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.

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

<p>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.  </p><p>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.</p>

Improve Tropical Cyclone Prediction of TWRF with the Application of Advanced Observation Data

<p>With violent wind and severe rainfall, the tropical cyclone is one of the most disastrous weather systems over ocean and the coastal area. To provide accurate tropical cyclone (TC) track and intensity forecasts is one of the most important tasks of the national weather service of countries affected. Taiwan is one of the areas frequently influenced by tropical cyclones. Improving the tropical cyclone forecast is the highest priority task of Taiwan’s Central Weather Bureau (CWB).</p><p>Recent improvement of the TC forecast is due to the improvement of the numerical weather prediction. A version of the Advanced Research Weather Research and Forecasting Model (WRF), named TWRF (Typhoon WRF), was developed and implemented in CWB for operational TC forecasting from 2011. During the years, partial update cycling, cyclone bogus scheme, relocation scheme, 3DVAR with outer loop, analysis blending scheme, new trigger Kain–Fritsch cumulus scheme, and so on have been studied and applied in TWRF (Hsiao et al. 2010, 2012, 2015) to improve the model. We also improved the model by changing the TWRF configuration from a triple nested to a double nested grid and increasing the model resolution from 45/15/5 km 45-levels to 15/3 km 52-levels from 2016. Results showed increasing the model resolution improving the track, intensity and rainfall forecast. However, The averaged 24/48/72 hours TC track forecast errors of TWRF are 91/147/223, 84/133/197, 74/127/215, 64/122/202, 70/120/194 and 70/122/180 km in year 2014, 2015, 2016, 2017, 2018 and 2019 respectively.</p><p>In this study, WRF Four-dimensional data assimilation (FDDA) is adopted to assimilate the temperature, pressure, water vapor content which processed from the FORMOSAT-7 constellation, high-temporal frequency atmospheric motion vector (AMV) retrieved from Himawari-8 satellite images and radar data to generate a model balanced TC structure and thermodynamic state at the initial time. The specific goal is to improve the track, structure and intensity prediction of TCs and their associated rainfall distribution in Taiwan. The detail will be presented in the conference.</p><p>Keywords: tropical cyclone, Himawari-8 AMV, Four-dimensional data assimilation, FORMOSAT-7, radar data.</p><p>Corresponding author address:</p><p>Der-Song Chen,  [email protected]</p><p>Central Weather Bureau, 64 Gongyuan Rd., Taipei, Taiwan, R.O.C., 10048.</p>

Daytime Visibility Measurement with Camera during Spring in Qingdao

Based on digital camera method, there was using an improved dual-target method to measure atmospheric visibility. Using a digital camera to obtain images selected targets under different weather, to brightness contrast method and dual-target method were used to edge detection and Hough transform to obtain the image of the target and background, then respectively through the brightness contrast method and dual target method to calculate visibility value, in view of the different weather will dual target method and brightness contrast method and meteorological bureau issued the visibility value contrast experiment. Comparison results show that the digital camera method for visibility value and weather bureau data released by the trend is almost the same, especially dual target method. Digital camera method proved the effectiveness of Qingdao area visibility observation.