Enhanced baseline determination for formation flying LEOs by relative corrections of phase center and code residual variations

Formation flying Low Earth Orbiters(LEOs)are important for implementing new and advanced concepts in Earth observation missions.Precise Baseline Determination(PBD)is a prerequisite for LEOs to complete specified mission targets.PBD is usually performed based on space-borne GNSS data,the relative corrections of phase center and code residual variations play crucial roles in achieving the best relative orbit accuracy.Herein,the influences of antenna Relative Phase Centre Variations(RPCVs)and Single-Difference(SD)Melbourne-Wu¨bbena(MW)Combination Residuals Variations(SD MWVs)on PBD are studied.The methods were tested using flight data from Gravity Recovery And Climate Experiment(GRACE)and GRACE Follow-On(GRACE-FO).Results showed that the maximum values for RPCVs and SD MWVs were 14 mm and 0.32 cycles,respectively.Then,the RPCVs correction significantly enhanced the baseline accuracy;the K-Band Ranging(KBR)measurement consistency improved by 30.1%and 37.5%for GRACE and GRACE-FO,respectively.The application of SD MWVs further improved the accuracy and reliability of PBD results.For GRACE,the ambiguities fixing success rate increased from 85.1%to 97.9%and a baseline consistency of 0.57 mm was achieved for the KBR measurements.It was found that the correction of both RPCVs and SD MWVs reduced the carrier phase observation minus computation residuals from double-difference ionosphere-free combination.In addition,in-flight data processing demonstrated that RPCVs and SD MWVs estimations for the current period could be used for the previous and subsequent periods.

Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method——A Case Study of a Mesoscale Convective System

Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVarrh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVarrh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs;thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVarrh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.