Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data

Alif Bin Abdul Qayyum, Xihaier Luo, Nathan M. Urban, Xiaoning Qian, Byung-Jun Yoon

TL;DR

The paper tackles the challenge of obtaining high-resolution wind patterns from limited, low-dimensional data by introducing a multi-modal representation learning framework. It combines dimension-reducing encoders for each modality with a Local Implicit Image Function (LIIF) based coordinate decoder to realize continuous super-resolution across wind data at different heights, while employing self and cross modality prediction alongside a latent consistency loss to enable intra- and inter-modality extrapolation. Key contributions include the dual-modality setup (M0 and M1 at heights h0 and h1), a pair of self and cross encoders, and a LIIF-inspired decoder that supports continuous spatial outputs, validated on WIND Toolkit data with PSNR and SSIM metrics. The approach offers practical impact for wind energy resource assessment, enabling accurate predictions at inaccessible locations and efficient storage of large climate datasets through dimensionality reduction and cross-modal inference.

Abstract

World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Wind energy has significant potential to not only reduce greenhouse emission, but also meet the ever increasing demand for energy. To enable the effective utilization of wind energy, addressing the following three challenges in wind data analysis is crucial. Firstly, improving data resolution in various climate conditions to ensure an ample supply of information for assessing potential energy resources. Secondly, implementing dimensionality reduction techniques for data collected from sensors/simulations to efficiently manage and store large datasets. Thirdly, extrapolating wind data from one spatial specification to another, particularly in cases where data acquisition may be impractical or costly. We propose a deep learning based approach to achieve multi-modal continuous resolution wind data prediction from discontinuous wind data, along with data dimensionality reduction.

Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data

TL;DR

The paper tackles the challenge of obtaining high-resolution wind patterns from limited, low-dimensional data by introducing a multi-modal representation learning framework. It combines dimension-reducing encoders for each modality with a Local Implicit Image Function (LIIF) based coordinate decoder to realize continuous super-resolution across wind data at different heights, while employing self and cross modality prediction alongside a latent consistency loss to enable intra- and inter-modality extrapolation. Key contributions include the dual-modality setup (M0 and M1 at heights h0 and h1), a pair of self and cross encoders, and a LIIF-inspired decoder that supports continuous spatial outputs, validated on WIND Toolkit data with PSNR and SSIM metrics. The approach offers practical impact for wind energy resource assessment, enabling accurate predictions at inaccessible locations and efficient storage of large climate datasets through dimensionality reduction and cross-modal inference.

Abstract

World is looking for clean and renewable energy sources that do not pollute the environment, in an attempt to reduce greenhouse gas emissions that contribute to global warming. Wind energy has significant potential to not only reduce greenhouse emission, but also meet the ever increasing demand for energy. To enable the effective utilization of wind energy, addressing the following three challenges in wind data analysis is crucial. Firstly, improving data resolution in various climate conditions to ensure an ample supply of information for assessing potential energy resources. Secondly, implementing dimensionality reduction techniques for data collected from sensors/simulations to efficiently manage and store large datasets. Thirdly, extrapolating wind data from one spatial specification to another, particularly in cases where data acquisition may be impractical or costly. We propose a deep learning based approach to achieve multi-modal continuous resolution wind data prediction from discontinuous wind data, along with data dimensionality reduction.
Paper Structure (14 sections, 4 equations, 2 figures)

This paper contains 14 sections, 4 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Multi-modal Weather Data
  4. Dimension Reducing Encoder
  5. Local Implicit Image Function based Decoder
  6. Self & Cross Modality Prediction
  7. Results
  8. Wind Data
  9. Experimental Setup
  10. Data Set
  11. Training Details
  12. Evaluation Metrics
  13. Obtained Results
  14. Conclusion

Figures (2)

  • Figure 1: Illustration of the overall model architecture.
  • Figure 2: Experimental results.