IFREMER EMPIRICAL ROUGHNESS MODEL Joe Tenerelli, CLS, Brest, France, November 4, 2010

To derive the empirical roughness model, we consider ascending and descending passes over the central South Pacific Ocean. We remove from the brightness temperatures all contributions except for roughness emission, transform (Tx,Ty) near the ground track into the surface basis and then bin all the transformed brightness temperatures into 5 deg incidence angle bins and 1 m/s wind speed bins.

Looking for a Significant Wave Height Impact…

Solid curves: ascending passes Dashed curves: descending passes Green curves: 2-scale solution Black curves: SSA-SPM

Solid curves: ascending passes Dashed curves: descending passes Green curves: 2-scale solution Black curves: SSA-SPM

Solid curves: ascending passes Dashed curves: descending passes Green curves: 2-scale solution Black curves: SSA-SPM

New vs Old Empirical Roughness Model

Use of empirical roughness model removes large bulleyes in the hovmoller bias plots.

UPDATE OF SMOS ASCENDING AND DESCENDING PASS BIAS TRENDS

Here we have extended the analysis of bias between descending and ascending pass (Tx+Ty)/2 up to present. We find that descending pass brightness temperatures continue to be higher than those for ascending passes. This trend continues despite the significant reduction of the impact of scattered galactic noise on the bias. Most of the descending-ascending bias appears to be in Tx and not in Ty.

This is one example of the system temperature plots we have shown before but extended to the end of October Note the large increase in the difference between descending and ascending pass temperatures in October.

This figure shows contribution of flat surface reflected celestial sky noise on descending pass brightness (Tx+Ty)/2. Note that we are now leaving the time period of large contribution for descending passes.

This figure shows contribution of flat surface reflected celestial sky noise on ascending pass brightness (Tx+Ty)/2. Note that we are now leaving the time period of large contribution for descending passes.

This figure shows contribution of scattered surface reflected celestial sky noise to the descending-ascending pass brightness temperature biases in Tx and Ty (averaged over a large range of latitudes in the central Pacific Ocean). At the end of October the contribution is back to where it was in early July.

Despite this reduction in the galactic contribution to the descending-ascending bias, we are not seeing a corresponding reduction in the descending-ascending bias in the SMOS brightness temperatures. Descending-ascending pass bias in Tx remains quite strong at about 1 K.

Tropical cyclones are clearly evident in SMOS brightness temperature maps. Here we have removed all geophysical contributions to brightness temperature except roughness emission. HURRICANE DANIELLE HURRICANE EARL 75 kt 982 mb 100 kt 947 mb 115 kt 942 mb 90 kt 955 mb

Tropical cyclones are clearly evident in SMOS brightness temperature maps. Here we have removed all geophysical contributions to brightness temperature except roughness emission. HURRICANE DANIELLE HURRICANE EARL 75 kt 982 mb 100 kt 947 mb 115 kt 942 mb 90 kt 955 mb

HURRICANE IGOR IMAGED BY SMOS AT NEAR PEAK INTENSITY 125 kt 935 mb 110 kt 935 mb

Hurricane Earl was imaged by SMOS twice as it weakened from a category 3 to a category 1 hurricane.

Hurricane Igor was imaged by SMOS as a category 4 (Sep 15), category 3 (Sep 17) and category 1 (Sep 19) hurricane in mid-September.

SMOOTHING THE HWIND ANALYSES BY THE MEAN SMOS SYNTHETIC BEAMWEF WEF

DERIVING THE EMPIRICAL TROPICAL CYCLONE SURFACE WIND SPEED MODEL FOR SMOS To derive an empirical relationship between residual SMOS total power and surface wind speed we consider the best available HWIND matchups in terms of collocation time, distance from land, and range of wind speed. Up to this point two HWIND analyses for Igor near its peak intensity provide the best opportunity for deriving the empirical model. Unfortunately, the data for the analyses were obtained about a day apart with the SMOS overpass occuring midway between them, and in this time period the storm was beginning to weaken. To arrive at an approximate wind field for deriving the model we take the average of the two HWIND anayses and we recognize that some error may be introduced in this averaging. We then smooth the temporally averaged HWIND analysis by an average SMOS synthetic beam (the SMOS synthetic beam weighting function as projected onto the earth varies somewhat with position in the field of view). HURRICANE IGOR: SMOS RESIDUAL BRIGHTNESS HURRICANE IGOR: AVERAGE OF SUCCESSIVE HWIND ANALYSES AS APPROXIMATION TO WIND FIELD AT 09 UTC SEPTEMBER 17 AVERAGE SUCCESSIV E HWIND FIELDS

EXPANDED VIEW OF AVERAGE HWIND SURFACE WIND FIELD, SMOOTHED BY THE SMOS SYNTHETIC BEAM, USED TO DERIVE WIND MODEL FOR SMOS. THE BLACK CIRCLE SHOWS THE DOMAIN WITHIN WHICH ANALYSIS WAS USED TO DEVELOP THE SMOS ROUGHNESS MODEL

Here is the portion of the HWIND wind field used to derive the roughness model:

Below is a cumulative distribution function for the average HWIND analysis wind field within 300 km of the storm center:

Here is the corresponding dwell line averaged SMOS brightness temperature total power (Tx+Ty)/2:

And here is the cumulative distribution function of SMOS total power within 300 km of storm center as interpolated onto the same grid.

We derive the roughness model by forcing the wind and brightness temperature fields to have the same distribution within 300 km of storm center.

Comapring this hurricane roughness model (valid about about 20 m/s) with the model derived outside of hurricanes (valid below about 20 m/s), we see that they do not match up smoothly around 20m/s. (Tx+Ty) increases about 1 K per m/s (Tx+Ty) increases about 1 K per m/s

Sensitivity of the first Stokes parameter to a 1 psu increase in SSS: Roughly 1 K/psu in (Tx+Ty) in warm water Roughly 0.5 K/psu in (Tx+Ty) in cold water

Applying the empirical model for hurricanes, we obtain the following wind field for Hurricane Igor on September 17 based upon the SMOS brightness temperatures:

This wind field can be compared to the following averaged and smoothed HWIND analysis:

CROSS SECTIONS OF SURACE WIND SPEED THROUGH HURRICANE EARLHWIND WEF SMOS

CROSS SECTIONS OF SURACE WIND SPEED THROUGH HURRICANE IGORHWIND WEF SMOS

Atmospheric Impact on L-band Brightness Temperatures emission brightness can reach 4 K at nadir attentuation reaches about 1% at nadir typical atmospheric vapor and temperature profiles

Atmospheric Impact on L-band Brightness Temperatures typical atmospheric vapor and temperature profiles nadir Reduction in brightness temperature increases to 2% at larger incidence angles

At L-band precipitation attenuates the brightness by an amount that depends upon rain rate and path length. The white box outlines the typical rain rates and depths encountered in the atmosphere. In hurricanes rain rates may attain 100 mm/h in rain bands and in the eyewall. But even in this case attenuation only attains about 2%. Hurricane rainbands Typical atmospheric conditions

At L-band precipitation attenuates the brightness by an amount that depends upon rain rate and path length. The white box outlines the typical rain rates and depths encountered in the atmosphere. In hurricanes rain rates may attain 100 mm/h in rain bands and in the eyewall. But even in this case attenuation only attains about 2%. Negligibla attenuation in typical atmospheric conditions Hurricane rainbands

Based on a typical atmospheric droplet spectrum, the emission brightness temperatures at L-band can reach 2 K in hurricane rainband. There is significan uncertainty in this estimate, however, since hurricane rainbands often contain graupel and other forms of precipitating particles not accounted for in the MPM model used here. Negligible emission in typical atmospheric conditions K emission within hurricane rainbands