Search Results (19)

Data for solar radiation resources play a pivotal role in assessing the energy yield capability of solar applications. A nationwide database for the typical meteorological year from the 30 weather stations of the Central Weather Bureau (CWB) in Taiwan is used to determine the spatial distribution of global radiation over the terrain of Taiwan. There is no available beam radiation information in daily reports from all CWB stations. Information on the diffuse fraction for all CWB stations is estimated using three available correlation models that account for topographical and geographical effects in Taiwan. The databases for beam radiation are generated using these estimated diffuse fractions. The mappings of global and beam radiation on the Taiwanese mainland are performed with databases from 24 CWB stations using the residual kriging method. There are no mappings of the remote islands, where six CWB stations are located. The databases for global and beam radiation for these six CWB stations are applied to nearby remote islands. The effects of topography and geography on the distributions of global and beam radiation are discussed. The spatial distributions of solar radiation presented are good scientific references for assessing the performances of solar energy systems in Taiwan. Full article

Climate change poses a significant threat to humanity. Achieving net-zero emissions is a key goal in many countries. Among various energy resources, solar power generation is one of the prominent renewable energy sources. Previous studies have demonstrated that post-processing techniques such as bias correction can enhance the accuracy of solar power forecasting based on numerical weather prediction (NWP) models. To improve the post-processing technique, this study proposes a new day-ahead forecasting framework that integrates weather research and forecasting solar (WRF-Solar) irradiances and the total solar power generation measurements for five cities in northern, central, and southern Taiwan. The WRF-Solar irradiances generated by the Taiwan Central Weather Bureau (CWB) were first subjected to bias correction using the decaying average (DA) method. Then, the effectiveness of this correction method was verified, which led to an improvement of 22% in the forecasting capability from the WRF-Solar model. Subsequently, the WRF-Solar irradiances after bias correction using the DA method were utilized as inputs into the transformer model to predict the day-ahead total solar power generation. The experimental results demonstrate that the application of bias-corrected WRF-Solar irradiances enhances the accuracy of day-ahead solar power forecasts by 15% compared with experiments conducted without bias correction. These findings highlight the necessity of correcting numerical weather predictions to improve the accuracy of solar power forecasts. Full article

Due to the rapid development of computers, researchers have made efforts since the 1990s to develop typhoon forecasting models and stochastic typhoon simulation models to assess typhoon disasters and risks. Typhoon forecasting models are primarily used to predict and track the movement of typhoons and provide warning information to the general public before landfall. Stochastic typhoon simulation models can assess extreme wind speeds and compensate for the limitations of current observations and simulation data length. Taiwan experiences approximately three to four typhoons yearly, of varying intensities and paths. Whether the marine meteorological data includes events of strong typhoon centers passing through will affect the results of frequency analysis. The development of offshore wind power in Taiwan is closely related to the unique marine meteorological conditions throughout the lifecycle stages, including wind farm site selection, feasibility studies, planning and design, construction and installation, operation and maintenance, and decommissioning. This study references relevant research and analyzes sixty-three scenarios using nine types of maximum storm wind speed radii and seven Holland-B parameters. The data from Japan Meteorological Agency Best Track Data (JMA BTD) is utilized, explicitly selecting 20 typhoon events after 2000 for wind speed simulation using a typhoon wind speed model. After validating the typhoon wind speeds with observation data from the Central Weather Bureau (CWB) in Hsinchu and the Longdong buoy, the technique of Monte Carlo simulation is utilized to generate synthetic typhoons randomly. The average of the relative absolute errors for the simulated maximum wind speeds is calculated, and through comprehensive evaluation, optimal parameter combinations ($R_m$, $B$) are obtained. Full article

The forecast of typhoon tracks remains uncertain and is positively related to the accuracy of the storm surge forecast. The storm surge prediction error increases dramatically when the forecast track error is larger than 100 km. This study aims to develop an ensemble storm surge prediction system using parametric weather models to account for the uncertainty in typhoon track prediction. The storm surge model adopted in this study is COMCOT-SS storm surge forecast system. Two methods are introduced and analyzed to generate the ensemble members in this study. One is from the weather ensemble prediction system (WEPS), and the other is from the error distribution of the deterministic forecasts (EDF). The ensemble prediction results show that the ensemble mean of WEPS performs similarly to the deterministic forecast. However, the maximum surge height of WEPS is often lower than one from EDF. The verification results suggest that, for disaster prevention, EDF provides stronger warnings to the coastal region than WEPS. However, it may provide overestimated forecasts in some cases. Full article

Background: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. Method: We defined people aged 51–90 years who were free from ischemic heart disease (ICD9: 410–414) in 1996–2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). Results: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. Conclusions: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health. Full article

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. Full article

Revisiting the defined Meiyu onset of Central Weather Bureau (CWB), this study applied a newer reanalysis dataset and added multiple timing and duration criteria to improve the Meiyu onset index. The previous Meiyu onset index was based on horizontal and vertical wind shears using older-generation reanalysis data. The horizontal shear captures the cyclonic vorticity while the vertical shear depicts overturning. However, this older index tends to predict the onset date too early from the actual maximum precipitation. After applying the modification that is described in this paper, the newer Meiyu onset index consistently leads the maximum precipitation in Taiwan only by a few days, except for two years over the 30-year analysis period. The implication of this modified and improved Meiyu onset index is that it can substitute model precipitation that tends to be problematic, as well as studying climate change impacts. Full article

Understanding students’ subjective perceptions of universities is one of the main issues that needs to be addressed in order to improve aspects such as student retention and achieve sustainable development. Considering subjective well-being as an alternative term for happiness and satisfaction in higher education for sustainability, this study is to develop a measure conceptually and operationally for college campus well-being (CWB) with multiple dimensions, including a psychological, physical, financial, and social dimension of well-being. Subjected to factorial validity and composite reliability, the CWB scale validated by 2793 undergraduate students in central Taiwan was administered. The research demonstrates the appropriate construct validity and suitable-fit indices of the CWB multidimensional scale when used for measuring university-oriented happiness and sustainability in this research context. Differential effects were found among the colleges and between genders. The implications and future research lines are discussed. Full article

This paper provides an overview of the contributions of the space-based global navigation satellite system (GNSS) radio occultation (RO) measurements from the FORMOSAT-7/COSMIC2 (F7/C2) mission in advancing our understanding of ionospheric plasma physics in the purview of space weather. The global positioning system (GPS) occultation experiment (GOX) onboard FORMOSAT-3/COSMIC (F3/C), with more than four and half million ionospheric RO soundings during April 2006–May 2020, offered a unique three-dimensional (3D) perspective to examine the global electron density distribution and unravel the underlying physical processes. The current F7/C2 carries TGRS (Tri-GNSS radio occultation system) has tracked more than 4000 RO profiles within ±35° latitudes per day since 25 June 2019. Taking advantage of the larger number of low-latitude soundings, the F7/C2 TGRS observations were used here to examine the 3D electron density structures and electrodynamics of the equatorial ionization anomaly, plasma depletion bays, and four-peaked patterns, as well as the S4 index of GNSS signal scintillations in the equatorial and low-latitude ionosphere, which have been previously investigated by using F3/C measurements. The results demonstrated that the denser low-latitude soundings enable the construction of monthly global electron density maps as well the altitude-latitude profiles with higher spatial and temporal resolution windows, and revealed longitudinal and seasonal characteristics in greater detail. The enhanced F7/C2 RO observations were further applied by the Central Weather Bureau/Space Weather Operation Office (CWB/SWOO) in Taiwan and the National Oceanic and Atmospheric Administration/Space Weather Prediction Center (NOAA/SWPC) in the United States to specify the ionospheric conditions for issuing alerts and warnings for positioning, navigation, and communication customers. A brief description of the two models is also provided. Full article

Seismic instrumentation for earthquake early warnings (EEWs) has improved significantly in the last few years, considering the station coverage, data quality, and the related applications. The official EEW system in Taiwan is operated by the Central Weather Bureau (CWB) and is responsible for issuing the regional warning for moderate-to-large earthquakes occurring in and around Taiwan. The low-cost micro-electro-mechanical system (MEMS)-based P-Alert EEW system is operational in Taiwan for on-site warnings and for producing shakemaps. Since 2010, this P-Alert system, installed by the National Taiwan University (NTU), has shown its importance during various earthquakes that caused damage in Taiwan. Although the system is capable of acting as a regional as well as an on-site warning system, it is particularly useful for on-site warning. Using real-time seismic signals, each P-Alert system can provide a 2–8 s-long warning time for the locations situated in the blind zone of the CWB regional warning system. The shakemaps plotted using this instrumentation help to assess the damage pattern and rupture directivity, a key feature in the risk mitigation process. These shakemaps are delivered to the intended users, including the disaster mitigation authorities, for possible relief purposes. Earlier, the network provided only peak ground acceleration (PGA) shakemaps, but has now been updated to include peak ground velocity (PGV), spectral acceleration ($S_a$) at different periods, and CWB intensity maps. The PGA and PGV shakemaps plotted using this network have proven helpful in establishing the fact that PGV is a better indicator of damage detection than PGA. This instrumentation is also useful in structural health-monitoring and estimating co-seismic deformations. Encouraged by the performance of the P-Alert network, more instruments are installed in Asia-Pacific countries. Full article

The main purpose of this research was to evaluate and analyze the economic value of the meteorological information services (MIS) provided by the Central Weather Bureau (CWB) when applied to aquaculture in Taiwan. In this research, a contingent valuation method (CVM) was used to inquire about the subjective rating given to the CWB’s meteorological information services based on the responses to a national level questionnaire distributed among aquaculture farmers. The subjective rating revealed how the aquaculture farmers rated the accuracy of the MIS provided by the CWB and how they recognized the impact of the MIS on their aquaculture output. On this basis, this research determined the economic value brought about by the application of the meteorological information services. In order to understand the main factors affecting the respondents’ willingness-to-pay (WTP) for the MIS, this research also conducted an empirical estimation of the bid function. The results indicated that the main factors affecting the WTP were found to include the respondents’ subjective rating of the meteorological information services (including the accuracy rating and the effect rating), traditional social and economic background variables such as income and education level, as well as fish species. In addition, through testing the estimation of the bid function, it was also found that the survey sample used in this research had a significant starting point bias effect, which needed to be corrected using econometric methods. According to the empirical results, the median willingness-to-pay (WTP) of aquaculture farmers in Taiwan was 3544 New Taiwan Dollars (NTD)/person/year and the total economic value at the national level ranged from 157 million to 209 million NTD per year. Since the MIS service users have often lacked sufficient knowledge and ability to interpret the weather forecasts, how to strengthen the capabilities of service users in using MIS through the promotion of training programs and improve the value of the MIS may be an important policy insight. Full article

Using low-cost sensors to build a seismic network for earthquake early warning (EEW) and to generate shakemaps is a cost-effective way in the field of seismology. National Taiwan University (NTU) network employing 748 P-Alert sensors based on micro-electro-mechanical systems (MEMS) technology is operational for almost the last 10 years. This instrumentation is capable of recording the strong ground motions of up to ± 2g and is dense enough to record the near-field ground motion. It has proven effective in generating EEW warnings and delivering real-time shakemaps to the concerned disaster relief agencies to mitigate the earthquake-affected regions. Before 2020, this instrumentation was used to plot peak ground acceleration (PGA) shakemaps only; however, recently it has been upgraded to generate the peak ground velocity (PGV), Central Weather Bureau (CWB) Intensity scale, and spectral acceleration (S_a) shakemaps at different periods as value-added products. After upgradation, the performance of the network was observed using the latest recorded earthquakes in the country. The experimental results in the present work demonstrate that the new parameters shakemaps added in the current work provide promising outputs, and are comparable with the shakemaps given by the official agency CWB. These shakemaps are helpful to delineate the earthquake-hit regions which in turn is required to assist the needy well in time to mitigate the seismic risk. Full article

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. Full article

Although the annual rainfall in Taiwan is high, water shortages still occasionally occur owing to its nonuniform temporal and spatial distribution. At these times, the groundwater is considered an acceptable alternative water source. Groundwater is of particular value because it is considered a clean and reliable source of fresh water. To prevent water scarcity, this study utilized seasonal forecasting by incorporating hydrological models to evaluate the seasonal groundwater level. The seasonal prospective issued by the Central Weather Bureau of Taiwan (CWB) was combined with weather generator data to construct seasonal weather forecasts as the input for hydrological models. A rainfall-runoff model, HEC-HMS, and a coupled groundwater and surface water model, WASH123D, were applied to simulate the seasonal groundwater levels. The Fengshan Creek basin in northern Taiwan was selected as a study site to test the proposed approach. The simulations demonstrated stability and feasibility, and the results agreed with the observed groundwater table. The calibrations indicated that the average errors of river stage were 0.850 for R², 0.279 for root-mean-square error (RMSE), and 0.824 for efficiency coefficient (CE). The simulation also revealed that the simulated groundwater table corresponded with observed hydrographs very well (R² of 0.607, RMSE of 0.282 m, and CE of 0.621). The parameters were verified in this study, and they were deemed practical and adequate for subsequent seasonal assessment. The seasonal forecast of 2018 at Guanxi station indicated that the 25th and 75th percentiles of simulated annual rainfall were within 1921–3285 mm and the actual annual rainfall was 2031 mm. Its seasonal rainfall outlook was around 30% accurate for forecasts of three consecutive months in 2018. Similarly, at Xinpu station, its seasonal rainfall outlook was about 40% accurate, and the amount of annual rainfall (1295 mm) was within the range of the 25th and 75th percentiles (1193–1852 mm). This revealed that the actual annual precipitations at both Guanxi and Xinpu station corresponded with the range of 25th and 75th percentiles of simulated rainfall, even if the accurate rate for the 3 month seasonal forecast had some error. The subsequent groundwater simulations were overestimated because the amount of actual rainfall was far lower than the average of the historical record in some dry season months. However, the amount of rainfall returned to normal values during the wet seasons, where the seasonal forecast and observation results were similar. Full article

The purpose of this study was to develop an optimal estimation model for rainfall rate retrievals using radar reflectivity, thereby gaining an effective grasp of rainfall information for disaster prevention uses. A process was designed for evaluating the optimal retrieval models using various dataset combinations with radar reflectivity and ground meteorological attributes. Various ground meteorological attributes (such as relative humidity, wind speed, precipitation, etc.) were obtained using the land-based weather stations affiliated with Taiwan’s Central Weather Bureau (CWB). This study used nine radar reflectivity provided by the Hualien weather surveillance radar station’s Volume Cover Pattern 21 system. The developed models are built using multiple machine learning algorithms, including linear regression (REG), support vector regression (SVR), and extreme gradient boosting (XGBoost), in addition to the Marshall–Palmer formula (MP). The study examined 14 typhoons that occurred from 2008 to 2017 at Chenggong station in southeast Taiwan, and Lanyu station in the outlying islands, and the top four major rainfall events were designated as test typhoons—Nanmadol (2011), Tembin (2012), Matmo (2014), and Nepartak (2016). The results indicated that for rainfall retrievals, radar reflectivity at a scanning (elevation) angle of 6.0° combined with ground meteorological attributes were the optimal input variables for the Chenggong station, whereas radar reflectivity at an elevation angle of 4.3° combined with ground meteorological attributes were optimal for the Lanyu station. In terms of model performance, XGBoost models had the lowest error index at Chenggong and Lanyu stations compared with MP, REG, and SVR models. XGBoost models at Lanyu station had the highest efficiency coefficient (0.903), and those at Chenggong station had the second highest (0.885). As a result, pairing the combination of optimal radar reflectivity and ground meteorological attributes, as verified by the evaluation process, with a high-efficiency algorithm (XGBoost) can effectively increase the accuracy of rainfall retrieval during typhoons. Full article