A Hybrid Deep Learning Model for the Bias Correction of SST Numerical Forecast Products Using Satellite Data

Fei, Tonghan; Huang, Bin; Wang, Xiang; Zhu, Junxing; Chen, Yan; Wang, Huizan; Zhang, Weimin

doi:10.3390/rs14061339

Cited by 14 publications

(9 citation statements)

References 48 publications

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“…Deep learning methods can automatically learn important features related to the target from large amounts of data and establish a relationship between the data and the target (Fei et al, 2022). The proposed deep learning method is designed to discover the intrinsic relationship between the TC image data and R34 to provide accurate R34 estimates.…”

Section: Problem Definitionsmentioning

confidence: 99%

Tropical cyclone size estimation based on deep learning using infrared and microwave satellite data

Wang²,

Wang³

et al. 2023

Front. Mar. Sci.

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Tropical cyclone (TC) size is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Satellite observation data are the primary data used to estimate TC size. Traditional methods of TC size estimation rely on a priori knowledge of the meteorological domain and emerging deep learning-based methods do not consider the considerable blurring and background noise in TC cloud systems and the application of multisource observation data. In this paper, we propose TC-Resnet, a deep learning-based model that estimates 34-kt wind radii (R34, commonly used as a measure of TC size) objectively by combining infrared and microwave satellite data. We regarded the resnet-50 model as the basic framework and embedded a convolution layer with a 5 × 5 convolution kernel on the shortcut branch in its residual block for downsampling to avoid the information loss problem of the original model. We also introduced a combined channel-spatial dual attention mechanism to suppress the background noise of TC cloud systems. In an R34 estimation experiment based on a global TC dataset containing 2003–2017 data, TC-Resnet outperformed existing methods of TC size estimation, obtaining a mean absolute error of 11.287 nmi and a Pearson correlation coefficient of 0.907.

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Section: Problem Definitionsmentioning

confidence: 99%

Tropical cyclone size estimation based on deep learning using infrared and microwave satellite data

Wang²,

Wang³

et al. 2023

Front. Mar. Sci.

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“…In recent years, machine learning-based methods have been widely used in meteorology and oceanography. (Mecikalski et al, 2021;Wikner et al, 2021;Fei et al, 2022). Tropical cyclones (including severe typhoons) are a high-impact and disastrous weather phenomenon in meteorology and oceanography.…”

Section: Introductionmentioning

confidence: 99%

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

et al. 2023

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IntroductionSevere typhoons, as extreme weather events, can cause a large number of casualties and property damage in coastal areas. There are mainly three kinds of methods for the prediction of severe typhoon formation, which are the numerical-based methods, the statistical-based methods, and the machine learning-based methods. However, existing methods do not consider the unbalance between the number of ordinary typhoon samples and severe typhoon samples, which makes the accuracies of existing methods in the prediction of severe typhoons much lower than that of ordinary typhoons.MethodsIn this paper, we propose an unbalanced severe typhoon formation prediction (USFP) framework based on transfer learning. We first propose a severe typhoon pre-learning model which is used to learn prior knowledge from a constructed balanced dataset. Then, we propose an unbalanced severe typhoon re-learning model which utilizes the prior knowledge learning from the pre-learning model. Our USFP framework fuses three different variables, which are atmospheric variables, sea surface variables, and ocean hydrographic variables.ResultsExtensive experiments based on datasets of three different regions show that our USFP framework outperforms the numerical model IFS of ECMWF and existing machine learning methods.

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“…It allows a model to be fed with raw data as predictors without detailed feature extraction and transformation [34]. Numerous studies have applied DL methods to the bias correction of short-lead predictions ranging from weather to sub-seasonal time scales [29,30,[35][36][37] or to the bias correction of climate model's long-term simulation results [31,38]. Fewer DL-based bias-correction models have been built for improving the seasonal climate prediction.…”

Section: Introductionmentioning

confidence: 99%

Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

Ling

Li²,

Luo³

et al. 2022

Environ. Res. Lett.

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As most global climate models suffer from large biases in simulating/predicting summer precipitation over China, it is of great importance to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning (DL) models incorporated. One is the deterministic pathway (DP), in which the bias correction is directly applied to the precipitation forecasts. The other one, namely the probability pathway (PP), corrects the forecasted precipitation anomalies using a conditional probability method before being added to the observational climatology. These two pathways have been applied to correct the precipitation forecasts based on a global climate model prediction system NUIST-CFS1.0. The application of DL models in the both pathways yield higher resolution of corrected predictions than the uncorrected ones. Both pathways improve summer precipitation predictions at 4-month lead. Moreover, the DP correction shows a better performance in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China. The present results highlight the importance of the application of appropriate correction strategy for different prediction purposes.

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A Hybrid Deep Learning Model for the Bias Correction of SST Numerical Forecast Products Using Satellite Data

Cited by 14 publications

References 48 publications

Tropical cyclone size estimation based on deep learning using infrared and microwave satellite data

Tropical cyclone size estimation based on deep learning using infrared and microwave satellite data

USFP: An unbalanced severe typhoon formation prediction framework based on transfer learning

Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

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