AVHRR irradiance data

The NASA Distributed Active Archive Center (DAAC) that specialises in polar applications, is the National Snow and Ice Data Center (NSIDC), located in Boulder, Colorado. NSIDC archives an enormous variety of polar field data, and also hosts satellite data products – some of which are “pathfinder” products. These data products have been rigorously developed with proper attention to the unique remote-sensing challenges in polar regions. The long-term datasets are designed for the convenience of polar researchers – being easy to access and use. Twice-daily images in polar stereographic grids can be ordered using the GISMO tool (see Table 5.1) and are made available for download. Table 5.1 includes basic details of the AVHRR Polar Pathfinder (AVHRR PPF) dataset for the Southern Hemisphere, which was sourced for this project. Further information on the AVHRR PPF data (Fowler et al. 2000) can be found in documentation at http://nsidc.org/data/docs/daac/nsidc0066_avhrr_5km.gd.html.

Chapter 5 – Data

Table 5.1: Overview table of AVHRR Polar Pathfinder (PPF) data (Fowler et al. 2000).

Category Description

Data format 1-byte and 2-byte big-endian integer grid format Spatial coverage Extends poleward from 53.2°S latitude

Spatial resolution 5 km Temporal

coverage From 24 July 1981 to 30 June 2005 (the dataset documentation lists dates for which data is missing – see http://nsidc.org/data/docs/daac/nsidc0066_avhrr_5km.gd.html)

Temporal

resolution Twice-daily composites approximately 0200 hours and 1400 hours for the Southern Hemisphere Grid type and

size Equal Area Scalable Earth-Grid (EASE-Grid) projection, 1605 pixels by 1605 pixels (centred on South Pole) Parameters Channel 1 Top of the Atmosphere (TOA) Reflectance

Channel 2 TOA Reflectance

Channel 3 TOA Brightness Temperature Channel 3A TOA Reflectance

Channel 3B TOA Brightness Temperature Channel 4 TOA Brightness Temperature Channel 5 TOA Brightness Temperature Clear Sky Surface Broadband Albedo Clear Sky Surface Skin Temperature Solar Zenith Angle

Satellite Elevation Angle

Sun-satellite Relative Azimuth Angle Surface Type Mask

Cloud Mask

Universal Coordinated Time (UTC) of Acquisition Data availability To order data use the GISMO tool, see link at

http://nsidc.org/data/gismo/index.html

The key parameter employed for the modelling in this project is the ‘surface type mask’ from which a surface UV albedo for the pixel was derived, based on whether it was open water or sea ice, and if sea ice, at what concentration (see Section 7.4.2 for an explanation of this process). The original intention was to use the ‘clear sky surface broadband albedo’ provided in the PPF dataset in the modelling calculations, but this idea was discarded for the following reasons. Firstly, the difficulty associated with discriminating between cloud and ice meant that the albedo in the PPF dataset might not necessarily have been that of the surface, but of clouds within the view of the satellite sensor instead. Secondly, insufficient research was found on which to base a reliable conversion from broadband to ultraviolet albedo (see Section 7.4.2 for details). Also, on occasions there were albedo values that were greater than 100%. This not only caused concern about the veracity of the albedo parameter overall, but it meant that these pixels did not display correctly in the plots. The derivation of the surface type mask was based on Special Sensor Microwave Imager (SSM/I) passive microwave brightness temperatures, regridded from the daily-averaged SSM/I Daily Polar Gridded Brightness Temperatures dataset to the 25 km EASE-Grid (see Figure 5.1). They were first partitioned into land (including ice sheets) or ocean. Over oceans, the NASA Team Sea Ice Algorithm (Fowler et al. 2000) was used to generate first- year and multi-year ice concentrations. For the purpose of the cloud detection procedure, all

Chapter 5 – Data

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ocean pixels with an estimated ice concentration greater than 0% were flagged as sea ice. In some cases, atmospheric effects and wind roughening of the ocean caused false ice concentration retrievals over open ocean. Such areas appeared as incorrectly-mapped areas of sea ice in the mask; however, for cloud detection, preliminary work suggests that it is important to avoid excluding areas with less than 15% true ice cover. Areas that consist of at least 50% multi-year ice are assigned the multi-year flag. The multi-year ice estimate is subject to considerable error and uncertainty, particularly during periods when surface melt is likely (Fowler et al. 2000). At such times, characteristic multi-year ice emission properties are partially or completely hidden, and melt-freeze metamorphosis or other changes in the snow cover on sea ice may cause first-year ice to appear as a fraction of multi-year ice (Fowler et al. 2000). Since thresholds were applied to the output from ice concentration, ice type, and snow depth algorithms, slight variations in the values yield a noisy appearance in the resulting binary-flag images, particularly in spring and summer due to algorithm limitations (Fowler et al. 2000). Table 5.2 describes the surface mask type (SMSK) cell contents. There is further information found in the code create_maps.pro (Appendix A) as to how the appropriate albedos were allocated to the different ice classes.

Chapter 5 – Data

Table 5.2: Table of surface mask type (SMSK) cell contents.

Surface Mask Type

Cell Value Description

10 Open water

20-29 Mostly first-year ice (range indicates total ice concentration in tens of percent; for example, a value of 20 indicates more first-year ice than multi-year ice with a total ice concentration between one and ten percent)

30-39 Mostly multi-year ice (range is same as above; however, multi-year ice is dominant)

40 Bare land

50 Snow-covered land 60 Ice sheet

For a test subset of 46 images, the plots of the ‘cloud mask’ provided in the PPF dataset were compared with plots of the classifications of cloud/ice by Williams et. al (2002) for summer and Borsche (2001) for spring. The decision was made to base the model on the latter algorithms as, on visual inspection, the surface classifications were more often close to surface types seen in plots of broadband surface albedo in the PPF dataset. Table 5.3 describes the cloud mask (CMSK) bit number values.

Table 5.3: Table of cloud mask (CMSK) bit number values.

Cloud Mask (CMSK) Version 3 Data (1994 to 2005) Bit

number Description Bit value

Bit 0 The least significant bit. Cloud mask from Cloud and _{Surface Parameter Retrieval (CASPR) single-day} algorithm (Fowler et al. 2000)

0 - clear 1 - cloud Bit 1 _{Cloud mask from updated CASPR multi-day algorithm} 0 - clear

1 - cloud Bit 2 _{Set if no valid data are present} 0 - valid data

1 - missing data