# Validating CYGNSS Wind Speeds with Surface-Based Observations and Triple Collocation Analysis
## 1 Introduction  
Reflectometry from global navigation satellite systems (GNSS-R) provides high temporal sampling of global ocean mean wind speeds to monitor storm events [1]. Navigation satellites (such as GPS) already broadcast cloud-penetrating signals that scatter directly off the ocean surface, allowing for the measurement of surface conditions in circumstances where visible and infrared signals are blocked. This makes it suitable for monitoring large-scale rapid changes in surface roughness, with many applications including tropical cyclone (TC) monitoring over the ocean or flood-extent on land [2, 3, 4, 5, 6, 7]. The use of GNSS-R for ocean wind-speed monitoring was first demonstrated in 2003 [8] to be followed by numerous national [9, 10], and private ventures [11, 12]. Some full reviews of recent GNSS-R advances are available in [13, 14]. Table 1 lists the current GNSS-R products, with many of these platforms not being publicly available. NASA's Cyclone GNSS (CYGNSS) mission is the exception, being publicly available and operating continually since 2016 [18]. Moored buoys near the equator have been used to validate CYGNSS wind-speed estimates lower than 20 m/s, with aircraft [19] and numerical weather prediction (NWP) models [20] being used for validation at higher wind regimes. To the best of our knowledge, the only study to validate CYGNSS using surface-based observations during a TC was [21]. These methods result in CYGNSS ocean high wind speeds being validated almost exclusively near the continental US. Since CYGNSS is known to have reduced performance at higher wind speeds [19], there is a growing need to broaden the benchmarks of validation to include more extreme events outside the continental US. To extend surface-based validation to other areas of the globe, weather stations on coral reefs or coastlines can be used to validate GNSS-R for TCs and mid-latitude storms. The mid-latitudes are more likely to experience higher winds that near the equator [22], and a greater number of high wind speed events are needed to provide validation [23]. There are limitations to using coastal surface-based observations to validate GNSS-R wind speed products. Measuring ocean roughness in coastal environments can be significantly different than in open ocean environments due to the influence of local topography, shallower water, and GNSS signals scattering from the land and water simultaneously [21]. Coastal hourly anemometer averages miss the extreme local gusts but provide a more reliable average of spatial variability [24]. This is beneficial, as the spatial resolution of GNSS-R and radiometers can be in the tens of kilometres [25]. One of the aims of this paper is to generalise the approach for regions with coastal anemometers instead of buoys. Coastal anemometer measurements are not the only way to increase sampling of high wind speed events. Gridded comparisons allow for greater spatiotemporal coverage and may be a closer comparison between two wind estimates. Each of these reference datasets (remote sensing, reanalysis, or NWP) has its own errors and uncertainties, which incentivizes the consideration of multiple references [26]. If two datasets provide a coincidental (similar time and location) measurement of meteorological parameter then GNSS-R can provide a third set of observations and enable triple-collocation analysis (TCA) [27]. TCA is more desirable than a single reference comparison, as it recognises errors in any reference dataset. It provides an error value to an approximated truth value--root mean square error, as opposed to root mean square _difference_. By making assumptions about the nature of the independence, stationarity, and linearity of errors in the datasets, TCA is able to yield a metric of correlation and error to an unknown truth value [28]. TCA has already been used for CYGNSS soil moisture products [29] and for developing an AI algorithm for TCs winds [29], but not as a validation for GNSS-R ocean winds.