Gerrit Holl MSc
Work for the SAT-group
- 15 August 2009 – ...: PhD. Student
- 1 September 2008 – 30 June 2009: Master Student
Please refer to my CV in PDF format, including my research interests.
- E-mail: firstname.lastname@example.org
- Phone: +46 980 79193
c/o SRT, avdelningen för Rymdteknik
Luleå Tekniska Universitet
981 28 Kiruna
SRT, avdelningen för Rymdteknik
Luleå Tekniska Universitet
981 92 Kiruna
ArticlesFull list of publications. See also my CV.
Full text is available here: Microwave and infrared remote sensing of ice clouds: measurements and radiative transfer simulations
This licentiate thesis considers the combination of multiple instruments for remote sensing of the Earth atmosphere from space. The primary focus is on remote sensing of atmospheric ice. Ice clouds are important for the Earth’s radiation budget, but their properties are difficult to measure and therefore poorly known. A better quantification of ice clouds is needed to improve global climate models. This thesis introduces the reader to the subject and describes how to combine measurements and radiative transfer simulations in an attempt to improve our understanding. A major part of this work is the development of a toolkit to find co-incident measurements, or collocations, between any pair of down-looking satellite sensors. Firstly, this toolkit is used to collocate passive microwave and thermal infrared sensors on meteorological satellites with the Cloud Profiling Radar on CloudSat. With the resulting collocated dataset, the Ice Water Path (IWP) signal in passive thermal radiation is studied and an improved IWP retrieval is presented. The toolkit is also used to better characterise the bias between different copies of passive microwave radiometers on-board polar-orbiting operational satellites. For the Atmospheric Radiative Transfer Simulator (ARTS), version 2, an optimised frequency grid for infrared broadband simulations is shown to be applicable for cloudy simulations. This frequency grid can and will be used to study the IWP signal in thermal infrared radiances. An outlook on a comparison between collocations and simulations is presented in the thesis.
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 μm) to around 183 GHz (≈ 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° 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.