Taiwan Central Weather Bureau Research Articles

Exploring the association between knee osteoarthritis outpatient visits and Asian dust storms: a time-series analysis

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases in Taiwan, posing a significant public health challenge. In recent years, outdoor air pollution has become an increasingly critical global health issue. Asian Dust Storms (ADS) are known to exacerbate various health conditions due to elevated levels of particulate matter and other pollutants. However, the relationship between ADS and knee OA remains insufficiently explored. This study investigates the association between ADS occurrences and knee OA outpatient visits from January 2006 to December 2012, aiming to understand the potential health impacts of dust storms on OA patients. Using data from the National Health Insurance Research Database (NHIRD), the Taiwan Environmental Protection Agency (TEPA), and the Taiwan Central Weather Bureau, we conducted a time-series analysis employing the autoregressive moving average with exogenous variables (ARMAX) model. This approach accounted for daily outpatient visits related to knee OA, ADS events, and various environmental and meteorological factors. The results revealed a significant increase in knee OA outpatient visits on days immediately following ADS events, with peaks observed one to two days after the event. This increase was most pronounced among females, individuals aged 61 and above, and residents in the western regions. The study demonstrates an association between ADS and increased knee OA outpatient visits, highlighting the need for public health strategies to mitigate the health impacts of dust storms.

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

The First Operational Version of Taiwan Central Weather Bureau’s One-Tier Global Atmosphere–Ocean Coupled Forecast System for Seasonal Prediction

Abstract The first version of the Taiwan Central Weather Bureau one-tier (TCWB1T) fully coupled global atmospheric and oceanic modeling forecast system had been developed and implemented as a routine operation for seasonal prediction at Central Weather Bureau (CWB) in 2017, with a minor revision in 2020. Based on NCEP CFSv1, the global atmospheric model in NCEP CFSv1 was replaced by CWB’s atmospheric global spectral model (GSM) and coupled with the GFDL MOM3. Several parameters have been tested and tuned in the CWB atmospheric GSM, achieving an optimal configuration with better sea surface temperature (SST) predictions for integration more than one year. Using NCEP CFSR as the initial condition, TCWB1T conducted hindcasts from 1982 to 2011 and forecasts from 2012 to 2019 to analyze its performance. The results of these hindcasts and forecasts show that the TCWB1T can make useful predictions as verified against the observations of OISST, ERSST, CFSR, and GPCP based on the methods of EOF, RMSE, anomaly correlation, ranked probability skill score (RPSS), reliability diagram (RD), and relative operating characteristics (ROCs). TCWB1T also has the same level of skill scores as NCEP CFSv2 and/or the ECMWF fifth-generation seasonal forecast system (SEAS5), based on EOF, anomaly pattern correlation, climatological bias, RMSE, temporal correlation, and anomaly correlation percentage of forecast skill. TCWB1T shows forecast skill that is better in winter than in summer. Overall, it indicates that TCWB1T can be used for seasonal ENSO predictions.

Impact of Assimilating FORMOSAT-7/COSMIC-2 Radio Occultation Data on Typhoon Prediction Using a Regional Model

As the successor of FORMOSAT-3/COSMIC (FS3/C1), FORMOSAT-7/COSMIC-2 (FS7/C2) was successfully launched on 25 June 2019. FS3 radio occultation (RO) data has contributed greatly to Taiwan’s meteorological progress, improving model representations of marine boundary layer heights, cyclogenesis, tropical cyclones/typhoons, and Mei-Yu front systems. The operational CWBWRF numerical weather prediction model with the 3DEnVar data assimilating system in the Taiwan Central Weather Bureau (CWB) was adopted to evaluate the impact of assimilating FS7 RO data. The following two experiments were conducted: one assimilated the in-situ observations as in the CWB operational task (nRO), and the other additionally assimilated FS7 RO refractivity profiles (wRO). Both experiments utilized 6-h assimilating window and full cycle data assimilation strategy and made 120-h forecasts after each assimilation. Within over 70 synoptic verification cases, the biases of geopotential height, temperature, and wind were reduced in the upper model levels in wRO results, and the typhoon track and intensity prediction error reductions were statistically significant. In addition, the wRO experiment improved the typhoon structure in the initial conditions and led to a better typhoon structure forecast. These results showed that the FS7 RO refractivity assimilation could improve model forecast performance, leading to its operational use in the CWB.

The Association of Visibility and Adenocarcinoma Lung Cancer in Southern Taiwan

Background/Aim Studies on association between lung cancer and long-term exposure to air pollution are limited due to the unavailability of air pollution data. The study aims to assess association of visibility, a proxy indicator of long-term air pollution exposure, with adenocarcinoma lung cancer. Methods Our study area is in southern Taiwan, an industrial area with 2.7 million people in 5.7 thousand km². Annual lung cancer incidence data with the ICD-O-3 codes from C34.0-C34.9 were obtained from Taiwan Cancer Registry (TCR) from 1997 to 2018. Age-adjusted incidence of lung cancers were standardized by the WHO 2000 world standard population. Hourly atmospheric visibility data from 1961 to 2022 were obtained from Taiwan Central Weather Bureau. 30-years moving averages were then calculated from 1967 onward to represent long-term visibility in the study area. Linear regression models were employed to estimate associations of 30-years averaging visibility annual age-adjusted lung cancers incidence for both adenocarcinoma and non-adenocarcinoma from 1997 to 2018. Results From 1997 to 2018, southern Taiwan's adenocarcinoma lung cancer incidence increased from 8.93 to 25.81 per 100,000 population, while non-adenocarcinoma incidence decreased from 14.61 to 10.41 per 100,000 population. The 30-years averaged visibility in the study area declined from 14.90 km to 7.65 km in the same period. Reducing visibility is significantly associated adenocarcinoma lung cancer, with a 2.43% (95% confidence interval [CI]: 2.77-2.11, adjusted R2 value: 0.92) increase in incidence by 1 km decrease in visibility during this period. Such associations exist for both females and males with increasing incidence of 2.57% (95% CI: 2.92-2.22, adj R2: 0.92) and 2.32% (95% CI: 2.69-1.95, adj R2: 0.89) by 1 km visibility reduction, respectively. By contrast, visibility reduction was not associated with increased in non-adenocarcinoma incidence. Conclusions Long-term visibility reduction is associated with increase in adenocarcinoma lung cancer incidence but not with non-adnocarcinoma.

Weather Impact on Acute Myocardial Infarction Hospital Admissions With a New Model for Prediction: A Nationwide Study.

Introduction: Cardiovascular disease is one of the leading causes of mortality worldwide. Acute myocardial infarction (AMI) is associated with weather change. The study aimed to investigate if weather change was among the risk factors of coronary artery disease to influence AMI occurrence in Taiwan and to generate a model to predict the probabilities of AMI in specific weather and clinical conditions.Method: This observational study utilized the National Health Insurance Research Database and daily weather reports from Taiwan Central Weather Bureau to evaluate the discharge records of patients diagnosed with AMI from various hospitals in Taiwan between January 1, 2008 and December 31, 2011. Generalized additive models (GAMs) were used to estimate the effective parameters on the trend of the AMI incidence rate with respect to the weather and health factors in the time-series data and to build a model for predicting AMI probabilities.Results: A total of 40,328 discharges were listed. The minimum temperature, maximum wind speed, and antiplatelet therapy were negatively related to the daily AMI incidence; however, a drop of 1° when the air temperature was below 15°C was associated with an increase of 1.6% of AMI incidence. By using the meaningful parameters including medical and weather factors, an estimated GAM was built. The model showed an adequate correlation in both internal and external validation.Conclusion: An increase in AMI occurrence in colder weather has been evidenced in the study, but the influence of wind speed remains uncertain. Our analysis demonstrated that the novel GAM model can predict daily onset rates of AMI in specific weather conditions.

Impact of Urbanization and Sunlight Exposure on Cataract Incidence

The impact of urbanization on cataract incidence is still inconclusive. This study aimed to examine the association of urbanization and sunlight exposure with cataract incidence using a nationwide population-based database in Taiwan. The researchers used data retrieved from the Taiwan Longitudinal Health Insurance Database from 2001 to 2010 (LHID2010). The LHID2010 consists of medical claims data for reimbursement for 1 million individuals randomly selected from all enrollees (N = 23.25 million) in the Taiwan National Health Insurance (NHI) program in 2010. For adults aged over 40, we identified a total of 3080 people diagnosed with senile cataracts (ICD-9:360) and 393,241 people without senile cataracts in the LHID2010. In addition, sunlight exposure data between 2001 and 2011 were obtained from 28 meteorological stations of the Taiwan Central Weather Bureau. Logistic regression was performed to test the hypothesis. When controlled for the confounding factors, such as demographic factors, comorbidities, and sunlight exposure, the logistic regression results showed that those living in highly urbanized areas are more likely to suffer from senile cataracts (p < 0.001).

A Hybrid Wind Power Forecasting Model with XGBoost, Data Preprocessing Considering Different NWPs

In recent years, wind energy has become a competitively priced source of energy around the world, which has created increasing challenges for system operators. Accurate wind power generation forecasting plays an important role in power systems to improve the reliable and efficient operation. Therefore, numerous artificial intelligent methods such as machine learning and deep learning have been considered as solutions for accurate wind power forecasts. In addition to deterministic forecasting, the probabilistic forecasting becomes more important, because it indicates the level of uncertainty. In this paper, a hybrid forecasting model considering different Numerical Weather Prediction (NWP) models and the XGBoost training model is proposed for short-term wind power forecasting. The proposed forecasting algorithm includes data preprocessing, in which an autoencoder model is used to reduce the dimension of 20 NWP ensembles. The performance of the proposed method is investigated using historical wind power measurements and NWP results by the Taiwan Central Weather Bureau (CWB); the NWP includes spot wind speeds from WRFD, RWRF, and ensemble wind speeds from WEPS. Based on the forecasting results, the proposed model produces better performance and forecasting accuracy among other forecasting models, which reveals the importance of data preprocessing using autoencoders and the use of deep learning models in deterministic or probabilistic forecasts.

A study of climate model responses of the western Pacific subtropical high to El Niño diversity

This study examines a climate model hindcast of the responses of the western Pacific subtropical high (WPSH) to three types of El Nino events: the Eastern Pacific (EP) El Nino and the types I and II of Central Pacific (CP-I and CP-II) El Nino. These El Nino types differ from each other in the central locations and patterns of their sea surface temperature (SST) anomalies. They invoke four different mechanisms to impact the WPSH. Hindcasts produced by the Taiwan Central Weather Bureau (CWB) Climate Forecast System 1-tiered model (TCWB1T1) are analyzed. These hindcasts realistically simulate the impacts on the WPSH during the CP-I El Nino, but overestimate the impacts during the decaying summer of the EP El Nino and during the developing autumn and early winter of the CP-II El Nino. The overestimates are mainly the result of an overly strong Maritime Continent regional circulation mechanism during EP El Ninos and an overly strong Indian Ocean capacitor mechanism during CP-II El Ninos. Further analyses show that these model biases are related to biases in the simulations of El Nino SST anomalies and the Walker circulation. Both model deficiencies are common to contemporary coupled climate models.

New land use regression model to estimate atmospheric temperature and heat island intensity in Taiwan

This paper is about spatial–temporal variability of atmospheric temperature across Taiwan, an island with diverse local emission sources partly because of its Asian cultural characteristics. To develop a new land use regression (LUR) model for this study, we used the temperature data collected by the Taiwan Central Weather Bureau from 2000 and 2016, while using the data from 2017 as the external data verification to assess model reliability. Because incense and joss money burning is a cultural-specific emission source in Asia, we further included location of temples, cemeteries, and crematoriums as potential predictors. The overall model performance and tenfold cross-validated are R2 of 0.88 and R2 of 0.87, respectively, which presents a high level of prediction performance. Moreover, we used our LUR model to estimate urban heat islands intensity (UHII) for six metropolises in Taiwan and found Taichung City has the highest UHII value (4.60 °C) among them. These results provide important insights in expanding the remote sensing application on spatial–temporal variation of atmospheric temperature and its further application on UHI effects.

Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

Mapping and Analyzing the Evolution of the Butangbunasi Landslide Using Landsat Time Series with Respect to Heavy Rainfall Events during Typhoons

Large rainfall-induced landslides are among the most dangerous natural hazards in Taiwan, posing a risk for people and infrastructure. Thus, better knowledge about the evolution of landslides and their impact on the downstream area is of high importance for disaster mitigation. The aim of this study is twofold: (1) to semi-automatically map the evolution of the Butangbunasi landslide in south-central Taiwan using satellite remote sensing data, and (2) to investigate the potential correlation between changes in landslide area and heavy rainfall during typhoon events. Landslide area, as well as temporary landslide-dammed lakes, were semi-automatically identified using object-based image analysis (OBIA), based on 20 Landsat images from 1984 to 2018. Hourly rainfall data from the Taiwan Central Weather Bureau (CWB) was complemented with rainfall data from Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to examine the potential relationship between landslide area changes and rainfall as a triggering factor. The OBIA mapping results revealed that the most significant landslide extension happened after typhoon Morakot in 2009. We found a moderate positive relationship between the landslide area change and the duration of the heavy rainfall event, whereas daily precipitation, cumulative rainfall and mean intensity did not present strong significant correlations.

Bias Adjustment of Satellite Precipitation Estimation Using Ground-Based Observation: Mei-Yu Front Case Studies in Taiwan

The Global Satellite Mapping of Precipitation (GSMaP) was used to estimate the accumulated rainfall in May from the Mei-Yu front in Taiwan. Rainfall estimation from GSMaP during 2002–2017 were evaluated using more than 400 local gauge observations, collected from the Taiwan Central Weather Bureau (CWB). Studies have demonstrated that the GSMaP rainfall estimation estimates can be biased, depending on the target region, elevation, and season. In this experiment, we have evaluated GSMaP over three elevation ranges. The GSMaP systemic errors for each elevation range were identified and corrected using regression analysis. The results indicated that GSMaP estimation can be improved significantly through adjustment over three elevation ranges (elevation less than 50 m, elevation of 50–100 m, and elevation higher than 100 m). For these three elevation ranges, the correlation coefficient between the GSMaP estimations and CWB rainfall data was 0.76, 0.78, and 0.59, respectively. This indicated that the GSMaP estimation was more accurate for low-elevation regions than high-elevation regions. After the proposed approaches were employed to correct the errors, the bias errors were respectively improved by 5.64(13.7%), 7.33(38.4%) and 10.52(31.2%) mm for low-, mid- and high-elevation regions. This study demonstrated that the local correction approaches can be used to improve GSMaP estimation of Mei-Yu rainfall in Taiwan.

Epidemiologic features of shigellosis and associated climatic factors in Taiwan.

The consistent, sporadic transmission of shigellosis in Taiwan necessitates an exploration of risk factors for the occurrence of shigellosis. The purpose of this study was to study the epidemiologic characteristics and the relationship between climatic factors and the incidence of shigellosis in Taiwan. We collected data from cases of shigellosis reported to the Taiwan Centers for Disease Control (Taiwan CDC) from 2001 to 2016. Climatic data were obtained from the Taiwan Central Weather Bureau. The relationships between weather variability and the incidence of shigellosis in Taiwan were determined via Poisson regression analyses. During the 16-year study period, a total of 4171 clinical cases of shigellosis were reported to the Taiwan CDC. Among them, 1926 (46.2%) were classified as confirmed cases. The incidence of shigellosis showed significant seasonality, with the majority of cases occurring in summertime (for oscillation, P < .001). The number of shigellosis cases started to increase when temperatures reached 21°C (r2 = 0.88, P < .001). Similarly, the number of shigellosis cases began to increase at a relative humidity of 70–74% (r2 = 0.75, P < .005). The number of shigellosis cases was positively associated with the mean temperature and relative humidity in the period preceding the infection. In conclusion, the occurrence of shigellosis is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures.

Impact of Boreal Summer Intra-Seasonal Oscillations on the Heavy Rainfall Events in Taiwan during the 2017 Meiyu Season

During May and June (the Meiyu season) of 2017, Taiwan was affected by three heavy frontal rainfall events, which led to large economic losses. Using satellite observations and reanalysis data, this study investigates the impact of boreal summer intra-seasonal oscillations (BSISOs, including a 30–60 day ISO mode named BSISO1 and a 10–30 day ISO mode named BSISO2) on the heavy rainfall events in Taiwan during the 2017 Meiyu season. Our examinations show that BSISO2 is more important than BSISO1 in determining the formation of heavy rainfall events in Taiwan during the 2017 Meiyu season. The heavy rainfall events generally formed in Taiwan at phases 4–6 of BSISO2, when the enhanced southwesterly wind and moisture flux convergence center propagate northward into the Taiwan area. In addition, we examined the forecast rainfall data (at lead times of one day to 16 days) obtained from the National Centers for Environmental Prediction Global Forecast System (NCEPgfs) and the Taiwan Central Weather Bureau Global Forecast System (CWBgfs). Our results show that the better the model’s capability in forecasting the BSISO2 index is, the better the model’s capability in forecasting the timing of rainfall formation in Taiwan during the 2017 Meiyu season is. These findings highlight the importance of BSISO2 in affecting the rainfall characteristics in East Asia during the Meiyu season.

Long-term exposure to high temperature associated with the incidence of major depressive disorder

Temperature was reported to have acute effects on mental disorders. However, the effect of long-term temperature exposure is unclear, especially in warmer regions. This research aimed to evaluate the association between long-term exposure to temperature and the incidence of major depressive disorder (MDD). A retrospective population-based study was conducted using Taiwan nationwide Longitudinal Health Insurance Database 2005. Subjects without MDD diagnosis were followed from 2003 to 2013 for MDD occurrence. Weather factors including temperature (°C), sunshine duration (hrs/day), and precipitation (mm/day) were 11-year average of daily data collected from weather monitoring stations of Taiwan Central Weather Bureau. Cox proportional hazard model was performed to examine the association between MDD incidence and weather factors. Covariates including age, gender, and insurance amount were adjusted. A total of 9,539,752 person-years were followed. People residing in regions with long-term temperature of 20–23 °C had the lowest risk of developing MDD. Risk for MDD incidence was higher among residents in regions with average temperature > 23 °C, at 7% increase per 1 °C increment (95% CI = 2–12%). The elderly was more vulnerable to such impact of heat exposure. For the elderly, males were more susceptible to heat-related MDD than females (HR = 1.18 vs. 1.14). However, the younger group showed a reverse finding (i.e. females 1.07 > males 1.04). Long-term residence in regions with extreme cold or heat may increase the risk of MDD incidence, especially higher temperature. Under global warming, these findings have great health implications and warrant further confirmation.

Influence of Latitude on the Prevalence of Kawasaki Disease: A Retrospective Cohort Study from the Taiwan National Health Insurance Database and Review of the Literature.

Background: Countries at higher latitudes have higher incidence rates of Kawasaki disease (KD) than do countries at lower latitudes in the Asian and West Pacific area. However, the precise influence of latitude on KD incidence rates requires further clarification. Methods: We searched the Longitudinal Health Insurance Database 2005 to retrieve patients’ medical records from 1996 to 2009. The patients with KD were categorized as living in northern, middle, and southern Taiwan; the period prevalence of KD for each area was determined. Climate variables, including temperature, sunshine duration, precipitation, and relative humidity, were collected from the Taiwan Central Weather Bureau. The effect of latitude on the period KD prevalence and the correlation between climate variables and KD prevalence were calculated. Results: After patients without complete data excluded, a total of 61,830 children up to 10 years old were retrieved, from which 404 patients with KD were recognized. The period prevalence of KD increased significantly with latitude (p = 0.0004). Climate variables associated with high temperature demonstrated a connection with KD prevalence; however, this correlation was not statistically significant. Conclusions: Our study demonstrated that higher latitude is associated with a higher KD prevalence in Taiwan.

Citizen Earthquake Science in Taiwan: From Science to Hazard Mitigation

Taiwan is located at the convergent plate boundary between the Eurasian and the Philippine Sea plates. As a result, intense earthquake activity and associated surface deformation are manifesting in this region. To implement and promote citizen earthquake science in Taiwan, we have developed several web-based platforms with multi-purpose themes, such as earthquake science information, popular science education, and crowdsourcing systems. First, with the rapid earthquake report issued from the Taiwan Central Weather Bureau (CWB), the available near real-time scientific results obtained from the Taiwanese seismology community are collected and published to a platform, the Taiwan Earthquake Science Information System (TESIS). The scientific information archived at TESIS includes CWB earthquake reports, focal mechanisms, shake maps, and finite source models (for strong earthquakes). All real-time results are integrated into a GIS system with background geospatial information, such as geological maps, traces of active faults, background seismicity, and inter-seismic GPS velocity fields. Second, by collaborating with Stanford University to maintain a regional Quake-Catcher Network (QCN) server in Taiwan, we have promoted citizen seismology in Asia by bringing earthquake information and scientific knowledge to the public. More than 200 school teachers have already installed the QCN sensors in Internet-enabled computers. Through two web-based educational platforms, users are able to access the guidelines and further interact with the recorded waveforms. Third, we also developed an earthquake damage reporting system – the Taiwan Scientific Earthquake Reporting (TSER) system – to encourage the citizen to collect field observation for significant earthquake-induced ground damages such as surface fault rupture, landslide, rock fall, liquefaction, and landslide-triggered dam or lake. The TSER system is constructed under the Ushahidi mapping platform, which has been widely used in crowdsourcing for the geospatial archiving of events. Trained high school teachers and public volunteers can send their ground damage observations, including photographs, through the TSER system. Most of these products and online systems are now being operated by the Taiwan Earthquake Research Center (TEC). With these newly developed platforms and materials, we aim to not only raise earthquake awareness and preparedness, but also encourage public participation in earthquake science in Taiwan.

Seasonal Patterns of Japanese Encephalitis and Associated Meteorological Factors in Taiwan.

The persistent transmission of Japanese encephalitis virus (JEV) in Taiwan necessitates exploring the risk factors of occurrence of Japanese encephalitis (JE). The purpose of this study was to assess the relationship between meteorological factors and the incidence of JE in Taiwan. We collected data for cases of JE reported to the Taiwan Centers for Disease Control (Taiwan CDC) from 2000 to 2014. Meteorological data were obtained from the Taiwan Central Weather Bureau. The relationships between weather variability and the incidence of JE in Taiwan were determined via Poisson regression analysis and a case-crossover methodology. During the 15-year study period, a total of 379 cases of JE were reported. The incidence of JE showed significant seasonality, with the majority of cases occurring in summertime (for oscillation, p < 0.001). The number of JE cases started to increase at temperatures of 22 °C (r2 = 0.88, p < 0.001). Similarly, the number of JE cases began to increase at a relative humidity of 70–74% (r2 = 0.75, p < 0.005). The number of JE cases was positively associated with mean temperature and relative humidity in the period preceding the infection. In conclusion, the occurrence of JE is significantly associated with increasing temperature and relative humidity in Taiwan. Therefore, these factors could be regarded as warning signals indicating the need to implement preventive measures.

Short‐term wind power forecasting and uncertainty analysis using a hybrid intelligent method

This study proposes a hybrid intelligent method for short-term wind power forecasting and uncertainty analysis. In practice, the power output of a wind turbine is a direct function of wind speed. Owing to the intermittent and irregular nature of wind, the wind power generation is not easily dispatched and the prediction of wind power is highly uncertain. To allow a procedure for more accurate forecasting, a deterministic wind power prediction method that uses multiple support vector regression (SVR) models is established based on the wind power capacity and wind speed forecasts obtained from the Taiwan Central Weather Bureau (TCWB). An enhanced harmony search (EHS) algorithm is then used to estimate the parameters for each SVR model. To assess the risk that is associated with wind power forecasts in a power grid, an EHS-based quantile regression method that accurately reflects the confidence intervals for wind power forecasts is presented. The proposed approach provides wind power forecasts for future 3 h in steps of 15 min and the associated forecasting uncertainty. During testing on three practical wind power generation systems, the proposed method gives better forecasting accuracy and produces more reasonable confidence intervals than existing methods.