Publikationsserver der Universitätsbibliothek Marburg
Titel:A machine learning based 24-h-technique for an area-wide rainfall retrieval using MSG SEVIRI data over Central Europe
Autor:Kühnlein, Meike
Weitere Beteiligte: Nauss, Thomas (Prof.)
Veröffentlicht:2014
URI:https://archiv.ub.uni-marburg.de/diss/z2014/0475
DOI: https://doi.org/10.17192/z2014.0475
URN: urn:nbn:de:hebis:04-z2014-04754
DDC:550 Geowissenschaften
Titel (trans.):Auf einem maschinellen Lernansatz basierende 24-Stunden-Technik für ein Verfahren zur flächenhaften Erfassung des Niederschlags unter Verwendung von MSG SEVIRI Daten in Mitteleuropa
Publikationsdatum:2014-12-16
Lizenz:https://rightsstatements.org/vocab/InC-NC/1.0/

Dokument

Schlagwörter:
Wolkeneigenschaften, Satellite remote sensing, Wolken, Random forests, Cloud properties, Random forests, Precipitation, Angewandte Geographie, Meteosat-8 SEVIRI, Satellitenfernerkundung, Maschinelles Lernverfahren, Maschine learning, Clouds, Meteosat-8 SEVIRI, Niederschlag

Summary:
The aim of the present study was to develop a 24-h-technique for the process-related and quantitative estimation of precipitation in connection with extra-tropical cyclones in the mid-latitudes based on MSG SEVIRI data using the machine learning algorithm random forest. The algorithms and approaches needed were successfully developed and implemented within three working packages: (WP1) The cloud property retrieval SLALOM, first developed for Terra MODIS, was successfully transferred and adapted to the specific requirements of the SEVIRI system and an extensive validation study was carried out. The cloud optical properties retrieved by SLALOM, namely cloud effective radius and cloud optical thickness that were needed for satellitebased rainfall estimation in WP2 and WP3, were compared against the well known and validated NASA MODIS cloud property product (MODIS 06) as well as the cloud optical depth product (2B-TAU) of CloudSat. The suitability of SLALOM has been shown over the North Atlantic and over the European continent (chapter 3). (WP2) A new 24-h-technique for rainfall rate assignment was developed for MSG SEVIRI using the machine learning algorithm random forest as fundamental prediction algorithm. Based on the precipitation processes in connection with extra-tropical cyclones, rainfall rates were assigned to advectivestratiform and convective precipitating areas by means of individual RF models. As predictor variables for the RF models satellite-based information on cloud top height, cloud top temperature, cloud phase and cloud water path were chosen. The different illumination conditions (daytime, twilight and night-time) were taken into account with a proper SEVIRI spectral channel selection as surrogates for theses cloud physical parameters. The development was realised in three steps: First, an extensive tuning study was carried out to customise each of the RF models. Secondly, the RF models were trained using the optimum model parameter values found in the tuning study. Finally, the final RF models were used to predict rainfall rates using an independent validation data set and the results were validated against co-located rainfall rates observed by the RADOLAN RW product of the DWD. The outstanding validation results during all times of the day confirmed the ability of RF as tool for the rainfall rate assignment technique from MSG SEVIRI data (chapter 4). (WP3) A new coherent daytime, twilight and night-time rainfall retrieval was developed for MSG SEVIRI. The technique aims to retrieve rainfall rates for precipitation events in connection with extra-tropical cyclones in the midlatitudes in a continuous manner resulting in a 24 hour prediction. Based on the dominant precipitation processes, the proposed rainfall retrieval consists of three steps which are applied consecutively by means of individual RF models to get the final product: (i) Identification of precipitating cloud areas. (ii) Separation of precipitating areas into predominately convective and advective-stratiform cloud regions. (iii) Individual process-oriented assignment of rainfall rates to these cloud areas. Again, the relationship between cloud top temperature, cloud top height, cloud water path and cloud phase was used to retrieve information about precipitation and according to the illumination conditions, a suitable selection of the predictor variables were taken into account as input to the RF models (chapter 5). The newly developed rainfall retrieval technique was tested in an extensive validation study over Germany using the radar-based RADOLAN RW product as reference data. The validation results show reliable performance of the new technique concerning rain area detection, rain process separation as well as rainfall rate assignment during all times of the day which enables the estimation of precipitation for 24 hours of a day. Hereby, the twilight applicability of the technique as well as good rainfall rate prediction performances even on an hourly basis are particularly remarkable and set this study apart from other rainfall retrievals. For the first time, a 24-h precipitation monitoring becomes possible for precipitating clouds of not only convective but also of advective-stratiform character, opening many areas of application.

Bibliographie / References

  1. Bézy, J.-L., D. Aminou, P. Bensi, R. Stuhlman, S. Tjemkes, and A. Rodriguez, 2005: Meteosat Third Generation: The future European Geostationary Mete- orological Satellite. Esa Bulletin, 123, 29–32.
  2. Cermak, J., 2006: SOFOS – A new satellite-based operational fog observation scheme. Ph.D. thesis, Philipps-University Marburg, 165 pp.
  3. Ruiz-Gazen, A. and N. Villa, 2007: Storms prediction: Logistic regression vs random forest for unbalanced data. Case Studies in Business, Industry and Government Statistics, 1 (2), 91–101.
  4. Austin, A. Benedetti, C. Mitrescu, and C. S. Team, 2002: The Cloudsat Mission and the A-Train: A new dimension of space-based observations of clouds and precipitation. Bulletin of the American Meteorological Society, 83 (12), 1771– 1790.
  5. Coppola, E., D. I. F. Grimes, M. Verdecchia, and G. Visconti, 2006: Validation of improved TAMANN neural network for operational satellite-derived rainfall estimation in Africa. American Meteorological Society, 45 (11), 1557–1572.
  6. Stephens, G., 2005: Cloud feedbacks in the climate system: A critical review. Journal of Climate, 18, 237– 273.
  7. Kiehl, J. and K. Trenberth, 1997: Earth's annual global mean energy budget. Bulletin American Meteorological Society, 78 (2), 197–208.
  8. Schmetz, J., P. Pili, S. Tjemkes, D. Just, J. Kerkmann, S. Rota, and A. Ratier, 2002: An introduction to Meteosat Second Generation (MSG). American Me- terological Society, 83 (7), 977–992.
  9. Breiman, L., 1996: Bagging predictors. Machine Learning, 24, 123–140.
  10. Briem, G., 2002: Multiple classifiers applied to multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 40 (10), 2291–2299.
  11. Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The Changing Character of Precipitation. Bulletin of the American Meteorological Society, 84 (9), 1205–1217.
  12. Bylander, T., 2002: Estimating generalization error on two-class datasets using out-of-bag estimates. Machine Learning, 48, 287–297.
  13. Krogh, A. and J. Vedelsby, 1995: Neural network ensembles, cross validation, and active learning. Advances in Neural Information Processing Systems, 7, 231–238.
  14. Ackerman, S. A., K. I. Strabala, W. P. Menzel, R. A. Frey, C. C. Moeller, and L. E. Gumley, 1998: Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research, 103 (D24), 32 141–32 157.
  15. Kummerow, C. D., Y. Hong, W. S. Olson, S. Yang, R. F. Adler, J. McCollum, R. Ferraro, G. Petty, D.-B. Shina, and T. T. Wilheit, 2001: The Evolution of the Goddard Profiling Algorithm (GPROF) for Rainfall Estimation from Passive Microwave Sensors. Journal of Applied Meteorology, 40, 1801–1820.
  16. Liu, Y., N. V. Chawla, M. P. Harper, E. Shriberg, and A. Stolcke, 2006: A study in machine learning from imbalanced data for sentence boundary detection in speech. Computer Speech and Language, 20 (4), 468–494.
  17. Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone, 1984: Classification and regression trees. Wadsworth and Brooks, Monterey, CA.
  18. Revolution Analytics, 2012a: doSNOW: Foreach parallel adaptor for the snow package. R package version 1.0.6, URL http://cran.r-project.org/ package=doSNOW.
  19. Revolution Analytics, 2012b: foreach: Foreach looping construct for R. R package version 1.4.0, URL http://cran.r-project.org/package=foreach.
  20. New, M., M. Todd, M. Hulme, and P. Jones, 2001: Precipitation measurements and trends in the twentieth century. International Journal of Climatology, 21 (15), 1889–1922.
  21. Kidd, C. and G. Huffman, 2011: Review Global precipitation measurement. Me- teorological Applications, 18, 334–353.
  22. Feidas, H. and A. Giannakos, 2012: Classifying convective and stratiform rain using multispectral infrared Meteosat Second Generation satellite data. Theo- retical and Applied Climatology, 108 (3-4), 613–630.
  23. Anagnostou, E. N., 2004: Overview of overland satellite rainfall estimation for hydro-meteorological applications. Surveys in Geophysics, 25, 511–537.
  24. Petty, G. W., 1995: The Status of Satellite-Based Rainfall Estimation over Land. Remote Sensing of Environment, 51 (1), 125–137.
  25. Grimes, D., E. Pardo-Igúzquiza, and R. Bonifacio, 1999: Optimal areal rainfall estimation using raingauges and satellite data. Journal of Hydrology, 222 (1- 4), 93–108.
  26. Friedl, M. A. and C. E. Brodley, 1997: Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment, 61 (3), 399–409.
  27. Cutler, A. and J. R. Stevens, 2006: Random Forests for Microarrays. Methods in Enzymology, A. Kimmel and O. Brian, Eds., Academic Press, San Diego, Vol. 411, 422–432.
  28. Munro, R., A. Ratier, J. Schmetz, and D. Klaes, 2002: Atmospheric measure- ments from the MSG and EPS systems. Advances in Space Research, 29 (11), 1609–1618.
  29. Capacci, D. and B. J. Conway, 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Meteoro- logical Applications, 12, 291–305.
  30. Todd, M. C., E. C. Barrett, M. J. Beaumont, and T. J. Bellerby, 1999: Estimation of daily rainfall over the upper Nile river basin using a continuously calibrated satellite infrared technique. Meteorological Applications, 6 (3), 201–210.
  31. Kokhanovsky, A. A., V. Rozanov, P. Zege, H. Bovensmann, and J. P. Burrows, 2003: A semianalytical cloud retrieval algorithm using backscattered radiation in 0.4-2.4 µm spectral region. Journal of Geophysical Research, 108 (D1), 1–19.
  32. Vicente, G. A., J. C. Davenport, and R. A. Scofield, 2002: The role of orographic and parallax corrections on real time high resolution satellite rainfall rate dis- tribution. International Journal of Remote Sensing, 23 (2), 221–230.
  33. Ghimire, B., J. Rogan, and J. Miller, 2010: Contextual land-cover classification: incorporating spatial dependence in land-cover classification models using ran- dom forests and the Getis statistic. Remote Sensing Letters, 1 (1), 45–54.
  34. Islam, T., M. a. Rico-Ramirez, P. K. Srivastava, and Q. Dai, 2014a: Non- parametric rain/no rain screening method for satellite-borne passive microwave radiometers at 19–85 GHz channels with the Random Forests algorithm. In- ternational Journal of Remote Sensing, 35 (9), 3254–3267.
  35. Porcu, F. and V. Levizzani, 1992: Cloud Classification using METEOSAT VIS-IR imagery. International Journal of Remote Sensing, 13 (5), 893–909.
  36. Foody, G., 1995: Land cover classification by an artificial neural network with ancillary information. International Journal of Geographical Information Sys- tems, 9 (5), 527–542. REFERENCES 135
  37. Arkin, P. A., R. Joyce, and J. E. Janowiak, 1994: The estimation of global monthly mean rainfall using infrared satellite data: The GOES Precipitation Index (GPI). Remote Sensing Reviews, 11, 107–124.
  38. Malley, J. D., K. G. Malley, and S. Pajevic, 2011: Statistical Learning for Biomed- ical Data. Cambridge University Press.
  39. Hollinger, J., J. Peirce, and G. Poe, 1990: SSM/I instrument evaluation. IEEE Transactions on Geoscience and Remote Sensing, 28 (5), 781–790.
  40. Guenther, B., G. D. Godden, X. Xiong, E. J. Knight, S.-Y. Qiu, H. Montgomery, M. M. Hopkins, M. G. Khayat, and Z. Hao, 1998: Prelaunch algorithm and data format for the Level 1 calibration products for the EOS-AM1 Moder- ate Resolution Imaging Spectroradiometer (MODIS). IEEE Transactions on Geoscience and Remote Sensing, 36 (4), 1142–1151.
  41. Friedl, M. A., C. E. Brodley, and A. H. Strahler, 1999: Maximizing land cover classification accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote Sensing, 37 (2), 969– 977.
  42. Capacci, D. and F. Porcù, 2009: Evaluation of a Satellite Multispectral VIS–IR Daytime Statistical Rain-Rate Classifier and Comparison with Passive Mi- crowave Rainfall Estimates. Journal of Applied Meteorology and Climatology, 48 (2), 284–300.
  43. Behrangi, A., K.-L. Hsu, B. Imam, S. Sorooshian, G. J. Huffman, and R. J. Kuligowski, 2009: PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis. Journal of Hydrometeorology, 10 (6), 1414–1429.
  44. Turk, J. and P. Bauer, 2006: The International Precipitation Working Group and Its Role in the Improvement of Quantitative Precipitation Measurements. Bulletin of the American Meteorological Society, 87 (5), 643–647.
  45. Nauss, T. and A. A. Kokhanovsky, 2007: Assignment of rainfall confidence values using multispectral satellite data at mid-latitude: First results. Advances in Geosciences, 10, 99–102.
  46. Chan, J. C.-W. and D. Paelinckx, 2008: Evaluation of Random Forest and Ad- aboost tree-based ensemble classification and spectral band selection for eco- tope mapping using airborne hyperspectral imagery. Remote Sensing of Envi- ronment, 112 (6), 2999–3011.
  47. Wilheit, T. T., R. F. Adler, S. Avery, E. Barrett, P. Bauer, W. Berg, A. Chang, J. Ferriday, N. Grody, S. Goodman, C. Kidd, D. Kniveton, C. Kummerow, A. Mugnai, W. Olsen, G. Petty, A. Shibata, E. A. Smith, and R. Spencer, 1994: Algorithms for the retrieval of rainfall from passive microwave measurements. Remote Sensing Reviews, 11, 163–194.
  48. Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol- Sanchez, 2012: An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104. REFERENCES 143
  49. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riédi, and R. A. Frey, 2003: The MODIS cloud products: Algorithms and examples from Terra. IEEE Transactions on Geoscience and Remote Sensing, 41 (2), 459–473.
  50. Prasad, A. M., L. R. Iverson, and A. Liaw, 2006: Newer Classification and Regres- sion Tree Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems, 9 (2), 181–199.
  51. Im, E., W. Chialin, and S. L. Durden, 2005: Cloud profiling radar for the cloudsat mission. IEEE International Radar Conference, Arlington, United States, 9-12 May 2005, Vol. 20, 483–486.
  52. Kidder, S. Q. and T. H. Vonder Haar, 1995: Satellite meteorology: An introduc- tion. Academic Press, London, 466 pp.
  53. Huffman, G. J., R. F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. E. Janowiak, A. McNab, B. Rudolf, U. Schneider, and J. Janoviak, 1997: The global precipitation climatology project (GPCP) combined precipitation dataset. Bulletin of the American Meteorological Society, 78 (1), 5–20.
  54. Platnick, S., 2000: Vertical photon transport in cloud remote sensing problems. Journal of Geophysical Research, 105, 22 919–22 935.
  55. Pruppacher, H. R. and J. D. Klett, 1997: Microphysics of clouds and precipita- tion. Kluwer Academic, Doedrecht.
  56. Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2001: A global determination of cloud microphysics with AVHRR remote sensing. Journal of Climate, 14, 2054–2068.
  57. Govaerts, Y. and M. Clerici, 2004: Evaluation of radiative transfer simulations over bright desert calibration sites. IEEE Transactions on Geoscience and Re- mote Sensing, 42, 176–187.
  58. Arkin, P. A. and P. E. Ardanuy, 1989: Estimating climatic-scale precipitation from space: A review. Journal of Climate, 2, 1229–1238.
  59. Adler, R. F. and A. J. Negri, 1988: A Satellite Infrared Technique to Estimate Tropical Convective and Stratiform Rainfall. Journal of Applied Meteorology, 27, 30–51.
  60. Ferreira, F., P. Amayenc, S. Oury, and J. Testud, 2001: Study and test of im- proved rain estimates from the TRMM precipitation radar. Journal of Applied Meteorology, 40, 1878–1899.
  61. Iguchi, T., T. Kou, R. Meneghini, J. Awaka, and K. Okamoto, 2000: Rain- profiling algorithm for the TRMM precipitation radar. Journal of Applied Me- teorology, 39, 2038–2052.
  62. Lensky, I. M. and D. Rosenfeld, 2003a: A night-time delineation algorithm for in- frared satellite data based on microphysical considerations. Journal of Applied Meteorology, 42 (9), 1218–1226. REFERENCES 139
  63. Lensky, I. M. and D. Rosenfeld, 2003b: Satellite-based insights into precipitation formation processes in continental and maritime convective clouds at night- time. Journal of Applied Meteorology, 42, 1227–1233.
  64. Tapiador, F. J., C. Kidd, V. Levizzani, and F. S. Marzano, 2004: A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1 degrees resolution from satellite passive microwave and infrared data. Jour- nal of Applied Meteorology, 43 (4), 576–594.
  65. King, M. D., 1987: Determination of the scaled optical thickness of clouds from reflected solar radiation measurements. Journal of the Atmospheric Sciences, 44 (13), 1734–1751.
  66. Nakajima, T. and M. D. King, 1990: Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measure- ments. Part I: Theory. Journal of the Atmospheric Sciences, 47 (15), 1878– 1893.
  67. Barth, M. C. and D. B. Parsons, 1996: Microphysical processes associated with intense frontal rainbands and the effect of evaporation and melting on frontal dynamics. Journal of the Atmospheric Sciences, 53 (11), 1569–1586.
  68. Vicente, G. A., R. A. Scofield, and P. W. Menzel, 1998: The Operational GOES Infrared Rainfall Estimation Technique. Bulletin of the American Meteorolog- ical Society, 79 (9), 1883–1898.
  69. Adler, R. F., C. Kidd, G. Petty, M. Morissey, and H. M. Goodman, 2001: In- tercomparison of global precipitation products: The third Precipitation Inter- comparison Project (PIP-3). Bulletin of the American Meteorological Society, 82 (7), 1377–1396.
  70. Arkin, P. A., 1979: The relationship between the fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Monthly Weather Review, 107, 1382–1387.
  71. Arkin, P. A. and B. N. Meisner, 1987: The relationship between large-scale con- vective rainfall and cold cloud over the western hemisphere during 1982-84. Monthly Weather Review, 115, 51–74.
  72. Grimes, D., E. Coppola, M. Verdecchia, and G. Visconti, 2003: A neural network approach to real-time rainfall estimation for Africa using satellite data. Journal of Hydrometeorology, 4, 1119–1133.
  73. Joyce, R. J., J. E. Janowiak, P. Arkin, P. A. Arking, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrom- eteorology, 5, 487–503.
  74. Bellerby, T. J., 2004: A feature-based approach to satellite precipitation monitor- ing using geostationary IR imagery. Journal of Hydrometeorology, 5, 910–921.
  75. Stephans, G. L. and C. D. Kummerow, 2007: The Remote Sensing of Clouds and Precipitation from Space: A Review. Journal of the Atmospheric Sciences, 64, 3742–3765.
  76. Roebeling, R. A., H. M. Deneke, and A. J. Feijt, 2008: Validation of cloud liquid water path retrievals from SEVIRI using one year of CloudNET observations. Journal of Applied Meteorology and Climatology, 47 (1), 206–222.
  77. Greuell, W. and R. A. Roebeling, 2009: Toward a standard procedure for valida- tion of satellite-derived cloud liquid water path: A study with SEVIRI data. Journal of Applied Meteorology, 48, 1575–1590.
  78. Ebert, E. E., J. E. Janowiak, and C. Kidd, 2007: Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bulletin of the American Meteorological Society, 88 (1), 47–64.
  79. Kühnlein, M., T. Appelhans, B. Thies, and T. Nauss, 2014b: Precipitation esti- mates from MSG SEVIRI daytime, night-time and twilight data with random forests. Journal of Applied Meteorology and Climatology, 53, 2457–2480.
  80. Kidd, C., P. Bauer, J. Turk, G. J. Huffman, R. Joyce, K.-L. Hsu, and D. Braith- waite, 2012: Intercomparison of High-Resolution Precipitation Products over Northwest Europe. Journal of Hydrometeorology, 13 (1), 67–83.
  81. Rao, N. X., S. C. Ou, and K. N. Liou, 1995: Removal of the Solar Component in AVHRR 3.7-µm Radiances for the Retrieval of Cirrus Cloud Parameters. Journal of Applied Meteorology, 34 (2), 482–499.
  82. Levizzani, V., J. Schmetz, H. J. Lutz, J. Kerkmann, P. P. Alberoni, and M. Cervino, 2001b: Precipitation estimations from geostationary orbit and prospects for Meteosat Second Generation. Meteorological Applications, 8 (1), 23–42.
  83. Feidas, H. and A. Giannakos, 2010: Identifying precipitating clouds in Greece using multispectral infrared Meteosat Second Generation satellite data. Theo- retical and Applied Climatology, 104 (1-2), 25–42.
  84. Giannakos, A. and H. Feidas, 2013: Classification of convective and stratiform rain based on the spectral and textural features of Meteosat Second Generation infrared data. Theoretical and Applied Climatology, 113 (3-4), 495–510.
  85. Baum, B. A. and S. Platnick, 2006: Introduction to MODIS cloud products. Earth Science Satellite Remote Sensing. Vol.1: Science and Instruments, J. J. Qu, W. Gao, M. Kafatos, R. E. Murphy, and V. V. Salomonson, Eds., Springer, Berlin, vol. 1 ed., chap. 5, 74–91.
  86. Alpaydin, E., 2010: Introduction to machine learning. The MIT Press, Cam- bridge, Massachusetts, London, England, 537 pp.
  87. Mohri, M., A. Rostamizadeh, and A. Talwalkar, 2012: Foundations of Machine Learning. The MIT Press, 412 pp.
  88. Boulesteix, A.-L., S. Janitza, J. Kruppa, and I. R. König, 2012: Overview of Ran- dom Forest Methodology and Practical Guidance with Emphasis on Compu- tational Biology and Bioinformatics. Tech. Rep. 129, Department of Statistics, University of Munich, 31 pp., Munich.
  89. Ba, M. B. and S. E. Nicholson, 1998: Analysis of Convective Activity and Its Relationship to the Rainfall over the Rift Valley Lakes of East Africa during 1983–90 Using the Meteosat Infrared Channel. Journal of Applied Meteorology, 37 (10), 1250–1264.
  90. Negri, A. J. and R. F. Adler, 1993: An intercomparison of three satellite infrared rainfall techniques over Japan and surrounding waters. Journal of Applied Me- teorology, 32, 357–373.
  91. Kerrache, M. and J. Schmetz, 1988: A precipitation index from the ESOC cli- matological data set. ESA Journal, 12, 379–383.
  92. Kuligowski, R., 2002: A self-calibrating real-time GOES rainfall algorithm for short-term rainfall estimates. Journal of Hydrometeorology, 3, 112–130.
  93. Platnick, S. and F. P. J. Valero, 1995: A Validation of a Satellite Cloud Retrieval during ASTEX. Journal of Atmospheric Sciences, 52, 2985–3001.
  94. Liaw, A. and M. Wiener, 2002: Classification and Regression by randomForest. R News, 2 (3), 18–22.
  95. Strabala, K. I., S. A. Ackerman, and W. P. Menzel, 1994: Cloud properties Inferred from 8-12-µm data. Journal of Applied Meteorology, 33, 212–229.
  96. Kraus, H., 1995: Das neue Bild von den atmosphärischen Fronten. Erdkunde, 49 (2), 81–105.
  97. Wu, R., J. A. Weinman, and R. T. Chin, 1985: Determination of rainfall rates from GOES satellite images by a pattern recognition technique. Journal of Atmospheric and Oceanic Technology, 2, 314–330.
  98. Dietterich, T., 2002: Ensemble learning. The handbook of brain theory and neural networks, Second edition, M. Arbib, Ed., The MIT Press, Cambridge.
  99. King, M. D. and R. Greenstone, 1999: 1999 EOS reference handbook: a guide to NASA's Earth Science Enterprise and the Earth Observing System. Tech. Rep. NASA NP-1999-08-134-GSFC, NASA/Goddard Space Flight Center, Green- belt, Md. REFERENCES King, M. D., S.-C. Tsay, S. E. Platnick, M. Wang, and K.-N. Liou, 1997: Cloud retrieval algorithms for MODIS: Optical thickness, effective particle radius, and thermodynamic phase. Algorithm Theoretical Basis Document ATBD-MOD- 05, NASA.
  100. Turk, and G. A. Vicente, 2001a: EURAINSAT – European satellite rainfall analysis and monitoring at the geostationary scale. 11th Conf. Satellite Mete- orol. Oceanography, AMS, Madison, 650–653, October.
  101. Sorooshian, S., K.-L. Hsu, X. Gao, H. V. Gupta, B. Imam, and D. Braithwaite, 2000: Evaluation of PERSIANN System Satellite-Based Estimates of Tropical Rainfall. Bulletin of the American Meteorological Society, 81 (9), 2035–2046.
  102. Ba, M. B. and A. Gruber, 2001: GOES multispectral rainfall algorithm (GM- SRA). Journal of Applied Meteorology, 40, 1500–1514.
  103. Reudenbach, C., 2003: Konvektive Sommerniederschläge in Mitteleuropa Eine Kombination aus Satellitenfernerkundung und numerischer Modellierung zur automatischen Erfassung mesoskaliger Niederschlagsfelder. Bonner Ge- ographische Abhandlungen, 109, 152.
  104. Han, Q., W. B. Rossow, and A. A. Lacis, 1994: Near-global survey of effective droplet radii in liquid water clouds using ISCCP data. Journal of Climate, 7, 465–497.
  105. Polonsky, I. N., L. C. Labonnote, and S. Cooper, 2008: Level 2 cloud optical depth product process description and interface control document, version 5.0. CloudSat Project, CIRA. Tech. rep., Colorado State University, Fort Collins, 21 pp. REFERENCES Pompei, A., M. Marrocu, P. Boi, and G. Dalu, 1995: Validation of retrieval algorithms for the infrared remote sensing of precipitation with the Sardinian gauge network data. II Nuovo Cimento, 18 C, 483–496.
  106. Rossow, W. B., 1989: Measuring cloud properties from space: A review. Journal of Climate, 2, 201–213.
  107. Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent cirrus clouds by bi-spectral measurements in the 10-µm win- dow region. Journal of the Meteorological Society of Japan, 63, 88–99.
  108. Levizzani, V., F. Porcu, and F. Prodi, 1990: Operational rainfall estimation using Meteosat infrared imagery. An application in Italy's Arno river basin. Its potential and drawbacks. ESA Journal, 14, 313–323.
  109. Liou, K.-N. and G. D. Wittman, 1979: Parameterization of the radiative proper- ties of clouds. Journal of the Atmospheric Sciences, 36 (7), 1261–1273.
  110. Ferraro, R. R., 2007: Past, present and future of microwave operational rainfall algorithms. Measuring precipitation from space, V. Levizzani, P. Bauer, and F. J. Turk, Eds., Springer Netherlands, Dordrecht, chap. 15, 189–198.
  111. Mota, J. F., F. J. Perez-Garcia, M. L. Jimenez, J. J. Amate, and J. Penas, 2002: Phytogeographical relationships among high mountain areas in the Baetic Ranges (South Spain). Global Ecology and Biogeography, 11, 497–504.
  112. Heymsfield, A. J., 1977: Precipitation development in stratiform ice clouds: A microphysical and dynamical study. Journal of the Atmospheric Sciences, 34, 367–381.
  113. Thies, B., T. Nauss, and J. Bendix, 2008d: Precipitation process and rainfall in- tensity differentiation using Meteosat Second Generation SEVIRI data. Journal of Geophysical Research, 113 (D23206).
  114. Prigent, C., 2010: Precipitation retrieval from space: An overview. Comptes Rendus Geoscience, 342 (4-5), 380–389.
  115. Liou, K. N., 1992: Radiation and cloud processes in the atmosphere. Theory, Observation and Modeling. Oxford University Press, New York, 504 pp.
  116. O'Sullivan, F., C. H. Wash, M. Stewart, and C. E. Motell, 1990: Rain estimation from infrared and visible GOES satellite data. Journal of Applied Meteorology, 29, 209–223.
  117. Hsu, K. K.-L., X. Gao, and S. Sorooshian, 2002: Rainfall estimation using cloud texture classification mapping. Proceedings of the 1st intl. Precipitation Work- ing Group (IPWG) Workshop, IPWG, Ed., Madrid, Spain, 23-27 September 2002, 6, September.
  118. Kühnlein, M., B. Thies, T. Nauss, and J. Bendix, 2010: Rainfall rate assign- ment using MSG SEVIRI data – a promising approach to spaceborne rainfall rate retrieval for midlatitudes. Journal of Applied Meteorology and Climatology, 49 (7), 1477–1495.
  119. Anagnostou, E. N. and W. F. Krajewski, 1999a: Real-Time Radar Rainfall Esti- mation. Part I: Algorithm Formulation. Journal of Atmospheric and Oceanic Technology, 16, 189–197.
  120. Arking, A. and J. D. Childs, 1985: Retrieval of cloud cover parameters from multispectral satellite images. Journal of Applied Meteorology, 24, 322–333.
  121. Reudenbach, C., T. Nauss, and J. Bendix, 2007: Retrieving precipitation with GOES, Meteosat and Terra/MSG at the tropics and mid-latitudes. Measuring precipitation from space, V. Levizzani, P. Bauer, and F. J. Turk, Eds., Springer Netherlands, Vol. 88, 509–519.
  122. Todd, M. C., E. C. E. Barrett, M. J. Beaumont, and J. L. Green, 1995: Satellite identification of rain days over the upper Nile river basin using an optimum infrared rain no-rain threshold temperature model. Journal of Applied Meteo- rology, 34, 2600–2611.
  123. Germogenova, T. A., 1963: Some formulas to solve the transfer equation in the plane layer problem. Spectroscopy of Scattering Media. Academy of Sciences of BSSR, 36–41.
  124. Twomey, S. and T. Cocks, 1982: Spectral reflectance of clouds in the near- infrared: Comparison of measurements and calculations. Journal of the Me- teorological Society of Japan, 60, 583–592.
  125. Anagnostou, E. N. and C. Kummerow, 1997: Stratiform and convective classi- fication of rainfall using SSM/I 85-GHz brightness temperature observations. Journal of Atmospheric and Oceanic Technology, 14, 570–575.
  126. Schiffer, R. A. and W. B. Rossow, 1983: The International Satellite Cloud Cli- matology Project (ISCCP): The first project of the World Climate Research Programme. Bulletin of the American Meteorological Society, 64 (7), 779–784.
  127. Rutledge, S. A. and P. V. Hobbs, 1983: The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. VIII: a model for the "seeder-feeder" process in warm-frontal rainbands. Journal of the Atmospheric Sciences, 40, 1185–1206.
  128. Stone, R. S., G. L. Stephens, C. M. R. Platt, and S. Banks, 1990: The remote sensing of thin cirrus cloud using satellites, lidar and radiative transfer theory. Journal of Applied Meteorology, 29 (5), 353–366.
  129. Kokhanovsky, A. A., V. V. Rozanov, T. Nauss, C. Reudenbach, J. S. Daniel, H. L. Miller, and J. P. Burrows, 2005: The semianalytical cloud retrieval algorithm for SCIAMACHY – I. The validation. Atmospheric Chemistry and Physics Discussions, 5, 1995–2015.
  130. Adler, R. F. and R. A. Mack, 1984: Thunderstorm cloud height-rainfall rate relations for use with satellite rainfall estimation techniques. Journal of Climate and Applied Meteorology, 23, 280–296.
  131. Schutgens, N. A. J. and R. A. Roebeling, 2009: Validating the validation: the influence of liquid water distribution in clouds on the intercomparison of satel- lite and surface observations. Journal of Atmospheric and Ocean Technology, 26 (8), 1457–1474. REFERENCES Scofield, R. A. and R. J. Kuligowski, 2003: Status and Outlook of Operational Satellite Precipitation Algorithms for Extreme-Precipitation Events. Weather and Forecasting, 18, 1037–1051.
  132. Ebert, E. E., 2002: Verifying satellite precipitation estimates for weather and hydrological applications. Proceedings of the 1st intl. Precipitation Working Group (IPWG) Workshop, IPWG, Ed., Madrid, Spain, 23-27 September 2002, IPWG Workshop Report, Vol. 1, 10.
  133. Tjemkes, S. A., L. van de Berg, and J. Schmetz, 1997: Warm water vapour pixels over high clouds as observed by Meteosat. Beiträge zur Physik der Atmosphäre, 70 (1), 15–21.
  134. Nakajima, T. Y. and T. Nakajima, 1995: Wide-Area Determination of Cloud Microphysical Properties from NOAA AVHRR Measurements for FIRE and ASTEX Regions. American Meteorological Society, 52, 4043–4059.
  135. Kidd, C., 2001: Satellite rainfall climatology: a review. International Journal of Climatology, 21 (9), 1041–1066.
  136. Thies, B. and J. Bendix, 2011: Review Satellite based remote sensing of weather and climate: recent achievements and future perspectives. Meteorological Ap- plications, 295, 262–295.
  137. Matejka, T. J., R. A. Houze, J. R. A. Houze, and P. V. Hobbs, 1980: Microphysics and dynamics of clouds associated with mesoscale rainbands in extratropical cyclones. Quarterly Journal of the Royal Meteorological Society, 106, 29–56. REFERENCES Menz, G. and A. Zock, 1997: Regionalisation of precipitation models in east Africa using Meteosat data. Journal of Climate, 17, 1011–1027.
  138. Browning, K. A. and N. M. Roberts, 1996: Variation of frontal and precipitation structure along a cold front. Quarterly Journal of the Royal Meteorological Society, 122 (536), 1845–1872.
  139. Amorati, R., P. P. Alberoni, V. Levizzani, and S. Nanni, 2000: IR-based satellite and radar rainfall estimates of convective storms over northern Italy. Meteoro- logical Applications, 7 (1), 1–18.
  140. Menzel, P. Yang, and A. Steven, 2000: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS 2. Cloud thermodynamic phase. Journal of Geophysical Research, 105 (D9), 11 781–11 792.
  141. Weng, F. W., L. Zhao, R. Ferraro, G. Poe, X. Li, and N. C. Grody, 2003: Ad- vanced Microwave Sounding Unit (AMSU) cloud and precipitation algorithms. Radio Science, 38, 8068–8083.
  142. Min, Q., P. Minnis, and M. M. Khaiyer, 2004: Comparison of cirrus optical thickness depths derived from GOES 8 and surface measurements. Journal of Geophysical Research, 109, D15 207.
  143. Kokhanovsky, A. A. and T. Nauss, 2005: Satellite based retrieval of ice cloud properties using a semi-analytical algorithm. Journal of Geophysical Research, 110 (D19206).
  144. Roebeling, R. A., A. J. Feijt, and P. Stammes, 2006: Cloud property retrievals for climate monitoring: Implications of differences between Spinning Enhanced Visible and Infrared Imager (SEVIRI) on METEOSAT-8 and Advanced Very High Resolution Radiometer (AVHRR) on NOAA-17. Journal of Geophysical Research, 111 (D20210), 1–16.
  145. Roebeling, R. A. and I. Holleman, 2009: SEVIRI rainfall retrieval and val- idation using weather radar observations. Journal of Geophysical Research, 114 (D21), 1–13.
  146. Bennartz, R., P. Watts, J. F. Meirink, and R. Roebeling, 2010: Rainwater path in warm clouds derived from combined visible/near-infrared and microwave satellite observations. Journal of Geophysical Research, 115 (D19120), 1–16.
  147. Kidd, C., V. Levizzani, J. Turk, R. Ferraro, and V. Turk, 2009: Satellite precip- itation measurements for water resource monitoring. Journal of the American Water Resource Association, 45 (3), 567–579.
  148. Cutler, D. R., T. C. Edwards, K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler, 2007: Random forests for classification in ecology. Ecology, 88 (11), 2783–2792.
  149. Thies, B., T. Nauss, and J. Bendix, 2008c: First results on a process-oriented rain area classification technique using Meteosat Second Generation SEVIRI night-time data. Advances in Geosciences, 8, 1–9.
  150. Nauss, T. and A. A. Kokhanovsky, 2006: Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data. Atmospheric Chem- istry and Physics, 6 (1), 5031–5036.
  151. Goldstein, B. A., E. C. Polley, and F. B. S. Briggs, 2011: Random forests for genetic association studies. Statistical applications in genetics and molecular biology, 10 (1), 32.
  152. Aminou, D. M. A., 2002: MSG's SEVIRI Instrument. ESA Bulletin, 111, 15–17.
  153. Hong, Y., K.-L. Hsu, S. Sorooshian, and G. Hiaogang, 2004: Precipitation esti- mation from remotely sensed imagery using an artificial neural network cloud classification system. Journal of Applied Meteorology, 43, 1834–1852.
  154. Stanski, H. R., L. J. Wilson, and W. R. Burrows, 1989: Survey of Common Verfication Methods in Meteorology. Tech. rep., WMO World Weather Watch No.8, WMO/TD No. 358, Geneva.
  155. EUMETSAT, 2013: Meteosat Third Generation (MTG) will see the launch of four new satellites from 2018. URL http://www.eumetsat.int/website/home/ Satellites/FutureSatellites/MeteosatThirdGeneration/index.html.
  156. Kidd, C. and V. Levizzani, 2011: Status of satellite precipitation retrievals. Hy- drology and Earth System Sciences, 15 (4), 1109–1116.
  157. Thornes, J., W. Bloss, S. Bouzarovski, X. Cai, L. Chapman, J. Clark, S. Dessai, S. Du, D. van der Horst, M. Kendall, C. Kidd, and S. Randalls, 2010: Commu- nicating the value of atmospheric services. Meteorological Applications, 17 (2), 243–250.
  158. Strobl, C., J. Malley, and G. Tutz, 2009: An Introduction to Recursive Parti- tioning: Rationale, Application and Characteristics of Classification and Re- gression Trees, Bagging and Random Forests. Psychological Methods, 14 (4), 323–348.
  159. Ou, S. C., K. N. Liou, W. M. Gooch, and Y. Takano, 1993: Remote sensing of cirrus cloud parameters using advanced very-high-resolution radiometer 3.7- and 10.9-µm channels. Applied Optics, 32 (12), 2171–2180.
  160. Platnick, S., P. A. Hubanks, G. Wind, M. D. King, S. A. Ackerman, B. Maddux, T. Zinner, and A. Ackerman, 2009: The MODIS Cloud Optical and Micro- physical Product: An Evaluation of Effective Radius Retrieval Statistics and Model Simulations, Hyperspectral Imaging and Sensing of the Environment, OSA Technical Digest (CD). Advances in Imaging, Optical Society of America, HWB1.
  161. R Development Core Team, 2014: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, URL http://www.r-project.org/.
  162. Levizzani, V., 2000: Satellite rainfall estimates: A look back and a perspective. EUMETSAT Meteorological Satellite Data Users' Conference, EUMETSAT, Bologna, Italy, 29 May -2 June 2000, EUMETSAT, 344–353, No. 2000 in EUMETSAT Proceedings.
  163. Rosenfeld, D. and G. Gutman, 1994: Retrieving microphysical properties near the tops of potential rain clouds by multispectral analysis of AVHRR data. Atmospheric Research, 34, 259–283.
  164. Mishchenko, M. I., J. M. Dlugach, E. G. Yanovitskij, and N. T. Zakharova, 1999: Bidirectional reflectance of flat, optically thick particulate layers: an efficient radiative transfer solution and applications to snow and soil surfaces. Journal of Quantitative Spectroscopy and Radiative Transfer, 63, 409–432.
  165. Steele, B., 2000: Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sensing of Environment, 74 (3), 545–556.
  166. Pérez, J., A. González, and M. Armas-Padilla, 2011: Remote sensing of water cloud properties from MSG/SEVIRI nighttime imagery. Remote Sensing of Environment, 115 (2), 738–746.
  167. Kühnlein, M., T. Appelhans, B. Thies, and T. Nauss, 2014a: Improving the accuracy of rainfall rates from optical satellite sensors with machine learning – A random forests-based approach applied to MSG SEVIRI. Remote Sensing of Environment, 141, 129–143.
  168. Islam, T., M. a. Rico-Ramirez, and D. Han, 2012a: Tree-based genetic pro- gramming approach to infer microphysical parameters of the DSDs from the polarization diversity measurements. Computers & Geosciences, 48, 20–30. REFERENCES 137
  169. Feijt, A., D. Jolivet, R. Koelemeijer, and H. Deneke, 2004: Recent improvements to LWP retrievals from AVHRR. Atmospheric Research, 72, 3–15.
  170. Islam, T., M. a. Rico-Ramirez, D. Han, and P. K. Srivastava, 2012b: Artificial intelligence techniques for clutter identification with polarimetric radar signa- tures. Atmospheric Research, 109-110, 95–113.
  171. Kühnlein, M., T. Appelhans, B. Thies, A. A. Kokhanovsky, and T. Nauss, 2013: An evaluation of a semi-analytical cloud property retrieval using MSG SEVIRI, MODIS and CloudSat. Atmospheric Research, 122, 111–135.
  172. Islam, T., P. K. Srivastava, M. a. Rico-Ramirez, Q. Dai, D. Han, and M. Gupta, 2014b: An exploratory investigation of an adaptive neuro fuzzy inference sys- tem (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR. Atmospheric Research, 145-146, 57–68.
  173. Schmetz, J., S. A. Tjemkes, M. Gube, and L. van de Berg, 1997: Monitoring deep convection and convective overshooting with Meteosat. Advances in Space Research, 19 (3), 433–441.
  174. Dupret, G. and M. Koda, 2001: Bootstrap re-sampling for unbalanced data in supervised learning. European Journal of Operational Research, 134 (1), 141– 156.
  175. Guo, L., N. Chehata, C. Mallet, and S. Boukir, 2011: Relevance of airborne lidar and multispectral image data for urban scene classification using Random Forests. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (1), 56– 66.
  176. Mountrakis, G., J. Im, and C. Ogole, 2011: Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), 247–259.
  177. Heinemann, G., C. Reudenbach, E. Heuel, J. Bendix, and M. Winiger, 2001: In- vestigation of summertime convective rainfall in Western Europe based on a synergy of remote sensing data and numerical models. Meteorology and Atmo- spheric Physics, 76 (1-4), 23–41.
  178. Bendix, J., 2000: Precipitation dynamics in Ecuador and northern Peru during the 1991/92 El Nino: a remote sensing perspective. International Journal of Remote Sensing, 21 (3), 533–548. REFERENCES 133
  179. Pal, M., 2005: Random forest classifier for remote sensing classification. Interna- tional Journal of Remote Sensing, 26 (1), 217–222.
  180. Mas, J. F. and J. J. Flores, 2008: The application of artificial neural networks to the analysis of remotely sensed data. International Journal of Remote Sensing, 29 (3), 617–663.
  181. Hansen, M., R. Dubayah, and R. Defries, 1996: Classification trees: an alternative to traditional land cover classifiers. International Journal of Remote Sensing, 17 (5), 1075–1081.
  182. Bendix, J., 1997: Adjustment of the Convective-Stratiform Technique (CST) to estimate 1991/93 El Niño rainfall distribution in Ecuador and Peru by means of Meteosat-3 IR data. International Journal of Remote Sensing, 18 (6), 1387– 1394.
  183. Marrocu, M. A., A. Pompei, G. Dalu, G. L. Liberti, and A. J. Negri, 1993: Pre- cipitation estimation over Sardinia from satellite infrared data. International Journal of Remote Sensing, 14, 115–134.
  184. Cermak, J. and J. Bendix, 2008: A novel approach to fog/low stratus detection using Meteosat 8 data. Atmospheric Research, 87, 279–292.
  185. Rivolta, G., F. S. Marzano, E. Coppola, and M. Verdecchia, 2006: Artificial neural-network technique for precipitation nowcasting from satellite imagery. Advances in Geosciences, 7, 97–103.
  186. Pandey, P., K. De Ridder, D. Gillotay, and N. P. M. van Lipzig, 2012: Estimating cloud optical thickness and associated surface UV irradiance from SEVIRI by implementing a semi-analytical cloud retrieval algorithm. Atmospheric Chem- istry and Physics Discussions, 12 (1), 691–721.
  187. Levizzani, V., 2003: Satellite rainfall estimates: new perspectives for meteorology and climate from the EURAINSAT project. Annals of Geophysics, 46 (2), 363– 372.
  188. Nauss, T. and A. A. Kokhanovsky, 2011: Retrieval of warm cloud optical prop- erties using simple approximations. Remote Sensing of Environment, 115 (6), 1317–1325. REFERENCES 141
  189. Früh, B., J. Bendix, T. Nauss, M. Paulat, A. Pfeiffer, J. W. Schipper, B. Thies, and H. Wernli, 2007: Verification of precipitation from regional climate sim- ulations and remote-sensing observations with respect to ground-based obser- vations in the upper Danube catchment. Meteorologische Zeitschrift, 16 (3), 275–293.
  190. Nauss, T., A. A. Kokhanovsky, T. Y. Nakajima, C. Reudenbach, and J. Bendix, 2005: The intercomparison of selected cloud retrieval algorithms. Atmospheric Research, 78 (1-2), 46–78.

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