CYGNSS Ocean Surface Wind Validation in the Tropics

Abstract

Surface wind plays a crucial role in many local/regional weather and climate processes, especially through the exchanges of energy, mass and momentum across the Earth’s surface. However, there is a lack of consistent observations with continuous coverage over the global tropical ocean. To fill this gap, the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016, consisting of a constellation of eight small spacecrafts that remotely sense near surface wind speed over the tropical and sub-tropical oceans with relatively high sampling rates both temporally and spatially. This current study uses data obtained from the Tropical Moored Buoy Arrays to quantitatively characterize and validate the CYGNSS derived winds over the tropical Indian, Pacific, and Atlantic Oceans. The validation results show that the uncertainty in CYGNSS wind speed, as compared with these tropical buoy data, is less than 2 m s-1 root mean squared difference, meeting the NASA science mission Level-1 uncertainty requirement for wind speeds below 20 m s-1. The quality of the CYGNSS wind is further assessed under different precipitation conditions, and in convective cold-pool events, identified using buoy rain and temperature data. Results show that CYGNSS winds compare fairly well with buoy observations in the presence of rain, though at low wind speeds the presence of rain appears to cause a slight positive wind speed bias in the CYGNSS data. The comparison indicates the potential utility of the CYGNSS surface wind product, which in turn may help to unravel the complexities of air-sea interaction in regions that are relatively under-sampled by other observing platforms.

Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones

“…For this reason, particularly in the tropical regions, the CCMP analysis corresponds more closely with the background field and thus is not improved by satellite measurements. CYGNSS provides wind measurements that are not, or only slightly, affected by rain at low wind speeds [17].…”

“…Our work focuses on validating all CYGNSS wind products that are currently available to the public in order to document the differences among them, illustrate advantages and shortcomings of each data product, and guide users in the optimal choice for their application/investigation. Most of these datasets have been independently validated by the team of investigators who developed them [13,[16][17][18][19], with analyses at the global scale, in collective statistics, at regional scales, and for sample tracks in storms. The main objective of this paper is to focus on wind observations within tropical cyclones.…”

Sea Surface Salinity and Wind Speed Retrievals Using GNSS-R and L-Band Microwave Radiometry Data from FMPL-2 Onboard the FSSCat Mission

“…Moreover, the GNSS-R technique has been proven to retrieve wind speed products, both for the UK TechDemoSat-1 [29] and the CyGNSS mission [30,31]. Several algorithms have been used to estimate WS from GNSS-R data, by looking at the Delay-Doppler Map (DDM) dispersion (as illustrated in Figure 2), computing the normalized bi-static radar cross-section, by looking to the leading edge slope of the waveform, or to other metrics, such as the Delay-Doppler Map average or variance, as shown in [32].…”

Daily Flood Monitoring Based on Spaceborne GNSS-R Data: A Case Study on Henan, China

“…Thus, compared with other remote sensing technologies (such as optical and active remote sensing varieties), CYGNSS observations for flood monitoring can meet the requirements of high dynamics. At present, spaceborne GNSS-R data are being used to conduct research on sea ice and sea altimetry [16][17][18], soil moisture [19][20][21], sea surface wind speed [22,23], flood monitoring [24,25], surface vegetation water content [26] etc. The papers [15,21] indicate that the reflectivity obtained from TDS-1 and CYGNSS data can be used to perceive surface water bodies, such as lakes and rivers.…”