Altimeter Observation-Based Eddy Nowcasting Using an Improved Conv-LSTM Network

Ma, Chunyong; Li, Siqing; Wang, Anni; Yang, Jie; Chen, Ge

doi:10.3390/rs11070783

Cited by 25 publications

(19 citation statements)

References 37 publications

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“…ConvLSTM has recently been applied to various research tasks that need to consider both time-series patterns and spatial information, such as segmentation, change detection, forecasting video frames, forecasting sea surface temperature, and air pollution research [57][58][59][60][61][62]. ConvLSTM models space-time structures through encoding spatial information, which can overcome the major limitations of LSTM, namely the loss of spatial information [63]. The structure of ConvLSTM is similar to that of LSTM, which consists of memory cells and three gates (i.e., forget, input, and output gates).…”

Section: Methodsmentioning

confidence: 99%

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Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Park

Han

et al. 2020

Remote Sensing

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Drought forecasting is essential for effectively managing drought-related damage and providing relevant drought information to decision-makers so they can make appropriate decisions in response to drought. Although there have been great efforts in drought-forecasting research, drought forecasting on a short-term scale (up to two weeks) is still difficult. In this research, drought-forecasting models on a short-term scale (8 days) were developed considering the temporal patterns of satellite-based drought indices and numerical model outputs through the synergistic use of convolutional long short term memory (ConvLSTM) and random forest (RF) approaches over a part of East Asia. Two widely used drought indices—Scaled Drought Condition Index (SDCI) and Standardized Precipitation Index (SPI)—were used as target variables. Through the combination of temporal patterns and the upcoming weather conditions (numerical model outputs), the overall performances of drought-forecasting models (ConvLSTM and RF combined) produced competitive results in terms of r (0.90 and 0.93 for validation SDCI and SPI, respectively) and nRMSE (0.11 and 0.08 for validation of SDCI and SPI, respectively). Furthermore, our short-term drought-forecasting model can be effective regardless of drought intensification or alleviation. The proposed drought-forecasting model can be operationally used, providing useful information on upcoming drought conditions with high resolution (0.05°).

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Section: Methodsmentioning

confidence: 99%

“…information, which can overcome the major limitations of LSTM, namely the loss of spatial information [63]. The structure of ConvLSTM is similar to that of LSTM, which consists of memory cells and three gates (i.e., forget, input, and output gates).…”

Section: Step 1: Convolutional Long Short Term Memory (Convlstm)mentioning

confidence: 99%

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Park

Han

et al. 2020

Remote Sensing

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“…Ocean mesoscale eddies, the rotating vortices with typical horizontal scales of tens to hundreds km and timescales on the order of weeks to months, are ubiquitous in world ocean. They induce significant transport of water mass, heat, salt, dissolved CO 2 , and other important oceanic tracers, which may have profound climatological impact (Bryden and Brady, 1989;Martin and Richards, 2001;McGillicuddy et al, 2007;Chelton et al, 2011a;Chen et al, 2011Chen et al, , 2021Sarangi, 2012;Frenger, 2013;Dong et al, 2014;Zhang Y. et al, 2014;Zhang Z. G. et al, 2014Zhang Z. G. et al, , 2017Zhang et al, 2016;Ma et al, 2019;Yang et al, 2019;Tian et al, 2020;Zhang and Qiu, 2020;Martínez-Moreno et al, 2021;Thoppil et al, 2021). The mesoscale eddies account for 90% oceanic kinetic energy and dominate the upper ocean flow field (Pascual et al, 2006;Wunsch, 2007;Martínez-Moreno et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly

Nian

Cai

et al. 2021

Front. Mar. Sci.

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Ocean mesoscale eddies are ubiquitous in world ocean and account for 90% oceanic kinetic energy, which dominate the upper ocean flow field. Accurately predicting the variation of ocean mesoscale eddies is the key to understand the oceanic flow field and circulation system. In this article, we propose to make an initial attempt to explore spatio-temporal predictability of mesoscale eddies, employing deep learning architecture, which primarily establishes Memory In Memory (MIM) for sea level anomaly (SLA) prediction, combined with the existing mesoscale eddy detection. Oriented to the western Pacific ocean (125°−137.5°E and 15°−27.5°N), we quantitatively investigate the historic daily SLA variability at a 0.25° spatial resolution from 2000 to 2018, derived by satellite altimetry. We develop the enhanced MIM prediction strategies, equipped with Gated Recurrent Unit (GRU) and spatial attention module, in a scheduled sampling manner, which overcomes the gradient vanishing and complements to strengthen spatio-temporal features for long-term dependencies. At the early stage, the real value SLA input guides the model training process for initialization, while the scheduled sampling intentionally feeds the newly predicted value, to resolve the distribution inconsistency of inference. It has been demonstrated in our experiment results that our proposed prediction scheme outperformed the state-of-art approaches for SLA time series, with MAPE, RMSE of the 14-day prediction duration, respectively, 5.1%, 0.023 m on average, even up to 4.6%, 0.018 m for the effective sub-regions, compared to 19.8%, 0.086 m in ConvLSTM and 8.3%, 0.040 m in original MIM, which greatly facilitated the mesoscale eddy prediction. This proposed scheme will be beneficial to understand of the underlying dynamical mechanism behind the predictability of mesoscale eddies in the future, and help the deployment of ARGO, glider, AUV and other observational platforms.

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“…LSTM based on attention [20,21] can define the region in the image that corresponds to the current word and provide a useful method for recognizing the geographic objects and their spatial relationships simultaneously. Currently, the convolutional long-short term memory (Conv LSTM) [21] is getting more attention in the research about semantic segmentation, because its input can be expended from 1D to 2D, which is better for processing the remote sensing images [22][23][24][25]. On the basis of the above researches, we proposed a novel method to recognize landslides and hazard-affected bodies simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

Cui

Meng

et al. 2020

IJGI

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When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making.

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Altimeter Observation-Based Eddy Nowcasting Using an Improved Conv-LSTM Network

Cited by 25 publications

References 37 publications

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

Short-Term Forecasting of Satellite-Based Drought Indices Using Their Temporal Patterns and Numerical Model Output

The Identification and Prediction of Mesoscale Eddy Variation via Memory in Memory With Scheduled Sampling for Sea Level Anomaly

Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM

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