Data for: Attributes of CloudSat identified echo objects

This data set contains a collection of attributes associated with CloudSat
identified echo objects (or contiguous regions of radar/dBZ echo) from15
June 2006 till 17 January 2013. CloudSat is a NASA satellite that carries
a 94 GHz (3 mm) nadir pointing cloud profiling radar (CPR). CloudSat makes
approximately 14 orbits per day with an equator passing time of 0130 and
1330 local time. Echo objects were identified using CloudSat's
2B-GEOPROF product that includes 2D arrays (alongtrack x vertical) of the
radar reflectivity factor and gaseous attenuation correction. Also
included in the product is a "cloud mask" with values ranging
between 0 and 40 with higher values indicating a greater likelihood of
cloud detection. An EO was defined as a contiguous region of cloud mask
greater than or eaqual to 20, consisting of at least three pixels with
their edges and not merely their corners touching. Each echo object (EO)
is assigned multiple attributes. The geographic attributes include
minimum, mean, and maximum latitude and longitude, minimum and maximium
location along the CloudSat orbit track, and the underlying surface
altitude and land mask data, which allows the EOs to be catagorized as
occuring over land, sea, or the coast. The geometric attributes include
top, mean, and bottom height, width, and the total number of pixels within
the EO. Attributes describing the internal structure of the EO are also
available including the number of pixels and cells (i.e., group of pixels)
greater than 0 dBZ and -17 dBZ. Finally, the time of day of occurance was
also recorded to compare the statistics of EOs ocurring during the daytime
versus nighttime. In total, we identified 15,181,193 EOs from 15 June 2006
to 17 January 2013. After 17 April 2011, data were only collected during
the day due to a battery failure onboard CloudSat. Each attribute is
organized as a 1D array where the size of the array corresponds to the
number of EOs. This organization allows subsets of EOs to be easily
identified using simple "where" statements when writing code.
The attributes were used to identify cloud types and analyze global cloud
climatology according to season, surface type, and region (i.e., Riley
2009; Riley and Mapes 2009). The varability of EOs across the MJO was also
analyzed (Riley et al. 2011).