# On-Board Wind Scatterometry
## 5 Summary and Discussion  
In this research, an on-board wind scatterometry environment and data processing chain were established and tested in the simulation system. The simulation system was established by on-board processing modules and PCs. Data acquisition and distribution were simulated from PCs. In addition to traditional on-board data collection and processing steps, ground product generation procedures were realized on-board. Newly implemented modules were composed of a FPGA, DDR, and a  
Figure 12: Cost of calculation vs. accuracy.  
GPU assembled into different motherboards. They can function for center control, I/O flow control, program execution, data swapping, and storage. Based on the setting of the on-board environment, modifications were made to the traditional scatterometer wind generation chain. Data packing flows were reduced before wind inversion, and LUTs were developed for accelerating calculation while avoiding iterations in the traditional chain. The results of experiments using CFOSCAT L0 data showed a comparable accuracy of on-board results taking reference from ground products in geolocation, NRCS derivationwith RMSE less than 0.93 m, \(-\)0.53 dB. The RMSE for wind speed is 0.17 m/s and stdev. in wind direction is \(-\)0.06\({}^{\circ}\). An analysis of compromising accuracy for shorter processing time was achieved via simplifications of LUTs that had been established for the on-board processing chain. Results indicated that as LUTs are more simplified, random noise is more likely to be introduced and deteriorate accuracies. At the same time, decreases in time-consumption were not obvious. The most time-consuming procedure for the whole processing is at the observing step, which is dependent on scatterometer observation systems. The existing processing time, including observations was about 10.2 min for the most time-consuming WVC cases, which is much faster than existing ground processing chains. With next-generation satellite information transformation networks, the proposed on-board system and method can cover the global ocean's surface and make a good complement to existing satellite-observing-ground-processing-distributing chain for RT applications. Further research should explore the possibility of more complex wind direction ambiguity removal methods with NWP as background information, as well as the possibility of developing rain flags for processing in an on-board environment.  
[PERSON], [PERSON], and [PERSON] conceived the main idea. [PERSON] and [PERSON] performed the experiments. The manuscript was written by [PERSON], and it was improved by [PERSON], [PERSON] and [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work was supported by funds in part by the National Key Research and Development Program of China under Grant 2017 YFB0502800 and 2017 YFB0502802. The authors also want to thank the China National Space Administration (CNSA), Centre National d'Etudes Spatiales (CNES) for funding the CFOSAT project and the National Satellite Ocean Application Service (NSOAS) of China for providing data products.  
The authors declare no conflict of interest.