Spatial Variability of Wet Troposphere Delays Over Inland Water Bodies

Abstract

Satellite radar altimetry has enabled the study of water levels in large lakes and reservoirs at a global scale. The upcoming Surface Water and Ocean Topography (SWOT) satellite mission (scheduled launch 2020) will simultaneously measure water surface extent and elevation at an unprecedented accuracy and resolution. However, SWOT retrieval accuracy will be affected by a number of factors, including wet tropospheric delay—the delay in the signal's passage through the atmosphere due to atmospheric water content. In past applications, the wet tropospheric delay over large inland water bodies has been corrected using atmospheric moisture profiles based on atmospheric reanalysis data at relatively coarse (tens to hundreds of kilometers) spatial resolution. These products cannot resolve subgrid variations in wet tropospheric delays at the spatial resolutions (of 1 km and finer) that SWOT is intended to resolve. We calculate zenith wet tropospheric delays (ZWDs) and their spatial variability from Weather Research and Forecasting (WRF) numerical weather prediction model simulations at 2.33 km spatial resolution over the southwestern U.S., with attention in particular to Sam Rayburn, Ray Hubbard, and Elephant Butte Reservoirs which have width and length dimensions that are of order or larger than the WRF spatial resolution. We find that spatiotemporal variability of ZWD over the inland reservoirs depends on climatic conditions at the reservoir location, as well as distance from ocean, elevation, and surface area of the reservoir, but that the magnitude of subgrid variability (relative to analysis and reanalysis products) is generally less than 10 mm.

Potential of SWOT for Monitoring Water Volumes in Sahelian Ponds and Lakes

“…A complete error budget should also take into account errors expected from satellite orbit, signal delay from the troposphere, vegetation effects and wind effects on the radar backscattering from water [39,40]. Wet tropospheric delays on the radar return have been analysed in details by Mehran et al [26] over inland reservoirs of souhtwest US and have been found to be between 4 and 18 mm, depending on climatic conditions, elevation and surface area of the reservoir. Given the high content in the tropospheric water vapor during the monsoon season [25], tropospheric effects on the delay of the radar signal in the Sahel are supposed to be in the highest range of the values reported in [26].…”

“…The objective of the present study is to assess the potential of SWOT for monitoring water volumes in Sahelian ponds and lakes. The Sahelian region can be challenging for remote sensing given the poorly documented and highly variable values in surface characteristics (water roughness, soil type and soil moisture) impacting the radar backscattering coefficients [23,24] and the wide range of atmospheric moisture content [25] that can affect the propagation of electromagnetic waves in the atmosphere [26]. Simulations can be used to investigate some of these issues before launch.…”

Synthesis Analysis of SWOT KaRIn‐Derived Water Surface Heights and Local Cross‐Calibration of the Baseline Roll Knowledge Error Over Lake Baikal

“…Research on SWOT hydrology is still in pre-study and verification stages. Most of previous studies have focused on rivers (Biancamaria et al, 2016;Durand et al, 2008Durand et al, , 2014Durand et al, , 2016Durand et al, , 2020Frasson et al, 2017;Gejadze and Malaterre, 2017;Häfliger et al, 2019;Hagemann et al, 2017;Mehran et al, 2017;Moramarco et al, 2019;Munier et al, 2015;Oubanas et al, 2018;Pavelsky et al, 2014;Yoon et al, 2012), and relatively few studies have focused on lakes (Bergeron et al, 2020;Biancamaria et al, 2010Biancamaria et al, , 2016Grippa et al, 2019;Lee et al, 2010). Lee et al (2010) took lakes in three high-latitude areas as research objects, simulated SWOT lake level heights using radar altimetry and in situ data and then combined the heights with image data to study the changes in water storage.…”