Precise Weather Research Articles

A framework for optimal agent deployment and opportunistic routing in flying Ad-Hoc networks for precision weather forecasting

Drone based precision weather prediction and forecasting is a prevailing area of research in recent times. Modern flying ad-hoc network (FANET) leverages advanced low latency communication protocol between small flying drones and base stations. In this work, a FANET framework for efficient and precision weather prediction and forecasting that leverages ultra low latency opportunistic message transfer protocol (MTP) is proposed. An, edge empowered intelligent computing quasi-stationary sink nodes are employed to develop the FANET ecosystem further. Flying sensor nodes and edge computing quasi-stationary sink nodes are deployed in the target area in order to achieve optimal sensor coverage minimizing the cost of data transmission power thus ensuring long endurance operation of drones. An ensemble model is deployed in edge level for localized weather prediction. Opportunistic routing strategies are utilized for converge casting. The experimental results show the maximum of 0.98 message delivery ratio and a minimum of 820 ms latency is achieved through opportunistic MTP. An accuracy of 96.5% is achieved in predicting the temperature and humidity data metric.

Enhancing Bright Band Prediction Through RadioSonde RadioWind for International Telecommunication Union- Radio Communications (ITU-R)

As a vital source of information on atmospheric conditions at different altitudes, upper air observations are critical for precise weather forecasting, climate monitoring, and atmospheric research. In coastal areas like Vishakhapatnam that are subject to tropical cyclones and monsoonal fluctuations, radiosondes provide precise vertical profiles that are essential for weather forecasting. They also measure temperature, humidity, and pressure. The addition of wind speed and direction readings via radiowind devices, often known as "rawinsondes," improves severe weather forecasts and our understanding of atmospheric dynamics. Forecasting floods, maintaining aviation safety, and managing water resources are all aided by the ability to predict the bright band, a radar indication of the precipitation layer melting. Weather models and readiness for disasters are much improved in Vishakhapatnam when radiosondes, rawinsondes, and bright band predictions are used in tandem.

Effect of climate on emergency department admissions for renal colic: a comparative analysis of sedentary and non-sedentary lifestyles.

To investigate the impact of climate and seasonal variations on emergency department (ED) admissions for renal colic, while specifically comparing the differences between individuals with sedentary and non-sedentary lifestyles. A retrospective, single center study was conducted. Between the years 2017- 2020, medical records of patients admitted to the ED with renal colic, found to harbor ureteric stones on CT scans, were examined. Data on patients' occupational activities was collected through telephone questionnaires. Patients were categorized into two groups: sedentary and active. Precise weather data was obtained from the Israeli Meteorological Service website. The monthly average daily maximum temperatures were calculated. In the final sample of 560 participants, 285 were in the sedentary group, and 275 were in the active group. The study population consisted of 78.1% males and 21.9% females, with consistent gender ratios in both occupational groups. Prevalence of uric acid stones was higher in the sedentary group (p < 0.05). While there was a slight increase in admissions during the summer, seasonal distribution did not significantly differ among occupational groups. The study found no significant differences in admissions across different temperature ranges. Both groups exhibited a pattern of increased referrals during the summer and reduced referrals in the colder winter months. The baseline data revealed notable differences between the sedentary and active groups, particularly in the prevalence of uric acid stones. Climate factors, including temperature and seasonal variations, had limited impact on ED admissions for renal colic in patients with kidney stones, irrespective of their sedentary or active lifestyles. Both groups exhibited similar admission patterns, with a higher rate of admissions during the summer and a lower rate of admissions during the winter.

Assessing Solar Radiation Forecasting: a WRF-Solar Model Evaluation in Kupang, Indonesia

In response to the challenges faced by solar power plants in Kupang, Indonesia – particularly during the peak of the rainy season – this study aims to enhance the WRF-Solar (Weather Research and Forecasting–Solar) numerical weather prediction model. Accurate short-term solar radiation forecasting is crucial for managing electricity supply disruptions and controlling operational costs in the City of Kupang and its surroundings. The unpredictable weather patterns – characterized by intense cloud activity during these seasons – pose significant challenges for reliable solar energy generation, thereby making precise weather modeling an essential tool for effective power plant management. Spanning 2020–2022, the research focused on the peak rainy seasons, utilizing a specifically configured WRF-Solar model to simulate solar radiation. Validation of the WRF-Solar model using observational data from the Automatic Solar Radiation Stations at the Kupang Climatological Station showed correlation values consistently above 0.50. However, the model exhibited a tendency to overestimate radiation intensity for GHI and DNI parameters (correlation 0.78 and 0.54) and underestimate for DHI (correlation 0.80), with RMSE values ranging from 47–320 W/m² and MBE values from –41.84 to 116.34 W/m². The model's accuracy was notably higher under clear conditions, with lower RMSE and MBE values but decreased significantly under overcast conditions, hence indicating sensitivity to cloud cover. This study highlights the WRF-Solar model's capability in capturing the diurnal pattern and hourly fluctuations of solar radiation while also underscoring the need for improved cloud representation to enhance accuracy under diverse climatic conditions.

Designing a Weather Forecasting Application using API

A web-based tool called Weather Forecasting App gives users precise weather information according to their location. Numerous characteristics are employed in the proposed online application, including temperature, humidity, wind pressure, wind speed, dawn, and sunset times for the area. The precise locations of a forested and hilly environment can be found in this. All of the weather's parameter data was retrieved for this web application using a JavaScript API. To retrieve weather information specific to the user's location, the open weather map API is utilized in the suggested app.This web-based project is also a very responsive app that can provide a very interactive easy-to-use interface to the user on all their devices. Mean this app is a dynamic site that can change its orientation and size depending upon the user's devices. The purpose of weather forecasting is to provide individuals and organizations with information they can use to reduce climate-related losses and grow the community benefits, which include health and property protection, public health and safety, and support economic prosperity and a standard of living

Spatiotemporal Convolutional Approach for the Short-Term Forecast of Hourly Heavy Rainfall Probability Integrating Numerical Weather Predictions and Surface Observations

Abstract The accurate prediction of short-term rainfall, and in particular the forecast of hourly heavy rainfall (HHR) probability, remains challenging for numerical weather prediction (NWP) models. Here, we introduce a deep learning (DL) model, PredRNNv2-AWS, a convolutional recurrent neural network designed for deterministic short-term rainfall forecasting. This model integrates surface rainfall observations and atmospheric variables simulated by the Precision Weather Analysis and Forecasting System (PWAFS). Our DL model produces realistic hourly rainfall forecasts for the next 13 h. Quantitative evaluations show that the use of surface rainfall observations as one of the predictors achieves higher performance (threat score) with 263% and 186% relative improvements over NWP simulations for the first 3 h and the entire forecast hours, respectively, at a threshold of 5 mm h−1. Noting that the optical-flow method also performs well in the initial hours, its predictions quickly worsen in the final hours compared to other experiments. The machine learning model, LightGBM, is then integrated to classify HHR from the predicted hourly rainfall of PredRNNv2-AWS. The results show that PredRNNv2-AWS can better reflect actual HHR conditions compared with PredRNNv2 and PWAFS. A representative case demonstrates the superiority of PredRNNv2-AWS in predicting the evolution of the rainy system, which substantially improves the accuracy of the HHR prediction. A test case involving the extreme flood event in Zhengzhou exemplifies the generalizability of our proposed model. Our model offers a reliable framework to predict target variables that can be obtained from numerical simulations and observations, e.g., visibility, wind power, solar energy, and air pollution.

An empirical study of the influences of gustiness on vertical mixing at the air-sea boundary

The exchange of momentum across the air-sea interface is a key driver of the earth system and its accurate parameterization is essential for precise weather and climate forecasting. However, our understanding of gustiness as an independent factor that can contribute to the momentum flux is limited. Using data collected from the R/P FLIP, as part of the Couple Air-Sea Processes and Electromagnetic ducting Research (CASPER) experiment, we explored the mechanisms by which gustiness contributes to the total interfacial momentum flux. We investigate how gustiness affects both the temporal and spatial (vertical) variance of turbulence in the atmospheric surface layer and show that high gustiness was associated with strong anisotropic turbulence at each measurement height. This was found to increase vertical wind fluctuations and inject additional momentum across the air-sea interface at lower wind speeds. Increased gustiness was also associated with the breakdown of the constant flux layer, which is generally assumed to exist over the ocean. This study has implications for both momentum flux parameterization and the use of similarity theory to model the flux-gradient relationship in the gusty atmospheric surface layer, thereby influencing the forecasting of climate and weather.

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Abstract We developed an advanced postprocessing model for precipitation forecasting using a microgenetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multimodel yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multimodel outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard postprocessing operations. This approach can significantly improve the accuracy of precipitation forecasts. Significance Statement We developed an optimized multimodel for predicting precipitation occurrence using advanced techniques. By integrating various weather models with their optimized weights, our approach outperforms the method of using an arithmetic average of all models. This study underscores the potential to enhance regional precipitation forecasts, thereby facilitating more precise weather predictions for the public.

Weather Prediction: Predicting Rain Using Weather Conditions

Weather forecasting is a major discipline that plays an important role in fields such as agriculture, transport, and emergency management, and it largely depends on accurate forecasts. Concerning this problem, this work aimed to analyze the effectiveness of recurrent neural networks, particularly the Long Short-Term Memory (LSTM), for estimating rainfall depending on precipitation, maximum temperature, minimum temperature, and wind speed. We will therefore use a large database containing recorded weather data obtained over several years to calibrate accurate predictive models designed to distinguish between drizzle, rain, sun, snow, and fog. The main idea of the work is to teach LSTM models that are capable of revealing temporal relations and patterns in sequential data, which makes them suitable to work on various time series forecasting such as weather prediction. The data is preprocessed effectively to clean it and make it ideal for our analysis to accurately compare the performance of one model against the others, we have divided the data into training, validation, and testing sets. The concurrency of the proposed LSTM model is then evaluated with the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) to measure the forecasting accuracy. The findings show a better predictive performance uplift whereby the best-performing LSTM model has an MSE of 8.74, RMSE of 2.96, MAE of 2.35, and R² of 0.83. Such metrics represent logical dependence between the predicted and actual weather conditions, proving thus the efficiency of the model. Also, the evaluation shows how hyper parameters’ optimization, features’ selection, and normalization, make a huge difference in the model’s performance and indicate that the precise management of weather parameters can result in better forecasts. However, the contributors of this research are not recluded to theoretical perspective; the present study can be useful for various subjects since the dependability of weather forecasts can be improved. They will be advantaged to have more precise weather data for crop growing, road networks, and other transport systems to prepare for the worst conditions, and emergency, rescue operations to be in a better position to handle certain disasters. Consequently, this study improves the academic literature on weather peculiarities with unforeseen downpours through a demonstration and explanation of the potential of LSTM networks to analyze key meteorological characteristics for rainfall prediction. Possible future study directions are outlined, proposing the expansion of features beyond those analyzed in the existing study to improve the predictive models, the usage of continuous rather than weekly data, as well as considering the mixed-ingredients approach for increasing the prediction accuracy. This inclusive strategy seeks to enhance the realistic stages in the phased meteorological prognosis and also timely resource allocation and management tactics within climate volatility.

IMPLEMENTATION OF GABELLA METHOD AND RANDOM FOREST FOR GROUND CLUTTER DETECTION IN PADANG WEATHER RADAR DATA

Weather radar is an active remote sensing instrument for various hydrological and meteorological applications. One advantage of weather radar is its ability to detect rainfall in space and time with high spatial resolution. However, one of the issues that contaminate radar observations is ground clutter. Ground clutter is a signal or echo from non-meteorological objects on the earth’s surface that are stationary in the time domain. Detecting and mitigating clutter effects is crucial to achieve precise weather measurements. This research aims to implement the Gabella and random forest methods to detect ground clutter in Padang weather radar data and determine the optimal method between the two. The implementation of the Gabella method for detecting ground clutter in Padang weather radar data was suboptimal. This was due to the most duplicated data at the same point being only 15.97% of the total data. Meanwhile the random forest method obtained a kappa value of 92.03%. This indicates that the random forest model created using 2000 trees as the parameter performs well. Based on these results, the random forest method identified as the most optimal approach for detecting ground clutter in Padang weather radar data.

Research and Application of Intelligent Weather Push Model Based on Travel Forecast and 5G Message

In the realm of daily activity planning, precise weather forecasting services hold paramount significance. However, the prevalent dissemination of weather forecasts through conventional channels like radio, television, and the internet often yields only generalized regional predictions. This limitation contributes to diminished forecast reach, inadequate accuracy, and a lack of individualization, thwarting the effective distribution of meteorological insights and inhibiting the fulfillment of personalized forecast demands. Addressing these concerns, our study proposes a personalized weather forecasting approach that harnesses machine learning techniques and leverages the 5G messaging platform. By amalgamating projected user travel data, we augment personalized weather reports and extend user coverage to achieve tailored, timely, and high-quality weather services. Concretely, our research commences with an extensive analysis of large-scale user travel behavior data to extract pertinent travel attributes. Subsequently, we construct a user’s future location prediction model—dubbed the Loc-PredModel—by employing the Extreme Gradient Boosting (XGBoost) algorithm to forecast users’ trip destinations and arrival times. Anchored in the anticipated outcomes of user travel behavior, personalized weather data reports are formulated. Experimental results underscore the Loc-PredModel’s remarkable predictive prowess, demonstrating a root mean squared error (RMSE) value of 0.208 and a coefficient of determination (R2) value of 0.935, affirming its efficacy in prognosticating users’ trip destinations and arrival times. Furthermore, our 5G message-driven platform, rooted in intelligent personalized meteorological services, underwent testing within Chengdu city and garnered positive user feedback. Our research effectively surmounts the limitations of conventional weather forecasting platforms by furnishing users with more precise and customized weather information predicated on behavioral analysis and the 5G information ecosystem. This study not only advances the theoretical groundwork of intelligent meteorology but also offers invaluable insights and guidance for future advancement. By providing users with a more personalized and timely intelligent meteorological service experience, our approach exhibits transferability, with the research methodology and model potentially extendable nationwide or even on a larger scale beyond the study’s Chengdu-based scope.

DAS with telecommunication fibre-optic cable in urban areas can record storm-induced seismic noise

SUMMARY Extreme weather events threaten life and property in populated areas. Timely and precise weather event monitoring and risk assessment are critical, but can be hampered by limited meteorological stations in cities. Recent studies have shown that seismic stations are sensitive to storm-induced noise. This study aims to investigate the sensitivity of distributed acoustic sensing (DAS), a technology capable of turning existing optical fibres into dense seismic sensors, for recording storm-induced seismic noise. We analyse 4-month continuous DAS recordings (June–September 2021) from a 4.2-km-long underground fibre-optic array in State College, PA. We calibrate the DAS data by comparing it to various meteorological data (rainfall and wind speed) from nearby weather stations. We first show DAS-recorded low-frequency wind-induced noise (0.5–8 Hz) probably caused by light poles swaying in the wind, as observed resonant frequencies agree with theoretical natural frequencies of nearby light poles. We find a strong linear correlation between DAS energy and wind speed. We further characterize rain-induced noise. Detailed observations from two rain events: a moderate rain and a heavy rain from Hurricane Ida, suggest that rain-induced noise is not generated by direct raindrops hitting the ground. Instead, the low-frequency noise (2–8 Hz) is attributed to the acoustic noise generated by water flow in stormwater drainage systems. Strong high-frequency noise up to 125 Hz is likely related to the rapid rainwater filling from the surface to the drainage system during heavy rain. We show linear relations between rain-induced DAS energy and rainfall rate, where the slopes of relations are related to the volume of rainwater, suggesting the influence of surface water and rainwater flow in the drainage system on DAS signals. Our results show the possibility of using DAS-equipped pre-existing telecom fibre-optic cables for sensing windstorms and rainstorms in urban areas and their interactions with urban infrastructures.

WEATHER EVENTS FORECASTING IN KOTA KINABALU USING A FUZZY RULE-BASED SYSTEM

A weather forecast is a prediction of weather phenomena in the future by collecting as much data as possible. Major worldwide climate studies include the breakthrough and interpretation of complicated weather patterns in order to successfully forecast varied weather events. It is quite complex to screen so many parameters from several factors to discover precise weather conditions. In this paper, a fuzzy rule-based system (FRBS) is applied for weather forecasting in Kota Kinabalu. There are 234 fuzzy rules being developed as the FRBS are constructed along and successfully implemented in MATLAB. Based on the results, the comparison between actual and forecast data shows moderate precision as the accuracy level is higher than the level of error analysis.

Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks

One of the most controversial issues in the mid-term load forecasting literature is the treatment of weather. Because of the difficulty in obtaining precise weather forecasts for a few weeks ahead, researchers have, so far, implemented three approaches: a) excluding weather from load forecasting models altogether, b) assuming future weather to be perfectly known and c) including weather forecasts in their load forecasting models. This article provides the first systematic comparison of how the different treatments of weather affect load forecasting performance. We incorporate air temperature into short- and mid-term load forecasting models, comparing time-series methods and feed-forward neural networks. Our results indicate that models including future temperature always significantly outperform models excluding temperature, at all-time horizons. However, when future temperature is replaced with its prediction, these results become weaker.

An appraisal on the determinants of adaptation responses to the impacts of climate variability on coffee Production: Implication to household food security in Nensebo Woreda, Ethiopia

Climate change and variability already had and will continue to harm coffee production throughout the world. Between 80% and 90% of the world’s coffee is produced by smallholder farmers who depend on nature. This study examines the determinants of climate variability adaptation response in coffee production and its implication for household food security in Nensebo Woreda, Ethiopia. Quantitative and qualitative data were collected from randomly selected 181 coffee grower farmers as well as purposively selected key informants, and focus group discussion participants. Arial gridded dikadal meteorological data were also used. Data were analyzed using thematic content analysis, descriptive statistics, and econometric analysis methods. The study reveals that in the study area monthly, seasonal, and annual minimum and maximum temperature has been increasing over the last three decades. Rainfall has been erratic both in amount and timing. Multinomial logit analysis showed that age, farm experience, farm size, family size, average days spent on non-farm and off-farm activities, DA contact, climate change information, and income from coffee were the factors that significantly influenced adaptation responses to climate variability. The study also revealed the presence of a varying degrees of household food insecurity. Therefore, to enhance the adaptive capacity of coffee grower farmers, it is necessary to provide area specific and precise weather forecast information. Likewise, the study shows that building the adaptive capacity of coffee grower farmers requires expanding access to credit and savings services as well as market information. Doing all of these requires extensive and continuous awareness creation intervention about the actual and potential effects of climate variability on coffee production.

Using body size as an indicator for age structure in field populations of Aedesaegypti (Diptera: Culicidae)

BackgroundThe Aedesaegypti mosquito is a vector of several viruses including dengue, chikungunya, zika, and yellow fever. Vector surveillance and control are the primary methods used for the control and prevention of disease transmission; however, public health institutions largely rely on measures of population abundance as a trigger for initiating control activities. Previous research found evidence that at the northern edge of Ae.aegypti’s geographic range, survival, rather than abundance, is likely to be the factor limiting disease transmission. In this study, we sought to test the utility of using body size as an entomological index to surveil changes in the age structure of field-collected female Aedesaegypti.MethodsWe collected female Ae.aegypti mosquitoes using BG sentinel traps in three cities at the northern edge of their geographic range. Collections took place during their active season over the course of 3 years. Female wing size was measured as an estimate of body size, and reproductive status was characterized by examining ovary tracheation. Chronological age was determined by measuring transcript abundance of an age-dependent gene. These data were then tested with female abundance at each site and weather data from the estimated larval development period and adulthood (1 week prior to capture). Two sources of weather data were tested to determine which was more appropriate for evaluating impacts on mosquito physiology. All variables were then used to parameterize structural equation models to predict age.ResultsIn comparing city-specific NOAA weather data and site-specific data from HOBO remote temperature and humidity loggers, we found that HOBO data were more tightly associated with body size. This information is useful for justifying the cost of more precise weather monitoring when studying intra-population heterogeneity of eco-physiological factors. We found that body size itself was not significantly associated with age. Of all the variables measured, we found that best fitting model for age included temperature during development, body size, female abundance, and relative humidity in the 1 week prior to capture . The strength of models improved drastically when testing one city at a time, with Hermosillo (the only study city with seasonal dengue transmission) having the best fitting model for age. Despite our finding that there was a bias in the body size of mosquitoes collected alive from the BG sentinel traps that favored large females, there was still sufficient variation in the size of females collected alive to show that inclusion of this entomological indicator improved the predictive capacity of our models.ConclusionsInclusion of body size data increased the strength of weather-based models for age. Importantly, we found that variation in age was greater within cities than between cities, suggesting that modeling of age must be made on a city-by-city basis. These results contribute to efforts to use weather forecasts to predict changes in the probability of disease transmission by mosquito vectors.Graphical abstract

The contribution of weather forecast information to agriculture, water, and energy sectors in East and West Africa: A systematic review

The provision of timely and precise weather information could reduce the vulnerability of people to climate change risks. In this study, we conduct a systematic review to synthesize the existing evidence on weather information services for the agriculture, water, and energy sectors of East and West Africa and identify priorities for future research. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement checklist. The review identified relevant peer-reviewed publications using ScienceDirect and Scopus databases for original research articles published in English from 2000 to 2022. After applying the eligibility criteria, 25 articles were included in the final review. The themes emerging from the articles were extracted, and a summary was provided to illustrate each theme. The review revealed that weather information studies focus more on the agriculture sector than energy production and water resource management. Users of weather information mainly accessed information related to rainfall and temperature, and the information was accessed mainly through radio, mobile phones, and television. Most of the information provided focused on generic meteorological forecasts instead of tailored impact-based forecasts. Only very few users can access, or benefit from the information produced due to poor communication and technical understanding of weather information. In addition, a lack of downscaled information, logistics, and trust hinders the uptake and use of climate information. Consequently, mainstreaming capacity-building of key stakeholders is required to promote effective adoption and strengthening of climate information services across East and West Africa.Systematic Review Registration: [website], identifier [registration number].

Forecasts of the Warm-Sector Heavy Rainfall With a Warm Shear Pattern Over Coastal Areas of the Yangtze–Huaihe River in a Regional Business Forecast Model

Based on the observational hourly precipitation data and the ERA5 reanalysis datasets, the short-term forecasts of the warm-sector heavy rainfall with warm-type shear line (WRWS) events over the coastal areas of the Yangtze–Huaihe River (YHR) are investigated in the regional business model Precision Weather Analysis and Forecasting System (PWAFS). Evaluations and diagnoses are carried out via objective estimations and composition analyses for the rainy season of 2017. Results show that the short-term forecasts of PWAFS are characterized by considerable skills for WRWS events in the coastal areas of YHR in view of the object-based diagnostic evaluation, which, however, tend to generate the rain belts with northeast shift phases and weaker intensities. Meantime, the threat score results for WRWS-associated processes show that the model forecasting skill declines sharply as the precipitation intensity increases. Moreover, composition differences of the synoptic-scale thermodynamic characteristics between observations and forecast results are diagnosed to reveal the possible mechanisms of the short-term forecast biases toward WRWS. The zonal westerlies are overestimated in the model, while the southerlies are underestimated in the lower troposphere over coastal areas of YHR, leading to the northeastward shifted shear line and the absent moisture channel associated with the East China Sea at the boundary layer. Attributed to these atmospheric circulation biases, the accumulated warm and moist energy is weaker at the boundary layer, and hence, the short-term forecasts of the rain-belt location for WRWS over the YHR coastal areas have northeast shifting phases with weaker intensities of precipitation in forecasts of the regional business model PWAFS.

IMPLEMENTASI METODE K-NEAREST NEIGHBOUR DALAM MEMPREDIKSI CURAH HUJAN DI KOTA BOGOR

Accurate weather prediction information is important for various fields that are closely related to weather forecasting, such as agriculture, fisheries and many more. Because precise weather forecasts are very useful for various fields of carrying out various activities. Because of that, it is necessary to make an application to find weather or rainfall prediction information, so that the information can be utilized optimally by the community. In this journal the authors apply the k-nearest neighbors (k-NN) method based on rainfall data obtained from the Bogor climatology station from 2016-2017 and the test results show that the predicted rainfall for the Bogor area with the K-Nearest Neighbor algorithm obtained a value of 0, 93148.&#x0D;

The retrospective comparison of Olympic rowing results considering weather

The purpose of the paper is comparing the rowing results in different aeroand hydrodynamics conditions. We analyzed the winners’ results of the single sculls (1x) men finals at the Olympic Games regattas from Sydney to Tokyo. We used precise weather hour diary and satellite maps to reconstruct the exact weather race conditions.We found data on the sport results and parameters of the rowers on the official websites of the competitions and the WorldRowing website. The created physical and mathematical model of the weather conditions influence is based on the second law of mechanics and includes 22 variables which characterise environment and features of the rowing biomechanical system. The physical and mathematical model was implemented in the form of a computer application. The computer application made it possible to accurately assess the preparedness of athletes by clearing the distance time off the environment influence. The pure characteristic time allowed comparing the results obtained in different weather conditions. Calculation reveals that the weather conditions have an effect on race time significantly. The weather influence on Olympic race was in the range of - 10.10 to +5.94 seconds. The presented approach allows to register world and Olympic records for the first time in rowing history.