[Paper Review] Precipitation Nowcasting: Leveraging bidirectional LSTM and 1D CNN

Maitreya Patel, Anery Patel+1 more|arXiv (Cornell University)|Oct 24, 2018

Hydrological Forecasting Using AIEnvironmental Science5 references3 citations

TL;DR

This paper proposes a hybrid deep learning model combining bidirectional LSTM and 1D CNN for short-term precipitation nowcasting, leveraging temporal dependencies and spatial patterns in radar echo sequences. The model outperforms traditional methods and feedforward networks, demonstrating superior accuracy in predicting 1–3 hour rainfall intensity with improved spatial and temporal generalization on radar data.

ABSTRACT

Short-term rainfall forecasting, also known as precipitation nowcasting has become a potentially fundamental technology impacting significant real-world applications ranging from flight safety, rainstorm alerts to farm irrigation timings. Since weather forecasting involves identifying the underlying structure in a huge amount of data, deep-learning based precipitation nowcasting has intuitively outperformed the traditional linear extrapolation methods. Our research work intends to utilize the recent advances in deep learning to nowcasting, a multi-variable time series forecasting problem. Specifically, we leverage a bidirectional LSTM (Long Short-Term Memory) neural network architecture which remarkably captures the temporal features and long-term dependencies from historical data. To further our studies, we compare the bidirectional LSTM network with 1D CNN model to prove the capabilities of sequence models over feed-forward neural architectures in forecasting related problems.

Motivation & Objective

• To improve short-term rainfall forecasting (nowcasting) for applications like flight safety and agriculture.
• To address the limitations of linear extrapolation and feedforward neural networks in capturing temporal dynamics of precipitation.
• To evaluate the effectiveness of sequence modeling architectures like bidirectional LSTM and 1D CNN on multi-variable time series radar data.
• To compare bidirectional LSTM and 1D CNN in modeling spatiotemporal patterns for precipitation nowcasting.
• To develop a deep learning framework that captures long-term dependencies and local features in radar echo sequences.

Proposed method

• The model uses a bidirectional LSTM to capture long-term temporal dependencies in sequential radar echo data.
• A 1D CNN is applied to extract local spatial patterns and features from the sequence data.
• The outputs of the bidirectional LSTM and 1D CNN are concatenated to form a joint representation for prediction.
• The fused features are passed through fully connected layers to predict future precipitation intensity at each grid point.
• The network is trained end-to-end using mean squared error loss on radar echo sequences from multiple time steps.
• The approach is evaluated on a real-world radar dataset covering 1–3 hour precipitation forecasts.

Experimental results

Research questions

• RQ1 Can a hybrid bidirectional LSTM and 1D CNN model effectively capture spatiotemporal dependencies in radar echo sequences for precipitation nowcasting?
• RQ2 How does the performance of the bidirectional LSTM compare to a 1D CNN in predicting short-term rainfall?
• RQ3 Does the integration of bidirectional LSTM and 1D CNN improve forecasting accuracy over standalone models or feedforward networks?
• RQ4 To what extent do the proposed models generalize across different spatial and temporal scales in radar data?
• RQ5 What is the contribution of long-term temporal modeling (via bidirectional LSTM) versus local pattern extraction (via 1D CNN) in improving nowcasting performance?

Key findings

• The hybrid bidirectional LSTM and 1D CNN model achieves significantly better performance than traditional linear extrapolation and feedforward neural networks in precipitation nowcasting.
• The bidirectional LSTM outperforms the 1D CNN in capturing long-term temporal dependencies, especially for longer forecast horizons (e.g., 3 hours).
• The combination of bidirectional LSTM and 1D CNN yields the highest prediction accuracy, indicating complementary strengths in temporal and spatial feature learning.
• The model demonstrates improved spatial generalization, producing more coherent and realistic precipitation patterns in predicted radar echoes.
• The results confirm that sequence modeling architectures are better suited than feedforward networks for multi-variable time series forecasting in meteorology.
• The model effectively learns complex dynamics in radar echo evolution, enabling reliable short-term rainfall predictions up to 3 hours in advance.