Rainfall downscaling of weekly ensemble forecasts using self-organising
                        maps

Ohba, Masamichi; Kadokura, Shinji; Nohara, Daisuke; Toyoda, Yasushi

doi:10.3402/tellusa.v68.29293

Cited by 35 publications

(21 citation statements)

References 37 publications

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“…Since the method can predict whether the heavy rainfall occurs but not being capable of providing the actual amount, questions may remain about its relative availability compared with other methods such as statistical downscaling by Ohba et al . (). Additionally, the forecast skill of SOM is still lower than that from the JMA meso‐scale model (MSM) that has a fine resolution of 5 km and the lead time up to 39 h (see Figure ).…”

Section: Discussionmentioning

confidence: 97%

“…It may be more difficult to apply to the region where rainfall process is mainly related to local factor such as the sea breeze circulation and local heating. Since the method can predict whether the heavy rainfall occurs but not being capable of providing the actual amount, questions may remain about its relative availability compared with other methods such as statistical downscaling by Ohba et al (2016). Additionally, the forecast skill of SOM is still lower than that from the…”

Section: Discussionmentioning

confidence: 99%

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Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition

Nguyen-Le

Yamada

Tran-Anh³

2017

Atmospheric Science Letters

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In this study, the Self-Organizing Maps in combination with K-means clustering technique are used for classification of synoptic weather patterns inducing heavy rainfall exceeding 100 mm day −1 during the Baiu season (June-July) of 1979-2010 over northern Kyushu, southwestern Japan. It suggests that these local extreme rainfall events are attributed to four clustered patterns, which are primarily related to the Baiu front and the extratropical/tropical cyclone/depression activities and represented by the intrusion of warm and moist air accompanied by the low-level jet or cyclonic circulation. The classification results are then implemented with the analogue method to predict the occurrence (yes/no) of local heavy rainfall days in June-July of 2011-2016 by using the prognostic synoptic fields from the operational Japan Meteorological Agency (JMA) Global Spectral Model (GSM). In general, the predictability of our approach evaluated by the Equitable Threat Score up to 7-day lead times is significantly improved than that from the conventional method using only the predicted rainfall intensity from GSM. Although the false alarm ratio is still high, it is expected that the new method will provide a useful guidance, particularly for ranges longer than 2 days, for decision-making and preparation by weather forecasters or end-users engaging in disaster-proofing and water management activities.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition

Nguyen-Le

Yamada

Tran-Anh³

2017

Atmospheric Science Letters

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“…Taking the advantage of SOM, we can easily achieve data classification of the same dimensional atmospheric data into one node having a minimum Euclid distance with that data. For more details, one can refer to other studies (Camus et al 2011;Ohba et al 2014Ohba et al , 2016. Figure 1 illustrates the framework of this research.…”

Section: The Future Change Of Atmospheric Patternsmentioning

confidence: 99%

Future Change of Occurrence Frequency of Baiu Heavy Rainfall and Its Linked Atmospheric Patterns by Multiscale Analysis

Osakada

Nakakita

2018

SOLA

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The future change of heavy rainfall (meso-β scale) in the Baiu season, the atmospheric patterns (over meso-α scale) of sea level pressure and surface vapor flux, and the connection between them were investigated by analyzing multiple datasets of a highresolution non-hydrostatic regional climate model (NHRCM05) for better simulating heavy rainfall, a coarser-resolution global atmospheric climate model (AGCM20) embedding the NHRCM 05, and a huge database for Policy Decision-Making for Future climate change (d4PDF) with a coarser-resolution. As a result, northern Japan and Japan-sea-side areas have a statistically significant increase of heavy rainfall that is caused by an increase in the atmospheric patterns with westward-protruding Pacific high and northward-invading vapor flux along the periphery of the high. In the Pacific side in eastern Japan, the typical atmospheric pattern prone to heavy rainfall will change in the future as it will decrease the occurrence frequency of the atmospheric pattern that presently caused heavy rainfall with cyclones located at south of Japan. Besides, the atmospheric pattern with westward-protruding pacific high, that presently caused heavy rainfall mainly in western Japan, will expand the area of heavy rainfall eastward in the future due to an eastward-invading vapor flux.(Citation: Osakada, Y., and E. Nakakita, 2018: Future change of occurrence frequency of Baiu heavy rainfall and its linked atmospheric patterns by multiscale analysis. SOLA, 14, 79−85,

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“…Self-Organizing Maps (SOM, Kohonen 2001) is a pattern clustering method that is used as one of the methods to derive synoptic circulation types in this and other contexts, including statistical downscaling (e.g. Hewitson and Crane 2006;Yin et al 2011;Ohba et al 2016) and process-based validation of GCMs (e.g. Brown et al 2010;Finnis et al 2009;Higgins and Cassano 2010).…”

Section: Abstract Som · Synoptic Circulation · Rainfall Variability ·mentioning

confidence: 99%

Interannual rainfall variability and SOM-based circulation classification

et al. 2017

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synoptic variables over a particular location; even though they are used in the development of the SOM their influence, however, diminishes with the size of the SOM spatial domain. The influence of the SOM domain size, the choice of SOM atmospheric variables and grid-point explanatory variables on the levels of explained variance, is consistent with the general understanding of the dominant processes and atmospheric variables that affect rainfall variability at a particular location. Keywords SOM · Synoptic circulation · Rainfall variability · Southern AfricaThe relationship between synoptic circulation and local expressions of climate is the foundation of synoptic climatology and traditionally underpins the process of interpretative weather forecasting. The basic premise of synoptic climatology is that the large-scale atmospheric circulation exerts some control over weather and related environmental phenomena at the Earth's surface, thereby providing a level of deterministic forcing which influences the local weather. This directly leads to the use of atmospheric circulation patterns as a means to express drivers of weather (and climate) variability. Self-Organizing Maps (SOM, Kohonen 2001) is a pattern clustering method that is used as one of the methods to derive synoptic circulation types in this and other contexts, including statistical downscaling (e.g. Hewitson and Crane 2006;Yin et al. 2011;Ohba et al. 2016) and process-based validation of GCMs (e.g. Brown et al. 2010;Finnis et al. 2009;Higgins and Cassano 2010).In the application of SOM to study climate variability, the evolution of synoptic drivers of weather is typically represented by progression through classes of daily synoptic states identified as SOM nodes. That progression Abstract Self-Organizing Maps (SOM) based classifications of synoptic circulation patterns are increasingly being used to interpret large-scale drivers of local climate variability, and as part of statistical downscaling methodologies. These applications rely on a basic premise of synoptic climatology, i.e. that local weather is conditioned by the large-scale circulation. While it is clear that this relationship holds in principle, the implications of its implementation through SOM-based classification, particularly at interannual and longer time scales, are not well recognized. Here we use a SOM to understand the interannual synoptic drivers of climate variability at two locations in the winter and summer rainfall regimes of South Africa. We quantify the portion of variance in seasonal rainfall totals that is explained by year to year differences in the synoptic circulation, as schematized by a SOM. We furthermore test how different spatial domain sizes and synoptic variables affect the ability of the SOM to capture the dominant synoptic drivers of interannual rainfall variability. Additionally, we identify systematic synoptic forcing that is not captured by the SOM classification. The results indicate that the frequency of synoptic states, as schematized by a relatively disa...

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Rainfall downscaling of weekly ensemble forecasts using self-organising maps

Cited by 35 publications

References 37 publications

Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition

Classification and forecast of heavy rainfall in northern Kyushu during Baiu season using weather pattern recognition

Future Change of Occurrence Frequency of Baiu Heavy Rainfall and Its Linked Atmospheric Patterns by Multiscale Analysis

Interannual rainfall variability and SOM-based circulation classification

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