Weather Scenarios Research Articles

Wind power ultra-short-term prediction method based on NWP wind speed correction and double clustering division of transitional weather process

Wind power prediction technology is important for building novel power systems with a high proportion of renewable energy. The quality of Numerical weather prediction (NWP) has a significant impact on the accuracy of ultra-short-term wind power prediction (USTWPP). However, existing NWP do not reflect the adaptability of different weather processes, because of it’ s forecasting errors. In view of this, this paper proposes an USTWPP method based on NWP wind speed correction and division of transitional weather process. The combined prediction method was first used to correct the NWP wind speed, and then we use the double clustering method to divide the transitional weather processes to establish a model for USTWPP based on different scenarios, the overall method was finally applied to a wind farm in west inner Mongolia, China. Compared to the pre-correction, the wind speed forecasted RMSE was reduced by 1.702 and the MAE by 1.366. Based on the wind power ultra-short-term prediction method proposed in this paper, the average reduction in RMSE is 5.93% and in MAE is 4.82% compared to the various comparison methods in the four seasons. The USTWPP method combining wind speed correction and double clustering division of transitional weather scenarios can significantly improve accuracy of USTWPP.

Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods

Crop simulation models are an important tool for assessing agroecosystem performance and the impact of agrotechnologies on soil cover condition. However, the high uncertainty and labor intensiveness of long-term weather forecasting limits the applicability of such models. A possible solution may be to use time series forecasting models (SARIMAX and Prophet) and artificial neural-network-based technologies (Neural Prophet). This work compares the applicability of these methods for modeling soil condition dynamics and agroecosystem performance using the MONICA simulation model for Voronic Chernozems in the Kursk region of Russia. The goal is to determine which weather indicators are most important for the yield forecast and to choose the most appropriate methods for forecasting weather scenarios for agricultural modeling. Crop rotation of soybean and sugar beet was simulated, with agricultural techniques and fertilizer usage considered as factors. We demonstrated the high sensitivity of aboveground biomass production and soil moisture dynamics to daily temperature fluctuations and precipitation during the vegetation period. The dynamics of the leaf area index and nitrate content showed less sensitivity to the daily fluctuations of temperature and precipitation. Among the proposed forecasting methods, both SARIMAX and the Neural Prophet algorithm demonstrated the ability to forecast weather to model the dynamics of crop and soil conditions with the highest degree of approximation to actual observations. For the dynamic of the crop yield of soybean, the SARIMAX model exhibited the most favorable coefficient of determination, R2, while for sugar beet, the Neural Prophet model achieved superior R2 levels of 0.99 and 0.98, respectively.

Application of Severe Weather Nowcasting to Case Studies in Air Traffic Management

Effective and time-efficient aircraft assistance and guidance in severe weather environments remains a challenge for air traffic control. Air navigation service providers around the globe could greatly benefit from specific and adapted meteorological information for the controller position, helping to reduce the increased workload induced by adverse weather. The present work proposes a radar-based nowcasting algorithm providing compact meteorological information on convective weather near airports for introduction into the algorithms intended to assist in air-traffic management. The use of vertically integrated liquid density enables extremely rapid identification and short-term prediction of convective regions that should not be traversed by aircraft, which is an essential requirement for use in tactical controller support systems. The proposed tracking and nowcasting method facilitates the anticipation of the meteorological situation around an airport. Nowcasts of centroid locations of various approaching thunderstorms were compared with corresponding radar data, and centroid distances between nowcasted and observed storms were computed. The results were analyzed with Method for the Object-Based Evaluation from the Model Evaluation tools software (MET-10.0.1, Developmental Testbed Center, Boulder, CO, US) and later integrated into an assistance arrival manager software, showing the potential of this approach for automatic air traffic assistance in adverse weather scenarios.

Examining the Impact of Climate Change on Residential Footing Design in Australia: A Case Study Analysis

The impact of climate change on building performance is anticipated to be significant, affecting both present and future constructions on clay soils. In Australia, the Thornthwaite Moisture Index (TMI) is widely used to classify climate zones and determine the characteristic ground movement for designing residential footings. However, the existing TMI map provided by the Australian Standard AS2870 for Victoria, Australia, is based on meteorological data from 1940 to 1960, an outdated and thus potentially insufficient basis for current and future climate scenarios. To address this challenge, the study endeavours to develop an updated TMI map using weather data over a 20-year period (1994–2013) with predictions for 2030 and 2070 based on climate models to visualize climate zone changes. The updated TMI map shows significant changes in climate zones and suggests that current footing designs may be inadequate for future weather conditions. A case study was conducted to evaluate the influence of climate change on footing size and construction material cost. The findings indicate that the footing constructed in 1992 is inadequate for the projected climatic conditions of 2070. Coping with such severe weather scenarios necessitates larger footing sizes and upgrading steel reinforcements, raising the material cost by approximately 111%.

Climate Change Impacts on Rainwater Tank’s Potential Water Savings, Efficiency and Reliability Presenting Relationship Between ‘Seasonality Index’ and Water Savings Efficiency

The paper presents potential impacts of climate change on rainwater tanks outcomes such as water savings, reliability and water savings efficiency under two projected climate change scenarios; RCP4.5 and RCP8.5 utilising a case study using rainfall data from four stations within the city of Brisbane, Australia. Historical rainfall data were collected from the Australian Bureau of Meteorology website for the selected stations. Projected daily rainfall data were collected from Australian government data portal for the same stations within Brisbane. Using an earlier developed daily water balance model, eTank potential annual water savings were calculated for several weather and climate change scenarios with varied roof areas and rainwater demandValues of ‘Seasonality Index (SI)’, a commonly used factor representing rainfall variability within a year, were explored to validate earlier developed relationships between SI and water savings efficiency through rainwater tanks. It is found that in most cases water savings in future periods are expected to decrease and such decrease is not necessarily attributed to the expected decrease in rainfall amounts in future, rather also affected by future reductions in rainwater tank reliability. Linear relationships between potential future water savings and reliabilities for all the stations are found. Relationship between SI and water savings efficiency was established, and it is found that the relationship slightly varies with an earlier developed relationship using historical data. Compared to earlier established relationship, for lower SI values water savings efficiency is expected to become better, while for higher SI values the water savings efficiency is expected to become worse. Also, an increase of roof area from 100 m2 to 200 m2 will cause an average increase of water savings efficiency by 25% and an increase of rainwater usage from 200 L/day to 300 L/day will cause an average increase of water savings efficiency by 20%.

A high-fidelity approach to modeling weather-dependent fuel consumption on ship routes with speed optimization

In this paper, we present the scheduling problem on a given route where speed optimization under various weather conditions is to be performed. Different approaches for calculating fuel consumption for vessels are introduced with a discussion of how this might influence the speed optimization strategies on predetermined multi-stop routes in a short sea shipping service within offshore logistics. Due to both spatial and temporal changes in weather conditions, fuel consumption as a function of speed becomes time-dependent as a vessel performs its route in varying weather. In our novel approach, the weather impact on fuel consumption for the considered vessels is modeled with a higher level of detail than in previously conducted studies, including both wave direction and wave period as input together with the wave height. We test our approach for optimizing schedules on a large set of routes of different lengths and number of stops, as well as for a set of different weather samples based on historical observations. When comparing the new approach to current industry practice, the computational study reveals on average a 4.5% reduction in fuel consumption across the different routes and weather scenarios. The magnitude of the reduction potential increases for worsening weather conditions. Furthermore, it is demonstrated how the approach commonly used for modeling weather impacts in the literature tends to greatly miscalculate the true cost of performing a voyage in realistic weather conditions. Finally, we discuss how the model fidelity is likely to affect the outcome of the routing decisions at a higher planning level, representing a potential for even further reductions of fuel consumption in various weather conditions.

Volume scan modeling simulator of a 1D phased array weather radar

Phased array weather radar (PAWR) for hazardous weather phenomena observation has attracted considerable attention. In this paper, a PAWR simulator for volume scan mode modeling is designed and the volume scan model has been used to evaluate the one-dimensional (1D) PAWR beam pattern characteristics and the effect of scanning mode. Firstly, the three-dimensional weather field has been generated by interpolating spectral moment parameters from next-generation weather radar data (NEXRAD), including reflectivity factor, radial velocity and spectral width. Secondly, based on the parameters of radar system and volume scanning modeling, the relationship between phased array beam characteristics and volume scanning mode is established, which can fully simulate the scanning process of phased array radar at different elevation cuts. Finally, according to the concept of 'scattering center', the time series of each range bin during volume scanning is reconstructed. The performance of the PAWR simulator has been evaluated under typical weather scenario, such as tornadoes and the results indicated the effectiveness and superiority over traditional Doppler weather radar. Simulation examples are provided to illustrate the flexibility and practicability of the PAWR simulator, and the factors affecting its PAWR detection performance are evaluated. The research results represent that reducing the number of linear array elements and increasing the array angle will further exacerbate the degradation of data quality.

Data Generation Using Simulation Technology to Improve Perception Mechanism of Autonomous Vehicles

Recent advancements in computer graphics technology allow more realistic rendering of car driving environments. They have enabled self-driving car simulators such as DeepGTA-V and CARLA (Car Learning to Act) to generate large amounts of synthetic data that can complement the existing real-world dataset in training autonomous car perception. Furthermore, since self-driving car simulators allow full control of the environments, they can generate dangerous driving scenarios that the real-world dataset lacks such as bad weather and accident scenarios. In this paper, we will demonstrate the effectiveness of combining data gathered from the real-world with data generated in simulated world to train perception system on object detection and localization task. We will also propose a multi-level deep learning perception framework that aims to emulate a human learning experience in which series of tasks from simple to more difficult ones are learnt in a certain domain. The autonomous car perceptron can learn from easy-to-drive scenarios to more challenging ones customized by simulation software.

Wrong Way Vehicle Detection in Single and Double Lane

Wrong-way driving is one of the primary causes of traffic jams and accidents globally. It is possible to identify vehicles going the wrong direction, which lessens accidents and traffic congestion. Surveillance footage has become an important source of data due to the accessibility of less priced cameras and the expanding use of real-time traffic management systems. In this paper, we propose a technique for automatically identifying automobiles moving against traffic. Our system uses the You Only Look Once (CNN) algorithm to recognize and track vehicles from video inputs and the centroid tracking method to determine each vehicle's orientation inside a given region of interest (ROI) in order to identify vehicles traveling in the wrong direction. It functions in three steps. The Deep sort tracking method is particularly good in detecting and tracking objects, and the centroid tracking technique can effectively monitor the direction of travel. Experiments with a variety of traffic films show that the suggested method can detect and identify wrong-way moving vehicles in a variety of lighting and weather scenarios. The interface of the system is quite simple and easy to use.

A process-based model for quantifying the effects of canal blocking on water table and CO2emissions in tropical peatlands

Abstract. Drainage in tropical peatlands increases CO2 emissions, the rate of subsidence, and the risk of forest fires. To a certain extent, these effects can be mitigated by raising the water table depth (WTD) using canal or ditch blocks. The performance of canal blocks in raising WTD is, however, poorly understood because the WTD monitoring data are limited and spatially concentrated around canals and canal blocks. This raises the following question: how effective are canal blocks in raising the WTD over large areas? In this work, we composed a process-based hydrological model to assess the peatland restoration performance of 168 canal blocks in a 22 000 ha peatland area in Sumatra, Indonesia. We simulated daily WTD over 1 year using an existing canal block setup and compared it to the situation without blocks. The study was performed across two contrasting weather scenarios representing dry (1997) and wet (2013) years. Our simulations revealed that, while canal blocks had a net positive impact on WTD rise, they lowered WTD in some areas, and the extent of their effect over 1 year was limited to a distance of about 600 m around the canals. We also show that canal blocks are most effective in peatlands with high hydraulic conductivity. Averaging over all modeled scenarios, blocks raised the annual mean WTD by only 1.5 cm. This value was similar in the dry (1.44 cm) and wet (1.57 cm) years, and there was a 2.13 fold difference between the scenarios with large and small hydraulic conductivities (2.05 cm versus 0.96 cm). Using a linear relationship between WTD and CO2 emissions, we estimated that, averaging over peat hydraulic properties, canal blocks prevented the emission of 1.07 Mg ha−1 CO2 in the dry year and 1.17 Mg ha−1 CO2 in the wet year. We believe that the modeling tools developed in this work could be adopted by local stakeholders aiming at a more effective and evidence-based approach to canal-block-based peatland restoration.

YOLOv5s-Fog: An Improved Model Based on YOLOv5s for Object Detection in Foggy Weather Scenarios.

In foggy weather scenarios, the scattering and absorption of light by water droplets and particulate matter cause object features in images to become blurred or lost, presenting a significant challenge for target detection in autonomous driving vehicles. To address this issue, this study proposes a foggy weather detection method based on the YOLOv5s framework, named YOLOv5s-Fog. The model enhances the feature extraction and expression capabilities of YOLOv5s by introducing a novel target detection layer called SwinFocus. Additionally, the decoupled head is incorporated into the model, and the conventional non-maximum suppression method is replaced with Soft-NMS. The experimental results demonstrate that these improvements effectively enhance the detection performance for blurry objects and small targets in foggy weather conditions. Compared to the baseline model, YOLOv5s, YOLOv5s-Fog achieves a 5.4% increase in mAP on the RTTS dataset, reaching 73.4%. This method provides technical support for rapid and accurate target detection in adverse weather conditions, such as foggy weather, for autonomous driving vehicles.

Experimental Performance of a Nonlinear Control Strategy to Regulate Temperature of a High-Temperature Solar Reactor

Abstract Despite the significant potential of solar thermochemical process technology for storing solar energy as solid-state solar fuel, several challenges have made its industrial application difficult. It is important to note that solar energy has a transient nature that causes instability and reduces process efficiency. Therefore, it is crucial to implement a robust control system to regulate the process temperature and tackle the shortage of incoming solar energy during cloudy weather. In our previous works, different model-based control strategies were developed namely a proportional integral derivative controller (PID) with gain scheduling and adaptive model predictive control (MPC). These methods were tested numerically to regulate the temperature inside a high-temperature tubular solar reactor. In this work, the proposed control strategies were experimentally tested under various operation conditions. The controllers were challenged to track different setpoints (500 °C, 1000 °C, and 1450 °C) with different amounts of gas/particle flowrates. Additionally, the flow controller was tested to regulate the reactor temperature under a cloudy weather scenario. The ultimate goal was to produce 5 kg of reduced solar fuel magnesium manganese oxide (MgMn2O4) successfully, and the controllers were able to track the required process temperature and reject disturbances despite the system's strong nonlinearity. The experimental results showed a maximum error in the temperature setpoint of less than 0.5% (6 °C), and the MPC controller demonstrated superior performance in reducing the control effort and rejecting disturbances.

Evaluation on the performance of tree view factor in a high-density subtropical city: A case study in Hong Kong

Outdoor thermal comfort is a primary concern when designing urban environments suitable for pedestrian activities. In previous research, urban greenery had been considered as an effective strategy to mitigate the deterioration of the street-level thermal environment. Tree view factor (TVF), a parameter that can estimate the amount of tree cover visible in the overlying hemisphere, was applied in recent thermal environment assessment studies. However, a detailed understanding of TVF's suitability and application in a subtropical high-rise compact metropolis with diversified land uses is still lacking. To fill the knowledge gap, this study used Hong Kong as a study case and conducted ENVI-met simulations to analyse the human thermal comfort at two levels of TVF sites and two weather conditions. Results showed that high TVF (Htvf) sites had a pronounced reduction in mean radiant temperature (TMRT) of at least 15 °C after 12:00 under both typical and extreme weather scenarios. A lower mean physiological equivalent temperature (PET) of below 30 °C and 35 °C remained in the daytime of typical and extreme weather scenarios, respectively. It was also found that low TVF sites inside parks received more solar radiation, while Htvf sites inside parks exhibited a more substantial cooling potential than that inside street canyons. Htvf sites could maintain better pedestrian thermal comfort due to the shading effect. This study also proved that TVF is more suitable than green coverage ratio (GCR) in urban thermal environment assessments and urban green infrastructure design to create a thermally comfortable high-rise and high-density metropolis.

A Water Cycle Approach for Maximum Power Point Tracking through an Interleaved Boost Converter

In this work, an Interleaved Boost Converter (IBC) is developed for Sun Powered E-vehicles (SPEVs) to reduce input/output filters, improve dynamic characteristics, and minimize device stress compared to conventional designs. The IBC achieves these goals by utilizing current control loops and lowering the size of passive components, resulting in excellent efficiency and reduced input/output voltage variation. To ensure an uninterrupted power supply (UPS) and support vehicle movement under erratic conditions, an MPPT (Maximum Power Point Tracking) controller is essential in the PV battery system. However, under non-ideal conditions such as shading, irradiance, temperature, and inclination angle variations, PV systems may experience multiple power peaks, making it challenging to identify the global peak. To address this issue, a unique Water Cycle Optimized Perturb and Observe (WCO-PO) method is proposed in this work. The proposed technique demonstrates shorter computation time, rapid convergence rate, and minimal variations after reaching the MPP (Maximum PowerPoint) in various weather scenarios, as demonstrated using MATLAB/Simulink software. The test results are presented with a comparison of the proposed approach against other methods, showcasing the advantages of the Water Cycle approach in MPPT through an Interleaved Boost Converter.

Resilience of wave energy farms using metocean dependent failure rates and repair operations

Emerging offshore renewable energy technologies are expected to become an important part of the future energy system, and reliability for these new technologies in different metocean scenarios must be guaranteed. This poses a challenge in extreme weather scenarios like storms, in particular for less mature technologies such as wave energy. Not only the offshore survivability must be controlled; the restoration after disruptive events and failures should be addressed and optimized. Offshore operations are costly and cannot be carried out if the weather is too harsh, and the resulting downtime after failures may be financially devastating for projects. In this paper, the resilience of large wave energy systems is studied with respect to wave conditions, metocean dependent failure rates, and weather windows available for offshore repair operations. A metocean- and time-dependent failure rate is derived based on a Weibull distribution, which is a novelty of the paper. The performance of the farm is assessed using the varying failure rates and metocean data at different offshore sites. Critical metocean thresholds for different offshore vessels are considered, and the resilience is quantified using relevant measures such as unavailability and expected energy not supplied. The resilience analysis is coupled to an economic assessment of the wave farm and different repair strategies. Our results show that the commonly used assumption of constant failure rates is seen to overestimate the annual energy production than when a more realistic varying failure rate is used. Two offshore sites are compared, and the availability is found to be higher at the calmer site. Most of the evaluated repair strategies cannot be considered to be economically justified, when compared to the cost of the energy not supplied.

Observation of the vertical structure of atmospheric boundary layer in subtropical UHI by radiosonde

Existing studies on the urban heat island (UHI) effect tend to focus on the planar scale. However, the UHI effect has obvious three-dimensional properties. This research focuses on the vertical structure of UHI. The study combined big data from sounding balloon and surface measurements in the Database for Atmospheric and Hydrologic Research, Taiwan. Data organization and classification were conducted using the Python programming language, the NumPy mathematical function library, and the Pandas data manipulation library. Results confirmed the existence of the atmospheric boundary layer and the canopy. Furthermore, the temperature inversion of the atmospheric boundary layer fell between heights of 130 m and 667 m. The height of the inversion is affected by the wind speed and cloudiness. Thereby demonstrating that the inversion fluctuates with weather scenario changes. The research results can be applied as the direction of urban planning thinking for urban heat island countermeasures.

Accounting for Weather Variability in Farm Management Resource Allocation in Northern Ghana: An Integrated Modeling Approach

Smallholder farmers in Northern Ghana face challenges due to weather variability and market volatility, hindering their ability to invest in sustainable intensification options. Modeling can help understand the relationships between productivity, environmental, and economical aspects, but few models have explored the effects of weather variability on crop management and resource allocation. This study introduces an integrated modeling approach to optimize resource allocation for smallholder mixed crop and livestock farming systems in Northern Ghana. The model combines a process-based crop model, farm simulation model, and annual optimization model. Crop model simulations are driven by a large ensemble of weather time series for two scenarios: good and bad weather. The model accounts for the effects of climate risks on farm management decisions, which can help in supporting investments in sustainable intensification practices, thereby bringing smallholder farmers out of poverty traps. The model was simulated for three different farm types represented in the region. The results suggest that farmers could increase their income by allocating more than 80% of their land to cash crops such as rice, groundnut, and soybeans. The optimized cropping patterns have an over 50% probability of increasing farm income, particularly under bad weather scenarios, compared with current cropping systems.

Sensitivity analysis and weather condition effects on hygrothermal performance of green roof models characterized by recycled and artificial materials’ properties

This study conducted a sensitivity analysis and assessed the effects of long-term weather conditions on green roof models, including recycled and artificial materials. Climate conditions can affect the hygrothermal performance of green roof materials, but this important issue has hardly been evaluated for drainage and substrate layers made of recycled and artificial materials. Climate change makes it unclear how well green roofs will perform hygrothermally. Moreover, the heat flux sensitivity to the thickness and physical characteristics of green roofs with artificial and recycled materials has received less attention. This study applied three weather scenarios on green roof models with artificial and recycled materials: the beginning, middle, and end of the 21st century. As per the results, at the beginning and middle of the 21st century, substrate layers' water content was roughly nine times more than the drainage layers'. At the end of the 21st century, the comparable difference was 6.5 times larger. During the summer and the beginning of autumn, the green roofs’ thermal performance with recycled and artificial materials was improved until the end of the 21st century. The entire parameter change demonstrated the scatter of thermal conductivity, density, and thickness effectively influenced the dispersion of heat flux for the green roof layers. Also, the scatter of density was more effective in heat flux dispersion for substrate layer than drainage layer.

Examining the impact of stochastic multi-year weather and air infiltration on hygrothermal moisture risks

The analysis of heat, air, and moisture (H.A.M.) transport for building envelopes are known to be highly dependent on climate loads and air infiltration rates. Moisture content within the assembly is often a key H.A.M. analysis outcome to assess risk and transport behavior. ASHRAE Standard 160-2016 states that building envelope H.A.M. analysis should be done using moisture design reference data or using a minimum of 10 consecutive years of weather. While there has been progress and methods for selecting or designing moisture reference years there has been a lack of study in the impact of multi-year (particularly 10-year) weather scenarios on simulation results in comparison to reference year simulations. This paper presents research using stochastic 1, 2, and 10-year weather data and air infiltration rates to study the range of simulated moisture content outcomes for four wall assemblies in Philadelphia and compares these to the outcomes when using reference years. Results from the study show that air infiltration, starting month, and multi-year duration have significant impacts on simulated moisture content, mold, and corrosion analysis results. Regression analysis using annual averages of climate input parameters did not yield useable models for selecting weather years, however an estimated mold index value using outdoor climate data may be useful in selecting weather years with varying starting months for mold growth assessment.

Influences of Changes in Rainfall and Solar Radiation on Performance of Kharif Sorghum

Kharif sorghum is an important crop of the northern transition zone (NTZ) of Karnataka. Historically this zone was characterized by the assured and uniform distribution of rainfall during the southwest monsoon. The last decade has witnessed increased erraticity in the onset, progress and distribution of rainfall, but days without rain also remain cloudy for weeks during Kharif season, thus lower the crop canopy, which affects the yield, interrupts solar radiation. Solar radiation, rainfall are the two important climatic factors affecting crop performance, but it is logistically difficult, and resource demanding to artificially create study-growing environment under field conditions. Alternately, Crop Simulation Models can be effectively used for such studies by creating customized weather scenarios within the model. Four rainfall scenarios (±10 and ±20 % over observed) and four solar radiation scenarios (±10 and ±20 % over observed) were created by using 32 years’ observed weather data (1985-2016) within the calibrated and validated DSSAT-CERES-Sorghum model [1].
 Simulations were run across all the above scenarios for 32 years' seasonal analysis with the best four kharif sorghum cultivars sown across three dates of sowing under the standard package of practices followed for NTZ. Model simulated annual outputs for grain yield over 32 years were averaged and presented. The model simulated results revealed that for NTZ changes in solar radiation was found to have more effect on yield than rainfall. Any reduction in solar radiation over observed drastically reduces the yield. Across cultivars and dates of sowing under observed weather (1985-2016), on average, 1720 kg ha-1 yield was simulated. When solar radiation was reduced by 10 % across rainfall scenarios the average yield was reduced to 1424 kg ha-1 which further reduced to 670 kg ha-1 (61% reduction) when solar radiation was reduced by 20 %. In contrast, when solar radiation was increased by 10 % and 20 %, the model simulated 2967 kg ha-1 and 3181 kg ha-1 yield, respectively which is 42 and 46 % more over the yield of observed weather. This study showed that for NTZ of Karnataka during the Kharif season increased cloudy period will have a more adverse effect on yield than changes to rainfall.