The Potential for Self-Organizing Maps to Identify Model Error Structures

Kolczynski, Walter C.; Hacker, Joshua P.

doi:10.1175/mwr-d-13-00189.1

Cited by 5 publications

(4 citation statements)

References 21 publications

(12 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They have also been applied to gridded mesoscale model data to investigate parameters associated with tornado events (Anderson-Frey et al 2017) or surface fronts (Hope et al 2014). In general, SOMs have provided useful insight into model behavior and have been advocated for as a tool to identify model and forecast issues (Kolczynski and Hacker 2014).…”

Section: A Brief Review and The Configuration Of Som Methods Used In Tmentioning

confidence: 99%

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

Wang

Dong

Kennedy

et al. 2019

Weather and Forecasting

View full text Add to dashboard Cite

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.

show abstract

Section: A Brief Review and The Configuration Of Som Methods Used In Tmentioning

confidence: 99%

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

Wang

Dong

Kennedy

et al. 2019

Weather and Forecasting

View full text Add to dashboard Cite

show abstract

“…Iseri et al, 2009). Because SOM can obtain a spatially organised set of patterns from temporally varying input data, it has been used in meteorological studies such as climate characterisation (Reusch et al, 2007;Johnson and Feldstein, 2010), identification of model errors and model evaluation (Radicá nd Clarke, 2011; Kolczynski and Hacker, 2014), and analysis of extreme events (Cavazos, 1999;Nishiyama et al, 2007;Ohba et al, 2015). These previous studies successfully separate visually clear-cut WPs from complex non-linear relationships.…”

Section: Som Techniquementioning

confidence: 99%

Rainfall downscaling of weekly ensemble forecasts using self-organising maps

Ohba

Kadokura

Nohara

et al. 2016

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

This study presents an application of self-organising maps (SOMs) to downscaling medium-range ensemble forecasts and probabilistic prediction of local precipitation in Japan. SOM was applied to analyse and connect the relationship between atmospheric patterns over Japan and local high-resolution precipitation data. Multiple SOM was simultaneously employed on four variables derived from the JRA-55 reanalysis over the area of study (south-western Japan), and a two-dimensional lattice of weather patterns (WPs) was obtained. Weekly ensemble forecasts can be downscaled to local precipitation using the obtained multiple SOM. The downscaled precipitation is derived by the five SOM lattices based on the WPs of the global model ensemble forecasts for a particular day in 2009Á2011. Because this method effectively handles the stochastic uncertainties from the large number of ensemble members, a probabilistic local precipitation is easily and quickly obtained from the ensemble forecasts. This downscaling of ensemble forecasts provides results better than those from a 20-km global spectral model (i.e. capturing the relatively detailed precipitation distribution over the region). To capture the effect of the detailed pattern differences in each SOM node, a statistical model is additionally concreted for each SOM node. The predictability skill of the ensemble forecasts is significantly improved under the neural network-statistics hybrid-downscaling technique, which then brings a much better skill score than the traditional method. It is expected that the results of this study will provide better guidance to the user community and contribute to the future development of dam-management models.

show abstract

“…The SOM method has been used in many fields, such as for oceanography studies [28][29][30], climate characterization over the Northern Hemisphere [31,32], the identification of spatially varying systematic numerical model errors [33], global climate model evaluations [34], rainfall predictions in the monsoon systems [35][36][37], and examining the connection between the circulation field and the weather element field [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Features and Evolution of Autumn Weather Regimes in the Southeast China

Wang

Sun

2022

Atmosphere

View full text Add to dashboard Cite

Autumn is the transitional season when the atmospheric circulation pattern changes from summer to winter. The temperature and precipitation in Southeastern China in autumn are significantly influenced by the change in circulation patterns, and both show significant uniqueness. The clustering method can be used to observe the changes of circulation patterns in detail and to observe and analyze the transition from warm to cold seasons from a detailed view of the daily circulation pattern perspective. This method may have important research implications on how to study the generation and dissipation of extreme weather events. The Self-Organizing Maps (SOM) method is used to a 500 hPa geopotential height and 850 hPa wind and sea level pressure for 1981–2020 to identify the characteristic weather patterns (WTs) in autumn (September–November) over Southeastern China. Characteristics of the captured WTs are also analyzed in terms of the distribution characteristics of weather patterns, occurrence frequency, typical progression, precipitation and extreme precipitation (EP), temperature and extreme high temperature (EHT), and the relationship with atmospheric teleconnection. Nine WTs were identified in autumn, which represents a series of weather situations consisting of troughs and ridges. On this basis, these WTs were used to carry out the differentiation of seasonal differences between early and late autumn. The maximum mean and extreme precipitation occur in several early season patterns (WT1, WT2, WT4, and WT7). It is highly likely that extremely high temperatures occur in the WT1 and WT2 patterns. The most common progression between WTs is WT7−WT1−WT2−WT4 in the early season. This seasonality allows us to distinguish between early and late seasons based on daily weather types. A preliminary trend analysis suggests that patterns in the early season occur more frequently and last longer in the early season, and patterns in the late season occur less frequently and later. That is, the longer cool season pattern is shifting to the shorter warm season pattern. In addition, the persistence of both cool and warm patterns increased during 2001–2020 relative to 1981–2000, and the risk of both flooding and drought occurrence is on the rise.

show abstract

The Potential for Self-Organizing Maps to Identify Model Error Structures

Cited by 5 publications

References 21 publications

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

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

Rainfall downscaling of weekly ensemble forecasts using self-organising maps

Features and Evolution of Autumn Weather Regimes in the Southeast China

Contact Info

Product

Resources

About