Development and validation of a multilayer cloud detection algorithm for the airborne demonstrator

139 Pages·2008·12.34 MB·English

Development and validation of a
multilayer cloud detection algorithm
for the airborne demonstrator of the
direct detection Doppler lidar on
ADM-Aeolus
Stephan Kox
Ludwig-Maximilians-Universit¨at Mu¨nchen
Meteorologisches Institut Mu¨nchen
Dezember 2008

Development and validation of a multilayer cloud detection
algorithm for the airborne demonstrator of the direct detection
Doppler lidar on ADM-Aeolus
Stephan Kox
Diplomarbeit
durchgefu¨hrt am
Deutschen Zentrum fu¨r Luft- und Raumfahrt
im Rahmen eines Projektes der
Europa¨ischen Raumfahrtagentur
vorgelegt von
Stephan Kox
aus Brunnthal
Mu¨nchen, den 19.12.2008
Erstgutachter: Dr. Oliver Reitebuch
Zweitgutachter: Dr. Matthias Wiegner
Contents
1 Introduction 1
1.1 Impact of Clouds on Climate and Climate Change . . . . . . . . . . . . 2
1.2 Cloud Climatologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Cloud Observations with Spaceborne Lidars 5
2.1 Lidar Principle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Doppler Wind Lidar . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 ADM-Aeolus - ALADIN . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.4 ICESat - GLAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.5 CALIPSO - CALIOP . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3 Cloud Detection with the ALADIN Airborne Demonstrator 33
3.1 ALADIN Airborne Demonstrator - A2D . . . . . . . . . . . . . . . . . 33
3.1.1 Transmitter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.1.2 Telescope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.1.3 Receiver . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.2 A2D - Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2.1 Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.2 Clouds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.3 Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3 Cloud Detection from Mie Spectrometer . . . . . . . . . . . . . . . . . 49
3.3.1 Signal-to-Noise Ratio . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.2 Scattering Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
vi CONTENTS
3.3.3 Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3.4 Simulated Signals and Minimum Detectable Cloud Optical Depth 59
3.3.5 Observed Signals and Multilayer Cloud Detection . . . . . . . . 65
3.4 Cloud Detection from Rayleigh Spectrometer . . . . . . . . . . . . . . . 70
3.4.1 Signal-to-Noise Ratio . . . . . . . . . . . . . . . . . . . . . . . . 70
3.4.2 Detection Algorithm . . . . . . . . . . . . . . . . . . . . . . . . 72
3.4.3 Simulated Signals and Minimum Detectable Cloud Optical Depth 74
3.4.4 Observed Signals and Multilayer Cloud Detection . . . . . . . . 77
4 Comparison of A2D, MULIS and Cloud Radar 81
4.1 Ground Campaign 2007 - Overview . . . . . . . . . . . . . . . . . . . . 81
4.1.1 ADM-Aeolus Ground Campaign Objectives . . . . . . . . . . . 81
4.1.2 METEK MIRA 36 - Cloud Radar of DWD . . . . . . . . . . . . 83
4.1.3 MULIS - Multiple Wavelength Lidar System of the University
Munich . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.2 Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.2.1 Multilayer Cirrus Cloud - 08.07.2007 . . . . . . . . . . . . . . . 85
4.2.2 Deep Broken Cumulus Clouds - 11.07.2007 . . . . . . . . . . . . 89
4.2.3 Thin Cloud at Medium Altitudes - 13.07.2007 . . . . . . . . . . 92
4.2.4 Thin Cirrus Cloud - 14.07.2007 . . . . . . . . . . . . . . . . . . 96
4.2.5 Clear Sky and Thin Single Layer Cloud - 17.07.2007 . . . . . . . 100
4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5 Conclusions 105
6 Outlook 107
List of Figures 109
List of Tables 115
Appendix 117
Bibliography 121
CONTENTS vii
Acknowledgements 129
viii CONTENTS
Chapter 1
Introduction
Over one hundred years ago Arrhenius [1896] elucidated that the burning of fossil fuels
would increase the concentration of CO within the earth’s atmosphere, increasing the
2
atmospheric greenhouse effect and thus leading to global warming.
More recently in 2008, the Intergovernmental Panel on Climate Change (IPCC)
definedinits“FourthAssessmentReport”[IPCC,2008]thatchangesintheatmospheric
abundance of greenhouse gases and aerosols, in solar radiation and in land surface prop-
erties alter the energy balance of the climate system. These changes can be expressed
in terms of Radiative Forcing (RF), which is used to compare how a range of human
and natural factors drive warming or cooling influences on global climate.
The changes in RF are different for each mechanism taking place. Especially long-lived
greenhouse gases lead to a positive RF while other mechanisms, for example, forced by
aerosols result in a negative RF. The total aerosol radiative forcing can be split in the
direct and the cloud albedo effect. Both effects are listed with a quite low scientific
understanding.
But before giving an explanation of the influence of anthropogenic produced aerosols
on clouds, the general influence of clouds on the climate and the atmospheric radiation
budget has to be known.
2 1. Introduction
Figure 1.1: Schematic view on the earth’s annual global energy budget [Kiehl and
Trenberth, 1997].
1.1 Impact of Clouds on Climate and Climate Change
Beside the importance for the global and regional water circulation, clouds regulate
solar and terrestrial radiation very effectively. Figure 1.1 shows, that the surface in-
frared radiation of 390 Wm−2 corresponds to a blackbody emission at a temperature of
15◦ C for the annual and global mean of outgoing fluxes. Some of the radiation leaving
the atmosphere originates near the earth’s surface and is transmitted relatively unim-
peded through the atmosphere. This radiation originates from areas with no clouds
and that is present in the part of the spectrum known as the atmospheric window
[Kiehl and Trenberth, 1997]. The estimate of longwave radiation leaving via the at-
mospheric window decreases in the cloudy case to 80 Wm−2, showing that there is
considerable absorption and re-emission at wavelengths in the atmospheric window by
clouds. The value is assigned to 40 Wm−2 (Figure 1.1), which is simply 38% of the clear
sky case, corresponding to the observed cloudiness of about 62% on the global scale.
This emphasizes that very little radiation is transmitted directly to space although the
atmosphere is transparent. The annual and global mean of shortwave incoming (so-
lar) radiation of 342 Wm−2 is reflected in parts by aerosols, clouds, and atmospheric