Yves Quilfen

Observation of tropical cyclones by high‐resolution scatterometry

Unprecedented views of surface wind fields in tropical cyclones (hereafter TCs) are provided by the European Remote Sensing Satellite (ERS) C band scatterometer. Scatterometer measurements at C band are able to penetrate convective storms clouds, observing the surface wind fields with good accuracy. However the resolution of the measurements (50x50 km 2) limits the interpretation of the scatterometer signals in such mesoscale events. The strong gradients of the surface wind existing at scales of a few kms are smoothed in the measured features such as the intensity and location of the wind maxima, and the position of the center. Beyond the ERS systems, the scatterometers on-board the ADEOS and METOP satellites, designed by the Jet Propulsion Laboratory and by the European Space Agency, respectively, will be able to produce measurements of the backscattering coefficient at about 25x25 km 2 resolution. A few sets of ERS-1 orbits sampling TC events were produced with an experimental 25x25 km 2 resolution. Enhancing the resolution by a factor of 2 allows location of the wind maxima and minima in a TC with a much better accuracy than at 50 km resolution. In addition, a better resolution reduces the geophysical noise (variability of wind speed within the cell and effect of rain) that dominates the radiometric noise and hence improves the definition of the backscattering measurements. A comprehensive analysis of the backscattering measurements in the case of high winds and high sea states obtained within TCs is proposed in order to refine the interpretation of the wind vector derived from a backscattering model that is currently only calibrated up to moderate winds (< 20 m/s) in neutral conditions. Observations of the TOPEX-POSEIDON dual-frequency altimeter are also used for that purpose. Patterns of the surface winds in TCs are described and characteristic features concerning asymmetries in the maximum winds and in the divergence field are discussed.

Sea Surface Salinity Observations from Space with the SMOS Satellite: A New Means to Monitor the Marine Branch of the Water Cycle

While it is well known that the ocean is one of the most important component of the climate system, with a heat capacity 1,100 times greater than the atmosphere, the ocean is also the primary reservoir for freshwater transport to the atmosphere and largest component of the global water cycle. Two new satellite sensors, the ESA Soil Moisture and Ocean Salinity (SMOS) and the NASA Aquarius SAC-D missions, are now providing the first space-borne measurements of the sea surface salinity (SSS). In this paper, we present examples demonstrating how SMOS-derived SSS data are being used to better characterize key land–ocean and atmosphere–ocean interaction processes that occur within the marine hydrological cycle. In particular, SMOS with its ocean mapping capability provides observations across the world’s largest tropical ocean fresh pool regions, and we discuss from intraseasonal to interannual precipitation impacts as well as large-scale river runoff from the Amazon–Orinoco and Congo rivers and its offshore advection. Synergistic multi-satellite analyses of these new surface salinity data sets combined with sea surface temperature, dynamical height and currents from altimetry, surface wind, ocean color, rainfall estimates, and in situ observations are shown to yield new freshwater budget insight. Finally, SSS observations from the SMOS and Aquarius/SAC-D sensors are combined to examine the response of the upper ocean to tropical cyclone passage including the potential role that a freshwater-induced upper ocean barrier layer may play in modulating surface cooling and enthalpy flux in tropical cyclone track regions.

Ocean surface wind fields estimated from satellite active and passive microwave instruments

This study examines the consistency of surface wind-speeds estimated from the European Remote Sensing Satellite (ERS-1) scatterometer, ERS-1 altimeter, and the special sensor microwave/imager (SSM/I). The goal is to combine these wind estimates to produce surface wind fields. With this in mind, a comparison with buoy wind measurements and comparison among the three sensors is performed. According to the in situ data, the rms errors of the three wind estimates are all within 2 m/s. The differences between the remotely sensed and buoy windspeeds are studied according to atmospheric and oceanic variables, and their impact is shown. A large data base is obtained from the comparisons among the three sensor winds. The rms values of the differences between the scatterometer and the altimeter and between the scatterometer and the SSM/I are 1.67 and 1.45 m/s, respectively. There is no global bias between the scatterometer and the SSM/I, but between the scatterometer and the altimeter windspeeds, the bias is about 0.30 m/s. Furthermore, it is shown that the difference between the scatterometer and the altimeter wind estimates is dependent on the significant wave height, while the difference between the scatterometer and the SSM/I winds is dependent on the integrated water vapor content. The comparison enables some corrections to be made for consistency and combining products. The use of combining scatterometer, altimeter, and SSM/I wind estimates is illustrated by two examples.

Haline hurricane wake in the Amazon/Orinoco plume: AQUARIUS/SACD and SMOS observations

At its seasonal peak the Amazon/Orinoco plume covers a region of 10 6 km 2 in the western tropical Atlantic with more than 1 m of extra freshwater, creating a near-surface barrier layer (BL) that inhibits mixing and warms the sea surface temperature (SST) to >29°C. Here new sea surface salinity (SSS) observations from the Aquarius/SACD and SMOS satellites help elucidate the ocean response to hurricane Katia, which crossed the plume in early fall, 2011. Its passage left a 1.5 psu high haline wake covering >10 5 km 2 (in its impact on density, the equivalent of a 3.5°C cooling) due to mixing of the shallow BL. Destruction of this BL apparently decreased SST cooling in the plume, and thus preserved higher SST and evaporation than outside. Combined with SST, the new satellite SSS data provide a new and better tool to monitor the plume extent and quantify tropical cyclone upper ocean responses with important implications for forecasting.

The ERS Scatterometer Wind Measurement Accuracy: Evidence of Seasonal and Regional Biases

Abstract A validation of European Space Agency (ESA) remote sensing satellite (ERS) scatterometer ocean wind measurements is performed using a formalism recently proposed for and applied to NASA scatterometer (NSCAT) and Special Sensor Microwave Imager (SSM/I) measurements. This simple analytical model relates scatterometer measurements to true winds, taking into account errors in the satellite winds as well as errors in the data used for reference. In this study, National Data Buoy Center (NDBC) buoy winds are the chosen reference. In addition, ECMWF analysis winds are used as a third data source to completely determine the errors via a triple collocation analysis. According to this development, the resulting wind speed error analysis indicates that ERS scatterometer estimates are negatively biased at light winds. This result differs from recent results determined using standard regression analysis. It is also shown that ERS and NSCAT measurement accuracies are comparable in an overall sense. This error ...

SMOS satellite L-band radiometer: A new capability for ocean surface remote sensing in hurricanes

The Soil Moisture and Ocean Salinity (SMOS) mission currently provides multiangular L-band (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, 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, we present SMOS data 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 (TB) were co-located and compared to H*Wind analysis. We find 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−1 and 0.7 K/m s−1 below and above the hurricane-force wind speed threshold (∼32 m s−1), 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 GFDL 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.

Impact of rain cell on scatterometer data: 1. Theory and modeling

The two scatterometers currently in operation, the Ku-band NASA Seawinds on the QuikScat satellite and the C-band AMI-Wind on the ERS-2 satellite, are designed to infer the ocean wind vectors from sea surface radar backscatter measurements. They provide excellent coverage of the ocean, and their wind products are of great value for ocean and meteorological communities. However, the presence of rain within scatterometer cells can significantly modify the sea surface backscatter coefficient and hence alter the wind vector retrieval. These perturbations can hamper the analysis of wind fields within atmospheric low-pressure systems or tropical cyclones. Rain perturbations result from volume scattering and attenuation by raindrops in the atmosphere as well as changes of sea surface roughness by impinging drops. For scatterometers operating at Ku-Band, attenuation and volume scattering are strong and one order of magnitude larger than at C-band. The wind retrieval will thus be less affected for the C-band AMI-Wind instrument than for the Ku-band Seawinds. A theoretical model, based on radiative transfer formulation including rain attenuation and scattering, has been developed to quantify the modification by rain of the measured backscatter and of the retrieved wind vectors. Changes in surface roughness, a complex phenomenon not yet fully understood and parameterized, is not considered here although it could be of importance for high rain rates. As a scatterometer cell covers several hundred square kilometers, inhomogeneities of rain within the cell will further modify the measured backscatter, particularly in case of small, intense precipitating rain cells. Using analytical rain cell models and constant wind fields, the effects of partial beam filling by rain is investigated. The model results show that Ku-band scatterometer data are greatly affected by rain and are extremely sensitive to the distribution of rain within scatterometer cells, i.e., to the distance between the rain cell center and the scatterometer resolution cell center. When the scatter from the sea surface is low, the additional volume scattering from rain will have a marked effect leading to an overestimation of the low wind speed actually present. Conversely, when the backscatter is already high (at high winds), attenuation by rain will reduce the signal causing an underestimation of the wind speed. The wind direction is modified in a complex manner and mainly depends on the rain distribution within the scatterometer cell. These results show that, especially at low and moderate wind speed, rain data such as the Special Sensor Microwave/Imager (SSM/I) rain fields are too coarse for correction of Normalized Radar Cross Section (NRCS) and that high-resolution rain data (such as the Tropical Rainfall Mapping Mission (TRMM) ones) are necessary. They also show that a good rain flagging is still an important issue for the operational use of Ku-band scatterometer data. A succeeding paper will present an example of application of the model for the correction of QuikScat data using TRMM rain data within a tropical cyclone.

The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions

[1] The physics of remote sensing sea surface measurements is still poorly understood under severe weather conditions. Wind vector algorithms are usually developed for non-precipitating atmospheres and for wind speeds less than 20 m/s. In this study, we analyze observations from the QuikSCAT Ku-band scatterometer collocated with the WindSat full polarimetric microwave radiometer to estimate the potential of these two instruments for sea surface wind retrieval under severe weather conditions. The Jason altimeter provides independent measurements of wind speed and rain rate for comparison purposes. The sensitivity of the radar cross-sections and brightness temperatures to the wind speed and direction is directly studied from the observations and compared with semi empirical models. This study clearly demonstrates that wind vector retrieval under extreme condition is feasible. Comparisons between QuikSCAT and WindSat coincident observations evidence a better sensitivity of the active mode to low and moderate winds and more sensitivity to high wind speeds in the passive mode. Although the WindSat observations are affected by water vapor, cloud, and rain, especially at and above 18 GHz, the measurements are sensitive to wind speed even at high wind speeds. Contrarily to the active instrument, there is no saturation at high winds. The sensitivity clearly tends to increase for winds above 15 m/s. For the wind direction, the amplitude of the azimuthal modulation in the active mode decreases with increasing wind speed, while it increases for the passive measurements. The development of specific wind retrievals under severe weather conditions is encouraged and a simple illustration is provided.

Calibration/Validation of an Altimeter Wave Period Model and Application to TOPEX/Poseidon and Jason-1 Altimeters

The altimeter radar backscatter cross-section is known to be related to the ocean surface wave mean square slope statistics, linked to the mean surface acceleration variance according to the surface wave dispersion relationship. Since altimeter measurements also provide significant wave height estimates, the precedent reasoning was used to derive empirical altimeter wave period models by combining both significant wave height and radar backscatter cross-section measurements. This article follows such attempts to propose new algorithms to derive an altimeter mean wave period parameter using neural networks method. Two versions depending on the required inputs are presented. The first one makes use of Ku-band measurements only as done in previous studies, and the second one exploits the dual-frequency capability of modern altimeters to better account for local environmental conditions. Comparison with in situ measurements show high correlations which give confidence in the derived altimeter wave period parameter. It is further shown that improved mean wave characteristics can be obtained at global and local scales by using an objective interpolation scheme to handle relatively coarse altimeter sampling and that TOPEX/Poseidon and Jason-1 altimeters can be merged to provide altimeter mean wave period fields with a better resolution. Finally, altimeter mean wave period estimates are compared with the WaveWatch-III numerical wave model to illustrate their usefulness for wave models tuning and validation.

Sensitivity of an oceanic general circulation model forced by satellite wind stress fields

Satellite wind and wind stress fields at the sea surface, derived from the scatterometers on European Remote Sensing satellites 1 and 2 (ERS-1 and ERS-2) are used to drive the ocean general circulation model (OGCM) “OPA” in the tropical oceans. The results of the impact of ERS winds are discussed in terms of the resulting thermocline, current structures, and sea level anomalies. Their adequacy is evaluated on the one hand by comparison with simulations forced by the Arpege-Climat model and on the other hand by comparison with measurements of the Tropical Atmosphere-Ocean (TAO) buoy network and of the TOPEX/Poseidon altimeter. Regarding annual mean values, the thermal and current responses of the OGCM forced by ERS winds are in good agreement with the TAO buoy observations, especially in the central and eastern Pacific Ocean. In these regions the South Equatorial Current, the Equatorial Undercurrent, and the thermocline features simulated by the OGCM forced by scatterometer wind fields are described. The impact of the ERS-1 winds is particularly significant to the description of the main oceanic variability. Compared to the TAO buoy observations, the high-frequency (a few weeks) and the low-frequency of the thermocline and zonal current variations are described. The correlation coefficients between the time series of the thermocline simulated by ERS winds and that observed by the TAO buoy network are highly significant; their mean value is 0.73, over the whole basin width, while it is 0.58 between Arpege model simulation and buoy observations. At the equator the time series of the zonal current simulated by the ERS winds, at three locations (110°W, 140°W, and 165°E) and at two depths, are compared to the TAO current meter and acoustic Doppler current profiler (ADCP) measurements. The mean value of the significant correlation coefficients computed with the in situ measurements is 0.72 for ERS, while it is 0.51 for the Arpege-Climat model. Thus ERS wind fields through the OGCM generate more realistic current variations than those obtained with Arpege climate winds, and they are particularly efficient in capturing abrupt changes (“wind bursts”) which may be important regarding ocean dynamics.