Next Article in Journal
Stable Isotopic Characteristics and Influencing Factors in Precipitation in the Monsoon Marginal Region of Northern China
Previous Article in Journal
Optimisation of Heat Loss through Ventilation for Residential Buildings
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Atmosphere 2018, 9(3), 96; https://doi.org/10.3390/atmos9030096

Weather Radar Data Compression Based on Spatial and Temporal Prediction

1,2,* , 1,2
,
1,2
and
1,2
1
College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China
2
CMA Key Laboratory of Atmospheric Sounding, Chengdu 610225, China
*
Author to whom correspondence should be addressed.
Received: 6 February 2018 / Revised: 6 March 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
(This article belongs to the Section Climatology and Meteorology)
Full-Text   |   PDF [3611 KB, uploaded 8 March 2018]   |  

Abstract

The transmission and storage of weather radar products will be an important problem for future weather radar applications. The aim of this work is to provide a solution for real-time transmission of weather radar data and efficient data storage. By upgrading the capabilities of radar, the amount of data that can be processed continues to increase. Weather radar compression is necessary to reduce the amount of data for transmission and archiving. The characteristics of weather radar data are not considered in general-purpose compression programs. The sparsity and data redundancy of weather radar data are analyzed. A lossless compression of weather radar data based on prediction coding is presented, which is called spatial and temporal prediction compression (STPC). The spatial and temporal correlations in weather radar data are utilized to improve the compression ratio. A specific prediction scheme for weather radar data is given, while the residual data and motion vectors are used to replace the original values for entropy coding. After this, the Level-II product from CINRAD SA is used to evaluate STPC. Experimental results show that the STPC achieves a better performance than the general-purpose compression programs, with the STPC yield being approximately 26% better than the next best approach. View Full-Text
Keywords: data compression; weather radar; spatial prediction; temporal prediction; radar signal processing data compression; weather radar; spatial prediction; temporal prediction; radar signal processing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zeng, Q.; He, J.; Shi, Z.; Li, X. Weather Radar Data Compression Based on Spatial and Temporal Prediction. Atmosphere 2018, 9, 96.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Atmosphere EISSN 2073-4433 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top