DATA PROCESSING:

SeaWiFS data are freely available for scientific research purposes subject to prior approval by the SeaWiFS project and with a two-week embargo period. Level 1A Local Area Coverage (LAC) 1km resolution data were downloaded from the password protected Distributed Active Archive Center (DAAC) site at http://daac.gsfc.nasa.gov/data/dataset/SEAWIFS/. L1 data consists of at- spacecraft raw radiance counts with calibration and navigation information available separately in the data file. All satellite passes available from the HRPT (High Resolution Picture Transmission) station HDUN (Dundee University) between March and October 1997-2003 were ordered. Data prior to March and after October were very cloud-contaminated and the low incidence angle of solar radiation masked large parts of the study region. In addition the ancillary data files containing TOVS and EPTOMS ozone measurements and NCEP

limited by size constraints on the NASA ftp site (maximum 2GB per order) and file storage space at NOCS. After a delay of approximately 6 hours the data were available to download from the NASA ftp site (ftp:\\daac.gsfc.nasa.gov) as Hierarchical Data Format (.hdf) files.

NASA provide IDL-based software, SeaDAS, which enables users to process and display Level 1 to 3 SeaWiFS data (http://seadas.gsfc.nasa.gov). SeaDAS v4.0 has an interactive user-interface and allows the user to generate a Level 2 product from the Level 1 files supplied from the DAAC. L2 data consists of five normalized water-leaving radiances and seven geophysical parameters (including chlorophyll-a concentration: chl-a) derived from the radiance data. Processing to L2 with the SeaDAS software also outputs the L2 flags, which mark areas where confidence in the chl-a product is low. Each flag has a code denoting why the pixel has been flagged. The reasons for low confidence cover a wide range of atmospheric and geophysical effects, such as sun glint, too high a wind speed, ice and coccolithophore blooms. At this stage in the processing the ozone and meteorological data files are used to correct for atmospheric effects, such as light scattering and sun angles differing from the nadir. The calculation of chl-a uses the NASA OC4 algorithm:

4 10 ) 555 / ) 510 490 443 log(( ) * 3 * 2 * 1 0 ( 2 3 a C Rrs Rrs Rrs Rrs R R a R a R a a ₊ = > > = + + +

where Rrs is the remote sensing reflectance at a particular wavelength, C is the derived chlorophyll-a and a0 to a4 are constants calibrated to the SeaBASS in situ

chl-a dataset (see Section 3.2.2 for a discussion of SeaBASS). In the multi-band technique the maximum ratio between the three wavebands at 443, 490 and 510 nm is used as the numerator in the above equation. This method should improve estimation of chl-a at low concentrations and prevent saturation at concentrations of chl-a > 1.5 mgm-3 (O’Reilly et al., 1998).

The next stage is to project the L2 product onto a suitable reference grid. The navigation data are included so that land masks may be correctly placed. For this dataset a cylindrical projection was chosen and the region bounded by 55N-

66N, 44W-20W was extracted into an image 1000 x 1000 pixels, so that each pixel covered an area ~1km2. To carry out this procedure using the SeaDAS interface for the ~1000 images downloaded would have been extremely time consuming. In response Dave Poulter (Laboratory for Satellite Oceanography, NOCS) wrote an IDL script, SeaPiCK, which allowed the user to batch process L1 files to L2 projected files. This increased the efficiency of processing immensely and allowed each 2 week long batch of data to be processed in ~12 hours without any further input from the user.

The resulting hdf files contain mapped chl-a and L2 flag data, each a single swath from every satellite pass that was within range of the Dundee University receiving station. For each day up to 3 images were available, each covering a different part of the study region. Daily and 3-day composites of the data were created from the swaths. A further IDL script written by Dave Poulter created the composites by calculating the median, for each pixel, across all images where data for that pixel were available. 3-day means were only created if at least two out of the three days contained data. The script outputs a bitmap file with pixels on a digital colour scale from 0 (black) to 255 (white) with the land masses overlaid. To convert from digital number to chl-a the following equation must be applied: 100 )) 035 . 0 * (exp(D C=

where D = digital number and C = chl-a concentration.

A script was written to read the bitmap files into Matlab. The script allows the user to specify the latitude and longitude of the study region and the averaging interval required (e.g. into 0.5° x 0.5° boxes). The resulting images were found to be rather ‘speckled’ – that is they contained isolated pixels of unreasonably high chl-a values, probably due to cloud contamination. In an attempt to remove these spurious values pixels which lay outside 2 standard deviations of the mean chl-a value were set to NaNs (Not a Number – the Matlab designation for missing data). The data are then split into squares of size specified by the user and a mean value

for that box is calculated. Where sufficient data were available the chl-a data were interpolated – this bridged small data gaps, but could not fill in large areas affected by cloud. This script outputs a 3-D matrix of chl-a for each year of data. The matrix size is ((lat_start-lat_end)/size of box, (lon_start-lon_end)/size of box, number of weeks) e.g. for 55N-66N and 44W-20W split into 0.5° boxes for 365 days the matrix will be of size (22 x 48 x 365). This format allows easy

manipulation and plotting of the data and comparison with other datasets. Monthly means were also created from the daily files for March to October for 1998 – 2003.