A NEW WIND VECTOR RETRIEVAL ALGORITHM FOR OCEANSAT-2 ...

A NEW WIND VECTOR RETRIEVAL ALGORITHM FOR OCEANSAT-2 E.Fiorda, IEEE Student Member, M.Migliaccio, IEEE Senior Member, A.Montuori, IEEE Student Member, F.Nunziata, IEEE Student Member Università degli Studi di Napoli Parthenope, Dipartimento per le Tecnologie Centro Direzionale, isola C4 - 80143 Napoli ABSTRACT Wind field availability at mesoscale is fundamental in meteorological and climatological applications. On this purpose the active microwave scatterometer sensor has been designed and operated. Retrieval of ocean-surface wind vectors from scatterometer data is performed by using a proper geophysical model function (GMF). The GMF relates ocean surface backscatter at scatterometer scale to the wind vector. In simple terms, the GMF is related to the wind direction by a harmonic relationship and to the wind speed by a power-law model. The harmonic dependence of backscatter ocean wind direction causes multiple solutions of the scatterometer based wind vector. Accordingly a nontrivial inversion problem has to be solved. In this study, the retrieval scatterometer inversion chain is tested over Oceansat-2 look-alike data. Index Terms – Wind scatterometer, Wind-retrieval, Oceansat-2 1. INTRODUCTION Knowledge of surface wind fields over the sea is essential for a range of oceanographic, meteorological, and climate investigations, as well as for improving regional and global operational weather predictions. The surface wind field is a key variable in estimating the exchanges of momentum (kinetic energy) between the atmosphere and sea. Surface wind field is fundamental to estimate fluxes of heat longwave radiation, moisture across the interface, and for development and validation of coupled climate model integrations. The global ocean surface wind field is observable from space at near-mesoscale resolution. Measurement techniques using satellite-borne active microwave scatterometers have been developed tested and refined over the past three decades. Scatterometers are real aperture radar capable to perform a set of simultaneous normalized radar cross section (NRCS) measurements under different azimuth angles for each resolution cell at high radiometric accuracy. Oceansat-2 is India’s second satellite built for the study of oceans as well as the interaction of oceans and the atmosphere to facilitate climatic studies [1].

Oceansat-2 consists of three payloads: Ocean Colour Monitor (OCM), Ku-band pencil beam scatterometer and Radio Occultation Sounder for Atmospheric Studies (ROSA) (Fig.1).

Figure 1: Oceansat-2 stowed configuration viewed from +Yaw side (from ISRO website)

The Ku-band pencil beam scatterometer is an active microwave radar operating at 13.515 GHz. By using two offset feed at the focal plane of the antenna, two beams (Inner beam and Outer beam) are generated which will conically scan the ground surface. The inner beam operates with HH polarization and outer beam with VV polarization. Oceansat-2 satellite mainframe systems derive their heritage from previous IRS missions and launched by PSLV-C14 from Satish Dhawan Space Centre, Sriharikota on September 23, 2009. 2. THEORETICAL BASIS The inversion method is a point-wise procedure that minimize an objective function obtained with a least square approach [2]. The point-wise inversion procedure is applied over a data set which represents the frame, i.e. the geographical area observed by the scatterometer in whose nodes are associated the multiple measurements. Within the point-wise inversion procedure each node of the frame, corresponding to a resolution cell, is analyzed singularly to estimate the possible solutions. In the case of Oceansat-2, similarly to the SeaWinds case [3], measurement diversity is achieved by combining angular diversity (pencil-beam scanning) and polarization diversity (HH polarization for inner beam and VV polarization for outer beam).

filter is run for each cell in the field and the number of vectors that change is recorded. This process is then iterated until either no vectors change or a very small number of them do, ensuring that the chosen ambiguities form the best fitting wind field. 3. EXPERIMENTS

Figure 2: Scatterometer viewing geometry (from ISRO website)

Note that since a pencil-beam configuration is implemented, the GMF, once that the beam has been set, has no dependency on the incidence angle which is constant. As a result we have a semi-empirical accurate geophysical model function (GMF) symbolically referred as · at variance of different relative azimuth angles. In each frame node, the objective function that we have to minimize is: ,

∑

,

In this section, experiments, accomplished over SeaWinds mission data, are presented. Because Oceansat-2 data aren’t available and Seawinds has the same nearly technical characteristics, these data are used to validate our algorithm. Considering QuikSCAT acquisition geometry, there are two data product used: Level 2A and Level 2B. Level 2A consist of Surface Flagged Sigma-0’s and Attenuations, while Level 2B is Ocean Wind Vectors in a 25Km Swath Grid. Each analyzed file contains data for one full orbital revolution of QuikSCAT. These data are grouped by rows of 25km wind vector cells (WVC). Each row contains a total of 76 WVC values and corresponds to a single cross-track cut of the SeaWinds 1800 km wide swath. Below are illustrated our experiments.

(1)

is the Euclidean space objective function, is where the weight associated to each antenna measure, is the , is the NRCS value measured NRCS and modeled by GMF, U and are the free parameters to be estimated. Hereafter, since no official Oceansat-2 GMF has been made available and the sensor configuration is practically identical to the SeaWinds one, the GMF as specified in [4] and [5] has been considered. The function has several local minima and only one of them will be the solution, while the other will lead to ambiguities. To remove these ambiguities and then complete the retrieval procedure, a median filter approach is used [6]. The purpose of ambiguity removal is to select the most likely alias from the set of aliases generated during the estimation step. Ambiguity removal can be thought of as putting a puzzle together with each alias in each cell being a possible piece of the puzzle [7]. The goal is to choose the aliases so that the final wind field best matches the true wind field. The median filter is based on the principle of choosing the wind alias that minimizes the error between it and the wind vectors in a window surrounding it. These method take advantage of the fact that the wind is correlated from one cell to the next and the difference in wind direction from cell to cell should be quite small [7]. To implement the median filter on a wind field the field must first be initialized. Most often the wind field is initialized to all the first aliases since the first alias is most likely the correct one. Once the field is initialized, a median

(a)

(b) Figure 3: Ocean-surface wind-vector field derived from QuikSCAT data for March,11 2001 (a) and wind-retrieval using our procedure (b).

Data take 03/11 01/16 01/24

Wind speed mean Wind direction error (m/s) mean error (°) total analyzed cells = 2160 0.3623 2.5685 0.3910 1.0493 0.5026 6.1096 Table I: Results comparison

4. CONCLUSIONS (a)

The reconstruction algorithm implemented with, Oceansat-2 like data, have showed high performance as identified in previous experiments: the estimate of the wind-field reaches accuracies with much lower error (2 m/s speed and 20° direction) and then applied to the nominal requirements make it particularly attractive in the field of meteorology and climatology. The inversion procedure will be further tested when Oceansat-2 data are available. 5. ACKNOWLEDGEMENTS

(b) Figure 4: Ocean-surface wind-vector field derived from QuikSCAT data for January,16 2003 (a) and wind-retrieval using our procedure (b).

Thanks are due to ISRO (Indian Space Research Organisation), and D.G.Long for useful suggestions. 6. REFERENCES [1] “Oceansat-2 brochure”, available at http://www.isro.org/

(a)

[2] M.Migliaccio, M.Sarti, S.Marsili, “C-band Scatterometer and wind field retrieval”, Canadian Journal of Remote Sensing, vol.29, no.4, pp. 472-480, August 2003. [3] M.Migliaccio, A.Reppucci, “Wind vector retrieval using SeaWinds data”, Rivista italiana di telerilevamento, vol.35, pp.103-114, 2006. [4] B.S.Gohil, A.Sarkar, V.K.Agarwal, “A new algorithm for wind-vector retrieval from scatterometers” IEEE Geoscience and Remote Sensing Letters, vol.5, no.3, pp. 387-391, July 2008. [5] D.G.Long, private communication, 2010. [6] S.J.Shaffer,R.S.Dunbar,S.V.Hsiao,D.G.Long,“A median filter-based ambiguity removal algorithm for NSCAT”, IEEE Transactions on Geoscience and Remote Sensing, vol.29, no.1, pp.167-174, 1991. [7] S.L.Richards, “A field-wise retrieval algorithm for SeaWinds”, Department of Electrical and Computer Engineering, Brigham Young University, 1999.

(b) Figure 5: Ocean-surface wind-vector field derived from QuikSCAT data for January,24 2003 (a) and wind-retrieval using our procedure (b).