scholarly journals Recurrent Self-Organizing Map for Severe Weather Patterns Recognition

10.5772/38823 ◽  
2012 ◽  
Author(s):  
Jos Alberto ◽  
Brgida Rocha ◽  
Arthur Almeida ◽  
Jos Ricardo
Keyword(s):  
Severe Weather ◽  
Self Organizing Map ◽  
Weather Patterns ◽  
Self Organizing

scholarly journals S-SOM v1.0: a structural self-organizing map algorithm for weather typing

2021 ◽  
Vol 14 (4) ◽  
pp. 2097-2111
Author(s):  
Quang-Van Doan ◽  
Hiroyuki Kusaka ◽  
Takuto Sato ◽  
Fei Chen
Keyword(s):  
Pearson Correlation ◽  
Structural Similarity ◽  
Self Organizing Map ◽  
Weather Patterns ◽  
Weather Typing ◽  
Synoptic Weather Typing ◽  
Clustering Quality ◽  
Som Algorithm ◽  
The Difference ◽  
Self Organizing

Abstract. This study proposes a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data with spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based on a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the locations of highs or lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the S-SOM's superiority compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error. Better performance of S-SOM versus ED is consistent with results from different tests and node-size configurations. S-SOM performs better than a SOM using the Pearson correlation coefficient (or COR-SOM), though the difference is not as clear as it is compared to ED-SOM.

scholarly journals A Validation of the Antarctic Mesoscale Prediction System Using Self-Organizing Maps and High-Density Observations from SNOWWEB

2016 ◽  
Vol 144 (9) ◽  
pp. 3181-3200 ◽  
Author(s):  
Ben Jolly ◽  
Adrian J. McDonald ◽  
Jack H. J. Coggins ◽  
Peyman Zawar-Reza ◽  
John Cassano ◽  
...  
Keyword(s):  
Surface Wind ◽  
Austral Summer ◽  
Meridional Wind ◽  
Self Organizing Map ◽  
Prediction System ◽  
Self Organizing Maps ◽  
Weather Patterns ◽  
Good Surface ◽  
The Antarctic ◽  
Self Organizing

This study compares high-resolution output (1.1-km horizontal grid length) from twice-daily forecasts produced by the Antarctic Mesoscale Prediction System (AMPS) with a dense observational network east of Ross Island. Covering 10 000 km2, 15 SNOWWEB stations significantly increased the number of observation stations in the area to 19 during the 2014–15 austral summer. Collocated “virtual stations” created from AMPS output are combined with observations, producing a single dataset of zonal and meridional wind components used to train a self-organizing map (SOM). The resulting SOM is used to individually classify the observational and AMPS datasets, producing a time series of classifications for each dataset directly comparable to the other. Analysis of class composites shows two dominant weather patterns: low but directionally variable winds and high but directionally constant winds linked to the Ross Ice Shelf airstream (RAS). During RAS events the AMPS and SNOWWEB data displayed good temporal class alignment with good surface wind correlation. SOM analysis shows that AMPS did not accurately forecast surface-level wind speed or direction during light wind conditions where synoptic forcing was weak; however, it was able to forecast the low wind period occurrence accurately. Coggins’s regimes provide synoptic-scale context and show a reduced synoptic pressure gradient during these classes, increasing reliance on the ability of Polar WRF to resolve mesoscale dynamics. Available initialization data have insufficient resolution for the region’s complex topography, which likely impacts performance. The SOM analysis methods used are shown to be effective for model validation and are widely applicable.

Trends and projection of heavy snowfall in Hokkaido, Japan as an application of self-organizing map

2021 ◽  
Author(s):  
Masaru Inatsu ◽  
Sho Kawazoe ◽  
Masato Mori
Keyword(s):  
Global Warming ◽  
Historical Change ◽  
Self Organizing Map ◽  
Heavy Snowfall ◽  
Sea Level Pressure ◽  
Historical Simulation ◽  
Weather Patterns ◽  
Pressure Anomaly ◽  
Group 2 ◽  
Self Organizing

AbstractThis paper showed the frequency of local-scale heavy winter snowfall in Hokkaido, Japan, its historical change, and its response to global warming using self-organizing map (SOM) of synoptic-scale sea-level pressure anomaly. Heavy snowfall days were here defined as days when the snowfall exceeded 10 mm in water equivalent. It was shown that the SOMs can be grouped into three categories for heavy snowfall days: 1) a passage of extratropical cyclones to the south of Hokkaido, 2) a pressure pattern between the Siberian high and the Aleutian low, and 3) a low-pressure anomaly just to the east of Hokkaido. Groups 1 and 2 were associated with heavy snowfall in Hiroo (located in southeastern Hokkaido) and in Iwamizawa (western Hokkaido), respectively, and heavy snowfall in Sapporo (western Hokkaido) was related to Group 3. The large-ensemble historical simulation reproduced the observed increasing trend in Group 2 and future projection revealed that Group 2 was related to a negative phase of the Western Pacific pattern and the frequency of this group would increase in the future. Heavy snowfall days associated with SOM Group 2 would also increase due to the increase in water vapor and preferable weather patterns in global warming climate, in contrast to the decrease of heavy snowfall days in other sites associated with SOM Group 1.

scholarly journals A structural self-organizing map (S-SOM) algorithm for weather typing

2020 ◽  
Author(s):  
Quang-Van Doan ◽  
Hiroyuki Kusaka ◽  
Takuto Sato ◽  
Fei Chen
Keyword(s):  
Input Data ◽  
Structural Similarity ◽  
Superior Performance ◽  
Self Organizing Map ◽  
Weather Patterns ◽  
Weather Typing ◽  
Synoptic Weather Typing ◽  
Clustering Quality ◽  
Som Algorithm ◽  
Self Organizing

Abstract. In this study, we propose a novel structural self-organizing map (S-SOM) algorithm for synoptic weather typing. A novel feature of the S-SOM compared with traditional SOMs is its ability to deal with input data that have spatial or temporal structures. In detail, the search scheme for the best matching unit (BMU) in a S-SOM is built based upon a structural similarity (S-SIM) index rather than by using the traditional Euclidean distance (ED). S-SIM enables the BMU search to consider the correlation in space between weather states, such as the location of highs of lows, that is impossible when using ED. The S-SOM performance is evaluated by multiple demo simulations of clustering weather patterns over Japan using the ERA-Interim sea-level pressure data. The results show the superiority of the S-SOM compared with a standard SOM with ED (or ED-SOM) in two respects: clustering quality based on silhouette analysis and topological preservation based on topological error analysis. The superior performance of the S-SOM compared with an ED-SOM is probably independent of both the input data and SOM configuration.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

2011 ◽  
Vol 131 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Yutaka Suzuki ◽  
Mizuya Fukasawa ◽  
Osamu Sakata ◽  
Hatsuhiro Kato ◽  
Asobu Hattori ◽  
...  
Keyword(s):  
Support System ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son
Keyword(s):  
Heat Wave ◽  
Self Organizing Map ◽  
Self Organizing
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