scholarly journals FLUKTUASI DEMAM BERDARAH DENGUE TERKAIT VARIABILITAS CUACA DI KLATEN, INDONESIA

2021 ◽  
Vol 13 (1) ◽  
pp. 45-60
Author(s):  
Tri Baskoro Tunggul Satoto ◽  
Nur Alvira Pascawati ◽  
Ajib Diptyanusa ◽  
Luthfan Lazuardi ◽  
Alvin Harjono Dwiputro ◽  
...  
Keyword(s):  
Wind Speed ◽  
Warning System ◽  
Hemorrhagic Fever ◽  
Weather Conditions ◽  
Specific Humidity ◽  
Weather Data ◽  
Dominant Factor ◽  
Regional Patterns ◽  
Central Java ◽  
Preventive Actions

Klaten Regency is one of the Dengue Hemorrhagic Fever (DHF) endemic areas in Central Java. Weather conditions can have an impact on vector dynamics, dengue virus development, and interactions between mosquitoes and humans. The purpose of this study was to determine the pattern of dengue transmission in twenty-six sub-districts in Klaten Regency based on wind speed, specific humidity, rainfall, and temperature. This study was conducted using a retrospective cohort design based on Giovanni-National Aeronautics and Space Administration (NASA) data during the last three years (2016-2018). The independent variables in this study were: wind speed (m/s), specific humidity (g/kg), rainfall (mm/month), and temperature (oC), while the dependent variable was the number of dengue cases in 26 sub-districts in 2014-2014. 2016. Data were analyzed based on monthly patterns and regional patterns using correlation and regression tests with =0.05. The results showed that a total of 1,434 dengue cases were reported during this time period. Weather data analysis revealed that DHF fluctuations were correlated with wind speed in four sub-districts, specific humidity in seven sub-districts, rainfall in three sub-districts, and temperature in three sub-districts. Specific humidity variation plays a role of 21.8% as the dominant factor that can explain the case of DHF in the Klaten Regency. The results of this study can be applied to mitigate the transmission of DHF by determining preventive actions according to place and time and increasing the early warning system to deal with the threat of DHF outbreaks. Abstrak  Kabupaten Klaten adalah salah satu daerah endemis Demam Berdarah Dengue (DBD) di Jawa Tengah. Kondisi cuaca dapat berdampak pada dinamika vektor, perkembangan virus dengue, dan interaksi antara nyamuk dengan manusia. Tujuan dari penelitian ini adalah untuk mengetahui pola penularan DBD di dua puluh enam kecamatan yang berada di Kabupaten Klaten berdasarkan kecepatan angin, kelembaban spesifik, curah hujan dan suhu. Penelitian ini dilakukan menggunakan desain kohort retrospektif berdasarkan pada data Giovanni-National Aeronautics and Space Administration (NASA) selama 3 tahun terakhir (2016-2018). Variabel bebas dalam penelitian ini adalah: kecepatan angin (m/s), kelembaban spesifik (g/kg), curah hujan (mm/bulan) dan suhu (oC), sedangkan variabel terikat adalah jumlah kasus DBD di 26 kecamatan pada tahun 2014-2016. Data dianalisis berdasarkan pola bulanan dan pola wilayah dengan menggunakan uji korelasi dan regresi dengan α=0,05. Hasil penelitian menunjukkan bahwa  total sebanyak 1.434 kasus dengue dilaporkan selama periode waktu tersebut. Analisis data cuaca mengungkapkan bahwa fluktuasi DBD berkorelasi dengan kecepatan angin di empat kecamatan, kelembaban spesifik di tujuh kecamatan, curah hujan di tiga kecamatan dan suhu di tiga kecamatan. Variasi kelembaban spesifik berperan sebesar 21,8% sebagai faktor dominan yang dapat menjelaskan kasus DBD di Kabupaten Klaten.  Hasil studi ini dapat diaplikasikan untuk mitigasi penularan DBD dengan menentukan tidakan pencegahan menurut tempat dan waktu serta meningkatkan sistem kewaspadaan dini untuk menghadapi ancaman KLB DBD.

scholarly journals The Vertical City Weather Generator (VCWG v1.0.0)

2019 ◽  
Author(s):  
Mohsen Moradi ◽  
Benjamin Dyer ◽  
Amir Nazem ◽  
Manoj K. Nambiar ◽  
M. Rafsan Nahian ◽  
...  
Keyword(s):  
Wind Speed ◽  
Potential Temperature ◽  
Building Energy ◽  
Energy Model ◽  
Weather Generator ◽  
Specific Humidity ◽  
Weather Data ◽  
Rural Site ◽  
Vertical Diffusion ◽  
Urban Microclimate

Abstract. The Vertical City Weather Generator (VCWG) is a computationally efficient urban microclimate model developed to predict temporal and vertical variation of temperature, wind speed, and specific humidity. It is composed of various sub models: a rural model, an urban microclimate model, and a building energy model. In a nearby rural site, a rural model is forced with weather data to solve a vertical diffusion equation to calculate vertical potential temperature profiles using a novel parameterization. The rural model also calculates a horizontal pressure gradient. The rural model outputs are then forced on a vertical diffusion urban microclimate model that solves vertical transport equations for momentum, temperature, and specific humidity. The urban microclimate model is also coupled to a building energy model using feedback interaction. The aerodynamic and thermal effects of urban elements and vegetation are considered in VCWG. To evaluate the VCWG model, a microclimate field campaign was held in Guelph, Canada, from 15 July 2018 to 5 September 2018. The meteorological measurements were carried out under a comprehensive set of wind directions, wind speeds, and thermal stability conditions in both the rural and the nearby urban areas. The model evaluation indicated that the VCWG predicted vertical profiles of meteorological variables in reasonable agreement with field measurements for selected days. In comparison to measurements, the overall model biases for potential temperature, wind speed, and specific humidity were within 5 %, 11 %, and 7 %, respectively. The performance of the model was further explored to investigate the effects of urban configurations such as plan and frontal area densities, varying levels of vegetation, seasonal variations, different climate zones, and time series analysis on the model predictions. The results obtained from the explorations were reasonably consistent with previous studies in the literature, justifying the reliability and computational efficiency of VCWG for operational urban development projects.

Modeling the threats of dangerous weather conditions using the theory of blurred sets

2021 ◽  
Vol 94 ◽  
pp. 187-200
Author(s):  
D. A. Berezhnoy ◽  
◽  
S. Yu. Butuzov ◽  
O. A. Kosorukov ◽  
◽  
...  
Keyword(s):  
Wind Speed ◽  
Warning System ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Winter Period ◽  
Summer Period ◽  
Emergency Situations ◽  
Threat Level ◽  
The Republic ◽  
Theory Of Fuzzy Sets

Introduction. The author's methods of forecasting the threat of emergencies caused by hazardous meteorological conditions are considered to ensure the required level of safety, including on the territory of the Republic of Crimea. Research objective: increasing the efficiency of the warning system and informing the population about emergency situations caused by hazardous weather conditions. Methods. To solve the problem of decision-making when predicting emergency situations caused by hazardous meteorological conditions, the methods of the theory of fuzzy sets are used. The article presents a method for determining the criteria, with the help of which it is possible to establish exactly whether the presented object belongs to the corresponding class. To implement the goal of timely informing and alerting the population and services of the Unified State System for the Prevention and Response of Emergencies (RSChS), lowering the entropy of an adequate assessment of the situation and taking effective measures to preserve the life and health of people, it is proposed to rank the criteria by wind speed, precipitation and air temperature , according to the levels of danger, indicating specific preventive measures for the population, management bodies and RSChS services. Results and discussion. A method is presented for determining the criteria by which it is possible to establish exactly whether the presented object belongs to the appropriate class. The method allows you to determine to what level of danger the situation can go. Conclusions. As the analysis has shown, the likelihood of the occurrence of a yellow level of threat during the year is practically the same. At the same time, the likelihood of a deterioration in the situation with the already emerging yellow threat level in the autumn-winter period is slightly higher. When the wind speed reaches the upper boundaries of the yellow level of threat in the autumn-winter period, the situation worsens twice as often than with the same values of the indicators in the spring-summer period. Key words: meteorological conditions, forecasting, fuzzy set theory, decision support system.

scholarly journals Prediction model of dengue hemorrhagic fever transmission to enhance early warning system in Gergunung Village, Klaten District, Central Java

2019 ◽  
Vol 51 (03) ◽  
Author(s):  
Tri Baskoro Tunggul Satoto ◽  
Alfin Harjuno Dwiputro ◽  
Rifa Nadhifa Risdwiyanto ◽  
A. Ulil Fadli Hakim ◽  
Nur Alvira Pascawati ◽  
...  
Keyword(s):  
Prediction Model ◽  
Dengue Hemorrhagic Fever ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Hemorrhagic Fever ◽  
Central Java

scholarly journals Examining the role of wind in human illness due to pesticide drift in Washington state, 2000–2015

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Edward J. Kasner ◽  
Joanne B. Prado ◽  
Michael G. Yost ◽  
Richard A. Fenske
Keyword(s):  
Wind Speed ◽  
Precision Agriculture ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Complete Information ◽  
Washington State ◽  
Weather Data ◽  
Target Movement ◽  
Pesticide Drift ◽  
Spatio Temporal

Abstract Background Pesticides play an important role in protecting the food supply and the public’s health from pests and diseases. By their nature, pesticides can be toxic to unintended target organisms. Changing winds contribute to pesticide drift— the off-target movement of pesticides—and can result in occupational and bystander illness. Methods We systematically linked historical weather data to documented pesticide drift illnesses. We used Washington State Department of Health data to identify 252 drift events that included 690 confirmed cases of illness from 2000 to 2015. To characterize wind speed and direction at the time of the events, we paired these data with meteorological data from a network of 171 state weather stations. We report descriptive statistics and the spatio-temporal extent of drift events and compare applicator-reported weather conditions to those from nearby meteorological stations. Results Most drift events occurred in tree fruit (151/252 = 60%). Ground spraying and aerial applications accounted for 68% and 23% of events, respectively; 69% of confirmed cases were workers, and 31% were bystanders. Confirmed cases were highest in 2014 (129) from 22 events. Complete applicator spray records were available for 57 drift events (23%). Average applicator-reported wind speeds were about 0.9 m •sec− 1 (2 mi •hr− 1) lower than corresponding speeds from the nearest weather station values. Conclusions Drift events result from a complex array of factors in the agricultural setting. We used known spatio-temporal aspects of drift and historical weather data to characterize these events, but additional research is needed to put our findings into practice. Particularly critical for this analysis is more accurate and complete information about location, time, wind speed, and wind direction. Our findings can be incorporated into new training materials to improve the practice of pesticide application and for better documentation of spray drift events. A precision agriculture approach offers technological solutions that simplify the task of tracking pesticide spraying and weather conditions. Public health investigators will benefit from improved meteorological data and accurate application records. Growers, applicators, and surrounding communities will also benefit from the explanatory and predictive potential of wind ramping studies.

Association of Wind Speed and the Occurrence of Sickle Cell Acute Painful Episodes: Results of a Case-Crossover Study.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 3402-3402
Author(s):  
Vikki G. Nolan ◽  
Yuqing Zhang ◽  
Timothy Lash ◽  
Paola Sebastiani ◽  
Martin H. Steinberg
Keyword(s):  
Wind Speed ◽  
Crossover Study ◽  
Sickle Cell ◽  
Induction Time ◽  
Weather Conditions ◽  
Weather Data ◽  
Case Crossover ◽  
Measure Of Association ◽  
Local Weather

Abstract The role of weather as a possible trigger of sickle cell acute painful episodes has been debated for over 30 years. Early studies based on anecdotal evidence, such as patients reporting pain during the colder parts of the day or when swimming in the cold ocean on a particularly hot day, argued for an association between weather and the occurrence of pain. Recently published studies have shown an association with cold and rainy seasons and with windy weather and low humidity. Other studies however, have found no associations. A limitation of these studies is that they are based on seasonal trend data, mean monthly temperatures, hospital-wide visit rates, but not data at the individual level. To more accurately describe the role of weather as a trigger of painful events, we conducted a case-crossover study of the association of local weather conditions with the occurrence of individual pain crises. From the Cooperative Study of Sickle Cell Disease, 813 patients with 3,580 acute painful episodes were identified. For each pain episode, the hazard period was defined as the 48 hours preceding the onset of pain, and control periods were two periods of 48 hours, two weeks before, and two weeks after the pain crisis. Local weather data including temperature, wind speed and relative humidity, were downloaded from weather-source.com for each of the 23 participating centers for the years 1979 through 1982. Weather data were merged with clinical data and the association between the occurrence of pain crises and local weather conditions were studied using conditional logistic regression. We found an association between wind speed and the onset of pain, specifically wind speed during the 24 hour period preceding the onset of pain. Continuous measures of wind speed, mean and median wind speed during the 24 first hours of the hazard/control windows, showed significant associations with the occurrence of pain (p = 0.03 and p = 0.009, respectively). Analyzing wind speed as a categorical trait, dichotomized at the median (10 mph) for the same 24 hour period, showed a 14% increase (95% CI: 4% – 12%) in odds of pain, when comparing the high wind speed to lower wind speed (p = 0.005). To determine the most likely induction time, average wind speeds were determined for 4 hour intervals and their association with the onset of pain analyzed. Assuming a non-specific induction time will bias the measure of association toward the null, the interval with the highest OR should contain the most relevant induction time. We found that the interval from 13 hours to 16 hours prior to onset of pain has the largest measure of association [OR =1.01 (1.00 – 1.02), p = 0.026]. These results are in agreement with another study that found an association between wind speed and hospital visits for pain in the United Kingdom (Jones et. al, BJH 2005). These findings lend support to recent physiological and clinical studies that have suggested that skin cooling is associated with sickle vasoocclusion (Mohan et al. Clin Sci, 1998), and perhaps pain (Resar et al., J Pediatr 1991). Though pain is a common complication, and likely to have many potential triggers, physicians may wish to advise patients to take precautions on windy days by limiting skin exposure.

scholarly journals ANALISIS HUBUNGAN VARIABEL CUACA DENGAN KEJADIAN DBD DI KOTA YOGYAKARTA

2019 ◽  
Vol 10 (1) ◽  
pp. 1-7
Author(s):  
Rinaldi Daswito ◽  
Lutfan Lazuardi ◽  
Hera Nirwati
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Hemorrhagic Fever ◽  
Weather Data ◽  
Weather Variables ◽  
Administrative Area ◽  
Relationship Of ◽  
The Relationship ◽  
The City ◽  
Yogyakarta City

Dengue Hemorrhagic Fever (DHF) is the main public health issues in Indonesia, even endemic in all provinces. The incidence of DHF is still fluctuated annually in the city of Yogyakarta. This study aims to determine the pattern of the relationship between weather variables (air temperature, rainfall, humidity, and wind speed) on the incidence of DHF in the city of Yogyakarta for 5 years (2010-2014). This study used the ecological study design with spatial-temporal approach. Population was the incidence of dengue for the period 2010-2014 in the administrative area of Yogyakarta city. Spearman-rho correlation test showed that the pattern of the relationship of DHF incidence was more significant (p <0.05) and had a stronger correlation coefficient with an increase in weather variables in the previous few months. Rainfall in the previous two months (r = 0.5617), air temperature three months earlier (r = 0.4399), and humidity in the previous month (r = 0.6097) had a positive relationship pattern with an increase in the incidence of DHF. Wind speed is negatively related to the incidence of DHF in the same month (r = -0.3743). Based on graph/ time-trend analysis and spatial analysis of weather variables had a relationship with the incidence of DHF in the city of Yogyakarta. The Yogyakarta City Health Office is advised to use weather data from BMKG every year in planning DHF prevention programs and determine the timing of mass mosquito eradication (PSN) activities. Keywords: Dengue, vector-borne disease, climate, temporal

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3030
Author(s):  
Simon Liebermann ◽  
Jung-Sup Um ◽  
YoungSeok Hwang ◽  
Stephan Schlüter
Keyword(s):  
Neural Network ◽  
Goodness Of Fit ◽  
Renewable Energy Sources ◽  
Weather Conditions ◽  
Solar Irradiation ◽  
Weather Data ◽  
Short Term ◽  
Climate Conditions ◽  
Lstm Network ◽  
The Time Domain

Due to the globally increasing share of renewable energy sources like wind and solar power, precise forecasts for weather data are becoming more and more important. To compute such forecasts numerous authors apply neural networks (NN), whereby models became ever more complex recently. Using solar irradiation as an example, we verify if this additional complexity is required in terms of forecasting precision. Different NN models, namely the long-short term (LSTM) neural network, a convolutional neural network (CNN), and combinations of both are benchmarked against each other. The naive forecast is included as a baseline. Various locations across Europe are tested to analyze the models’ performance under different climate conditions. Forecasts up to 24 h in advance are generated and compared using different goodness of fit (GoF) measures. Besides, errors are analyzed in the time domain. As expected, the error of all models increases with rising forecasting horizon. Over all test stations it shows that combining an LSTM network with a CNN yields the best performance. However, regarding the chosen GoF measures, differences to the alternative approaches are fairly small. The hybrid model’s advantage lies not in the improved GoF but in its versatility: contrary to an LSTM or a CNN, it produces good results under all tested weather conditions.

scholarly journals Advanced Flight Planning and the Benefit of In-Flight Aircraft Trajectory Optimization

2021 ◽  
Vol 13 (3) ◽  
pp. 1383
Author(s):  
Judith Rosenow ◽  
Martin Lindner ◽  
Joachim Scheiderer
Keyword(s):  
New York ◽  
Traffic Control ◽  
Traffic Management ◽  
Trajectory Optimization ◽  
Service Providers ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Weather Forecasts ◽  
Aircraft Trajectory

The implementation of Trajectory-Based Operations, invented by the Single European Sky Air Traffic Management Research program SESAR, enables airlines to fly along optimized waypoint-less trajectories and accordingly to significantly increase the sustainability of the air transport system in a business with increasing environmental awareness. However, unsteady weather conditions and uncertain weather forecasts might induce the necessity to re-optimize the trajectory during the flight. By considering a re-optimization of the trajectory during the flight they further support air traffic control towards achieving precise air traffic flow management and, in consequence, an increase in airspace and airport capacity. However, the re-optimization leads to an increase in the operator and controller’s task loads which must be balanced with the benefit of the re-optimization. From this follows that operators need a decision support under which circumstances and how often a trajectory re-optimization should be carried out. Local numerical weather service providers issue hourly weather forecasts for the coming hour. Such weather data sets covering three months were used to re-optimize a daily A320 flight from Seattle to New York every hour and to calculate the effects of this re-optimization on fuel consumption and deviation from the filed path. Therefore, a simulation-based trajectory optimization tool was used. Fuel savings between 0.5% and 7% per flight were achieved despite minor differences in wind speed between two consecutive weather forecasts in the order of 0.5 m s−1. The calculated lateral deviations from the filed path within 1 nautical mile were always very small. Thus, the method could be easily implemented in current flight operations. The developed performance indicators could help operators to evaluate the re-optimization and to initiate its activation as a new flight plan accordingly.

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