Cloud Statistics from Calipso Lidar Data for the ... - espace-tum.de

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Chapter 3 Methodology behind cloud free and cloud gap statistics 3.1 Introduction In order to obtain statistically significant data from the CALIPSO mission and to draw concrete conclusions for the aspired MERLIN mission, a software was developed as a part of this thesis. While by now there is no such analysis and statistic tool publicly available for the CALIPSO data set, the need for a fast and easy way to process the data is apparent. For this reason a Python program with a PyQt GUI (Graphical User interface) was developed. If not otherwise stated, the data sets used for the analysis and the development of the algorithms are Level 2 1/3 km Lidar Cloud Layer v3.01 and Level 2 5 km Lidar Cloud Layer v3.01 from 2007-01-01 00:22:49 until 2008-01-01 00:37:39. The year 2007 was chosen because of the minor losses of only 5% of data due to orbit maneuvers. The way the CALIPSO data sets are stored by NASA and CNES does not allow an enhanced analysis for periods larger than a day without storing them locally, reprocessing them and compressing the original half orbit data sets to a new data set for the desired analysis period. With a required disk space of over 700 GB in total for the 333 m and 5 km product for the entire analysis year 2007, the first processing step has to focus on reducing the data set by deleting unnecessary parameters that do not contribute to the cloudless gap analysis (see Section 3.2). A next step is to use a gzip (GNU zip) compression to reduce the total required disk space to less than 20 GB for all monthly data sets of both products for the entire year. 24
Chapter 3. Methodology behind cloud free and cloud gap statistics 25 HDF 4 half orbit files ICARE FTP Server CALIPSO DATA READING AND MERGING Section 3.2 05kmCLay.v3.01 333mCLay.v3.01 calipso convert HDF5 daily files 333 m / 5 km calipso combination HDF5 monthly files 333 m / 5 km Graphical User Inferface of Calipso Statistics PROCESSING Section 3.3 Worldmaps Layer plots Histograms Cloudless Statistics Cloudless Maps Data set merging HDF Export Configuration of parameters Merging of the data sets Plotting routine Processed and calculated monthly results HDF Result Export VISUALIZATION AND ANALYSIS Section 3.4 Broken cloud scene Multilayered cloud scene Multilayered cloud flattness scene Low maritime flat cloud scene HDF result merging Cloudless fraction worldmap (annual/monthly) Latitude averaged histograms (annual) Cloudless statistics and global power law fit (annual) Worldmap of powerlaw fit and respective scatter plots (annual) Total number, mean and median worldmaps of cloud gaps (annual) Cloud top flatness events worldmap and histograms (annual) Cloud height distribution (annual) Figure 7: The diagram introduces all components of the developed software and the typical data flow beginning from the original HDF 4 file from NASA/ICARE to the final products presented in this thesis. Thereby red boxes indicate data outputs of the program.
Page 1: Technische Universität München Ma
Page 5: ”Science never solves a problem w
Page 9: Acknowledgements I would like to th
Page 12 and 13: Contents xii 3.1 Introduction . . .
Page 14 and 15: List of Figures xiv 33 Global cloud
Page 17 and 18: Abbreviations ABL ATBD BSP CALIOP C
Page 19: SI Units and Abbreviations deg degr
Page 23: Dedicated to the ones I love and to
Page 26 and 27: Chapter 1. Introduction and motivat
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Page 50 and 51: Chapter 3. Methodology behind cloud
Page 60 and 61: Chapter 4. Case studies 36 4.1 Typi
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Page 64 and 65: Chapter 5 Cloud free statistics 5.1
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Page 82 and 83: Chapter 6. Cloud flatness occurrenc
Page 88 and 89: Chapter 7 Cloud top height distribu
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Page 96 and 97: Chapter 8. Conclusion 72 the fact t
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Appendix. Detailed results 74 CALIP
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Appendix. Detailed results 76 Logar
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Appendix. Detailed results 78 (a) J
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Bibliography J. Alstott, E. Bullmor
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Bibliography 82 A. Klaus, S. Yu, an
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Bibliography 84 E. P. White, B. J.
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Cloud Statistics from Calipso Lidar Data for the ... - espace-tum.de

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