Rainfall In Taiwan Research Articles

The role of sea surface temperature in shaping the characteristics of future convective afternoon rainfall in Taiwan

Convective afternoon rainfall (CAR) is a significant summer rainfall feature in Taiwan. This study investigates the projected uncertainties in summer CAR in Taiwan, using a dynamical downscaling approach with the Weather Research and Forecasting Model (WRF) and the High-Resolution Atmospheric Model (HiRAM). The projections were driven by four different sea surface temperature (SST) categories derived from CMIP5 model simulations under the RCP8.5 scenario. All projections indicate a reduced frequency but increased intensity of CAR over Taiwan by the end of the 21st century. However, notable differences in the degree of CAR changes were observed among simulations with varying degrees of Pacific SST warming. These variations in potential future changes in CAR frequency and intensity can be attributed to the variations in daytime thermal instability, local inland wind convergence, and moisture flux convergence over Taiwan. Furthermore, this study explored the linkage between local thermodynamic conditions and projected large-scale circulation patterns.

Sensitivity of Extreme Rainfall in Taiwan to SST Over the South China Sea Through Modulation of Marine Boundary Layer Jet: A Mei‐Yu Front Event During 1–4 June 2017

AbstractWater vapor transport from the South China Sea (SCS) by the marine boundary layer jet (MBLJ) can be an important moisture source for heavy rainfall events in Taiwan during the mei‐yu season. However, the variability in the MBLJ due to sea surface temperature (SST) changes and its impact on extreme rainfall events have yet to be well understood. This study investigates how varying the SST over SCS modifies the structure of MBLJ and further impacts an extreme mei‐yu rainfall in Taiwan during 1–4 June 2017. Results show that SST over the SCS can affect rainfall in Taiwan through its modulation of the MBLJ. Increasing SST leads to a southward shift of the mei‐yu rainfall. This is attributed to the weakening of the MBLJ and the enhanced convection at the leading edge of the mei‐yu front. The opposite effect is observed when the SST is decreased but with a smaller impact.

Interdecadal variability of meridional tropical cyclone genesis over the western North Pacific and associated modulating processes

AbstractDuring the period 1970–2020, evident interdecadal variability of meridional tropical cyclone (TC) genesis occurs in the western North Pacific (WNP) from September through October (SO). From the periods 1970–1996 (IP1) to 1998–2020 (IP2), TC genesis during SO is enhanced (suppressed) in the region north (south) of 20°N. The large‐scale modulating processes feature a general warming of sea surface temperature (SST) over the WNP and systematic northward displacement in atmospheric circulations. The northward‐shifted Pacific subtropical high and monsoon trough result in an anomalous anticyclone across the subtropical region and an anomalous cyclone across the South China Sea and the northern WNP east of Taiwan. This anomalous cyclone facilitates TC genesis to increase in the northern WNP. Meanwhile, the southeastward‐extending monsoon trough retreats northwestward, leading to an anomalous anticyclone over the tropical eastern WNP and a decrease in TC genesis over the southern region. Correlation analyses indicate that the Pacific Decadal Oscillation (PDO) is more effective than global warming in modulating interdecadal increases of TC genesis in the northern region due to inclusions of both trend and oscillation features. Interdecadal decreases of TC genesis in the southern region exhibit a major oscillation feature and are thus mainly affected by interdecadal oscillation components of PDO. More TCs forming in the oceans east of Taiwan are steered by the overlying anomalous cyclone to recurve northward toward oceans to the northeast and north of Taiwan. TC‐induced rainfall over Taiwan thus increases by 25% from IP1 to IP2, which accounts for about 82% of increased total rainfall in Taiwan, showing the notable impact of interdecadal TC genesis on local climate in the WNP.

Eight-Day Typhoon Quantitative Precipitation Forecasts in Taiwan by the 2.5 km CReSS Model, Part II: Reduced Control of Track Errors on Rainfall Prediction Quality for Typhoons Associated with Southwesterly Flow

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm’s circulation, and the QPF was degraded without a good enough track prediction.

Cloud-resolving time-lagged rainfall ensemble forecasts for typhoons in Taiwan: Examples of Saola (2012), Soulik (2013), and Soudelor (2015)

As high resolution is required for numerical models to adequately simulate convective storms and thus produce quantitative precipitation forecasts (QPFs), a time-lagged ensemble out to 8 days at 6-h intervals using a 2.5-km cloud-resolving model is applied to three rainy typhoons that made landfall in Taiwan in recent years: Saola (2012), Soulik (2013), and Soudelor (2015), following an earlier study.For the three typhoons where the worst-case rainfall scenario turned out to happen in Taiwan, the system was able to predict this particular scenario with high accuracy (with a rainfall pattern similar to the observed) at an earliest lead time of about 162 h, 79 h, and 164 h before landfall, and thus provided key information for early preparation. Within the short range (≤72 h), as the predicted tracks converged toward the best track, high-quality QPFs were consistently generated starting at about 36 h, 55 h, and 80 h prior to landfall, respectively, with derived probabilities in good to excellent agreement with the observations, even at extreme thresholds (≥500 and 700 mm in 24 h). Leading up to the track convergence, the probabilities across various rainfall thresholds increased markedly, so their time evolution also provided useful information for decision makers and hazard preparation.The underlying reason behind our results is the high predictability of topographic rainfall in Taiwan produced by the typhoon circulation, which cannot be properly captured without high model resolution. Thus, without compromising the resolution, we demonstrate the advantages of the time-lagged strategy for ensemble forecasting, assuming QPF as the key target forecast parameter.

Causes of the Record-breaking Drought in Taiwan in 2020-2021

<p>2020年梅雨季提早結束，夏季沒有侵臺颱風，秋冬季雨量亦明顯偏少，2021年春雨也是寥寥可數，就在水庫快要見底之際，望眼欲穿的梅雨終於在5月底到來，臺灣旱象才得以逐漸緩解。此次旱災除在經濟上有許多損失外，亦對民眾生活造成諸多不便，值得深入了解形成旱象的物理機制。</p> <p>統計2020年6月至2021年5月的臺灣雨量，達到1910年有紀錄以來的最少，堪稱為百年大旱。本報告分析此段期間的大尺度環流，推測造成臺灣百年大旱的可能原因，並歸納主要發現如下：①2020年6月至9月的少雨，主要是極端偏強的副熱帶高壓造成，當時的副高不僅在強度上達到最強，持續時間亦為1949年之後的最持久；副高偏強可能與熱帶各洋面同時出現海溫異常有關，包含反聖嬰在中東太平洋的偏冷、印度洋及大西洋的偏暖。②臺灣2020年秋冬季雨量偏少，可能與反聖嬰及菲律賓海偏暖有關。③2021年臺灣春雨偏少，主因應該是菲律賓海溫偏暖，其次為逐漸減弱的反聖嬰，負相位的太平洋年代際震盪亦有少許的貢獻；當菲律賓海偏暖時，會在其西側產生氣旋式環流，為臺灣及東亞沿岸帶來水氣較少的東北風距平，進一步造成臺灣春雨較少；反聖嬰有利上述偶合系統的維持。④2021年3月中旬至5月期間的熱帶季內振盪較為活躍，季內振盪可能使臺灣4月中下旬的西南水氣源減少，5月季內振盪乾區逐漸接近臺灣，進而造成臺灣少雨。簡而言之，2020-2021年的臺灣百年大旱不是單一因素造成的極端事件，而是許多因素同時出現的複合現象。</p> <p> </p><p>During 2020, an earlier-ended Mei-Yu season combined with a typhoon-absent summer, followed by notably low rainfall from following autumn to spring in 2021, Taiwan’s accumulated rainfall from June 2020 to May 2021 broke the lowest record since 1910, we report the unique characteristics and physical mechanism of this extreme event.</p> <p>The possible causes of the drought event are presented as follows. ①The duration and strength of subtropical high over the Western North Pacific from Jun to September were the longest and strongest since 1949. The abnormal subtropical high maybe caused by the compounding effects from cool sea surface temperature (SST) over the central to eastern equatorial Pacific associated with La Niña, and warm SST in the northern Indian Ocean and the tropical Atlantic. ②The warmest SST appeared in the Philippine Sea through October 2020 to Marth 2021, concurrent with La Niña, generated the dry condition near Taiwan. ③ The less spring rainfall in Taiwan in 2021 could be directly attributed to the warmest SST in the Philippine Sea and indirectly to the weakening La Niña , whereas negative phase of the Pacific Decadal Oscillation seemed to have little effect. An anomalous cyclone usually appeared in the western side of the warm Philippine Sea and brought dry northeasterly anomaly to the East Asian coast, resulting in less spring rainfall in Taiwan, and the La Niña would favor and maintain this atmosphere-ocean coupled system. ④ In April-May 2021, active tropical intra-seasonal oscillation caused weaker moist southeasterly in middle to late April, and also resulted in dry condition before the end of May.</p> <p>In conclusion, the most serious drought event in 2020-2021 since 1910 was caused by the compounding effect from various influencing factors that occurred concurrently and sequentially. How much of these abnormal conditions were caused by the warming trend in recent decades, which is not investigated in this study, warrants further studies.</p> <p> </p>

Characteristics and causes of Taiwan's extreme rainfall in 2022 January and February

Over the past 40 years (1982–2021), the January and February (JF) rainfall in Taiwan has shown a clear decreasing trend. However, in 2022, the JF rainfall suddenly climbed to the third-highest value ever recorded in Taiwan. This study aims to explore the characteristics and causes of this unique event. Our results show that the significant increase in JF 2022 rainfall is contributed by both an increase in rainfall occurrence frequency and an increase in rainfall intensity. Further analysis shows that most of the JF 2022 rainfall is contributed by the rainy systems propagating from South China or the north of South China Sea to Taiwan but without the frontal structure, which is different from the general concept that JF rainfall is dominated by winter monsoon circulation-induced orographic rainfall and frontal rainfall. As a result, southern Taiwan experienced more than a 130% increase in JF 2022 rainfall compared to the JF climate mean. Possible physical explanations for the increase in JF 2022 rainfall can be attributed to the enhanced winter background circulation, which featured an enhanced regional northeasterly wind at 925 hPa coupled with an enhanced southwesterly wind transporting moisture at 700 hPa covering the domain of (10°-30°N, 100°-120°E). Consequently, an active convection zone extending from the southeastern Bay of Bengal to Taiwan was revealed in JF 2022 to result in the extreme rainfall in Taiwan. Further analysis also indicates that the relationship between the Taiwan JF rainfall and the tropical sea surface temperature has changed since 2002.

Analysis of the Interdecadal and Interannual Variability of Autumn Extreme Rainfall in Taiwan Using a Deep-Learning-Based Weather Typing Approach

Analysis of the Interdecadal and Interannual Variability of Autumn Extreme Rainfall in Taiwan Using a Deep-Learning-Based Weather Typing Approach

Spatial Frequency Analysis by Adopting Regional Analysis with Radar Rainfall in Taiwan

This study proposed a spatially and temporally improving methodology adopting the Regional Frequency Analysis with an L-moments approach to estimate rainfall quantiles from 22,787 grids of radar rainfall in Taiwan for a 24-h duration. Due to limited radar coverage in the eastern region, significant discordant grids were found in the coastal area of the eastern region. A total of 171 grids with Di > 6 were set as discordant grids and removed for further analysis. A K-means cluster analysis using scaled at-site characteristics was used to group the QPESUMS grids in Taiwan into 22 clusters/sub-regions based on their characteristics. Spatially, homogeneous subregions with QPESUMS data produce more detailed homogeneous subregions with clear and continuous boundaries, especially in the mountain range area where the number of rain stations is still very limited. According to the results of z-values and L-moment ratio diagrams, the Wakeby (WAK), Generalized Extreme Value (GEV), and Generalized Pareto (GPA) distributions of rainfall extremes fitted well for the majority of subregions. The Wakeby distribution was the dominant best-fitted distribution, especially in the central and eastern regions. The east of the northern part and southern part of Taiwan had the highest extreme rainfall especially for a 100-year return period with an extreme value of more than 1200 mm/day. Both areas were frequently struck by typhoons. By using grid-based (at-site) as the basis for assessing regional frequency analysis, the results show that the regional approach in determining extreme rainfall is very suitable for large-scale applications and even better for smaller scales such as watershed areas. The spatial investigation was performed by establishing regions of interest in small subregions across the northern part. It showed that regionalization was correct and consistent.

Identification of synoptic patterns for extreme rainfall events associated with landfalling typhoons in China during 1960–2020

Extreme rainfall associated with landfalling typhoon (ERLTC) can cause severe disasters and economic impacts throughout China. Improving the accuracy of ERLTC forecasts is therefore crucial in disaster prevention and mitigation. The top 26 ERLTC events in China during 1960–2020 are investigated based on multi-source datasets. These ERLTC events are categorized into five main types according to the geographical location of the extreme precipitation and its position relative to the tropical cyclone (TC) center, namely: the typhoon inner-core rainfall in Taiwan (TWIC), typhoon inverted trough rainfall in Taiwan (TWIT), weak typhoon rainfall in Hainan (HNWK), strong typhoon rainfall in Zhejiang (ZJST) and inland typhoon remnant rainfall (ILRM). All the ERLTC events occurred in the weakening stage of TC after reaching its lifetime maximum intensity in convective cloud (TBB ≤ −32 °C) regions over complex local terrain. The translational speeds of 20 TCs (76.9% of the total) were smaller than the climatological average (20.6 km h−1) during the extreme precipitation events. The differences are as follows: the TWIC and TWIT types are featured with different season, track and water vapor channel although both occurred in Taiwan. The other three types are distinguished by spinning track and strong convective cloud for HNWK type, strong TC intensity and binary TC interactions for ZJST type; and stagnation and strong westerly trough activity for ILRM type, respectively. These results are expected to provide useful clues for an in-depth understanding of ERLTC events over China.

Time-Lagged Ensemble Quantitative Precipitation Forecasts for Three Landfalling Typhoons in the Philippines Using the CReSS Model, Part I: Description and Verification against Rain-Gauge Observations

In this study, the 2.5 km Cloud-Resolving Storm Simulator was applied to forecast the rainfall by three landfalling typhoons in the Philippines at high resolution: Mangkhut (2018), Koppu (2015), and Melor (2015), using a time-lagged strategy for ensemble. The three typhoons penetrated northern Luzon, central Luzon, and the middle of the Philippine Archipelago, respectively, and the present study verified the track and quantitative precipitation forecasts (QPFs) using categorical statistics against observations at 56 rain-gauge sites at seven thresholds up to 500 mm. The predictability of rainfall is the highest for Koppu, followed by Melor, and the lowest for Mangkhut, which had the highest peak rainfall amount. Targeted at the most-rainy 24 h of each case, the threat score (TS) within the short range (≤72 h) could reach 1.0 for Koppu at 350 mm in many runs (peak observation = 502 mm), and 1.0 for Mangkhut and 0.25 for Melor (peak observation = 407 mm) both at 200 mm in the best member, when the track errors were small enough. For rainfall from entire events (48 or 72 h), TS hitting 1.0 could also be achieved regularly at 500 mm for Koppu (peak observation = 695 mm), and 0.33 at 350 mm for Melor (407 mm) and 0.46 at 200 mm for Mangkhut (786 mm) in the best case. At lead times beyond the short range, one third of these earlier runs also produced good QPFs for both Koppu and Melor, but such runs were fewer for Mangkhut and the quality of QPFs was also not as high due to larger northward track biases. Overall, the QPF results are very encouraging, and comparable to the skill level for typhoon rainfall in Taiwan (with similar peak rainfall amounts). Thus, at high resolution, there is a fair chance to make decent QPFs even at lead times of 3–7 days before typhoon landfall in the Philippines, with useful information on rainfall scenarios for early preparation.

Idealized simulations of Mei-yu rainfall in Taiwan under uniform southwesterly flow using a cloud-resolving model

Abstract. In this study, idealized cloud-resolving simulations are performed for horizontally uniform and steady southwesterly flow at fixed direction–speed combinations to investigate rainfall characteristics and the role of the complex topography in Taiwan during the Mei-yu season without the influence of a front or other disturbances. Eight directions (180 to 285∘, every 15∘) and eight speeds (5 to 22.5 m s−1, every 2.5 m s−1) are considered, and near-surface relative humidity is also altered (from 55 %–100 %) in a subset of these tests to further examine the effects of moisture content, yielding a total 109 experiments each having a integration length of 50 h. Three rainfall regimes that correspond to different ranges of the wet Froude number (Frw) are identified from the idealized simulations (with a grid size of 2 km). The low-Frw regime (Frw ≤ ∼ 0.3) is where the island circulation from thermodynamic effects is the main driver of rainfall in local afternoon. The lower the wind speed and Frw are, the more widespread the rainfall is, as well as its amount. On the other hand, the high-Frw regime (Frw ≥ ∼ 0.4) occurs when the flow of at least 12.5 m s−1 impinges on Taiwan terrain at a large angle (not parallel). This favors the flow-over scenario, and topographic rainfall production becomes dominant through mechanical uplift of unstable air. In this scenario, the faster and wetter the flow is, the heavier the rainfall on the windward slopes is, and maximum amounts typically occur at wind directions from 240–255∘. Between the two regimes above, a third, mixed regime also exists. The idealized results are discussed for their applicability to the real atmosphere.

Evaluation of Mei-yu heavy-rainfall quantitative precipitation forecasts in Taiwan by a cloud-resolving model for three seasons of 2012–2014

Abstract. In this study, the performance of quantitative precipitation forecasts (QPFs) by the Cloud-Resolving Storm Simulator (CReSS) in Taiwan, at a horizontal grid spacing of 2.5 km and a domain size of 1500×1200 km2, in the range of 1–3 d during three Mei-yu seasons (May–June) of 2012–2014 is evaluated using categorical statistics, with an emphasis on heavy-rainfall events (≥100 mm per 24 h). The categorical statistics are chosen because the main hazards are landslides and floods in Taiwan, so predicting heavy rainfall at the correct location is important. The overall threat scores (TSs) of QPFs for all events on day 1 (0–24 h) are 0.18, 0.15, and 0.09 at thresholds of 100, 250, and 500 mm, respectively, and indicate considerable improvements at increased resolution compared to past results and 5 km models (TS < 0.1 at 100 mm and TS ≤ 0.02 at 250 mm). Moreover, the TSs are shown to be higher and the model more skillful in predicting larger events, in agreement with earlier findings for typhoons. After classification based on observed rainfall, the TSs of day − 1 QPFs for the largest 4 % of events by CReSS at 100, 250, and 500 mm (per 24 h) are 0.34, 0.24, and 0.16, respectively, and can reach 0.15 at 250 mm on day 2 (24–48 h) and 130 mm on day 3 (48–72 h). The larger events also exhibit higher probability of detection and lower false alarm ratio than smaller ones almost without exception across all thresholds. With the convection and terrain better resolved, the strength of the model is found to lie mainly in the topographic rainfall in Taiwan rather than migratory events that are more difficult to predict. Our results highlight the crucial importance of cloud-resolving capability and the size of fine mesh for heavy-rainfall QPFs in Taiwan.

Assessing the Impact of Dropsonde Data on Rain Forecasts in Taiwan with Observing System Simulation Experiments

This paper presents an observing system simulation experiment (OSSE) study to examine the impact of dropsonde data assimilation (DA) on rainfall forecasts for a heavy rain event in Taiwan. The rain event was associated with strong southwesterly flows over the northern South China Sea (SCS) after a weakening tropical cyclone (TC) made landfall over southeastern China. With DA of synthetic dropsonde data over the northern SCS, the model reproduces more realistic initial fields and a better simulated TC track that can help in producing improved low-level southwesterly flows and rainfall forecasts in Taiwan. Dropsonde DA can also aid the model in reducing the ensemble spread, thereby producing more converged ensemble forecasts. The sensitivity studies suggest that dropsonde DA with a 12-h cycling interval is the best strategy for deriving skillful rainfall forecasts in Taiwan. Increasing the DA interval to 6 h is not beneficial. However, if the flight time is limited, a 24-h interval of DA cycling is acceptable, because rainfall forecasts in Taiwan appear to be satisfactory. It is also suggested that 12 dropsondes with a 225-km separation distance over the northern SCS set a minimum requirement for enhancing the model regarding rainfall forecasts. Although more dropsonde data can help the model to obtain better initial fields over the northern SCS, they do not provide more assistance to the forecasts of the TC track and rainfall in Taiwan. These findings can be applied to the future field campaigns and model simulations in the nearby regions.

A Study on Synoptic Conditions Leading to the Extreme Rainfall in Taiwan during 10–12 June 2012

During 10–12 June 2012, heavy rainfall occurred three days in a row in southern and central Taiwan, with daily rainfall maxima exceeding 500 mm on each day. In the Mei-yu season (May–June) during 1993–2000, only two other rainfall events had a comparable amount and duration, but this case was the only one that occurred well before the arrival of the Mei-yu front. The synoptic conditions and their evolution leading to this unique event are thus important and are the foci of this study. Our analysis indicates that the 10–12 June 2012 event in Taiwan was caused by the strong and persistent west-southwesterly low-level jet (LLJ) that transported warm, moist, and unstable air from upstream and then impinged on the island. The LLJ developed due to the enhanced horizontal pressure (or height) gradient when the pressure at low-levels fell significantly (by ~8 hPa) in South China (north of the jet) during 8–10 June, but the subtropical high to the southeast maintained its strength. Further, through a diagnosis using the pressure tendency equation, it is found that both warm air advection and the dynamic effects (column divergence and transport of mass by vertical motion) contributed to the pressure fall in South China. The warm air advection occurred in the southern part of a large-scale confluent pattern in China, and the persistent west-southwesterly flow through deep layer (mainly above 800 hPa) in South China transported warmer and less dense air into the region from lower latitudes. On the other hand, South China was also located under the diffluent zone in the northeastern quadrant of the South Asian upper-level anticyclone, which strengthened during 5–10 June and provided divergence aloft, which exceeded the low-level convergence and upward transport of mass (at a fixed height) into the column by vertical motion on 9 June. As a result, the dynamic effects also contributed to the pressure fall, although secondary to the warm air advection. The destabilization process in South China during 8–10 June was also helpful to increase convective activity and upper-level divergence.

Extreme Rainfall in Taiwan: Seasonal Statistics and Trends

AbstractTaiwan regularly experiences precipitation extremes of hundreds of millimeters per day, especially between May and September. In this study, Taiwan’s extreme rainfall (ER) is analyzed over a 56-yr time period in different seasons and geographic regions, using a recently released, high-resolution gridded rainfall dataset. ER is defined using a seasonally and geographically varying 99th-percentile threshold to better resolve the characteristics of the most intense rainfall seen in different locations and times of year. The resulting monthly ER rates are largest in typhoon season and smallest in fall, winter, and spring. ER is spatially homogeneous in the mei-yu and typhoon seasons and concentrated in northern Taiwan during the rest of the year. A trend analysis revealed a positive trend in island-mean ER for the winter, spring, and typhoon seasons. In winter and spring, these trends are most pronounced in the north. In the mei-yu season, ER has increased most over the southwestern mountain slopes; in typhoon season, ER has increased consistently over much of Taiwan. These changes often exceed 1% yr−1. In many areas, typhoon season accounts for the largest fraction of the observed annual ER trend. TCs produce most of the observed typhoon season ER and ER trend, with nearly half of the typhoon season ER trend being associated with increases in TC frequency and duration around central and northern Taiwan. Certain regional changes in ER characteristics, particularly in areas with low sample size or complex seasonal contributions, merit further investigation in future work.

Multiple Modulating Processes for Intensive Tropical Cyclone Activity Affecting Taiwan in September 2016

September 2016 harbored record-breaking three tropical cyclones (TCs) affecting Taiwan within a month. Multiple modulating processes governing these three TC events and associated rainfall and intensification features are examined. September 2016 fell in a La Nina phase. For interannual variability, major warm sea surface temperature (SST) anomalies in September 2016 shifted eastward toward the tropical western North Pacific (WNP) compared to the Maritime Continent during other La Nina events. These SST anomalies induced strong convergence and convection anomalies facilitating TC formation. The TCs formed in the joint region between a northern anticyclonic anomaly over the northern WNP and a southern cyclonic anomaly extending from the tropical WNP toward Taiwan. They were steered by anomalous easterly/southeasterly flows northwestward toward an anomalous cyclonic center overlying Taiwan, leading to intensive TC activity affecting Taiwan. For intraseasonal variability, the three TCs of September 2016 were steered by anomalous easterly/southeasterly flows in the southern section of a 30–60-day anomalous anticyclone over the subtropical WNP northwestward toward a 30–60-day anomalous cyclone that lay over Taiwan. During these events, local rainfall in Taiwan was mainly affected by moisture convergence due to 3–10-day transient anomalies, rather than intraseasonal anomalies. For TC intensification processes, faster intensification was assisted by decreases in vertical wind shear and increases in upward motion, moisture convergence, upper-level divergence, and SST. Intensification processes were mainly affected by transient anomalies. Overall, interannual and intraseasonal anomalies modulated TC genesis and movement, while transient anomalies influenced local rainfall and intensification processes.

Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

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.

Joint modulations of Taiwan rainfall by tropical cyclone, northeast monsoon, and intraseasonal oscillation in October

Joint modulations of Taiwan rainfall by tropical cyclone, northeast monsoon, and intraseasonal oscillation in October

Assessing Groundwater Level with a Unified Seasonal Outlook and Hydrological Modeling Projection

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.