AUTHOR
Holl, Gerrit

DEPARTMENT
Space Science, Kiruna

SUMMARY
Remote sensing satellites can roughly be divided in operational satellites and scientific satellites. Generally speaking, operational satellites have a long lifetime and often several near-identical copies, whereas scientific satellites are unique and have a more limited lifetime, but produce more advanced data. An example of a scientific satellite is the CloudSat, a NASA satellite flying in the so-called "A-Train" formation with other satellites. Examples of operational satellites are the NOAA and MetOp meteorological satellite series.

CloudSat carries a 94 GHz nadir viewing radar instrument measuring profiles of clouds. The NOAA-15 to NOAA-18 and MetOp-A satellites carry radiometers at various frequencies ranging from the infrared (3.76 micrometer) to around 183 GHz (approximately 1.6 mm). The full range is covered by the High Resolution Infrared Radiation Sounder (HIRS) and the Advanced Microwave Sounding Units (AMSU-A and AMSU-B). On newer satellites, AMSU-B has been replaced by the Microwave Humidity Sounder (MHS) with nearly the same characteristics. Those instruments scan the atmosphere at angles from approximately -50 to +50 degrees perpendicular to the ground track.

The large amount of data from operational satellites is interesting to the scientific community, particularly when combined with measurements from a scientific satellite. The degree project focuses on this combination and consists of two parts:

* The first part of the project involves searching for collocations between the CloudSat radar and one of the NOAA or MetOp-A instruments. A collocation between two instruments is defined to occur when both look at the same place at the same time (within pre-set thresholds). This has been done with software developed by the student.

* Those collocations are then used to find the relation between the radiances and physical data (such as Ice Water Path (IWP)) derived from CloudSat measurements. For the tropical ocean, this relation has been compared with data from models. Additionally, an artificial neural network has been trained to retrieve IWP.

ISSN 1653-0187 / ISRN LTU-PB-EX--09/029--SE / NR 2009:029