Optimization filtering in meteor radar wind retrieval based on combinations of residual error and root mean square error

Meteor radar is an important ground-based radio detection equipment, which can retrieve horizontal winds in the mesopause region based on the scatter echoes of the meteor trail. Meteor radar plays an important role in the study of global middle and upper atmosphere dynamics. All-sky least squares fitting is widely used in the wind retrieval algorithm based on meteor echoes. However, this algorithm is greatly affected by the quantity and quality of meteor echoes. Poor meteor echoes may cause large errors in retrieval wind results, which needs to be filtered and eliminated. Residual filtering is one of the typical filtering methods in previous studies, which eliminates echoes with excessive residuals by iterative operations. Nevertheless, due to multiple iterations and strict thresholds, residual filtering may screen out much more echoes and lead to uncertainties in the final retrieval winds. Especially when the number of echoes is limited, residual filtering cannot ensure the stability and reliability of the wind results. Therefore, this paper presents a new strategy to optimize the filtering method, which combines the advantages of the residual filtering and Root Mean Square Error (RMSE) filtering. First, the residual filtering threshold is relaxed without iterations to retain more echoes. Then, evaluates the overall data quality by the RMSE test to ensure the reliability of wind retrieval results. Based on the observational meteor echoes provided by a meteor radar at Yinchuan established by Wuhan University, we verified that the new strategy can effectively eliminate the anomalies but maintaining a higher data volume, which ensures the retrieval winds more reliable and stable. This study proposes a practical optimization scheme for echo data filtering in meteor radar wind retrieval. Our method can be used as an important reference for valid meteor echoes filtering in meteor wind retrievals.