SMOS satellite data and high winds

Soil Moisture and Ocean Salinity (SMOS) is the European Space Agency’s water mission, an Earth Explorer Opportunity Mission belonging to the Living Planet Program. It was launched in November 2009. It aims to provide global and regular observations of soil moisture and sea surface salinity, which are crucial variables to understand and predict the evolution of the water cycle on our planet. 

As high wind observations are very often contaminated by heavy rain and clouds, Reul et al. (2012) show that the SMOS satellite L-band radiometer measurements present a unique opportunity to study the mesoscale evolution of surface winds and whitecap statistical properties under hurricanes and severe storms, and to complement existing active and passive observation systems. 

The SMOS mission currently provides multi-angular L-band brightness temperature images of the Earth. Because upwelling radiation at 1.4 GHz is significantly less affected by rain and atmospheric effects than at higher microwave frequencies, these new SMOS measurements offer unique opportunities to complement existing ocean satellite high wind observations that are often contaminated by heavy rain and clouds. To illustrate this new capability, SMOS data are presented over hurricane Igor, a tropical storm that developed to a Saffir-Simpson category 4 hurricane from 11 to 19 September 2010. Thanks to its large spatial swath and frequent revisit time, SMOS observations intercepted the hurricane 9 times during this period. Without correcting for rain effects, L-band wind-induced ocean surface brightness temperatures were co-located and compared to H*Wind analysis. The L-band ocean emissivity dependence with wind speed appears less sensitive to roughness and foam changes than at the higher C-band microwave frequencies. The first Stokes parameter on a 50 km spatial scale nevertheless increases quasi-linearly with increasing surface wind speed at a rate of 0.3 K/(m/s) and 0.7 K/(m/s) below and above the hurricane-force wind speed threshold (32 m/s), respectively. Surface wind speeds estimated from SMOS brightness temperature images agree well with the observed and modeled surface wind speed features. In particular, the evolution of the maximum surface wind speed and the radii of 34, 50 and 64 knots surface wind speeds are consistent with hurricane model solutions and H*Wind analyses. The SMOS sensor is thus closer to a true all-weather satellite ocean wind sensor with the capability to provide quantitative and complementary surface wind information of interest for operational hurricane intensity forecasts.

(a) Superimposed contours of the wind-excess L-band first Stokes brightness temperature parameter estimated from SMOS data during the 2010 hurricane Igor evolution from September 11 to 19. Contours are ranging from 4 K to 20 K by steps of 1 K. (b) Superimposed contours of the surface wind speed temporally interpolated at SMOS acquisition time from GFS/GFDL hurricane model and (c) H*WIND analysis. Contours are ranging from 15 m/s to 50 m/s by steps of 2.5 m/s. (d) Maximum sustained surface winds Vmax along Igor track from the National Hurricane Center Best Track ATCF system. The white dots indicate the location of the hurricane eye center at the SMOS acquisition time. From Reul et al. (2012)

 

SMOS passes over Igor for which rain rate estimates are available from other spaceborne sensors at an acquisition time less than half an hour from SMOS ones. (left) SMOS wind-excess first Stokes brightness temperature parameter Delta(Th + Tv)/2 in Kelvins. (a) 20:54 Z 11 Sept, (c) 21:16 Z 13 Sept, (e) 09:18 Z 15 Sept, (g) 10:05 Z 19 Sept. (right) Rain rate estimates in mm/h from (b) TRMM/ TMI 20:51 Z 11 Sept, (d) WindSat 21:16Z 13 Sept, SSMIS/F17: (f) 09:40Z 15 Sept and (h) 10:30 Z 19 Sept. The magenta dotted curve indicates the ATCF best-track. From Reul et al. (2012)

 

More information in

Reul, N., J. Tenerelli, B. Chapron, D. Vandermark, Y. Quilfen, Y. Kerr (2012), SMOS satellite L-band radiometer : a new capability for ocean surface remote sensing in hurricanes, J. Geophys. Res., 117, C02006, doi : 10.1029/2011JC007474.