A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 Hour Precipitation Nowcasting

Zhang, Fuhan; Wang, Xiaodong; Guan, Jiping

doi:10.3390/atmos12121596

Cited by 14 publications

(14 citation statements)

References 31 publications

(37 reference statements)

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“…Encoders and decoders are usually based on recurrent neural networks, which can be used to process time-series data. Therefore, the Seq2Seq model is widely used in meteorology for short-term precipitation forecasts (Zhang et al, 2021), other meteorological elements forecast, etc. (Geng et al, 2019) (Kong et al, 2022).…”

Section: Baseline Methodsmentioning

confidence: 99%

“…The spatiotemporal prediction model can utilize both the spatial distribution characteristics and the temporal evolution of the atmospheric state; therefore, it is extensively used in the meteorology field. Instead of using convolutional LSTM (Shi et al, 2015), Zhang et al (2021) proposed a novel multi-input multi-output recurrent neural network model based on multimodal fusion and spatiotemporal prediction for 0-4-h precipitation nowcasting; this model has evident benefits in heavy precipitation nowcasting. Because convolution is a position-invariant filter, Shi et al (2017) proposed a trajectory gate recurrent unit model, which can actively understand the location-variant structure of recurrent connections for precipitation nowcasting.…”

Section: Introductionmentioning

confidence: 99%

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Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

Xiang²,

Guan³

et al. 2022

Front. Earth Sci.

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Future weather conditions can be obtained based on numerical weather prediction (NWP); however, NWP is unsatisfied with precise local weather prediction. In this study, we propose a spatiotemporal convolutional network (STCNet) based on spatiotemporal modeling for local weather prediction post-processing. To model the spatiotemporal information, we use a convolutional neural network and an interactive convolutional module, which use two-dimensional convolution for spatial feature extraction and one-dimensional convolution for time-series processing, respectively. We performed experiments at several stations, and the results show that our model considerably outperforms the traditional recurrent neural network–based Seq2Seq model while demonstrating the effectiveness of the fusion of observation and forecast data. By investigating the influences of seasonal changes and station differences, we conclude that the STCNet model has high prediction accuracy and stability. Finally, we completed the hour-by-hour local weather prediction using the 3-h forecast data and attained similar results to the 3-h local weather prediction that efficiently compensated for the temporal resolution of the forecast data. Thus, our model can enhance the spatial and temporal resolutions of forecast data and achieve remarkable local weather prediction.

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Section: Baseline Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

Xiang²,

Guan³

et al. 2022

Front. Earth Sci.

Self Cite

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“…All the deep learning models are implemented in Pytorch [56] and trained end-to-end using the Adam optimizer with an initial learning rate of 10 −4 . To improve prediction quality and handle imbalanced rainfall levels, the loss function combines weighted mean square error (MSE), mean absolute error (MAE), and structural similarity index (SSIM) terms (Equation (2) [57]). The weights for different rainfall levels are set to 1, 2, 3, 5, and 10, corresponding to 0.5, 2, 5, 10, and 30 mm/h from the rainfall intensity of ground truth.…”

Section: Implementation Detailsmentioning

confidence: 99%

STPF-Net: Short-Term Precipitation Forecast Based on a Recurrent Neural Network

Wang,

Guan

et al. 2023

Remote Sensing

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Accurate and timely precipitation forecasts are critical in modern society, influencing both economic activity and daily life. While deep learning methods leveraging remotely sensed radar data have become prevalent for precipitation nowcasting, longer-term forecasting remains challenging. This is due to accumulated errors in deep learning models and insufficient information about precipitation systems over longer time horizons. To address these challenges, we introduce the Short-Term Precipitation Forecast Network (STPF-Net), a recurrent neural network designed for longer-term precipitation prediction. STPF-Net uses a multi-tier structure with varying temporal resolutions to mitigate the accumulated errors during longer forecasts. Additionally, its transformer-based module incorporates larger spatial contexts, providing more complete information about precipitation systems. We evaluated STPF-Net on radar data from southeastern China, training separate models for 6 and 12 h forecasts. Quantitative results demonstrate STPF-Net achieved superior accuracy and lower errors compared to benchmark deep learning and numerical weather prediction models. Visualized case studies indicate reasonably coherent 6 h predictions from STPF-Net versus other methods. For 12 h forecasts, while STPF-Net outperformed other models, it still struggled with storm initiation over longer forecasting time.

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“…For precipitation nowcasting, Zhang et al [27] proposed MFSP-Net, a multi-input multi-output recurrent neural network model based on multimodal fusion and spatiotemporal prediction. The proposed model used precipitation grid data, radar echo data and reanalysis data.…”

Section: Literature Review On Precipitation Estimation and Nowcastingmentioning

confidence: 99%

Enhancing the Performance of Quantitative Precipitation Estimation Using Ensemble of Machine Learning Models Applied on Weather Radar Data

et al. 2023

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Flash floods are a major weather-related risk, as they cause more than 5000 fatalities annually, according to the World Meteorological Organization. Quantitative Precipitation Estimation is a method used to approximate the rainfall over locations where direct field observations are not available. It represents one of the most valuable information employed by meteorologists and hydrologists for issuing early warnings concerning flash floods. The current study is in line with the efforts to improve radar-based rainfall estimates through the use of machine learning techniques applied on radar data. With this aim, as a proof of concept, six machine learning models are evaluated to make estimations of the radar-based hourly accumulated rainfall using reflectivity data collected on the lowest radar elevation angles, and we employ a new data model for representing these radar data. The data were collected by a WSR-98D weather radar of the Romanian Meteorological Administration, located in the central region of Romania, during 30 non-consecutive days of the convective seasons, between 2016 and 2021. We obtained encouraging results using a stacked machine learning model. In terms of the Root Mean Squared Error evaluation metric, the results of the proposed stacked regressor are better than the radar estimated accumulated rainfall by about 33% and also outperform the baseline computed using the Z-R relationship by about 13%.

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A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 Hour Precipitation Nowcasting

Cited by 14 publications

References 31 publications

Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

Spatiotemporal forecasting model based on hybrid convolution for local weather prediction post-processing

STPF-Net: Short-Term Precipitation Forecast Based on a Recurrent Neural Network

Enhancing the Performance of Quantitative Precipitation Estimation Using Ensemble of Machine Learning Models Applied on Weather Radar Data

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