Parameter estimations of a storm surge model using a genetic algorithm

You, Sung Hyup; Lee, Yong Hee; Lee, Woo Jeong

doi:10.1007/s11069-011-9900-y

Cited by 6 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…If the weight does not provide the optimal solution, the algorithm continues to the next generation through operators such as crossover, mutation, and replacement until an optimal solution is obtained (Henao, 2011). Here, the minimum RMSE between observation and model anomalies is used as the fitness function (Lee et al ., 2006; You et al ., 2012; Ahn and Lee, 2016). Ahn and Lee (2016) showed that the MME method applying GA to both single‐model and MME improved the performance for both winter temperature and precipitation, even over higher‐latitude land areas, compared to conventional SCM.…”

Section: Methodsmentioning

confidence: 99%

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Kim

Ahn

Kryjov

et al. 2020

Intl Journal of Climatology

View full text Add to dashboard Cite

Different multi-model ensemble (MME) methods were investigated for their potential to improve the skill of 1-month lead seasonal forecast products, based on six models from Global Producing Centers (GPCs) for long-range forecasts (LRFs) designated by the World Meteorological Organization (WMO). We first compared the hindcast performance of seven MME methods (simple composite method, SCM; simple linear regression, SLR; multiple linear regression, MLR; best selection anomaly, BSA; multilayer perceptron, MLP; radial basis function, RBF; genetic algorithm, GA) for the global 2-m temperature and precipitation for 1983-2009. The reference datasets for 2-m temperature and precipitation are the ERA-Interim from European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Precipitation Climatology Project (GPCP) for hindcast verification. For real-time verification, the data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis 1 for 2-m temperature and climate anomaly monitoring system and outgoing longwave radiation precipitation index (CAMS OPI) for precipitation are used. According to our analysis, GA was the most successful MME method in predicting both the global 2-m temperature and precipitation for all four seasons. GA also showed good performance in predicting the 2-m temperature and precipitation over the 13 regional climate outlook forum (RCOF) regions in all four seasons, but the range in performance among the RCOF regions varied significantly. In a realtime forecast period (MAM 2012-DJF 2015/16), GA outperformed in terms of time-averaged anomaly pattern correlation coefficient (ACC) and root-meansquare error (RMSE) of the 2-m temperature, although the forecast skill difference (0.02) between GA and SCM was not statistically significant. For the precipitation, both SCM and GA also reveal better performance than other MME methods. During the very strong El Niño event in 2015, individual models show better performance than other years. Nonetheless, these two MME methods outperform all the individual models.

show abstract

Section: Methodsmentioning

confidence: 99%

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Kim

Ahn

Kryjov

et al. 2020

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…The fitness function used in this study is the minimum root‐mean‐square error (RMSE) between observation and hindcast anomalies. RMSE, which is a simple and effective fitness function, has been used by several previous studies [e.g., Lee et al ., ; You et al ., ]. RMSE is defined as follows:

RMSE = \frac{1}{T} true {true\sum}_{j = 1}^{T} {(k_{j} - O_{j})}^{2}

k_{j} = true {true\sum}_{i = 1}^{N} ((), A_{i, italicj}^{'} \times w_{i})

where T and N are the total number of time and input data, respectively.…”

Section: Methodsmentioning

confidence: 99%

A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction

Ahn

Lee

2016

JGR Atmospheres

View full text Add to dashboard Cite

A new multimodel ensemble (MME) method that uses a genetic algorithm (GA) is developed and applied to the prediction of winter surface air temperature (SAT) and precipitation. The GA based on the biological process of natural evolution is a nonlinear method which solves nonlinear optimization problems. Hindcast data of winter SAT and precipitation from the six coupled general circulation models participating in the seasonal MME prediction system of the Asia‐Pacific Economic Conference Climate Center are used. Three MME methods using GA (MME/GAs) are examined in comparison with a simple composite MME strategy (MS0): MS1 which applies GA to single‐model ensembles (SMEs), MS2 which applies GA to each ensemble member and then performs a simple composite method for MME, and MS3 which applies GA to both MME and SME. MS3 shows the highest predictability compared to MS0, MS1, and MS2 for both winter SAT and precipitation. These results indicate that biases of ensemble members of each model and model ensemble are more reduced with MS3 than with other MME/GAs and MS0. The predictability of the MME/GAs shows a greater improvement than that of MS0, particularly in higher‐latitude land areas. The reason for the more improved increase of predictability over the land area, particularly in MS3, seems to be the fact that GA is more efficient in finding an optimum solution in a complex region where nonlinear physical properties are evident.

show abstract

“…This method extends the skillful ENSO forecast from 6 leading months to 12 leading months. Parameter adjustment, also known as parameter estimation or optimization, is commonly used to reduce the model errors [9][10][11][12][13][14]. Parameter adjustment automatically finds the optimal parameter values by assimilating observations over a period, to make the simulations approach the observations as much as possible [15].…”

Section: Introductionmentioning

confidence: 99%

Removing the systematic errors of the model in operational oceanography forecasting system using data assimilation method

Zu,

Zhu,

Ren

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

For the operational oceanography forecast, the synoptic forecast error is partly from the long-term systematic bias of the model, which can be partly counteracted by adjusting the values of the physical parameters. To this end, a four-dimensional optimization system is implemented into the South China Sea operational oceanography forecasting system, to adjust the values of multi-parameters using data assimilation method. By assimilating Argo temperature profiles of 51 days in the model domain, five physical parameters (coefficients of horizontal/vertical diffusion/viscosity and linear bottom drag) of the model have been adjusted simultaneously, and then the optimal values are obtained. The RMSE of temperature simulations in the assimilation window decreases from 1.17 to 0.97 K, when using the optimal values. The validation of the freerun experiments shows that the temperature RMSE decreases from 0.97 to 0.88 K, which indicates that the optimal values are still valid in a longer and independent period. Finally, the validation of the hindcast experiments shows that at the synoptic scale the temperature RMSE decreases from 0.90 to 0.80 K and other variables also present improvements. It hints that it is feasible to reduce the synoptic forecast errors by adjusting the parameter values at the climatological scale to partly counteract the systematic bias of the model. Therefore, it also provides a potential pathway to improve the synoptic forecast skill for the operational oceanography forecasting system.

show abstract

Parameter estimations of a storm surge model using a genetic algorithm

Cited by 6 publications

References 20 publications

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

Assessment of MME methods for seasonal prediction using WMO LC‐LRFMME hindcast dataset

A new multimodel ensemble method using nonlinear genetic algorithm: An application to boreal winter surface air temperature and precipitation prediction

Removing the systematic errors of the model in operational oceanography forecasting system using data assimilation method

Contact Info

Product

Resources

About