Implementation of the Unified Representation of Deep Moist Convection in the CWBGFS

This study implements the unified parameterization (UP) in the Central Weather Bureau Global Forecast System (CWBGFS) based on the relaxed Arakawa–Schubert scheme (RAS) at a horizontal resolution of 15 km. The new cumulus parameterization that incorporates the UP framework is called URAS. The UP generalizes the representation of moist convection between the parameterized and the explicitly resolved processes according to the process-dependent convective updraft fraction (σ). Short-term hindcasts are performed to investigate the impacts of the UP on the simulated precipitation variability and organized convective systems over the Maritime Continent when multiple scales of convection occurred. The result shows that σ is generally larger when convective systems develop, which adaptively reduces the parameterized convection and increases the spatial variation of moisture. In the URAS experiment, the moisture hotspots within organized convective systems contribute to the enhanced local circulation and the more significant variability of precipitation. Consequently, the URAS has a more realistic precipitation spectrum, an improved relationship between the maximum precipitation and the horizontal scale of the convective systems, and an improved column water vapor–precipitation relationship.

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).

Comparing Kriging Estimators Using Weather Station Data and Local Greenhouse Sensors

In order to minimize the impacts of climate change on various crops, farmers must learn to monitor environmental conditions accurately and effectively, especially for plants that are particularly sensitive to the weather. On-site sensors and weather stations are two common methods for collecting data and observing weather conditions. Although sensors are capable of collecting accurate weather information on-site, they can be costly and time-consuming to install and maintain. An alternative is to use the online weather stations, which are usually government-owned and free to the public; however, their accuracy is questionable because they are frequently located far from the farmers’ greenhouses. Therefore, we compared the accuracy of kriging estimators using the weather station data (collected by the Central Weather Bureau) to local sensors located in the greenhouse. The spatio-temporal kriging method was used to interpolate temperature data. The real value at the central point of the greenhouse was used for comparison. According to our results, the accuracy of the weather station estimator was slightly lower than that of the local sensor estimator. Farmers can obtain accurate estimators of environmental data by using on-site sensors; however, if they are unavailable, using a nearby weather station estimator is also acceptable.

Temporal b-value variation before and after ML ≥ 6.0 Taiwan earthquakes from 2012 to 2019

&lt;p&gt;Recent studies show that earthquake b values gradually decrease before large earthquakes at the epicenters and then immediately increase after the earthquakes. Temporal b-value variations may result from crustal stress changes associated with a large earthquake. However, the physical process is rarely observed and remains unclear. Taiwan island is a young orogeny leading to frequent earthquakes with magnitudes greater than M&lt;sub&gt;L&lt;/sub&gt; 6.0, which provides an excellent laboratory to examine the physical process. We calculated b-value variation before and after M&lt;sub&gt;L&lt;/sub&gt; &amp;#8805; 6.0 Taiwan earthquakes at the epicenters from 2012 to 2019. The time period is based on an enhancement of earthquake detection capability from the Central Weather Bureau Seismic Network in Taiwan, which allows the magnitude of completeness (M&lt;sub&gt;c&lt;/sub&gt;) down to 1.5 in the inland region. We used a relocated earthquake catalog to precisely estimate b value and M&lt;sub&gt;c &lt;/sub&gt;by the maximum likelihood method and maximum curvature method, respectively. We designed three steps in our research. First, we calculated the b value and M&lt;sub&gt;c&lt;/sub&gt; at the epicenters of the M&lt;sub&gt;L&lt;/sub&gt; &amp;#8805; 6.0 earthquakes in overall 8 years to know the background seismic activity. Based on this, second, we calculated b values and M&lt;sub&gt;c&lt;/sub&gt; per half year to test the sensitivity between the radius from epicenters (r) and the number of earthquakes with magnitudes greater than M&lt;sub&gt;c&lt;/sub&gt;&amp;#160;(n). Finally, we will apply moving window approach with specific criteria to continuously calculate temporal b-value variations. Our results showed that spatial b values in Taiwan in overall 8 years have an average of 1.0. The b values are systematically lower in the epicenters of M&lt;sub&gt;L&lt;/sub&gt; &amp;#8805; 6.0 earthquakes from 2012 to 2019. We have determined suitable r and n values for each earthquake at the epicenters and some epicenters share similar r and n values. We preliminarily observed temporal b-value decreases before the 2018 M&lt;sub&gt;w&lt;/sub&gt; 6.4 Hualien earthquake. Considering temporal b-value variation by moving windows, we aim to realize whether temporal b-value variation by a large earthquake can be frequently observed in Taiwan.&lt;/p&gt;

Development of a Small Intelligent Weather Station for Agricultural Applications

It is known that climate change causes a decrease in the profit gained from agricultural production. This work designs and establishes weather boxes equipped with functions of rainfall prediction, frosting forecast, and lightning detection. With the wireless connection and the build-in decision mode, weather boxes can deliver early-warning by sending texting messages to the users and actuating the corresponding action to response the extreme climate. To implement rainfall and frosting prognostication, two different datasets are analyzed by the technology of data mining. One of the datasets is acquired from the Central Weather Bureau, and the other is from the proposed weather box monitoring the agricultural environment. From the experimental results, the prediction model constructed from the data which is collected by the proposed weather box exhibits a higher accuracy in rainfall forecasting than those based on the Central Weather Bureau.

Real-Time Production of PGA, PGV, Intensity, and Sa Shakemaps Using Dense MEMS-Based Sensors in Taiwan

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 (Sa) 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.

Deep Learning for Satellite Rainfall Retrieval Using Himawari-8 Multiple Spectral Channels

Rainfall retrieval using geostationary satellites provides critical means to the monitoring of extreme rainfall events. Using the relatively new Himawari 8 meteorological satellite with three times more channels than its predecessors, the deep learning framework of &ldquo;convolutional autoencoder&rdquo; (CAE) was applied to the extraction of cloud and precipitation features. The CAE method was incorporated into the Convolution Neural Network version of the PERSIANN precipitation retrieval that uses GOES satellites. By applying the CAE technique with the addition of Residual Blocks and other modifications of deep learning architecture, the presented derivation of PERSIANN operated at the Central Weather Bureau of Taiwan (referred to as PERSIANN-CWB) expands four extra convolution layers to fully use Himawari 8&rsquo;s infrared and water vapor channels, while preventing degradation of accuracy caused by the deeper network. The development of PERSIANN-CWB was trained over Taiwan for its diverse weather systems and localized rainfall features, and the evaluation reveals an overall improvement from its CNN counterpart and superior performance over all other rainfall retrievals analyzed. Limitation of this model was found in the derivation of typhoon rainfall, an area requiring further research.

Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.

Improvement of the Numerical Tropical Cyclone Prediction System at the Central Weather Bureau of Taiwan: TWRF (Typhoon WRF)

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.

Impact of FORMOSAT-7/COSMIC-2 RO on High-Resolution Hybrid 3DEnVar System at Central Weather Bureau of Taiwan

&lt;p&gt;The FORMOSAT-7/COSMIC-2 (simplified as FS-7/C-2 in the following descriptions) is the constellation of satellites for meteorology, ionosphere, climatology, and space weather research. FS-7/C-2 was a joint Taiwan-U.S. satellite mission that makes use of the radio occultation (RO) measurement technique. FORMOSAT-7 is the successor of FORMOSAT-3 which was launched in 2006. the FORMOSAT-3 RO data has been shown to be extremely valuable for numerical weather prediction, such as improving the prediction of tropical cyclogenesis and reducing the typhoon track error. The follow-on FS-7/C-2 mission was launched on 25 June 2019, and is currently going through preliminary testing and evaluation. After it is fully deployed, FS-7/C-2 is expected to provide 6,000 GNSS (Global Navigation Satellite System) RO profiles per day between 40S and 40N. &amp;#160;&lt;/p&gt;&lt;p&gt;In this study, we conduct a preliminary evaluation of FS-7/C-2 GNSS RO data on heavy precipitation events associated with typhoon and southwesterly monsoon flows based on the operational NWP system of the Central Weather Bureau (CWB) in Taiwan. The FS-7/C-2 GNSS RO data are assimilated using a dual-resolution hybrid 3DEnVare system with a 15-3 km nested-grid configuration. In the 15km resolution domain, flow-dependent background error covariances (BECs) derived from the perturbation of ensemble adjustment Kalman filter (EAKF), will be used to conduct hybrid 3DEnVar analysis. In the 3 km resolution domain, the 15 km resolution flow-dependent BECs will be inserted to the 3 km grid using a Dual-Resolution (DR) technique, and then combined with 3 km resolution static BECs, to perform the high-resolution 3DEnVar analysis. The performance of the CWB operational NWP system on quantitative precipitation forecast of significant precipitation events with and without the assimilation of FS-7/C-2 GNSS RO data will be evaluated.&lt;/p&gt;