Typhoon Haiyan (known in the Philippines as Typhoon Yolanda) slammed into the Philippines less than 2 weeks ago with sustained winds of 310 kilometers per hour, making it one of the strongest tropical storms to date and the second-deadliest Philippine typhoon on record. Haiyan originated from an area of low pressure in the Federated States of Micronesia on November 2. Tracking generally westward, environmental conditions favoured tropical cyclogenesis and the system developed into a tropical depression the following day. After becoming a tropical storm and attaining the name Haiyan at 0000 UTC on November 4, the system began a period of rapid intensification that brought it to typhoon intensity by 1800 UTC on November 5. By November 6, the Joint Typhoon Warning Center (JTWC) assessed the system as a Category 5-equivalent super typhoon on the Saffir-Simpson hurricane wind scale; the storm passed over the Palau shortly after attaining this strength.
Figure 1: SMOS retrieved surface wind speed [km/h] along the eye track of super typhoon Haiyan from 4 to 9 Nov 2013.
SMOS intercepted the typhoon several times along its track. We selected only those passes where the signal was well detected and not too contaminated by RFI or land masses. As illustrated by Figure 1, this let one pass on the 4 as Haiyan was still a Tropical Storm, two on the 6th Nov (with the morning pass capturing only a small portion of the typhoon), one on the 7 prior landing towards Philippines and one interception on the 9, just before it passed over Vietnam. Passes on the evening of the 6 and during the 7th morning were close in time to the maximum intensity reached by that super storm (reached on the evening of the 7th).
As illustrated by Figure 2 top panel, the estimated excess brightness signal (First stokes parameter/2) due to surface roughness and foam-formation processes under the cyclone on the 7th morning overpass (i.e., after correcting for atmosphere, extra-terrestrial sources, salinity and temperature contributions) reached a record value of 41 K. To put such value in perspective of other natural oceanic signals, we plotted together the Tb jump measured during the passage of Hurricane Category 4-5 Igor in 2010, which was only 22 K! In contrast, global changes of surface salinity (32-38 pss) and temperature (0°C-30°C) only modify the Tb by ~5 K. So we believe such signal is very likely a natural extreme of sea surface emission at L-band over the oceans.
Figure 2: Top: North-south section trough the Haiyan Typhoon showing the change of residual brightness temperature (Th+Tv)/2 reconstructed from SMOS data at longitude of 130.05°E on the 7 Nov 2013 at 09:15Z Typhoon (black). The Blue curve is showing an equivalent section through the Igor Category 4 hurricane in 2010. The red line is illustrating the range of brightness temperature variation expected on earth due to sea surface salinity and temperature changes. Bottom: surface wind speed deduced from the excess brightness temperature.
Application of the wind-speed bi-linear retrieval algorithm that we derived in 2012 based on the established relationship between surface wind speed estimates during IGOR and the excess brightness temperature, we obtained the wind speed module shown in Figure 2, bottom. One can easily see that around the cyclone eye, wind speeds largely exceed the 64 knots threshold for typhoons within a more than 50 km radius. The spatial resolution of SMOS however does not allow to resolve the detailed wind speed structure around the eye. The maximum wind estimated from SMOS reaches 142 knots !
Figure 3: Maximum sustained 1 minute wind speed estimated during Haiyan Typhoon. From SMOS data (black filled dots) compared to Advanced Dvorak Technic (ADT=blue diamond), CIMSS (yellow filled dots), SATCON (red) and Best Track from NHC (cyan). Note the empty circle correspond to the SMOS measurements for the 11/06 morning for which only a small portion of the cyclone signal was intercepted. Maximum 10 minutes wind speed deduced from SMOS algorithm were multiplied by 1/0.93, adopting the conversion factor proposed in (Harper et al., 2010) between one minute winds and 10 min winds.
Given the spatial resolution of SMOS, the wind speed measured is more equivalent to a 10 minute sustained wind than to a 1 minute one, traditionally used by forecasters in the US. Using a 0.93 conversion factor from 1 mn to 10 mn winds (Harper et al., 2010), 1 minutes sustained winds can be estimated from SMOS. The evolution of the maximum sustained wind speed deduced from SMOS is compared to other estimates in Figure 3. SMOS estimate compares well with standard methods. The Advanced Dvorak Technique (ADT) uses longwave-infrared, temperature measurements from geostationary satellites to estimate tropical cyclone (TC) intensity. This step-by-step technique relies upon the user to determine a primary cloud pattern and measure various TC cloud top parameters in order to derive an initial intensity estimate. It continues to be the standard method for estimating TC intensity where aircraft reconnaissance is not available (all tropical regions outside the North Atlantic and Caribbean Sea), however it has several important limitations and flaws. The primary issue centers upon the inherent subjectivity of the storm center selection and scene type determination proceedures. Secondly, learning the Dvorak Technique and its regional nuances and adjustments can take a significant time to master. SMOS winds in TC will be produced operationally next year so as to be ingested by numerical weather forecast model within 6 hours from acquisition. In regions not too polluted by RFI, this new impressive example shows again that SMOS new information shall well complement existing satellite observation to better forecast TC intensification and evolution.