Regrouping refers to the process of resampling the backscattering coefficients that are stored according to the observation sequence and the corresponding parameters of wind vector cell observation geometry. Regrouping is important in scatterometer data processing because it directly affects the final measurement accuracy. In August 2011

China launched its first operational satellite-borne scatterometer HSCAT. Thus

designing a suitable regrouping algorithm for accurately retrieving wind speed and direction data from HACAT observations is necessary. On the basis of the regrouping method for QuikSCAT and Shenzhou-4 multi-mode microwave remote sensing scatterometer

the present work has designed a business operational regrouping algorithm for HSCAT in accordance with its observation characteristics. This study offers a re-designed regrouping algorithm that includes two critical steps: (1) the generation of a ground grid and (2) the resampling of observation results. To simplify the resampling process

HSCAT adopted a convenient grid model along the sub-track swath

which centered on the nadir track

with a coordinate system specifying the location of a given point in terms of “longitude” parallel to the nadir track and “latitude” perpendicular to the nadir track. The origin of the nadir track grid is at the southernmost orbit latitude (rev boundary)

and the resolution is set to 25 km×25 km. After the sub-track grid was set

for each “egg” this study searched the nadir track point with the shortest spherical distance to the observed backscattering coefficient

and this shortest distance was used to calculate the cross-track indices (column_number) in the sub-track grid for the “egg”. Then

the distance between this nadir point and the nadir point at the southernmost orbit latitude (rev boundary) was calculated. Moreover

this distance was used to calculate the along-track indices (column_number)) in the sub-track grid for the “egg”. To improve calculation efficiency

we used coarse and fine search strategies when searching for the minimum spherical distance between the backscattering coefficient and the nadir track point. To verify the effectiveness of the proposed regrouping algorithm

we first analyzed the location distribution of wind vector cells and the spatial distribution of the total independent observation number of each wind vector cell in high-latitude and low-latitude areas. Results show that the wind vector cells are uniformly distributed

and a sufficient number of independently observed backscattering coefficients are included in each cell

which can satisfy the requirements of wind vector retrieval. Meanwhile

comparison of NDBC buoy wind and the retrieved wind shows that the standard deviations of wind speed and wind direction are 1.7 m s

–1

and 23.4° for the retrieved wind

respectively

indicating that the proposed regrouping algorithm can meet the requirements for high-quality sea surface wind field retrieval. This article proposes a business operational regrouping method for HSCAT. To simplify the resampling process

this method adopts the sub-track grid in along-track and cross-track directions with grid cells having approximately 25 km resolution. Meanwhile

the shortest spherical distance between each independent backscattering coefficient measurement (“egg”) and the nadir track point was used to calculate the cross-track indices (column_number) in the sub-track grid for each “egg” and the distance between the nadir point with “shortest spherical distance” and the nadir point at the southernmost orbit latitude (rev boundary) was used to calculate the along-track indices (column_number) in the sub-track grid for the “egg” In this way

the grid cells corresponding to the observed backscattering coefficients in the along-track/cross-track coordinate network were determined

and the resampling was completed. We used coarse and fine search strategies when searching for the minimum spherical distance between the backscattering coefficient and the nadir track point

effectively improving calculation efficiency. According to the post-regrouping location distribution of wind vector cells and the spatial distribution of the observation number of each wind vector cell

the proposed regrouping algorithm can achieve effective resampling of backscattering coefficient. After resampling

the acquired wind vector cells are uniformly distributed

and a sufficient number of independently observed backscattering coefficients are included in each wind vector cell. These results indicate that the backscattering coefficients can be resampled effectively.