Improvement and Application Test of TREC Algorithm for Convective Storm Nowcast

At present, cross-correlation extrapolation is one of the main algorithms for convective storm now cast. Motion vectors of convective storm for every divided equal-sized two-dimensional arrays of radar echo or other data measured at two times several minutes apart by calculating optimal spatial cross-correlation are obtained in the algorithm. The obtained motion vectors are customarily called TREC (tracking radar echoes by correlation) vectors or TREC winds. And then, storm now cast can be achieved by extrapolating radar echoes or other data based on the obtained TREC vectors. The algorithm results involve not only the changing characteristics of magnitude and direction of the motion vectors, but also shape varieties of the whole echoes in the course of their movement. So the result of storm now cast based on the algorithm is assuredly reasonable and significant in meteorology. The basic principle of TREC is introduced firstly. And a number of methods to improve the algorithm result are presented, including noisy vector restriction and clutter contamination removal, discarded or missing motion vector supplement, vector smoothing, and so on. Analysis results of two cases indicate that the tracked motion vectors can be markedly improved after quality control and optimization processes to TREC algorithm. Finally, based on the optimized TREC algorithm and Tianjin radar data, storm nowcast tests and verifications of four intense convective storms that occur in Beijing-Tianjin-Hebei areas during 2004 and 2005 summertime, including two squall line cases, a hailstorm case and a strong thunderstorm case, are described in detail. The results indicate that the improved algorithm is available for convective storm nowcast.The algorithm can automatically produce 30-minute or 60-minute forecast of location and shape of radar echoes or storm characteristics based on the extrapolated vectors. The forecast results are close to what is actually happening. Because the algorithm can automatically produce forecast results in real time mode, it is helpful for convective storm now cast and warning.The forecast results are also clear at a glance, so abilities of forecasters for strong convective storm identification and forecast can be enhanced by the algorithm. An expectation is that the improved algorithm can be used for operational storm now cast in the near future.