Forecast Weather Conditions Research Articles

Forecasting and Seismic Detection of Proglacial Debris Flows at Mount Rainier National Park, Washington, USA

ABSTRACT The glaciated Mount Rainier volcano in southwestern Washington State (United States) has a rich history of outburst floods and debris flows that have adversely impacted infrastructure at Mount Rainier National Park in the 20th and 21st centuries. Retreating glaciers leave behind vast amounts of unconsolidated till that is easily mobilized during high-precipitation-intensity storms in the fall months, and during outburst floods during warm summer months. Over 60 debris flows and outburst floods have been documented between 1926 and 2019 at Mount Rainier. Debris-flow activity has led to the closure of campgrounds and visitor destinations, which has limited visitor access to large swaths of the park. This paper documents efforts to characterize and seismically monitor debris flows, map hazards, and develop forecasting approaches for wet and dry weather debris flows. Using the day-of and historic antecedent weather conditions on past debris-flow days, we developed a debris-flow hazard model to help predict those days with a higher relative hazard for debris-flow activity park-wide based on prevailing and forecasted weather conditions. Debris flows are detected in near-real-time using the U.S. Geological Survey Real-time Seismic Amplitude Measurement (RSAM) tool. If an event is detected, we can then provide evacuation alerts to employees and visitors working and recreating in the areas downstream. Our goal is to accurately forecast the debris-flow hazards up to 7 days ahead of time and then use RSAM to detect debris flows within minutes of their genesis.

Valley Winds at the Local Scale: Correcting Routine Weather Forecast Using Artificial Neural Networks

In regions of complex topography, local flows are difficult to forecast on a routine basis, especially in stable conditions, due to the coarse resolution of operational models. The Cadarache valley (southeastern France) features this sort of complex topography. The Weather Research and Forecasting (WRF) model is run daily to forecast the weather in this region with a horizontal resolution of 3 km. Such a resolution cannot resolve all topography details of the small Cadarache valley, and therefore its local wind patterns. Other variables, however, that are less dependent on the subgrid topography, are satisfactorily forecasted, and used as inputs to an artificial neural network (ANN) designed to reproduce wind observations inside the valley from WRF forecasts. A variable selection procedure identified 5 key input variables that best drive the ANN. With respect to the WRF output, the ANN significantly improves forecasted low-level winds, both for speed and direction. This study demonstrates the potential for the ANN technique to be used as a correcting tool to forecast weather conditions at the local scale when numerical modeling is performed at a resolution too coarse to take into account the effect of local topography.

Weather Forecasting for Batu Pahat Using Neural Network

Nowadays, weather forecasting plays a vital role in human activities. The complexity of data gathering in the meteorology department and high technical costs lead to inaccurate weather forecasting. Due to minimizing this problem, an application of using an artificial neural network (ANN) has been developed to forecast weather conditions using the Matlab compiler. This application will help users to define the weather conditions daily well and made early preparation to encounter uncertainty. This application performs in Multilayer Perceptron (MLP) using the back-propagation (BP) algorithm. The data is undergoing training and testing based on actual data obtained from the Malaysian Meteorological Department. The result will show in graph plotting for the training and testing process. Predictive accuracy for each step of stimulation will measure using Mean Square Error (MSE) graph in this application. Therefore, this application will be able to assist the Malaysian Meteorological Department to forecast weather, so our government and people have sufficient time to prepare and solve.

Assessing the impact of climate change on sugarcane and adaptation actions in Pakistan

Climate change is a challenging global issue for the sustainable production of various crops around the world as change in climatic patterns can create a stressful environment for plant growth. This study assesses the impact of climate change on future water demand and sugarcane yield in Pakistan for the baseline (1981–2005) and future timescales (2020s, 2050s and 2080s). For evaluating the crop water requirement and yield under future climate, CROPWAT 8.0 and AquaCrop models were used, respectively. For the estimation of future climate, three different Regional Climatic Models were applied under two projection scenarios i.e. RCP 4.5 and RCP 8.5. Maximum temperature, precipitation and minimum temperature displayed an increasing trend under the projected future climatic conditions. The results revealed a growth in the crop water requirement with a subsequent escalating irrigation demand caused due to rise in projected temperature; this is because the projected increase in precipitation under forecasted weather conditions cannot compensate for the increased evaporative demand. Moreover, the results showed a general increasing trend of sugarcane yield under projected climate. By delaying the crop calendar, an overall decrease in crop water requirement in the range of 10.7–12.6% and increase in the yield in the range of 0.37–6.48 tha−1 can be observed under both climate change scenarios. Moreover, 90% of control irrigation level proved beneficial in terms of saving around 10% irrigation water with acceptable yield reduction. The outcomes of the study are supportive for growers to gain more yield using less amount of water and to adapt to changing climate. The results are also helpful for policy makers to develop adaptation strategies to improve sugarcane productivity and to address water stress in Pakistan.

Using weather forecasts to forecast whether bikes are used

Although several papers have shown that bike ridership is affected by actual weather conditions, this is the first study to comprehensively investigate the impact of forecasted weather conditions on bike ridership. The results show that both actual and forecasted weather conditions can be used as useful explanatory variables for predicting bicycle usage. Even incorrect weather forecasts can impact on bike ridership, which underlines the importance of weather forecast effects for traffic planners; for example, forecasted rain can reduce bike traffic by 3.6% in periods that turn out to be rain-free. Additionally, a digital image-processing method is used to calculate the darkness of the cloud coverage displayed on weather forecast maps. The results imply that bike ridership is significantly smaller in regions with darker forecasted clouds. It is also shown that weather forecasts have a stronger impact on recreational bike traffic than on utilitarian traffic. Furthermore, various lagging and leading effects of rain forecasts are outlined. Morning rain forecasts can, for example, reduce bike ridership in midday and afternoon hours that were predicted to be rain-free. To derive these results, hourly bicycle counts from 188 automated counting stations in Germany are collected for the years 2017 and 2018. They are linked to actual weather data from Germany’s National Meteorological Service and with historical weather forecasts that are deduced from weather maps of Germany’s most-watched television news program. Log-linear and negative binomial regression models are then used to estimate the weather forecast effects.

Weather Prediction using Machine Learning and IOT

This project proposes a method for forecasting weather conditions and predicting rainfall by means of machine learning. Here, there are two set ups: one, to measure the weather parameters like temperature, humidity using sensors along with Arduino and another set up, to display the current values(status) and predicted rainfall based on the trained machine learning data sets. The weather forecasting and prediction is done based on the older datasets collected and compared with the current values. The user need not have a backup of huge data to predict the rainfall. Instead a machine learning algorithm can suffice the same. The temperature, humidity sensor modules are used to measure weather parameters and interfaced to an Arduino controller. The proposed setup will compare the forecast value with real-time data, and the predict rainfall based on the dataset fed to the machine learning algorithm.

Quantification and visualisation of extreme wind effects on transmission network outage probability and wind generation output

An approach is demonstrated to visualise overhead line failure rates and estimated wind power output during extreme wind events on transmission networks. Reanalysis data is combined with network data and line failure models to illustrate spatially resolved line failure probability with data corrected for asset altitude and exposure. Wind output is estimated using a corrected power curve to account for high speed shutdown with wind speed corrected for altitude. Case studies demonstrate these methods' application on representations of real networks of different scales. The proposed methods allow users to determine at-risk regions of overhead line networks and to estimate the impact on wind power output. Such techniques could equally be applied to forecasted weather conditions to aid in resilience planning. The methods are shown to be particularly sensitive to the weather data used, especially when modelling risk on overhead lines, but are still shown to be useful as an indicative representation of system risk. The techniques also provide a more robust method of representing weather-related failure rates on lines considerate of the altitude, voltage level, and their varying exposure to weather conditions than current techniques typically provide, which can be used to usefully represent failure probability of lines during storms.

UAVs fleet mission planning robust to changing weather conditions

Abstract A fleet of homogeneous UAVs fly in a 2D plane matching a distribution network to service customers in a collision-free manner. Limited UAVs’ battery capacity and UAVs’ weight reduction during they traveling along planned routes and goods delivery as well as changing weather conditions are also taken into account. The goal is to determine a set of routings covering all delivery points so that the total distance traveled by the UAVs fleet is constrained by a battery capacity limit. All customers’ demands are realized within a given time horizon and take into account forecasted weather constraints, focusing on changes in a wind speed and direction. In this context, the main objective is to propose a declarative model allowing one to prototype proactive routings of UAVs fleet mission. Computational experiments assessing alternative strategies of UAVs fleet mission robust to forecast weather conditions are discussed.

Determinants of farmers’ perception about climate change in Khyber Pakhtunkhwa-Pakistan

Climate change is a global environmental threat to almost all economic sectors, especially the agricultural sector and Pakistan is no exception to these threats. In Pakistan, farmers are the primary stakeholders in agriculture and are more at risk due to climatic vulnerability. This study, therefore examined farmers’ perception about climate change across four agro-climatic zones of Khyber Pakhtunkhwa-Pakistan. A total of 210 farmers were selected through multistage random sampling technique and interviewed by using a well-designed interview schedule. The study results revealed that maize and wheat were the two main crops cultivated by 99.05% and 97.14% respondents, respectively. Among the total targeted respondents, 89.52% respondents reported an increase in temperature, whereas 79.05% observed decrease in rainfall in the province. The logistic regression analysis was carried out to assess factors influencing farmers’ perceptions of climate change. The analysis revealed that 89.52%of farmers in Khyber Pakhtunkhwa had a perception that climate was changing. In this respect, age, educations, household size, farming experience and farm income were recorded having statistically significant effect on the probability of the respondents to perceive climate change. It is concluded that mean temperature was increased, while a significant decrease was noted in annual precipitation. Based on the study findings, it is recommended that policy makers should devise policies helping the farmers in coping with changing climate to avoid adverse effects of climatic pattern on overall crop yield. Agricultural extension and meteorological department should work together to forecast weather conditions and transfer the required information and mitigation strategies to the target farming community. Keywords: Climate change; Logit regression; Khyber Pakhtunkhwa-Pakistan; Perception http://dx.doi.org/10.19045/bspab.2019.80161

A Supervised Learning Algorithm to Forecast Weather Conditions for Playing Cricket

Now days, Machine learning is considered as the key technique in the field of technologies, such as, Internet of things (IOT), Cloud computing, Big data and Artificial Intelligence etc. As technology enhances, lots of incorrect and redundant data are collected from these fields. To make use of these data for a meaningful purpose, we have to apply mining or classification technique in the real world. In this paper, we have proposed two nobel approaches towards data classification by using supervised learning algorithm

Climate change implications for environmental performance of residential building energy use: The case of Qatar

Building energy demands in the future could increase due to the climate change driven increase in outdoor temperatures. Gulf countries characterized by extreme climate and dependency on fossil fuels would be especially vulnerable to such impacts. Considering that in such countries about 80% of energy produced is used for cooling services, an increase in cooling demand would affect both building environmental impact and grid stability. The main scope of this study is to quantify such implications by considering the residential sector of Qatar as a case study. A representative building of the national building stock was selected, and energy consumption was modelled for present and forecasted weather conditions. The results indicated that in the future, heat waves will occur more frequently, with a higher intensity and a longer duration. Consequently, building energy consumption will increase for up to 30%. Such increase in energy demand would result in higher CO2 emissions, water and fossil fuel depletion rates, as well as increased impact on already strained local marine ecosystem. In order to mitigate such impacts, New regional environmental policies should be developed for large-scale renovation of the existing building stock and installation of renewable energy systems in order to mitigate the environmental impacts.

Antarctic Verification of the Australian Numerical Weather Prediction Model

Abstract The Australian Community Climate and Earth-System Simulator-Global (ACCESS-G) features an atmosphere-only numerical weather prediction (NWP) suite used operationally by the Australian Bureau of Meteorology to forecast weather conditions for the Antarctic. The current operational version of the forecast model, the Australian Parallel Suite v2 (APS2), has been used operationally since early 2016. To date, the performance of the model has been largely unverified for the Antarctic and anecdotal reports suggest challenges for model performance in the region. This study investigates the performance of ACCESS-G south of 50°S over 2017 and finds that model performance degrades toward the poles and in proportion to forecast horizon against a range of performance metrics. The model exhibits persistent negative surface and mean sea level pressure biases around the Adelie Land coast, which is linked to the underrepresentation of model winds to the west, and driven by positive screen temperature biases that inhibit modeled katabatic outflow. These results suggest that an improved representation of boundary layer parameterization could be implemented to improve model performance in the region.

Indigenous knowledge for seasonal weather and climate forecasting across East Africa

Climate information and agro-advisory services are crucial in helping smallholder farmers and pastoralists in East Africa manage climate-related risks and adapt to climate change. However, significant gaps exist in provision of climate information that effectively addresses the needs of farmers and pastoralists. Most farmers and pastoralists, therefore, rely on indigenous knowledge (IK), where local indicators and experiences are used to observe and forecast weather conditions. While IK-based forecasting is inbuilt and established in many communities in East Africa, coordinated research and systematic documentation of IK for weather forecasting, including accuracy and reliability of IK is largely lacking. This paper documents and synthesizes existing IK for weather forecasting in East Africa using case studies from Ethiopia, Tanzania, and Uganda. The results show that farmers and pastoralists use a combination of meteorological, biological, and astrological indicators to forecast local weather conditions. IK weather forecasting is, therefore, crucial in supporting efforts to improve access to climate information in East Africa, especially in resource-poor and vulnerable communities. The paper draws valuable lessons on how farmers and pastoralists in East Africa use IK weather forecasts for making crop and livestock production decisions and demonstrates that the trust and willingness to apply scientific forecasts by farmers and pastoralists is likely to increase when integrated with IK. Therefore, a systematic documentation of IK, and a framework for integrating IK and scientific weather forecasting from national meteorological agencies can improve accuracy, uptake, and use of weather forecasts.

A Model Analysis of Observed and Predicted Climatic Changes in Groundwater Recharge in a Small River Basin

The relationship between groundwater recharge in a small river basin to the current and expected climatic changes in the European area of Russia has been analyzed using the catchment basin of the Zhizdra River (Kaluga Region) as an example. The analysis is based on modeling of moisture transformation processes on the earth’s surface and moisture transfer in the unsaturated zone. The global climate predictions of five CMI5 Coupled Atmosphere–Ocean General Circulation Models (GCM) have been applied to forecast the groundwater recharge in the second half of the 21st century using the LARSWG5 forecast weather conditions generator. Despite the fact that all used GCMs predict a warming by 2–6°C in the studied region, the difference in the predicted recharge values is still considerable due to the difference in the predicted dryness index.

Model analysis of observed and predicted climate changes of groundwater recharge in the basin of a small river

The analysis of the connection of groundwater recharge in the basin of a small river with the current and expected climatic changes in the European territory of Russia is carried out using the catchment basin of Zhizdra river Kaluga region as an example. The analysis was based on the modeling of the processes of transformation of moisture on the earth surface and moisture transfer in the aeration zone. The results of global climate predictions for five models of the general circulation of the atmosphere and ocean (GCM) from the CMI5 family were applied for the forecast in the second half of the 21st century using the LARSWG5 forecast weather conditions generator. The simulation results show that despite the fact that all the GCM used predict a warming in the region at 2–6 ᵒC, the difference in the predicted recharge is still significantly large, which is associated with the difference in the predicted dryness index.

Coupling computational fluid dynamics with the high resolution rapid refresh model for forecasting dynamic line ratings

This study looks at forecasted dynamic line ratings in southern Idaho by using data from the high resolution rapid-refresh (HRRR) model for forecasted weather conditions. The HRRR model can provide accurate 18-h forecasts with a 15-min temporal resolution. Typical static ratings used for overhead transmission lines use overly conservative assumptions for local weather conditions, such as using the maximum solar irradiance and ambient temperature measured during the summer, combined with low wind speed for an entire season. The HRRR forecast model used here has high spatial resolution to provide local forecast conditions along the entire length of a transmission line. The area that is of interest in this study is in southern Idaho, spanning a total of 15,000 square kilometers. The forecasted weather data are coupled with a computational fluid dynamics (CFD) model of the wind in the region for fine-scale resolution of convective cooling rates on the midpoint of each individual transmission-line span. This high-fidelity approach can be used to find the minimum ampacity across all given midpoints of a transmission line to determine the limit to be used for the line rating. The ability to increase the overhead line rating above the conservative static approach with the forecasted weather data provides a large potential to alleviate congestion and provide data for utility-market transactions, as well as benefits for wind energy generation through concurrent cooling. This study shows that for the region of interest, using forecasted weather data coupled with CFD modeling can calculate DLR ampacity rating above static over 90% of the time, with a small relative error in the forecasted ampacity over time.

Stochastic models of Ground Delay Program implementation for prediction, simulation, and insight

We use historical data to build two types of stochastic model of hourly Ground Delay Program (GDP) implementation for the three main airports near New York City: Newark Liberty International, LaGuardia, and John F. Kennedy International. The models predict the probability that a GDP will be initial ized or canceled in a given hour based on hundreds of features describing the situation at the airport, including features describing forecasted weather conditions and scheduled traffic. One is a regularized logistic regression model that ignores system dynamics and the other model is based on inverse reinforcement learning, so it considers system dynamics and the impact that GDP implementation actions have over time on some metrics. We evaluate the models based on two objectives: their ability to predict and simulate GDP implementation decisions in historical test data sets. As is expected based on the motivation for and objective of each type of model, regularized logistic regression models make superior predictions while simulations controlled by inverse reinforcement learning models produce average metric values that more closely match those produced by historical GDP implementation decisions. Finally, we draw insights about GDP implementation from the trained model parameters. For example, parameters of both models suggest while weather conditions and values of performance metrics such as airborne delay play a role in GDP decision making, parameters related to the predictability or continuity of existing GDP plans are also important.

Modelling the influence of crop density and weather conditions on field drying characteristics of switchgrass and maize stover using random forest

Field drying trials were conducted using both field baskets as well as grab sampling techniques to study drying behaviour of switchgrass and maize (corn) stover (CS). Environmental conditions such as hourly solar radiation, vapour pressure deficit (VPD), average wind speed, rainfall amount, harvesting method, and field operations such as swath density were used as variables for model development. A powerful classification-based algorithm, which uses a collection of decision trees called random forest (RF) was utilised to predict moisture content (MC) of switchgrass and CS on wet basis. RF predicted the MC of switchgrass and CS with a coefficient of determination of 0.77 and 0.79, respectively. Rainfall, hours after harvest, average change in solar radiation in past 12 h, average solar radiation in past 12 h, and swath density were found to be the important variables affecting the MC of CS. Drying CS in low density (LD) and medium density (MD) swaths facilitated quick drying even in moderate drying conditions. Rainfall events ranging from 1.5 to 7.5 mm were experienced during the switchgrass drying period which delayed crop drying by one day to several days depending on the weather conditions after rainfall. Several rewetting events were also observed due to dew at night which increased the MC in LD switchgrass and CS by 5–15%. The models developed in the current study will help in decision-making of switchgrass and CS collection after harvest, based on forecast weather conditions in lower Midwestern states.

Indigenous Knowledge and Practices on Climate Change in Selected Barangays of La Union: Towards Community Resilience

This study validated the indigenous knowledge and practices on climate change in selected upland barangays of the six indigenous municipalities of La Union. It utilized the participatory action research design in which those involved in the research participated and were consulted in the listing and validation of indigenous knowledge and practices. Barangays of the indigenous municipalities were selected as areas of the study due to the remoteness of the places which impedes the availability of access to scientific weather forecasts through television, newspapers, radios or even the internet. The key informants were senior citizens who have resided in the area for at least thirty years. They were chosen as respondents because they have a good knowledge of the environment and long periods of residence enabled them to acquire understanding on environmental indicators and how they are used to predict weather and adapt to climate change. This study found out that the key-informants have been using their observations of the following: a) environment, b) celestial bodies, and c) animal behavior in forecasting typhoons, droughts or even good weather conditions. Most of the indigenous knowledge and practices before a disaster were rated valid, meaning the prediction really happened, the indigenous knowledge and practices are widely used in the community, used for more than one generation, still being used and effective in predicting weather conditions. Use of plants to forecast weather conditions has low validity. Majority of the indigenous knowledge

Early warning method for transmission line galloping based on SVM and AdaBoost bi‐level classifiers

Transmission line galloping often causes structural and electrical failures, which is a serious threat to the security of transmission systems. Through analysing the influence factors of galloping, it reveals that weather conditions are the most significant excitation factors and conductors of any voltage level and region may gallop when the apt-galloping weather conditions are satisfied. This study proposes an early warning method for transmission line galloping based on support vector machine (SVM) and AdaBoost bi-level classifiers. First, a prediction model of apt-galloping weather conditions based on an SVM classifier is built through data mining of historical weather parameters in regions where galloping frequently occurred. When the forecast weather conditions of a particular region satisfy the apt-galloping weather conditions, the conductor type, cross-section and span of transmission line are further considered to realise early warning of galloping through an AdaBoost classifier. Finally, the historical galloping events of a power grid are adopted to verify the validity of the proposed methods. The test results indicate that both the accurate classification rate and accurate warning rate are above 90%, whereas the missed warning rate is below 10%. The models are suitable for early warning of transmission line galloping and can provide important decision support for operation staff of power grid.