Study On Method Of Exploring The Turbulence Areas Based On The Application Of AMDAR Data

Nowadays, the statistic results of the turbulence areas mainly depend on the collections from the PIREPs (Pilot Reports). The Pilot Reports are of low quantity and very subjective, therefore, the statistics results are not very accurate.In this paper, the turbulence cases are collected based on the AMDAR (aircraft meteorological data relay) data. Four turbulence forecasting indices are selected and used to simulate the turbulence cases, based on the NCEP data. And, four diagnostic indices are selected to evaluate the turbulence forecasting indices. Then, the distribution graphs of the best turbulence forecasting index are drawn. And the high-risk turbulence areas are found by these distribution graphs.Firstly, the spatial-temporal characteristics and the quality of AMDAR data were analyzed. The result shows that the AMDAR data has high spatial-temporal density and quality. Then, the turbulence cases were collected based on the DEVG of the AMDAR data.Secondly, four turbulence forecasting indices were selected to simulate the turbulence forecasting process, based on NCEP meteorological data. Four diagnostic criterias (PODy, PODn, POD, TS) were selected to evaluate the turbulence forecasting results. The results show that Moscat probability predictor index has a better forecasting effect.Finally, the turbulence distribution graphs were drawn based on the Moscat probability predictor. The distribution graphs of the best turbulence forecasting index were selected by the grayscale method. Then, the high-risk turbulence areas of a specific region and layer in different seasons were obtained by image processing.