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The blockage percentages from the first two tilts of RCMK (a and b) and RCCK (c and d). The color map indicates the blockage percentages.
Source publication
Complex terrain poses significant challenges to the radar based quantitative precipitation estimation (QPE) because of blockages to the lower tilts of radar observations. The blockages often force the use of higher tilts data to estimate precipitation at the ground and result in errors due to vertical variations of the radar variables. To obtain ac...
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In the present work, long-term (10 years) raindrop size distribution (RSD) measurements from Joss-Waldvogel Disdrometer (JWD) installed at National Central University (NCU, 24°58'6"N 121°11'27"E), Taiwan and vertical profile of radar reflectivity were used to analyze the variations in gamma parameters of six seasons (winter, spring, meiyu, summer,...
Citations
... In this study, observations from surface rainfall stations were utilized as input to the forecasting model. Model forecasting might be advanced if quantitative rainfall forecast data, such as QPESUM (Quantitative Precipitation Estimation and Segregation Using Multiple Sensors) and QPF (Quantitative Precipitation Forecast), are adopted as model inputs (Chiou et al. 2005;Wang et al. 2016). Urban flooding characteristics are determined by many factors, such as drainage system, vegetation and topography. ...
Since flooding in urban areas is rarely observed using sensors, most researchers use artificial intelligence (AI) models to predict flood hazards based on model simulation data. However, there is still a gap between simulation and real flooding phenomenon due to the limitation of the model. Few studies have reported on the AI model for flood inundation depth forecasting based on observed data. This study presents a novel method integrating long short-term memory (LSTM) with moving average (MA) for flood inundation depth forecasting based on observed data. A flood-prone intersection in Rende District, Tainan, Taiwan, was adopted as the study area. This investigation compared the forecasting performance of the backpropagation neural network (BPNN), recurrent neural network (RNN) and LSTM models. Accumulated rainfall (Ra) and the moving average (MA) method were applied to enhance the LSTM model performance. The model forecast accuracy was evaluated using root mean square error, coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE). Analytical results indicated that the LSTM had better forecasting ability than the RNN and BPNN, because LSTM had both long-term and short-term memory. Since Ra was an important factor in flooding, adding the Ra to the model input upgraded the LSTM forecasting accuracy for high inundation depths. Because MA reduced the noise of the data, processing the model output using the MA also elevated the forecasting accuracy for high inundation depths. For 3-step-ahead forecasting, the NSE of the model benchmark BPNN was 0.79. Using LSTM, Ra and MA, NSEs gradually increased to 0.83, 0.88 and 0.91, respectively.
... QPE studies [21][22][23] in mountainous regions are continuously being conducted. Most of the QPE studies on orographic rainfall focus on correcting rainfall errors by correcting the dual-polarization parameters according to mountain altitude. ...
The precipitation systems that pass over mountains develop rapidly due to the forcible ascent caused by the topography, and spatial rainfall distribution differences occur due to the local development of the system because of the topography. In order to reduce the damage caused by orographic rainfall, it is essential to provide rainfall field data with high spatial rainfall accuracy. In this study, the rainfall estimation relationship was calculated using drop size distribution data obtained from 10 Parsivel disdrometers that were installed along the long axis of Mt. Halla (oriented west–east; height: 1950 m; width: 78 km; length: 35 km) on Jeju Island, South Korea. An ensemble rainfall estimation relationship was obtained using the HSA (harmony search algorithm). Through the linear combination of the rainfall estimation relationships determined by the HSA, the weight values of each relationship for each rainfall intensity were optimized. The relationships considering KDP, such as R(KDP) and R(ZDR, KDP), had higher weight values at rain rates that were more than 10 mm h−1. Otherwise, the R(ZH) and R(ZH, ZDR) weights, not considering KDP, were predominant at rain rates weaker than 5 mm h−1. The ensemble rainfall estimation method was more accurate than the rainfall that was estimated through an independent relationship. To generate the rain field that reflected the differences in the rainfall distribution according to terrain altitude and location, the spatial correction value was calculated by comparing the rainfall obtained from the dual-polarization radar and AWS observations. The distribution of Mt. Halla’s rainfall correction values showed a sharp difference according to the changes in the topographical elevation. As a result, it was possible to calculate the optimal rain field for the orographic rainfall through the ensemble of rainfall relationships and the spatial rainfall correction process. Using the proposed methodology, it is possible to create a rain field that reflects the regional developmental characteristics of precipitation.
... Observed rainfall data from the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) are obtained from 6 Dopper radars of the Central Weather Bureau. The interval of the weather information was 10 min, and the grid resolution was 1.3 km (Chiou et al. 2005;Wang et al. 2016). ...
Coupled 1D–2D hydrodynamic models are widely utilized in flood hazard mapping. Previous studies adopted conceptual hydrological models or 1D hydrodynamic models to evaluate the impact of drainage density on river flow. However, the drainage density affects not only river flow, but also the flooded area and location. Therefore, this work adopts the 1D–2D model SOBEK to investigate the impact of drainage density on river flow. The uncertainty of drainage density in flood hazard mapping is assessed by a designed case and a real case, Yanshuixi Drainage in Tainan, Taiwan. Analytical results indicate that under the same return period rainfall, reduction in tributary drainages in a model (indicating a lower drainage density) results in an underestimate of the flooded area in tributary drainages. This underestimate causes higher peak discharges and total volume of discharges in the drainages, leading to flooding in certain downstream reaches, thereby overestimating the flooded area. The uncertainty of drainage density decreases with increased rainfall. We suggest that modeling flood hazard mapping with low return period rainfalls requires tributary drainages. For extreme rainfall events, a lower drainage density could be selected, but the drainage density of local key areas should be raised.
... The data presented are radar data that have been pre-processed from published literature in the form of scatter plots and tables [3][4][5][6][7][8][9][10][11][12]. Specifically, these are "Dual-Pol radar derived rainfall estimates" coupled with the "observed local rain gauge values" collected from multiple storms and geographical domains, of which the majority are categorized via total storm accumulation. ...
... Specifically, these are "Dual-Pol radar derived rainfall estimates" coupled with the "observed local rain gauge values" collected from multiple storms and geographical domains, of which the majority are categorized via total storm accumulation. The data from the published graphs [3][4][5][6][7][8][9][10][11][12] was read using the plot digitizer (http:// plotd igiti zer. sourc eforge. ...
Objective
Reported rainfall data from multiple rain gauges and its corresponding estimate from Dual-Polarization (Dual-Pol) radar is presented here. The ordered set of data pairs were collected from multiple peer reviewed publications spanning across the last decade.
Data description
Taken from multiple sources, the data set represents several radar sites and rain gauge sites combined for 12,734 data points. The data is relevant in various applications of hydrometeorology and engineering as well as weather forecasting. Further, the importance of accuracy in radar precipitation estimates continues to increase, necessitating the incorporation of as much data as possible.
... Previous studies have pointed out that when the representative area of a single station is reduced to less than 40 km 2 , the fluctuation range of average rainfall has not changed significantly (Zhang et al., 2018). Although Taiwan currently has radar rainfall data, owing to the high-relief topography, the estimated rainfall obtained from radar may be limited (Wang et al., 2016). This study chose traditional rain gauge data to ensure the accuracy of rainfall data used in the modeling. ...
Typhoon Aere in 2004 induced severe sedimentation and loss of storage capacity of the Shihmen Reservoir in northern Taiwan. The resulting dramatic increase in the turbidity of the water seriously affected the water supply. To effectively maintain the stability of the water supply and maintain the reservoir’s storage capacity, the government of Taiwan began to plan and construct a series of improvement measures, such as a sediment flushing tunnel, the JhongJhuang Bank-Side Reservoir, and the Amuping Desilting Tunnel. However, previous studies only focused on the impact of the sediment flushing tunnel and the Amuping Desilting Tunnel on the downstream riverbed, and did not consider the possibility of increasing sediment discharge after the completion of the JhongJhuang Bank-Side Reservoir. In addition, climate change will cause the intensity of extreme rainfall to increase enormously in the future. That rainfall and extra sediment flushing will challenge the existing flood prevention facilities. Therefore, this study considered that the JhongJhuang Bank-Side Reservoir will increase sediment discharge of the Shihmen Reservoir, and used dynamical downscaling extreme typhoon data of climate change under the RCP 8.5 scenario to explore the flood prevention and riverbed migration of the main channels of the Dahan and Tamsui Rivers in the future. We used the rainfall–runoff model of Hydrologic Modeling System to simulate rainfall and runoff, and used the hydraulic and sediment transport model of CCHE1D to holistically simulate flood events and consequent river scouring and deposition behaviors. Our results showed that the projected peak discharge during the late 21st century (2075 to 2099) will be at least 50% higher than that during the baseline (1979 to 2003) period. In terms of flood prevention, the potential of overbank flooding will increase in the downstream area, and the trend of long-term change in the riverbed will be dominated by degradation (-0.489 ± 0.743 m) in the future. The improvement measures will have a limited impact on riverbed migration (0.011 ± 0.094 m) in the Dahan and Tamsui Rivers. After the operation of the JhongJhuang Bank-Side Reservoir, the Shihmen Reservoir is expected to increase the sediment discharge ratio by 70% during floods, and it will not cause excessive water turbidity that may affect downstream water supply.
... As TC rainfall frequently leads to flooding, debris flows, and large economic losses [7,8], many studies have focused on understanding the spatio-temporal characteristics of TC rainfall in Taiwan over various timescales [9][10][11]. Local researchers might generally use high-density rain-gauge observations [11,12] or the weather radar network data [13][14][15][16][17][18] for the study of TC rainfall; however, the raw data of these observations usually are not free accessible for "non-local" researchers. Instead, free accessible satellite precipitation products (SPPs) become an important source of information for "non-local" researchers interested in studying TC rainfall over Taiwan. ...
This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.
... Flood and mudslide warnings and predictions in Taiwan are especially challenging because of the complex and steep terrain and extreme rainfall produced by typhoons and quasistationary fronts. Advancements in weather radar and computer technologies in recent decades have provided meteorologists and hydrologists new opportunities to improve the monitoring and prediction of flash floods and debris flows (e.g., Germann et al. 2006;Tabary 2007;Zhang et al. 2011Zhang et al. , 2016Gourley et al. 2017). Over the last two decades, The annual rainfall in Taiwan is abundant and varies from 1600 to 3100 mm with an average of approximately 2400 mm . ...
Over the last two decades, the Central Weather Bureau of Taiwan and the U.S. National Severe Storms Laboratory have been involved in a research and development collaboration to improve the monitoring and prediction of river flooding, flash floods, debris flows, and severe storms for Taiwan. The collaboration resulted in the Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system. The QPESUMS system integrates observations from multiple mixed-band weather radars, rain gauges, and numerical weather prediction model fields to produce high-resolution (1 km) and rapid-update (10 min) rainfall and severe storm monitoring and prediction products. The rainfall products are widely used by government agencies and emergency managers in Taiwan for flood and mudslide warnings as well as for water resource management. The 3D reflectivity mosaic and QPE products are also used in high-resolution radar data assimilation and for the verification of numerical weather prediction model forecasts. The system facilitated collaborations with academic communities for research and development of radar applications, including quantitative precipitation estimation and nowcasting. This paper provides an overview of the operational QPE capabilities in the Taiwan QPESUMS system.
... Depending on whether radars are equipped with this functionality, they are referred to as Doppler or non-Doppler radars. Polarimetric Doppler radars are the most sophisticated type of radars and their use is quickly expanding (Berne & Krajewski, 2013;Huuskonen et al., 2014;Wang et al., 2016). ...
... 1. Merging application under improved quality radar QPEs: as mentioned in section 2, Doppler, dualpolarization radars are becoming increasingly available and these additional radar hardware functionalities have the potential to lead to radar QPEs of increased accuracy (Berne & Krajewski, 2013;Huuskonen et al., 2014;Wang et al., 2016). As discussed in this review, particularly in section 7, the quality of the underlying radar QPEs plays an important role in the quality of final merged estimates and on the relative performance of a given merging technique, depending on how information from radar QPEs is employed. ...
Radar-rain gauge merging techniques have been widely used to improve the applicability of radar and rain gauge rainfall estimates by combining their advantages, while partially overcoming their individual weaknesses. Despite significant research in this area, guidance on the suitability of, and factors affecting merging techniques at the fine spatial temporal resolutions required for urban hydrological applications is still insufficient. In this paper, an in-depth review of radar-rain gauge merging techniques is conducted, with a focus on their potential for urban hydrological applications. An overview is first given of existing merging techniques and an application-oriented categorization is proposed: (1) radar bias adjustment methods; (2) rain gauge interpolation methods using radar spatial association as additional information; (3) radar-rain gauge integration methods. A detailed review is given of studies focusing on the evaluation and inter-comparison of merging methods, based upon which the most widely used and best performing techniques from each category are identified. These are: Mean Field Bias (MFB) adjustment, Kriging with External Drift (KED), and Bayesian (BAY) merging. Climatological, operational and methodological factors affecting merging performance are then reviewed and their relevance for urban applications discussed. Based on this review, conclusions on merging potential for urban applications are drawn and research gaps are identified which should be addressed to provide further guidance on the use of merging techniques for urban hydrological applications.
... The QPESUMS was based on six Doppler radars in Taiwan to estimate the precipitation and other hydrologic parameters, and it has been used to monitor severe weather since 2003. The weather information is updated every 10 min including 1.3-km resolution rainfall intensities covering all Taiwan land area [21,22]. The grid resolution of rainfall data was 1.3 km × 1.3 km, while the interval was 10 min. ...
An operational two-dimensional real-time flood forecasting system has been developed to prevent urban inundation in Taiwan, and it uses the Delft-FEWS (Flood EarlyWarning System) platform to integrate the SOBEK models to forecast flooding. A new generation of smart water level gauges, integrating power charging, recording, transmission, and sensing into one, can monitor the inundation on roads in real-time. This study took Annan District in Tainan City, Taiwan, as the study object and presented the application of the flood forecasting system and smart water level gauges in the urban environment. The collected data from Storm 0611 and Typhoon Megi in 2016 were used to assess the accuracy of the flood forecasting system. The analysis of three water level stations in the drainage showed that the one-dimensional simulation results were fairly accurate. According to the 18 observed and simulated inundation depths on the roads, the true positive, false positive, false negative, and true negative values were 8, 0, 2, and 8, respectively. The accuracy, sensitivity, and precision of the flood forecasting system were 0.889, 0.800, and 1.000, respectively, indicating that the two-dimensional simulation results were highly accurate.
... More water vapor can be transported into the inland mountainous area and lifted by the orography. Third, radar QPE may also underestimate the precipitation over complex terrain (Wang et al. 2016). Thus, one should not expect the simulated rainfall to match exactly with the radar observational estimate. ...
Convection-permitting numerical experiments using the Weather Research and Forecasting (WRF) Model are performed to explore the influence of monsoonal onshore wind speed and moisture content on the intensity and diurnal variations of coastal rainfall over south China during the mei-yu seasons. The focus of the analyses is on a pair of 10-day WRF simulations with diurnally cyclic-in-time lateral boundary conditions averaged over the high versus low onshore wind speed days of the 2007–09 mei-yu seasons. Despite differences in the rainfall intensity, the spatial distributions and diurnal variations of rainfall in both simulations verified qualitatively well against the mean estimates derived from ground-based radar observations, averaged respectively over either the high-wind or low-wind days.
Sensitivity experiments show that the pattern of coastal rainfall spatial distribution is mostly controlled by the ambient onshore wind speed. During the high-wind days, strong coastal rainfall is concentrated along the coastline and reaches its maximum in the early morning. The coastal lifting induced by the differential surface friction and small hills is the primary cause for the strong coastal rainfall, while land breeze enhances coastal lifting and precipitation from evening to early morning. In the low-wind days, on the other hand, coastal rainfall is mainly induced by the land–sea-breeze fronts, which has apparent diurnal propagation perpendicular to the coastline. With stronger land–sea temperature contrast, the land–sea breeze is stronger during the low-wind days. Both in the high-wind and low-wind days, the coastal rainfall intensity is sensitive to the incoming moisture in the upstream oceanic airflow, especially to the moisture content in the boundary layer.
