Jing-Shan Hong

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.

A 16-member Convective-scale Ensemble Prediction System (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 13 and 20 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 05,08, and 11 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread-skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 11 LST outperform those launched at 05 and 08 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 11 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses. A 16-member Convective-scale Ensemble Prediction System (CEPS) developed at the Central Weather Bureau (CWB) of Taiwan is evaluated for probability forecasts of convective precipitation. To address the issues of limited predictability of convective systems, the CEPS provides short-range forecasts using initial conditions from a rapid-updated ensemble data assimilation system. This study aims to identify the behavior of the CEPS forecasts, especially the impact of different ensemble configurations and forecast lead times. Warm-season afternoon thunderstorms (ATs) from 30 June to 4 July 2017 are selected. Since ATs usually occur between 13 and 20 LST, this study compares deterministic and probabilistic quantitative precipitation forecasts (QPFs) launched at 05,08, and 11 LST. This study demonstrates that initial and boundary perturbations (IBP) are crucial to ensure good spread-skill consistency over the 18-h forecasts. On top of IBP, additional model perturbations have insignificant impacts on upper-air and precipitation forecasts. The deterministic QPFs launched at 11 LST outperform those launched at 05 and 08 LST, likely because the most-recent data assimilation analyses enhance the practical predictability. However, it cannot improve the probabilistic QPFs launched at 11 LST due to inadequate ensemble spreads resulting from limited error growth time. This study points out the importance of sufficient initial condition uncertainty on short-range probabilistic forecasts to exploit the benefits of rapid-update data assimilation analyses.

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.

Tropical Cyclone Atsani occurred in late October 2020 and moved westward offshore south of Taiwan. During its offshore passage, the cyclone deflects northward as it closes to the southern end of Taiwan. A global model MPAS at a multi-resolution of 60-15-3-km is applied to explore the track responses of Atsani and identify the topographic effects of the Central Mountain Range (CMR) on the cyclone circulation and the associated track deflection. With a 3-km resolution targeted at the Taiwan area, the cyclone track deflection can be reasonably simulated, with more sensitivity to physics schemes and dynamic vortex initialization and less sensitivity to initial environmental perturbations. When the Taiwan terrain is removed, the cyclone indeed deflects more northward earlier, in particular for simulations with a stronger cyclone that tends to generate stronger east-west wind asymmetry in the absence of the terrain. Idealized simulations with a regional model WRF at 3-km resolution are also utilized to contrast the track deflection of different departing cyclones, similar to the real case. It was found that northward deflection will be induced near south of the CMR-like terrain for both stronger and weaker westbound cyclones departing at different latitudes south of the terrain. We have explained why a further northward track at earlier stages is induced in the absence of the terrain effects in regard to model initial states. In both real and idealized cases, the track deflection of the cyclone moving around the terrain is dominated by the wavenumber-one horizontal potential vorticity (PV) advection that is somewhat offset by both vertical PV advection and differential diabatic heating.

Because of climate changes, natural disasters are becoming more serious. For instance, the intensity of typhoons has been increasing in recent years. Typhoons and other natural disasters have high-impact low-probability (HILP) characteristics. Thus, procedures for preparing for natural disasters and increasing power system resilience are important issues. This paper proposes an all-inclusive process for system operators to make decisions for enhancing power system resilience and economic value during a severe weather event. This process first considers the typhoon track, the fragility curve and the recovery time of transmission lines. After collecting these data, system simulations and a calculated resilience index are implemented according to cases with and without disaster prevention. Next, the probability threshold and calculated economic value (EV) index are obtained based on numerical weather prediction (NWP) wind speeds and the cost-loss ratio. Finally, the two indexes are considered in combination to obtain the highest resilience with the greatest benefit. The proposed process helps system operators make decisions for appropriate preventive actions at the least cost. An actual Taiwan power system and a severe weather event are used as an example to demonstrate the proposed decision analysis. The simulation results indicate the feasibility of the proposed method, which can reduce potential risks caused by extreme weather events with the maximum economic benefits.

Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed forecast were diurnally variated and clustered with the usage of the planetary boundary layer (PBL) scheme. To correct the error characteristics of the ensemble wind speed forecast, three system-bias representations with decaying average algorithms were studied. One of the three system-bias representations is represented by the forecast error of the ensemble mean (BC01), and others are assembled from each PBC group (BC03) as well as an independent member (BC20). System bias was calculated daily and updated within a 5-month duration, and the verification was conducted in the last month, including 316 gauges around Taiwan. Results show that the mean of the calibrated ensemble (BC03) was significantly improved as the calibrated ensemble (BC20), but both demonstrated insufficient ensemble spread. However, the calibrated ensemble, BC01, with the best dispersion relation could be extracted as a more valuable deterministic forecast via the probability matched mean method (PMM).

Dealing with accumulated forecast error is a key issue in the rapid update cycle Implementing the blending scheme is expected to improve the larger-sc...

Recently, the Central Weather Bureau of Taiwan developed a WRF- and WRF data assimilation (WRFDA)-based convective-scale data assimilation system to increase model predictability toward high-impact weather. In this study, we focus on afternoon thunderstorm (AT) prediction and investigate the following questions: 1) Is the designation of a rapid update cycle strategy with a blending scheme effective? 2) Can surface data assimilation contribute positively to AT prediction under the complex geography of Taiwan island? 3) What is the relative importance between radar and surface observation to AT prediction? 4) Can we increase the AT forecast lead time in the morning through data assimilation? Consecutive ATs from 30 June to 8 July 2017 are investigated. Five experiments, each having 240 continuous cycles, are designed. Results show that employing continuous cycles with a blending scheme mitigates model spinup compared with downscaled forecasts. Although there are few radar echoes before AT initiation, assimilating radar observations is still crucial since it largely corrects model errors in cycles. However, assimilating surface observations is more important compared with radar in terms of extending forecast lead time in the morning. Either radar or surface observations contribute positively, and assimilating both has the highest QPF score. Assimilating surface observations systematically improves surface wind and temperature predictions based on 240 cases. A case study demonstrates that the model can capture the AT initiation and development by assimilating surface and radar observations. Its cold pool and outflow boundary prediction are also improved. In this case, the assimilation of surface wind and water vapor in the morning contributes more compared with temperature and pressure.

With the increasing proportion of renewable energy, some problems have gradually emerged. To reduce the operating cost and improve system reliability, renewable power forecasting is an indispensable part. Compared with the deterministic prediction, the probabilistic forecast considers the uncertainty, which helps manage power system operations. This study proposes a novel hour-ahead probabilistic forecasting method for wind power generation. It includes data preprocessing, adaptive neuro fuzzy inference system (ANFIS) training model with fuzzy c-means (FCM) clustering algorithm, and post processing of predicted-interval (PI). The input data of the proposed forecasting model include the numerical weather prediction (NWP) ensemble wind speeds, NWP spot wind speeds, and historical wind power measurements. The research results demonstrate that the proposed model supports better performance and prediction stability. Furthermore, this work reveals that data preprocessing and post processing of predicted interval are essential for wind power forecasting. These processes greatly improve the performance of the probabilistic wind power forecasts.

Typhoon WRF (TWRF) based on the Advanced Research Weather Research and Forecasting Model (ARW WRF) was operational at the Central Weather Bureau (CWB) for tropical cyclone (TC) predictions since 2010 (named TWRF V1). CWB has committed to improve this regional model, aiming to increase the model predictability toward typhoons over East Asia. In 2016, an upgraded version designed to replace TWRF V1 became operational (named TWRF V2). Compared with V1, which has triple-nested meshes with coarser resolution (45/15/5 km), V2 increased the model resolution to 15/3 km. Since V1 and V2 were maintained in parallel from 2016 to 2018, this study utilized the real-time forecasts to investigate the impact of model resolution on TC prediction. Statistical measures pointed out the superiority of the high-resolution model on TC prediction. The forecast performance was also found competitive with that of two leading global models. The case study further pointed out, with the higher resolution, the model not only advanced the prediction on the TC track and inner core structure but also improved the representativeness of the complex terrain. Overall, the high-resolution model can better handle the so-called terrain phase-lock effect and, therefore, improve the TC quantitative precipitation forecast over the complex Taiwanese terrain.