Circumventing rain‐related errors in scatterometer wind observations

Abstract

Satellite scatterometer observations of surface winds over the global oceans are critical for climate research and applications like weather forecasting. However, rain‐related errors remain an important limitation, largely precluding satellite study of winds in rainy areas. Here we utilize a novel technique to compute divergence and curl from satellite observations of surface winds and surface wind stress in rainy areas. This technique circumvents rain‐related errors by computing line integrals around rainy patches, using valid wind vector observations that border the rainy patches. The area‐averaged divergence and wind stress curl inside each rainy patch are recovered via the divergence and curl theorems. We process the 10 year Quick Scatterometer (QuikSCAT) data set and show that the line‐integral method brings the QuikSCAT winds into better agreement with an atmospheric reanalysis, largely removing both the “divergence bias” and “anticyclonic curl bias” in rainy areas noted in previous studies. The corrected QuikSCAT wind stress curl reduces the North Pacific midlatitude Sverdrup transport by 20–30%. We test several methods of computing divergence and curl on winds from an atmospheric model simulation and show that the line‐integral method has the smallest errors. We anticipate that scatterometer winds processed with the line‐integral method will improve ocean model simulations and help illuminate the coupling between atmospheric convection and circulation.

Diurnal Convection‐Wind Coupling in the Bay of Bengal

“…For the tandem mission wind observations, we first compute the surface wind divergence δ as described in Kilpatrick and Xie () and then fit δ to the diurnal harmonic at each grid cell in the Bay of Bengal, where t is the local time of day (h) of the observation. We determine the harmonic coefficients a and b via linear regression of the tandem mission data to equation ; the amplitude of the diurnal harmonic is .…”

“…Because our focus is the wind variability that is coupled to diurnal rainfall, we utilize the “line integral fill holes” (LIFE) technique to recover the surface wind divergence in rainy patches. Kilpatrick and Xie () demonstrated that LIFE can recover much of the convergence signal in rainy areas, bringing scatterometer wind fields into better agreement with reanalyses. In this study, the LIFE technique increases data coverage in the Bay of Bengal by 6–14% for QuikSCAT and 2–10% for SeaWinds; the resulting number of scatterometer‐derived surface divergence observations used in this study is shown in Figure .…”

Wind Limits on Rain Layers and Diurnal Warm Layers

“…To some extent, the presence of RLs and DWLs can be detected by comparing the uppermost Argo or mooring measurements of S and T with satellite‐derived SSS or SST (Anderson & Riser, ; Boutin et al, ; Drushka et al, ; Kawai & Wada, ). However, scatterometer‐ and radiometer‐based measurements of U 10 , SSS, and SST are contaminated by precipitation and their 25‐ to 100‐km scale footprints can also be too coarse spatially and temporally (e.g., updating only a few times per day) to resolve the atmospheric mesoscale footprint of rain (Kilpatrick & Xie, , ; Kummerow et al, ).…”

Factors influencing the skill of synthesized satellite wind products in the tropical Pacific

“…This can have a significant impact on wind speeds in the tropical oceans where surface currents can be of comparable magnitude to the surface winds. The quality of satellite wind retrievals from some sensors (e.g., the Ku-band sensors) is sensitive to rain, with an increased rain rate related to decreased accuracy [e.g., Atlas et al, 2011;Yu and Jin, 2012] and even spurious spatial derivatives like wind stress curl [e.g., Milliff et al, 2004;O'Neill et al, 2015;Kilpatrick and Xie, 2016].…”