northwest China to the middle and lower reaches of Yangtze River; The operational precipitation risk has decreased for Urumqi, Lanzhou, Qinghai-Tibet Group, Xi’an, and Wuhan railway bureaus (abbreviated as Bureau), while it has increased for Nanchang, Chengdu, Zhengzhou, Shanghai, and Shenyang Bureaus. Particularly noteworthy is that despite a decrease in total annual precipitation for Nanchang bureau (15.3 mm/a), the frequency of intense precipitation events has increased, leading to an increased operational precipitation risk. (2) During the 21st century, under high (SSP5-8.5), medium (SSP2-4.5), and low (SSP1-2.6) forcing scenarios, all projections indicate that most of the China will experience an increasing trend in precipitation, with significant increases in precipitation observed in the regions south of the Yangtze Rive, South and Southwest China. The higher the greenhouse gas emissions, the more pronounced the increasing trend in precipitation. (3) Compared to the 20th century, under high (SSP5-8.5), medium (SSP2-4.5), and low (SSP1-2.6) forcing scenarios, all projections indicate that the total annual precipitation hours, railway inspection, speed limit, and closure risk hours have all increased on a national scale during the 21st century. The operational precipitation risk for railways has also increased. The higher the alert level for railway precipitation (precipitation &amp;lt; inspection &amp;lt; speed limit &amp;lt; closure), the higher the proportion of risk hours compared to the 20th century. By the late 21st century, the railway inspection, speed limit, and closure risk hours have increased by 175%, 463%, and 647%, respectively, compared to the 20th century.