# Neural Network Based Quality Control of CYGNSS Wind Retrieval
## 1 Introduction  
Global Navigation Satellite System-Reflectometry (GNSS-R) is an emerging trend in space borne ocean remote sensing due to its ability to greatly improve measurement frequency using reflected GNSS signals and also for the simplicity in design and requirements of its receivers. The UK-DMC mission was the first to demonstrate the sensitivity of GNSS signals to ocean winds [1, 2], this was followed by the TechDemoSat (TDS) mission [3] and now the Cyclone Global Navigation Satellite System (CYGNSS) mission is actively making continuous measurements across the globe with its constellation of small satellites [4]. The CYGNSS mission consists of 8 small spacecraft deployed in a LEO orbit (~510 km altitude and 35 deg inclination). Each spacecraft has a Delay Doppler Mapping Instrument (DDMI) that can map the signal power of GPS reflections from the Earth's surface onto a range of time delay and Doppler frequency shifts relative to the position and velocity of the GPS transmitters and the CYGNSS spacecraft [5]. These passive measurements, referred to as Delay Doppler Maps (DDMs), enable the individual spacecraft to act as a bistatic scatterometer which estimates the near surface ocean wind speed by measuring the bistatic radar cross-section at the specular reflection point. The near surface ocean wind speed retrieval by CYGNSS uses empirical Geophysical Model Functions (GMFs) developed for 2 measurement observables--the Normalised Bistatic Radar Cross-Section (NBRCS) and the Leading Edge Slope (LES), derived from the DDMs [5]. Wind speed is then estimated by inverse mapping of these observables to reference winds (NWP models and aircraft measurements) using the GMFs and optimally combining the two estimates using a minimum variance estimate of the wind speed [6]. The error in wind speed retrieval can come from different levels of measurement processing and it is important to identify and eliminate erroneous measurements in order to provide high quality observations for scientific applications. While previous spaceborne GNSS-R missions were primarily focused on technology demonstrations, CYGNSS attempts to provide an operational service to meet its science goals. Hence a high data quality is a top priority. At the engineering level, the major sources of error can be attributed to errors in estimation of the GPS transmit power, GPS antenna patterns, spacecraft pointing knowledge, and star tracker/science antenna boresight misalignment [7]. At the instrument level, possible errors include science antenna gain error, instrument noise power estimation error, calibration load temperature error, and Digital-Analog quantization error [8]. At the measurement level, errors can occur due to specular point geo-location errors, dependence of observables on other geophysical parameters such as wave age, swell etc., non-unique mapping from wind speed to observable, and error in interpolation in space and time for co-location with reference winds [9]. A number of quality control flags are already in place at different levels of the data processing in order for the science community to pick and choose data with quality requirements specific to their application. At Level 1, some of the major quality flags are star tracker attitude status and one-hertz status flags indicating if Milky Way or the Sun is in the zenith antenna field of view (FOV). The Level 1 quality flags also look out for spacecraft attitude errors, radio frequency interference (RFI), DDM noise floor errors, rapid rate of change of receiver temperature, and telemetry errors [10]. At Level 2, the quality flags look out for retrieval ambiguity, low Range Corrected Gain (RCG) and other data processing errors [11]. Despite such stringent quality flags in place, there remain occasional outlier samples with large discrepancies between the CYGNSS retrieved wind speed and reference validation winds (shown in Figure 1) To improve the data quality of CYGNSS, another layer of quality control is needed which can effectively identify and eliminate these outliers.