Diagnosis of Synoptic Weather Patterns Causing Heavy Rainfall Occurrence Using Self-Organizing Maps
(2021) In TVVR21/5010 VVRM01 20211Division of Water Resources Engineering
- Abstract
- The annual number of events with precipitation over 50 mm/h have increased from 1970s and this trend is likely to be continued due to climate change in Kyushu, Japan. Therefore, it is important to recognize what kind of meteorological fields have contributed to the occurrence of heavy rainfall events and to reveal whether it is possible to diagnose heavy rainfall risk. Firstly, this study combines the Self-Organizing Map (SOM) and Radar/Rain gauge analyzed precipitation to provide the distribution of heavy rainfall frequency on the two-dimensional map. As a result, 19520 meteorological fields observed for 40 years are classified into 40 synoptic weather groups and the top 10 groups are characterized by four reasons, the existence of 1)... (More)
- The annual number of events with precipitation over 50 mm/h have increased from 1970s and this trend is likely to be continued due to climate change in Kyushu, Japan. Therefore, it is important to recognize what kind of meteorological fields have contributed to the occurrence of heavy rainfall events and to reveal whether it is possible to diagnose heavy rainfall risk. Firstly, this study combines the Self-Organizing Map (SOM) and Radar/Rain gauge analyzed precipitation to provide the distribution of heavy rainfall frequency on the two-dimensional map. As a result, 19520 meteorological fields observed for 40 years are classified into 40 synoptic weather groups and the top 10 groups are characterized by four reasons, the existence of 1) strong southwest wind and large amounts of precipitable water (PW), 2) counterclockwise circulation with large PW, 3) tropical cyclone and 4) stationary front. Secondly, the Global Spectral Model is combined with the structured SOM for diagnosing the probability of heavy rainfall occurrence within a range of few days. Following the case studies, the probability of heavy rainfall occurrence can be increased and stabilized around 36 h before heavy rainfall events. Furthermore, the SOM can relate the diagnosed patterns to historical rainfall events. (Less)
- Popular Abstract
- The fifth synthesis report (AR5) of the International Panel on Climate Change suggested the frequency and intensity of heavy rainfall events has likely increased over the second half of the 20th century due to the anthropogenic forcing. Owing to the recent increase of heavy rainfall frequency, the natural disaster events related to heavy rainfall have frequently happened and caused severe damage to infrastructures and human lives in Kyushu, Japan. Despite some recent progress on bending the emission curve, RCP8.5 is the most likely scenario under current and stated policies. Under RCP 8.5 scenario, extreme precipitation events will become more frequent in Kyushu. Then, it is important to recognize what kind of meteorological fields have... (More)
- The fifth synthesis report (AR5) of the International Panel on Climate Change suggested the frequency and intensity of heavy rainfall events has likely increased over the second half of the 20th century due to the anthropogenic forcing. Owing to the recent increase of heavy rainfall frequency, the natural disaster events related to heavy rainfall have frequently happened and caused severe damage to infrastructures and human lives in Kyushu, Japan. Despite some recent progress on bending the emission curve, RCP8.5 is the most likely scenario under current and stated policies. Under RCP 8.5 scenario, extreme precipitation events will become more frequent in Kyushu. Then, it is important to recognize what kind of meteorological fields have contributed to the occurrence of heavy rainfall events so far and to reveal whether it is possible to diagnose heavy rainfall risk and associated disasters.
This study employs an unsupervised algorithm called the Self-Organizing Map (SOM) to discover patterns of meteorological fields and provide a visualization of unlabeled multidimensional data into two-dimensional map. As a result, meteorological fields observed from 1979 to 2018 in warm seasons (June to September) are classified into 40 synoptic weather groups and they are related to the heavy rainfall frequency obtained by Radar/Rain gauge analyzed precipitation data. Then, the top 10 groups of precipitation frequency explain 76.8% of all precipitation over 50 mm/h in Kyushu area and explain all disastrous heavy rainfall events happened after 2000. Moreover, they are characterized by four reasons, the existence of 1) strong southwest wind and large amounts of precipitable water (PW), 2) counterclockwise circulation with large PW, 3) tropical cyclone and 4) stationary front. The conditions of 1) and 2) are highly contributed to heavy rainfall events the in the northern area, whereas, 3) and 4) can be particularly recognized in the southern area.
The Global Spectral Model (GSM) is combined with the structured SOM for diagnosing the probability of heavy rainfall occurrence within a range of few days. As a result of the case studies, the probability of heavy rainfall occurrence can be increased and stabilized around 36 hours before heavy rainfall events. The probability is around 40 to 80 % in three case studies. Furthermore, the SOM can relate these diagnosed patterns to historical rainfall events. For instance, the predicted unit (U596) on the SOM in Case 1 is so close to the unit including 2012/7 Northern Kyushu rainfall event (U595), which resulted in the flood disasters in the same prefecture (Fukuoka). Therefore, decision makers can learn where was mostly affected in the predicted unit on the SOM and estimate where will be affected in the upcoming rainfall events. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9057587
- author
- Kasai, Aoi LU
- supervisor
- organization
- course
- VVRM01 20211
- year
- 2021
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Self-Organizing Map, heavy rainfall frequency, classification, meteorological fields, prediction, heavy rainfall risk
- publication/series
- TVVR21/5010
- report number
- 21/5010
- ISSN
- 1101-9824
- language
- English
- additional info
- Examiner: Magnus Persson
- id
- 9057587
- date added to LUP
- 2021-06-22 10:56:38
- date last changed
- 2021-06-22 10:56:38
@misc{9057587,
abstract = {{The annual number of events with precipitation over 50 mm/h have increased from 1970s and this trend is likely to be continued due to climate change in Kyushu, Japan. Therefore, it is important to recognize what kind of meteorological fields have contributed to the occurrence of heavy rainfall events and to reveal whether it is possible to diagnose heavy rainfall risk. Firstly, this study combines the Self-Organizing Map (SOM) and Radar/Rain gauge analyzed precipitation to provide the distribution of heavy rainfall frequency on the two-dimensional map. As a result, 19520 meteorological fields observed for 40 years are classified into 40 synoptic weather groups and the top 10 groups are characterized by four reasons, the existence of 1) strong southwest wind and large amounts of precipitable water (PW), 2) counterclockwise circulation with large PW, 3) tropical cyclone and 4) stationary front. Secondly, the Global Spectral Model is combined with the structured SOM for diagnosing the probability of heavy rainfall occurrence within a range of few days. Following the case studies, the probability of heavy rainfall occurrence can be increased and stabilized around 36 h before heavy rainfall events. Furthermore, the SOM can relate the diagnosed patterns to historical rainfall events.}},
author = {{Kasai, Aoi}},
issn = {{1101-9824}},
language = {{eng}},
note = {{Student Paper}},
series = {{TVVR21/5010}},
title = {{Diagnosis of Synoptic Weather Patterns Causing Heavy Rainfall Occurrence Using Self-Organizing Maps}},
year = {{2021}},
}