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

2022 ◽  
Vol 22 (1) ◽  
pp. 23-40
Author(s):  
Chung-Chieh Wang ◽  
Pi-Yu Chuang ◽  
Chih-Sheng Chang ◽  
Kazuhisa Tsuboki ◽  
Shin-Yi Huang ◽  
...  

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.

2021 ◽  
Author(s):  
Chung-Chieh Wang ◽  
Pi-Yu Chuang ◽  
Chih-Sheng Chang ◽  
Kazuhisa Tsuboki ◽  
Shin-Yi Huang ◽  
...  

Abstract. In this study, the performance of quantitative precipitation forecasts (QPFs) by the Cloud-Resolving Storm Simulator (CReSS) in real-time in Taiwan, at a horizontal grid spacing of 2.5 km and a domain size of 1500 x 1200 km2, within a range of 72 h during three mei-yu seasons of 2012–2014 is evaluated using categorical statistics, with an emphasis on heavy events (≥ 100 mm per 24 h). 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 the threshold of 100, 250, and 500 mm, respectively, and indicate considerable improvements compared to past results and 5-km models. 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 weaker events almost without exception across all thresholds. The strength of the model lies mainly in the topographic rainfall in Taiwan rather than migratory events that are less predictable. Our results highlight the crucial importance of cloud-resolving capability and the size of fine mesh for heavy-rainfall QPFs in Taiwan.


Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 875
Author(s):  
Li Zhou ◽  
Lin Xu ◽  
Mingcai Lan ◽  
Jingjing Chen

Heavy rainfall events often cause great societal and economic impacts. The prediction ability of traditional extrapolation techniques decreases rapidly with the increase in the lead time. Moreover, deficiencies of high-resolution numerical models and high-frequency data assimilation will increase the prediction uncertainty. To address these shortcomings, based on the hourly precipitation prediction of Global/Regional Assimilation and Prediction System-Cycle of Hourly Assimilation and Forecast (GRAPES-CHAF) and Shanghai Meteorological Service-WRF ADAS Rapid Refresh System (SMS-WARR), we present an improved weighting method of time-lag-ensemble averaging for hourly precipitation forecast which gives more weight to heavy rainfall and can quickly select the optimal ensemble members for forecasting. In addition, by using the cross-magnitude weight (CMW) method, mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (CC), the verification results of hourly precipitation forecast for next six hours in Hunan Province during the 2019 typhoon Bailu case and heavy rainfall events from April to September in 2020 show that the revised forecast method can more accurately capture the characteristics of the hourly short-range precipitation forecast and improve the forecast accuracy and the probability of detection of heavy rainfall.


MAUSAM ◽  
2021 ◽  
Vol 60 (2) ◽  
pp. 175-184
Author(s):  
M. MOHAPATRA ◽  
H. R. HATWAR ◽  
S. R. KALSI

India Meteorological Department (IMD) issues heavy rainfall warning for a meteorological sub-division when the expected 24 hours rainfall over any rain gauge station in that sub-division is likely to be 64.5 mm or more. Though these warnings have been provided since long and are also now being issued for smaller spatial scales, very few attempts have been made for quantitative evaluation of these warnings.  Hence, a study is undertaken to verify the heavy rainfall warning over the representative meteorological sub-divisions of east Uttar Pradesh (UP), west UP and Bihar during main monsoon months of July and August. For this purpose data of the recent 5 years (2001-2005) and also for another epoch of 5 years in the early 1970s has been taken into consideration. In this connection, the day when heavy rainfall is recorded over atleast two stations in a sub-division, has been considered as a heavy rainfall day for that sub-division.   This study of verification shows that probability of detection of heavy rainfall is 64% over Bihar, 52% over east UP and 53% over west UP for the recent 5 years. Compared to early 1970s, there has been slight improvement in the forecast skill during 2001-2005 with probability of detection increasing by about 10-20% and with decrease in missing rate and false alarm rate. However, the false alarm rates are still large indicating higher bias towards over-prediction. The synoptic conditions associated with the heavy rainfall events have been collected for the period 2001-05 and analysed. The analysis of the unanticipated heavy rainfall events suggests that though proper interpretation of synoptic charts and NWP outputs could improve the warnings, the forecast system available even today is still not capable to capture every heavy rain event in advance.


Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc

The role of the large-scale atmospheric circulation in producing heavy rainfall events and floods in the eastern part of Europe, with a special focus on the Siret and Prut catchment areas (Romania), is analyzed in this study. Moreover, a detailed analysis of the socio-economic impacts of the most extreme flood events (e.g., July 2008, June–July 2010, and June 2020) is given. Analysis of the largest flood events indicates that the flood peaks have been preceded up to 6 days in advance by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent cut-off lows over the analyzed region, and increased water vapor transport over the catchment areas of Siret and Prut Rivers. The vertically integrated water vapor transport prior to the flood peak exceeds 300 kg m−1 s−1, leading to heavy rainfall events. We also show that the implementation of the Flood Management Plan in Romania had positive results during the 2020 flood event compared with the other flood events, when the authorities took several precaution measurements that mitigated in a better way the socio-economic impact and risks of the flood event. The results presented in this study offer new insights regarding the importance of large-scale atmospheric circulation and water vapor transport as drivers of extreme flooding in the eastern part of Europe and could lead to a better flood forecast and flood risk management.


2012 ◽  
Vol 69 (2) ◽  
pp. 521-537 ◽  
Author(s):  
Christopher A. Davis ◽  
Wen-Chau Lee

Abstract The authors analyze the mesoscale structure accompanying two multiday periods of heavy rainfall during the Southwest Monsoon Experiment and the Terrain-Induced Mesoscale Rainfall Experiment conducted over and near Taiwan during May and June 2008. Each period is about 5–6 days long with episodic heavy rainfall events within. These events are shown to correspond primarily to periods when well-defined frontal boundaries are established near the coast. The boundaries are typically 1 km deep or less and feature contrasts of virtual temperature of only 2°–3°C. Yet, owing to the extremely moist condition of the upstream conditionally unstable air, these boundaries appear to exert a profound influence on convection initiation or intensification near the coast. Furthermore, the boundaries, once established, are long lived, possibly reinforced through cool downdrafts and prolonged by the absence of diurnal heating over land in generally cloudy conditions. These boundaries are linked phenomenologically with coastal fronts that occur at higher latitudes.


Author(s):  
Chanil Park ◽  
Seok-Woo Son ◽  
Joowan Kim ◽  
Eun-Chul Chang ◽  
Jung-Hoon Kim ◽  
...  

AbstractThis study identifies diverse synoptic weather patterns of warm-season heavy rainfall events (HREs) in South Korea. The HREs not directly connected to tropical cyclones (TCs) (81.1%) are typically associated with a midlatitude cyclone from eastern China, the expanded North Pacific high and strong southwesterly moisture transport in between. They are frequent both in the first (early summer) and second rainy periods (late summer) with impacts on the south coast and west of the mountainous region. In contrast, the HREs resulting from TCs (18.9%) are caused by the synergetic interaction between the TC and meandering midlatitude flow, especially in the second rainy period. The strong south-southeasterly moisture transport makes the southern and eastern coastal regions prone to the TC-driven HREs. By applying a self-organizing map algorithm to the non-TC HREs, their surface weather patterns are further classified into six clusters. Clusters 1 and 3 exhibit frontal boundary between the low and high with differing relative strengths. Clusters 2 and 5 feature an extratropical cyclone migrating from eastern China under different background sea-level pressure patterns. Cluster 4 is characterized by the expanded North Pacific high with no organized negative sea-level pressure anomaly, and cluster 6 displays a development of a moisture pathway between the continental and oceanic highs. Each cluster exhibits a distinct spatio-temporal occurrence distribution. The result provides useful guidance for predicting the HREs by depicting important factors to be differently considered depending on their synoptic categorization.


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