The project around the scientific exploitation of the SMOS data to infer surface wind speed in stormy conditions have started in 2012 with the SMOS+STORM and SMOS+STORM evolution feasability projects and later evolved into an operational service: the SMOS Wind Data Service which produces Near Real Time wind speed from SMOS since mid-2018. These two steps and evolution are described herebelow:
THE "SMOS+ STORM "and "SMOS+STORM EVOLUTION" PROJECT (2012-2016)
Microwave Emission from the rough Sea Surface in Stormy conditions
The European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular L-band (electromagnetic frequency of 1.4 GHz) 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, the SMOS measurements offer unique opportunities to complement existing ocean satellite high wind observations in tropical cyclones and severe weather that are often erroneous in such conditions. The physical basis for surface wind speed retrievals in extreme weather from passive microwave radiometers involves emission from a rough, foam-covered sea surface. The sea state within tropical cyclones is complex (see picture above) and varies according to the storm sectors, but in the region where the wind speeds exceed tropical storm force (>17 m/s ~34 knots), breaking waves generate extensive foam patches and deep bubble layers. Foam patches are associated with high emissivity at microwave frequencies. The foam horizontal coverage and thickness extension as wind speed increases towards hurricane force and the associated emissivity increase are the basic principles for wind retrievals from radiometers. This information can be used as a means of remotely measuring surface wind speeds in hurricanes from airborne, or spaceborne, microwave radiometers. The Step Frequency Microwave Radiometer (SFMR) operating at C-band (4-8 GHz), which is the US/National Oceanic and Atmospheric Administration (NOAA)'s primary airborne sensor for measuring tropical cyclone surface wind speeds (Uhlhorn et al., 2003; 2007), is based on this principle.
Photograph of the sea surface during a hurricane (Beaufort Force 12) taken from a NOAA ‘‘Hurricane Hunter’’ aircraft (courtesy Black et al., 1986).
The objective of this project is to exploit the identified capability of SMOS satellite Brightness Temperatures acquired at L-band to monitor wind speed and whitecap statistical properties beneath Tropical Cyclones and severe Extra Tropical storms. Such new capability at the core of the project was recently demonstrated during the SMOS+ STORM Feasibility ESA-STSE project ran between January 2012 and concluded in September 2013. The primary aim of the study was to establish if SMOS could retrieve meaningful surface wind speed in tropical cyclones and storms. This was successfully demonstrated by analysing SMOS data over the category 4 hurricane IGOR that developed in September 2010 [Reul et al. JGR, 2012].
Artist View of SMOS satellite
Without correcting for rain effects, the wind-induced components of SMOS ocean surface brightness temperatures were co-located and compared to observed and modelled surface wind speed products. The evolution of the maximum surface wind speed and the radii of 34, 50 and 64 knots surface wind speeds retrieved from SMOS were shown to be consistent with hurricane model solutions and observation analyses.
Sea Surface Wind Speed in units of meter per seconds retrieved from SMOS data during the passage of Hurricane Igor North of the Antilles in September 2010.
The SMOS sensor is thus closer to a true all-weather ocean wind sensor with the capability to provide quantitative and complementary surface wind information of great interest for operational hurricane intensity forecasts. The methodology developed and validated on Igor was also successfully applied to several other important storms such as the Hurricane Sandy in 2012 and the Super Typhoon Hayian that devastated the Phillipines in 2013. Consistent comparisons between SMOS winds and SFMR transect-flight data were found in Sandy. The initial results from the SMOS+ STORM Feasibility study showed great promise. The Scientific Roadmap produced by the SMOS+ STORM Feasibility study listed the following next steps:
1. Consolidate the physical understanding of L-band emission characteristics and the physical processes that control its signal dynamics to solidify the theoretical framework required to improve the quality of SMOS derived high-wind speed products.
2. Systematically produce a large ensemble of TC Storm cases and associated validation data that can be used to improve the SMOS retrieval algorithm and product robustness.
3. Disseminate SMOS+ STORM products to the scientific and operational research user community to develop a user base that can nurture and exploit new information from SMOS.
A follow-on project called SMOS+STORMS Evolution started in April 2014 for a period of 2 years and was funded by ESA again under the support to science element program (Technical Officer: Craig Donlon). It aimed at targeting the above listed next steps. More precisely, the project had one overall aim which was to Demonstrate the performance, utility and impact of SMOS L-band measurements at high wind speeds over the ocean during Tropical and Extra-Tropical storm conditions.
The seven specific objectives to be addressed within the SMOS+ STORM Evolution project were:
1) Improve and consolidate our theoretical understanding of the L-band signal response and physical properties that can be inferred over the ocean during the passage of Tropical Cyclone (TC) and Extra-Tropical Cyclone (ETC) systems.
2) Consolidate, evolve, implement and validate the STSE SMOS+ STORM feasibility project Geophysical Model Function (GMF) and retrieval algorithm for high wind speed conditions.
3) Systematically produce and validate L-band SMOS high wind speed products with uncertainty estimates/flags for ETC and TC conditions over the entire SMOS Mission archive.
4) Develop, implement and validate new blended multi-mission oceanic wind speed products with uncertainty estimates incorporating SMOS+STORM Evolution L-Band measurements at high-wind speeds for TC and ETC events.
5) Generate a global database of TC and ETC events over the ocean surface and characterize each event using diverse Earth Observation and other observations in synergy.
6) Improve our understanding and parameterization of ocean-atmosphere coupling and mixed-layer dynamics for ETC and TC cases.
7) Demonstrate the utility, performance and impact of SMOS+ STORM Evolution products on TC and ETC prediction systems in the context of maritime applications.
In particular, five years of SMOS L-band brightness temperature data intercepting a large number of tropical cyclones (TCs) were analyzed. The storm-induced half-power radio-brightness contrast (ΔI) was determined and is defined as the difference between the brightness observed at a specific wind force and that for a smooth water surface with the same physical parameters. ΔI can be related to surface wind speed and has been estimated for ~ 300 TCs that intercept with SMOS measurements. ΔI, expressed in a common storm-centric coordinate system, shows that mean brightness contrast monotonically increases with increased storm intensity ranging from ~ 5 K for strong storms to ~ 24 K for the most intense Category 5 TCs. A remarkable feature of the 2D mean ΔI fields and their variability is that maxima are systematically found on the right quadrants of the storms in the storm-centered coordinate frame, consistent with the reported asymmetric structure of the wind and wave fields in hurricanes. These results highlight the strong potential of SMOS measurements to improve monitoring of TC intensification and evolution. An improved empirical geophysical model function (GMF) was derived using a large ensemble of co-located SMOS ΔI, aircraft and H*WIND (a multi-measurement analysis) surface wind speed data. The GMF reveals a quadratic relationship between ΔI and the surface wind speed at a height of 10 m (U10). ECMWF and NCEP analysis products and SMOS derived wind speed estimates are compared to a large ensemble of H*WIND 2D fields. This analysis confirms that the surface wind speed in TCs can effectively be retrieved from SMOS data with an RMS error on the order of 10 kt up to 100 kt. SMOS wind speed products above hurricane force (64 kt) are found to be more accurate than those derived from NWP analyses products that systematically underestimate the surface wind speed in these extreme conditions. Using co-located estimates of rain rate, we showed that the L-band radio-brightness contrasts could be weakly affected by rain or ice-phase clouds and further work is required to refine the GMF in this context.
Examples of SMOS L-band radio-brightness temperature contrasts ΔI [K] measured for tropical storms (a, b, c: 35 ≤ U10 ≤ 63 kt), Category 1 TC (d, e, f: 64 ≤ U10 ≤ 82 kt), Category 2 TCs (g, h, i: 83 ≤ U10 ≤ 95 kt), Category 3 TCs (j, k, l; 96 ≤ U10 ≤ 113 kt) and Category 4 TCs (m, n, o: 114 ≤ U10 ≤ 135 kt) on the SSWS. Note that the color-scale range is 0–12 K for TS and Category 1, 0–15 K for Categories 2 to 3 and 0–18 K for Category 4 on the SSWS. Each panel represents a domain of about 1000 km width centered on the TC eye. The pink dotted curves show the storm 6-hourly best track and the black arrow indicates the storm main propagation direction but not its motion speed. After Reul et al., 2016.
The following animation provides a graphical view of the study motivation and outcomes:
The study results are also summarized in the SMOS+STORM evolution project brochure, accessible here:
The findings of the SMOS+ STORMS Evolution project were then discussed with the scientific community at the “International Workshop on Measuring High Wind Speeds over the Ocean” that was held at the UK MetOffice in Exeter (UK) on 15-17 November 2016. Results presented during the workshop highlighted the added value that L-Band observations could bring in the context of the available datasets (workshop proceedings available here).. L-Band measurements from passive sensors (e.g. SMOS, SMAP) have shown good sensitivity to high surface wind speed over ocean, as confirmed by validation results presented during the workshop for extreme Tropical Cyclone events and in peer reviewed papers [e.g., see http://www.smosstorm.org/Document-tools/Publications].
Based on those results, the user community has expressed its interest for a systematic data generation of such innovative data products in near real time for TC and ETC prediction and monitoring systems in the context of maritime applications and Numerical Weather Prediction operational centres activities. In particular, during the WMO 9th International Workshop on Tropical Cyclones (IWTC-9), Honolulu, Hawaii, USA, 3-7 December 2018, it has been recognized (see WMO report available here) that in the last four years there has been significant changes in the tropical cyclone community’s ability to analyze and diagnose tropical cyclones, which are impacting research understanding and forecast operations. To highlight some of the recent progress, three sub topics are briefly discussed in this report. These subtopics include new and existing methods to estimate TC surface wind structure, the next generation geostationary satellites for TC monitoring, and new developments and science using aircraft-based reconnaissance. Recommendation 4 from this report is: "Encourage the use and evaluation of wind fields from L-band radiometers (SMOS and SMAP) for determining intensity and 34-, 50, and 64 kt radii in TC. (for Forecasters and Researchers)"
THE SMOS WIND DATA SERVICE
Considering the above mentioned interest from users for L-band surface wind speed over ocean, ESA has decided to implement an operational service to provide, in near real time (NRT), surface wind speed over ocean derived from SMOS brightness temperature measurements. The so-called "SMOS NRT wind data service" now provide NRT data as described here
The work is funded by ESA and conducted by a consortium between IFREMER : Institut Français pour la Recherche et l’Exploitation de la MER, Laboratory of Oceanography from Space, France and the French R&D company OceanDataLab
Combined SMOS and SMAP bring regular and consistent observations on the wind structure in storms to help in TC and associated wave and storm surge forecasting as well as to complement available data to operation and WMO warning centers.
Reul, N., J. Tenerelli, B. Chapron, D. Vandemark, Y. Quilfen, and 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
Reul Nicolas, Chapron Bertrand, Zabolotskikh E., Donlon C., Quilfen Yves, Guimbard Sebastien, Piolle Jean-Francois (2016). A revised L-band radio-brightness sensitivity to extreme winds under tropical cyclones: The 5 year SMOS-Storm database . Remote Sensing Of Environment , 180, 274-291 . Publisher's official version : https://doi.org/10.1016/j.rse.2016.03.011 , Open Access version : https://archimer.ifremer.fr/doc/00324/43542/
Organising Committee of the Workshop (2017). International workshop on measuring high wind speeds over the ocean. 15-17 november 2016, Exeter. Proceedings . https://archimer.ifremer.fr/doc/00374/48546/
Reul Nicolas, Chapron Bertrand, Zabolotskikh E., Donlon C., Mouche Alexis, Tenerelli Joseph, Collard F., Piolle Jean-Francois, Fore A., Yueh S., Cotton J., Francis P., Quilfen Yves, Kudryavtsev V. (2017). A new generation of Tropical Cyclone Size measurements from space . Bulletin Of The American Meteorological Society , 98(11), 2367-2386 . Publisher's official version : https://doi.org/10.1175/BAMS-D-15-00291.1 , Open Access version : https://archimer.ifremer.fr/doc/00376/48758/
Bourassa Mark A., Meissner Thomas, Cerovecki Ivana, Chang Paul S., Dong Xiaolong, De Chiara Giovanna, Donlon Craig, Dukhovskoy Dmitry S., Elya Jocelyn, Fore Alexander, Fewings Melanie R., Foster Ralph C., Gille Sarah T., Haus Brian K., Hristova-Veleva Svetla, Holbach Heather M., Jelenak Zorana, Knaff John A., Kranz Sven A., Manaster Andrew, Mazloff Matthew, Mears Carl, Mouche Alexis, Portabella Marcos, Reul Nicolas, Ricciardulli Lucrezia, Rodriguez Ernesto, Sampson Charles, Solis Daniel, Stoffelen Ad, Stukel Michael R., Stiles Bryan, Weissman David, Wentz Frank (2019). Remotely Sensed Winds and Wind Stresses for Marine Forecasting and Ocean Modeling . Frontiers In Marine Science , 6(443), 28p. Publisher's official version : https://doi.org/10.3389/fmars.2019.00443 , Open Access version : https://archimer.ifremer.fr/doc/00511/62312/