scholarly journals Heavy Rainfall Duration Bias in Dynamical Downscaling and Its Related Synoptic Patterns in Summertime Asian Monsoon

2018 ◽  
Vol 57 (7) ◽  
pp. 1477-1496 ◽  
Author(s):  
Yuta Tamaki ◽  
Masaru Inatsu ◽  
Dzung Nguyen-Le ◽  
Tomohito J. Yamada
Keyword(s):  
Heavy Rainfall ◽  
Dynamical Downscaling ◽  
Potential Temperature ◽  
Precipitation Amount ◽  
Horizontal Wind ◽  
Self Organizing Map ◽  
Positive Bias ◽  
Rainfall Events ◽  
Synoptic Patterns ◽  
Rainfall Duration

AbstractDynamical downscaling (DDS) was conducted over Japan by using a regional atmospheric model with reanalysis data to investigate the rainfall duration bias over Kyushu, Japan, in July and August from 2006 to 2015. The model results showed that DDS had a positive rainfall duration bias over Kyushu and a dry bias over almost all of Kyushu, which were emphasized for extreme rainfall events. Investigated was the rainfall duration bias for heavy rainfall days, accompanied by synoptic-scale forcing, in which daily precipitation exceeded 30 mm day−1 and covered over 20% of the Kyushu area. Heavy rainfall days were sampled from observed rainfall data that were based on rain gauge and radar observations. A set of daily climatic variables of horizontal wind and equivalent potential temperature at 850 hPa and sea level pressure, around southwestern Japan, corresponding to the sampled dates, was selected to conduct a self-organizing map (SOM) and K-means method. The SOM and K-means method objectively classified three synoptic patterns related to heavy rainfall over Kyushu: strong monsoon, weak monsoon, and typhoon patterns. Rainfall duration had a positive bias in western Kyushu for the strong monsoon pattern and a positive bias in southern and east-coast Kyushu for the typhoon pattern, whereas there was little rainfall duration bias in the weak monsoon pattern. The bias for the typhoon pattern was related to rainfall events with a strong rainfall peak. The results suggest that bias correction for rainfall duration would be required for accurately estimating direct runoff in a catchment area in addition to the precipitation amount.

Identification of typical synoptic patterns causing heavy rainfall in the rainy season in Japan by a Self-Organizing Map

2007 ◽  
Vol 83 (2-4) ◽  
pp. 185-200 ◽  
Author(s):  
Koji Nishiyama ◽  
Shinichi Endo ◽  
Kenji Jinno ◽  
Cintia Bertacchi Uvo ◽  
Jonas Olsson ◽  
...  
Keyword(s):  
Rainy Season ◽  
Heavy Rainfall ◽  
Self Organizing Map ◽  
Synoptic Patterns ◽  
Self Organizing

scholarly journals Individual Rainfall Change Based on Observed Hourly Precipitation Records on the Chinese Loess Plateau from 1983 to 2012

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2268
Author(s):  
Wenbin Ding ◽  
Fei Wang ◽  
Kai Jin ◽  
Jianqiao Han ◽  
Qiang Yu ◽  
...  
Keyword(s):  
Loess Plateau ◽  
Heavy Rainfall ◽  
Rainfall Intensity ◽  
Rainfall Amount ◽  
The Loess Plateau ◽  
Total Rainfall ◽  
Rainfall Events ◽  
Rainfall Duration ◽  
Rainfall Frequency ◽  
Annual Total

The magnitude and spatiotemporal distribution of precipitation are the main drivers of hydrologic and agricultural processes in soil moisture, runoff generation, soil erosion, vegetation growth and agriculture activities on the Loess Plateau (LP). This study detects the spatiotemporal variations of individual rainfall events during a rainy season (RS) from May to September based on the hourly precipitation data measured at 87 stations on the LP from 1983 to 2012. The incidence and contribution rates were calculated for all classes of rainfall duration and intensity to identify the dominant contribution to the rainfall amount and frequency variations. The trend rates of regional mean annual total rainfall amount (ATR) and annual mean rainfall intensity (ARI) were 0.43 mm/year and 0.002 mm/h/year in the RS for 1983–2012, respectively. However, the regional mean annual total rainfall frequency (ARF) and rainfall events (ATE) were −0.27 h/year and −0.11 times/year, respectively. In terms of spatial patterns, an increase in ATR appeared in most areas except for the southwest, while the ARI increased throughout the study region, with particularly higher values in the northwest and southeast. Areas of decreasing ARF occurred mainly in the northwest and central south of the LP, while ATE was found in most areas except for the northeast. Short-duration (≤6 h) and light rainfall events occurred mostly on the LP, accounting for 69.89% and 72.48% of total rainfall events, respectively. Long-duration (≥7 h) and moderate rainfall events contributed to the total rainfall amount by 70.64% and 66.73% of the total rainfall amount, respectively. Rainfall frequency contributed the most to the variations of rainfall amount for light and moderate rainfall events, while rainfall intensity played an important role in heavy rainfall and rainstorms. The variation in rainfall frequency for moderate rainfall, heavy rainfall, and rainstorms is mainly affected by rainfall duration, while rainfall event was identified as a critical factor for light rainfall. The characteristics in rainfall variations on the Loess Plateau revealed in this study can provide useful information for sustainable water resources management and plans.

scholarly journals A Regime-Based Evaluation of Southern and Northern Great Plains Warm-Season Precipitation Events in WRF

2019 ◽  
Vol 34 (4) ◽  
pp. 805-831 ◽  
Author(s):  
Jingyu Wang ◽  
Xiquan Dong ◽  
Aaron Kennedy ◽  
Brooke Hagenhoff ◽  
Baike Xi
Keyword(s):  
Great Plains ◽  
Precipitation Amount ◽  
Warm Season ◽  
Stage Iv ◽  
Northern Great Plains ◽  
Self Organizing Map ◽  
Synoptic Patterns ◽  
Competitive Neural Network ◽  
Convective Rain ◽  
Precipitation Events

Abstract A competitive neural network known as the self-organizing map (SOM) is used to objectively identify synoptic patterns in the North American Regional Reanalysis (NARR) for warm-season (April–September) precipitation events over the Southern and Northern Great Plains (SGP/NGP) from 2007 to 2014. Classifications for both regions demonstrate contrast in dominant synoptic patterns ranging from extratropical cyclones to subtropical ridges, all of which have preferred months of occurrence. Precipitation from deterministic Weather Research and Forecasting (WRF) Model simulations run by the National Severe Storms Laboratory (NSSL) are evaluated against National Centers for Environmental Prediction (NCEP) Stage IV observations. The SGP features larger observed precipitation amount, intensity, and coverage, as well as better model performance than the NGP. Both regions’ simulated convective rain intensity and coverage have good agreement with observations, whereas the stratiform rain (SR) is more problematic with weaker intensity and larger coverage. Further evaluation based on SOM regimes shows that WRF bias varies with the type of meteorological forcing, which can be traced to differences in the diurnal cycle and properties of stratiform and convective rain. The higher performance scores are generally associated with the extratropical cyclone condition than the subtropical ridge. Of the six SOM classes over both regions, the largest precipitation oversimulation is found for SR dominated classes, whereas a nocturnal negative precipitation bias exists for classes featuring upscale growth of convection.

scholarly journals On the Use of an Innovative Trend Analysis Methodology for Temporal Trend Identification in Extreme Rainfall Indices Over the Central Highlands, Vietnam

2021 ◽  
Author(s):  
Dang Nguyen Dong Phuong ◽  
Nguyen Thi Huyen ◽  
Nguyen Duy Liem ◽  
Nguyen Thi Hong ◽  
Dang Kien Cuong ◽  
...  
Keyword(s):  
Trend Analysis ◽  
Heavy Rainfall ◽  
Precipitation Amount ◽  
Extreme Rainfall ◽  
Rainfall Events ◽  
Monotonic Trend ◽  
Innovative Trend Analysis ◽  
Central Highlands ◽  
Warming World ◽  
Heavy Rainfall Events

Abstract Understanding past changes in the characteristics of climate extremes (such as frequency, intensity, and duration) forms an essential part of viable countermeasures to cope with climate-induced risks under a rapidly warming world. Thus, this paper endeavored to explore possible non-monotonic trend components in heavy rainfall events over the Central Highlands of Vietnam by employing the Şen’s innovative trend analysis (ITA) method in conjunction with the well-defined extreme rainfall indices developed by the Joint CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI). The outcomes show that the overall trends in most extreme rainfall indices exhibited significant increases at several stations. Moreover, the high-value subgroups of most analyzed indices (such as maximum 5-day precipitation amount (Rx5day), simple daily intensity index (SDII), very wet days (R95p), extremely wet days (R99p), number of extremely heavy precipitation days (R50mm), and consecutive dry days (CDD)) were characterized mainly by significant increasing trends, thereby implying that heavy rainfall events have become more frequent and intense over recent decades. Some stations also exposed significant increasing trend behaviors in a given extreme index within all low-, medium-, and high-value subgroups. In general, it is expected that these findings yield more insightful knowledge on rainfall extremes to local decision-makers and other stakeholders.

scholarly journals Anomalous Weather Patterns in Relation to Heavy Precipitation Events in Japan during the Baiu Season

2015 ◽  
Vol 16 (2) ◽  
pp. 688-701 ◽  
Author(s):  
Masamichi Ohba ◽  
Shinji Kadokura ◽  
Yoshikatsu Yoshida ◽  
Daisuke Nohara ◽  
Yasushi Toyoda
Keyword(s):  
Time Scales ◽  
Heavy Rainfall ◽  
Southern Oscillation ◽  
Potential Temperature ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Weather Patterns ◽  
Extreme Precipitation Events ◽  
Heavy Rainfall Events ◽  
Precipitation Events

Abstract Anomalous weather patterns (WPs) in relation to heavy precipitation events during the baiu season in Japan are investigated using a nonlinear classification technique known as the self-organizing map (SOM). The analysis is performed on daily time scales using the Japanese 55-year Reanalysis Project (JRA-55) to determine the role of circulation and atmospheric moisture on extreme events and to investigate interannual and interdecadal variations for possible linkages with global-scale climate variability. SOM is simultaneously employed on four atmospheric variables over East Asia that are related to baiu front variability, whereby anomalous WPs that dominated during the 1958–2011 period are obtained. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of heavy precipitation events. Each WP is associated with regional variations in the probability of extreme precipitation events. On interannual time scales, El Niño–Southern Oscillation (ENSO) affects the frequency of the WPs in relation to the heavy rainfall events. The warm phase of ENSO results in an increased frequency of a WP that provides a southwesterly intrusion of high equivalent potential temperature at low levels, while the cold phase provides southeastern intrusion. In addition, the results of this analysis suggest that interdecadal variability of frequency for heavy rainfall events corresponds to changes in frequency distributions of WPs and are not due to one particular WP.

scholarly journals Extreme Floods in the Eastern Part of Europe: Large-Scale Drivers and Associated Impacts

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Flood Events ◽  
Rainfall Events ◽  
Catchment Areas ◽  
Large Scale Atmospheric Circulation ◽  
Heavy Rainfall Events

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.

scholarly journals Prediction of factors affecting activation of soil erosion by mathematical modeling at pedon scale under laboratory conditions

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Saeed Shojaei ◽  
Zahra Kalantari ◽  
Jesús Rodrigo-Comino
Keyword(s):  
Soil Loss ◽  
Rainfall Intensity ◽  
Quadratic Function ◽  
Interactive Effects ◽  
Soil Protection ◽  
Single Factor ◽  
Rainfall Events ◽  
Rainfall Duration ◽  
The Impact ◽  
Runoff Production

AbstractSoil degradation due to erosion is a significant worldwide problem at different spatial (from pedon to watershed) and temporal scales. All stages and factors in the erosion process must be detected and evaluated to reduce this environmental issue and protect existing fertile soils and natural ecosystems. Laboratory studies using rainfall simulators allow single factors and interactive effects to be investigated under controlled conditions during extreme rainfall events. In this study, three main factors (rainfall intensity, inclination, and rainfall duration) were assessed to obtain empirical data for modeling water erosion during single rainfall events. Each factor was divided into three levels (− 1, 0, + 1), which were applied in different combinations using a rainfall simulator on beds (6 × 1 m) filled with soil from a study plot located in the arid Sistan region, Iran. The rainfall duration levels tested were 3, 5, and 7 min, the rainfall intensity levels were 30, 60, and 90 mm/h, and the inclination levels were 5, 15, and 25%. The results showed that the highest rainfall intensity tested (90 mm/h) for the longest duration (7 min) caused the highest runoff (62 mm3/s) and soil loss (1580 g/m2/h). Based on the empirical results, a quadratic function was the best mathematical model (R2 = 0.90) for predicting runoff (Q) and soil loss. Single-factor analysis revealed that rainfall intensity was more influential for runoff production than changes in time and inclination, while rainfall duration was the most influential single factor for soil loss. Modeling and three-dimensional depictions of the data revealed that sediment production was high and runoff production lower at the beginning of the experiment, but this trend was reversed over time as the soil became saturated. These results indicate that avoiding the initial stage of erosion is critical, so all soil protection measures should be taken to reduce the impact at this stage. The final stages of erosion appeared too complicated to be modeled, because different factors showed differing effects on erosion.

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