The combination of passive and active microwave satellite data products should give new and improved insight into the structure and development of sub-synoptic scale circulation systems in the Earth's atmosphere, as e.g. polar lows, comma clouds and tropical cyclones. The present study focuses on wind speeds in severe storm systems derived from microwave radiometers and altimeters in comparison with related products from the Envisat mission. Especially, the influence of precipitation on the accuracy of wind speed estimates is analysed through this combination technique. Preliminary results suggest only minor errors through undetected signal contamination by precipitation

Blechschmidt, A.

Graßl, H.

English

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COMPARISON OF SATELLITE DERIVED OCEAN SURFACE WIND SPEEDS AN DTHEIR ERROR DUE TO PRECIPITATIONA.-M. Blechschmidt and H. GraßlMeteorological Institute, University of Hamburg, Hamburg, GermanyABSTRACTThe combination of passive and active microwave satel-lite data products should give new and improved insightinto the structure and development of sub-synoptic scalecirculation systems in the Earth’s atmosphere, as e.g.polar lows, comma clouds and tropical cyclones. Thepresent study focuses on wind speeds in severe stormsystems derived from microwave radiometers and altime-ters in comparison with related products from the Envisatmission. Especially, the influence of precipitation on theaccuracy of wind speed estimates is analysed through thiscombination technique. Preliminary results suggest onlyminor errors through undetected signal contamination byprecipitation.Key words: Ocean surface wind speeds; rain attenuation;sensor intercomparison.1. INTRODUCTIONThe weather and climate of the extratropical latitudes aredominated by synoptic scale cyclones. Also mesoscalecyclonic systems of various dimensions and intensity areobserved over the extratropical oceans, some of which areof large impact on the weather and climate of the Euro-pean coastal regions and even continental Europe. Thesesystems include small and short-lived cyclones, knownas polar lows, which form poleward of the main baro-clinic zone over high latitude oceans. Polar lows are of-ten associated with strong winds and heavy precipitation,posing a serious danger to ships and coastal facilities.Because of their short life times and the remoteness ofthe genesis regions, not much is known about their phe-nomenology, origin and life cycle. Due to the lack ofmeteorological in-situ data, it can be argued that the mostreasonable approach for studies about sub-synoptic scalesystems is one that makes use of different satellite sen-sors. Especially the combination of passive and activemicrowave satellite data products should give new andimproved insight not only into the structure and devel-opment of these systems, but also into the accuracy ofsatellite retrieved meteorological parameters in extremeweather situations. The focus of this study is on colocatedwind speeds(u10) in severe storm systems including po-lar lows, atmospheric fronts and comma-clouds over theeastern North Atlantic Ocean derived from QuikSCAT,Jason-1, Envisat and HOAPS (Hamburg Ocean Atmo-sphere Parameters and Fluxes from Satellite Data). Theretrieval of satellite wind speeds in the presence of pre-cipitation is crucial and will be evaluated through thiscombination techique. Satellite based wind speeds andcolocation methods are described in section 2, followedby the results in section 3. First comparisons with EnvisatASAR wind speeds are given in section 4.2. SATELLITE WIND SPEEDS AND COLOCA-TION METHODS2.1. HOAPS-3The Hamburg Ocean Atmosphere Parameters and Fluxesfrom Satellite Data (HOAPS, http://www.hoaps.org) is acompletely satellite based climatology of precipitation,evaporation and all basic state variables needed for thederivation of these fluxes over the global ice free oceans.Except for the SST, all variables are derived from SSM/Ipassive microwave radiometers. The HOAPS-3 windspeed algorithm uses a neural network to derive windspeeds at 10 m height above the sea surface. It consistsof 3 layers: an input layer with 5 neurons (19V, 19H,22V, 37V, 37H), a hidden layer with 3 neurons and anoutput layer with one neuron (wind speed). The networkwas trained with a composite dataset of buoy measure-ments and radiative transfer simulations. The HOAPS-Sdataset, which is used in this study, contains all retrievedphysical parameters in the original SSM/I scan resolu-tion (that is 50 km for wind speed) for every individ-ual satellite. Horizontally polarized channels are moresensitive to the wind speed signal than vertically polar-ized channels, particulary at the SSM/I incidence angle[1]. Since surface roughness increases brightness tem-peratures, any phenomenon that roughens the ocean sur-face can result in erroneous wind speeds. Therefore windspeed is not accurate in heavy rain, and even light raincan degrade the signal. Additionally, virga and cloud wa-ter affect the SSM/I signal. Pixels containing substantialatmospheric contamination (rain) are filtered in HOAPS-3 to avoid misdetection of wind speeds. The detection is_____________________________________________________ Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)  Figure 1. Number of pixels in bins of 0.5 mm/d widthagainst rain rate resulting from 15 randomly selecteddays of scan data of all different SSM/I radiometers. Thedashed line shows the number of all pixels containingrain rates greater 0. The solid line shows the numberof pixels in which atmospheric contamination is detectedand which are flagged.done by brightness temperature thresholds for 19 and 37GHz channels. If one of these thresholds is exceeded thecorresponding pixel is flagged and wind values are notcalculated. Fig. 1 shows the efficiency of this procedure.The number of pixels in which atmospheric contamina-tion is detected and which are flagged is decreasing veryfast by several orders of magnitude with increasing rainrates, indicating the performance of the filter. For rainrates above 6 mm/h all pixels are flagged.2.2. Dual frequency altimetersThe Jason-1 and Envisat altimeters are two out of threemost recent dual-frequency altimeters which are cur-rently in space (the third one is TOPEX/Poseidon). Thesecond frequency is aimed at the determination of theionospheric electron content, which affects the radar sig-nal path delay. Launched by the end of 2001, Jason-1is the first follow-on to the TOPEX/Poseidon mission.The primary instrument is the Poseidon-2 altimeter whichoperates at 13.6 GHz (Ku band) and 5.3 GHz (C band).The second generation radar altimeter (RA-2), launchedin March 2002 onboard Envisat, emits pulses at Ku bandand S band (3.2 GHz). The Envisat and Jason-1 satel-lites carry microwave radiometers to correct the atmo-spheric path delay due to water vapor. Altimeter mea-surements are limited to nadir observations and are pri-marily dedicated to ocean topography. However, windspeeds are also estimated through relationships with theKu band backscatter coefficient. Additionally, significantwave height is included in the wind speed model func-tion for Poseidon-2. The main effect of rain on altime-ter data is the attenuation of the pulse through absorbingrain drops which increases with frequency [2]. Addition-ally, rain roughens the sea surface and changes its radarcross section. To identify pixels contaminated by rain,rain flags are assessed from a combination of microwaveradiometer liquid water and the difference between thebackscatter derived from the altimeter frequencies. Al-timeters provide high-resolution information of severalkilometers. Regarding the colocation of altimeter andHOAPS wind speeds every HOAPS pixel that lies withina circle of 22.5 km radius from the center of the altime-ter footprint is compared to the altimeter pixel. As forall other comparisons of satellite wind speeds presentedin this study the maximum time difference between colo-cated values is not larger than two hours.2.3. QuikSCATThe SeaWinds scatterometer was launched in June 1999on board the QuikSCAT satellite. SeaWinds collectsbackscattered power at Ku band via dual pencil beamscans at 46◦ and 55◦ incidence angles. Like the En-visat and Jason-1 altimeter, the SeaWinds scatterome-ter retrieves wind speeds through a relation with the Kuband backscatter coefficient. The influence of rain onQuikSCAT wind speeds is comparable to that on altime-ter data. However, the integration path through the at-mosphere is longer since QuikSCAT incidence angles aregreater than 45◦. This implies that atmospheric effects tothe Ku backscatter due to rain are more significant [3].For colocation with HOAPS wind speeds every HOAPSpixel that lies within a circle of 22.5 km radius fromthe center of the QuikSCAT footprint is compared to theQuikSCAT pixel.3. COMPARISON OF SATELLITE DERIVEDWIND SPEEDSScatterplots of satellite derived ocean surface winds fora region over the North Atlantic (40◦W to 20◦E longi-tude and 20◦N to 80◦N latitude) are calculated includingseveral comma cloud, atmospheric front and polar lowcases. A two-dimensional linear regression is appliedto quantify the coherence between the different datasets.The correlation coefficient is calculated and a t-test is ap-plied based on a level of significance ofα=0.05. The t-test shows that the results are significant in all cases pre-sented.Fig. 2 shows wind speed from the Envisat altimeteragainst that from HOAPS. There is a good agreementbetween both datasets resulting in a high correlation of0.949±0.005. The root mean squared error (rms) is 1.63m/s and the bias 0.804 m/s. Wind speeds reach valuesgreater than 15 m/s in most of the raining cases. Forthose wind speeds the agreement between HOAPS andthe Envisat altimeter is very good. The main result is thatthe rms and bias are smaller for the raining cases thanfor the the non-raining cases. The datapoints are muchmore spread for lower wind speeds, especially between7 m/s to 15 m/s. A closer look at the data indicates thatFigure 2. Envisat altimeter against HOAPS wind speedfor 1752 colocated wind speeds over the eastern NorthAtlantic Ocean. Cases with HOAPS rain rates greaterthan 0 mm/hr or Envisat altimeter rain flag eq 1 aremarked with red color. The dashed line is the refernceline (f(x)=x). The other two black lines result from a two-dimensional linear regression. The root mean squarederror (rms), the bias and variance of the error (var) aregiven for all datapoints, for the red datapoints (subscriptr) and the black datapoints (subscript nr). The correla-tion coefficient (cor) and the variance explained by thetwo-dimensional linear regression (varε) are also given.these are cases where the altimeter crossed atmosphericfronts, passed an ice-edge or has been close to the coast.The time difference between the HOAPS and the altime-ter satellite passes may lead to differences in wind speedsespecially in those cases. Additionally, the different reso-lutions of the remote sensing systems leads to deviationsin wind speeds. The sharp cut at 15 m/s may physicallybe related to the effect of sea foam which becomes dom-inant for values greater than 15 m/s. The two lines re-sulting from a two-dimensional linear regression showthat HOAPS seems to overestimate low and underesti-mate high wind speeds. This is also the case for the windspeed comparisons given below.The Jason-1 altimeter against HOAPS wind speed isshown in Fig. 3. The correlation between HOAPS andJason-1 altimeter wind speeds is 0.912± 0.007, whichis smaller than the correlation between HOAPS and theEnvisat altimeter wind speeds. The rms is 2.119 m/s andthe bias 1.013 m/s. The rms is larger for the raining thanfor the non-raining cases but the opposite is the case forthe bias. The agreement between the different datasets isbetter for wind speeds lower than 5 m/s than for higherones. In most of the raining cases the wind speed reachesvalues above 10 m/s. The largest deviations seem to beassociated with rain. But since those are only very fewFigure 3. Colocated wind speeds as in Fig. 2, butfor Jason-1 altimeter against HOAPS wind speed (2445cases).Figure 4. Colocated wind speeds as in Fig. 2, but forQuikSCAT against HOAPS wind speed (1399 cases).cases, the error due to contamination of the signal by pre-cipitation does not seem to be significant.The QuikSCAT versus HOAPS wind speed is presentedin Fig. 4. The correlation between HOAPS andQuikSCAT wind speeds is 0.920± 0.008. The windspeeds have a rms of 2.117 m/s and a bias of 0.133m/s. The raining cases are spread almost equally overall wind speed values, although there is a tendency to-wards winds peeds above 10 m/s like in the comparisonbetween HOAPS and the Jason-1 altimeter wind speeds.The rms and bias are higher in raining than in non-rainingcases. Some of the largest deviations between HOAPSand QuikSCAT wind speed are again associated with rainbut these are also few compared to the remaining cases.4. FIRST COMPARISONS WITH ENVISATASAR WIND SPEEDSIn the following colocated satellite wind speeds from adual polar low event, which is also included in the windspeed comparisons above, will be presented. In the morn-ing of 15 March 2005 two comma-shaped polar lowsdeveloped near the Norwegian coast (see Fig. 5). Thesouthern polar low propagated eastwards and reached theNorwegian coast in the evening of 15 March 2005. Forthis time an Envisat ASAR image is available allowingthe verification of the position of the cyclone center andthe determination of high resolution wind speeds (seeFig. 6). HOAPS and Jason-1 altimeter wind speeds areoverlaid on the Envisat ASAR wind speed. The time dif-ference between HOAPS and Envisat is about one and ahalf hour, but inspections of AVHRR images show thatthe polar low remained nearly stationary between the twosatellite observation times. The time difference betweenthe Jason-1 altimeter and the ASAR wind speeds is about10 minutes and therefore negligible. There is a high levelof agreement bewteen the several wind speed estimations.The increase in wind speed around the center of the polarlow is detected by all sensors.Figure 5. MERIS true-color image showing two polarlows on 15 March 2005 at 10:55 UTC near Norway.Fig. 7 shows HOAPS precipitation overlaid on ASARwind speed. This allows the detection of patterns in theASAR image that might result from rain. According to[4] the ASAR radar return can be affected by raindropsimpinging on the ocean surface and generating roughnessthat dampens the wind-generated capillary waves. Addi-tionally, rain can affect the radar signal propagation paththrough the atmosphere. The highest precipitation ratesof about 3 mm/h occur to the west of the eye of the polarlow. Since some of the HOAPS wind speed pixels at andnear the eye were flagged as being contaminated by atmo-spheric effects, there might be an influence on the ASARdata as well. This question must further be evaluated infuture research.Figure 6. Wind speed calculated from an Envisat ASARWS image with the CMOD-IFR2 algorithm for 15 March2005 at 20:45 UTC. The resolution of the ASAR windspeeds is 300 m. Overlaid are HOAPS wind speeds (blackencircled dots) and Jason-1 altimeter wind speeds (greyencircled dots).Figure 7. Wind speed calculated from Envisat ASAR asin Fig. 6 but now shown in shades of grey. Overlaid areHOAPS precipitation rates (black encircled dots).5. CONCLUSIONSWind speeds from active remote sensing systems havebeen compared to passive microwave data from HOAPSfor several comma cloud, atmospheric front and polar lowcases over the eastern North Atlantic Ocean. The analysishas shown that HOAPS overestimates low and underesti-mates high wind speeds compared to the wind speeds de-rived from active remote sensors. Satellite derived windspeeds in high latitude severe storm systems in the easternNorth Atlantic Ocean are associated with a rms of about2 m/s. The best agreement was found between HOAPSand Envisat altimeter wind speeds with a correlation of0.949± 0.005 and an rms of 1.630 m/s. Although unde-tected signal contamination by precipitation may result inlarge deviations between colocated wind speeds, the in-fluence of precipitation on HOAPS wind speed does notseem to be significant since those cases are only few com-pared to the remaining cases. Deviations in wind speedsbasically result due to different satellite observation timesand different resolutions of the remote sensors. The dif-ferent frequencies and algorithms used for the derivationof wind speed and different viewing angles further con-tribute to the wind speed deviations. First comparisonswith Envisat ASAR wind speeds for a polar low eventnear Norway show a high level of agreement between thedifferent data sets. HOAPS data may help to identify raincontaminated pixels in ASAR images.ACKNOWLEDGMENTSThanks goes to Dr. Jochen Horstmann from GKSS forprocessing the ASAR wind speed image with WISAR.The QuikSCAT and Jason-1 wind speed data were ob-tained from the Physical Oceanography Distributed Ac-tive Archive Center at the NASA Jet Propulsion Labo-ratory (http://podaac.jpl.nasa.gov). The Envisat data wasprovided by the European Space Agency.REFERENCES[1] Raytheon Systems Company (2000). SSM/I User’sInterpretation Guide.[2] Goldhirsh, J. & Rowland, J. R. (1982). A tutorial as-sessment of atmospheric height uncertainties for high-precision satellite altimeter missions to monitor oceancurrents.IEEE Trans. Geosci. Remote Sensing20(4),418-434.[3] Chan, S.H., Halterman, R.R. & Long, D.G. (2004).Cross validation of Jason-1 and QuikSCAT WindSpeeds. InProc. IGARSS’04 ’Geoscience and RemoteSensing Symposium’, IEEE International.[4] Katsaros, K.B., Vachon, P.W., Black, P.G., Dodge,P.P. & Uhlhorn, E.W. (2000). Wind fields from SAR:Could they improve our understanding of storm dy-namics?Johns Hopkins APL Technical Digest21, 86-93.

Comparison of satellite derived ocean surface wind speeds and their error due to precipitation

http://hdl.handle.net/21.11116/0000-000A-2813-4

Comparison of satellite derived ocean surface wind speeds and their error due to precipitation

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