Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Fast Data Assimilation using Kernel Tridiagonal Sparse Matrix for Performance Improvement of Air Quality Forecasting

대기질 예보의 성능 향상을 위한 커널 삼중대각 희소행렬을 이용한 고속 자료동화

  • Received : 2016.01.13
  • Accepted : 2017.01.26
  • Published : 2017.02.28
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Abstract

Data assimilation is an initializing method for air quality forecasting such as PM10. It is very important to enhance the forecasting accuracy. Optimal interpolation is one of the data assimilation techniques. It is very effective and widely used in air quality forecasting fields. The technique, however, requires too much memory space and long execution time. It makes the PM10 air quality forecasting difficult in real time. We propose a fast optimal interpolation data assimilation method for PM10 air quality forecasting using a new kernel tridiagonal sparse matrix and CUDA massively parallel processing architecture. Experimental results show the proposed method is 5~56 times faster than conventional ones.

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References

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