Abstract
The sensitivity of a recently published satellite-based rainfall detection scheme with differing frontal weather regimes is investigated for 676 precipitation scenes between January and August 2004. For this purpose, the rain area classified by the recent Rain Area Delineation Scheme during Night time (RADS-N) was compared to the rain area detected by the radar network of the German Weather Service. The validation results indicate that the rain area detected by RADS-N is highly consistent with the radar network (mean POD: 0.62; mean FAR: 0.52; mean ETS: 0.22). However, the bias indicates a mean overestimation of 42%. The classification results show that the satellite technique performs better in cold frontal situations and thunderstorms. Therefore, further investigations are needed to address the overall performance as well as the dependency on different weather situations and in order to allow reliable rain area detection during night-time, independent of the prevailing weather situation.
Similar content being viewed by others
References
Adler RF, Negri AJ (1988) A satellite technique to estimate tropical convective and stratiform rainfall. J Appl Meteor 27:30–51
Arking A, Childs JD (1985) Retrieval of cloud cover parameters from multispectral satellite images. J Appl Meteor 24:322–333
Ba MB, Gruber A (2001) GOES multispectral rainfall algorithm (GMSRA). J Appl Meteor 40:1500–1514
Bellon A, Lovejoy S, Austin GL (1980) Combining satellite and radar data for the short-range forecasting of precipitation. Mon Wea Rev 108:1554–1556
Bendix J, Reudenbach C, Rollenbeck R (2003) The Marburg Satellite Station. In: Proceedings of EUMETSAT 2002 conference, pp 139–146
Berliner Wetterkarte V (ed) (2004) Berliner Wetterkarte, vol. 53, pp 1–168
Bissolli P, Dittmann E (2001) The objective weather type classification of the German Weather Service and its possibilities of application to environmental and meteorological investigations. Met Ztsch 10:253–260
Bissolli P, Dittmann E (2005) Objektive Wetterlagenklassen 2004. In: DWD (ed) Klimastatusbericht 2004, Offenbach, pp 122–128
Cermak J, Bendix J, Dobbermann M (2008) FMet—an integrated framework for Meteosat data processing for operational scientific applications. Comput Geosci 34:1638–1644
Cheng M, Brown R, Collier CG (1993) Delineation of precipitation areas by correlation of METEOSAT visible and infrared data in the region of the United Kingdom. J Appl Meteor 32:884–898
Chiaravalloti F, Gabriele S (2009) Vibo Valentia flood and MSG rainfall evaluation. Atmos Res 93:286–294
Deutscher Wetterdienst (2005), Weather radar network, report, Offenbach, Germany. Online available at: http://www.dwd.de/bvbw/generator/Sites/DWDWWW/Content/Oeffentlichkeit/TI/TI2/Downloads/Radarbrosch_C3_BCre,templateId=raw,property=publicationFile.pdf/Radarbrosch%C3%BCre.pdf
Ebert E, Janowiak J, Kidd C (2007) Comparison of near-real-time precipitation estimates from satellite observations and numerical models. Bull Am Meteorol Soc 88:47–64
Feijt AJ, Jolivet D, Koelemeijer R, Deneke H (2004) Recent improvements to LWP retrievals from AVHRR. Atmos Res 72:3–15
Früh B, Bendix J, Nauss T, Paulat M, Pfeiffer A, Schipper JW, Thies B, Wernli H (2007) Verification of precipitation from regional climate simulations and remote-sensing observations with respect to ground-based observations in the upper Danube catchment. Meteorol Z 16:275–293
Greuell W, Roebeling RA (2009) Toward a standard procedure for validation of satellite-derived cloud liquid water path: A study with SEVIRI data. J Appl Meteorol Climatol 48:1575–1590
Han Q, Rossow WB, Lacis AA (1994) Near-global survey of effective droplet radii in liquid water clouds using ISCCP data. J Clim 7:465–497
Houze RA (1993) Cloud dynamics, Int Geophys Ser, vol 53. Academic, San Diego
Jaeneke M (1995) Der Radarverbund des DWD als Hilfsmittel der regionalen und lokalen Gewitterprognose. Promet 24:55–72
Jolliffe IT, Stephenson DB (ed) (2003) Forecast verification: a practitioner’s guide in atmospheric science. Wiley, London, 240 pp
Kawamoto K, Nakajima T, Nakajima TY (2001) A global determination of cloud microphysics with AVHRR remote sensing. J Clim 14:2054–2068
King MD, Tsay SC, Platnick SE, Wang M, Liou KN (1997) Cloud retrieval algorithms for MODIS: optical thickness, effective particle radius, and thermodynamic phase, NASA
Kokhanovsky AA, Nauss T (2005) Satellite-based retrieval of ice cloud properties using a semi-analytical algorithm. J Geophys Res Atmos 110/D19: D19206
Kokhanovsky AA, Rozanov VV, Zege PE, Bovensmann H, Burrows JP (2003) A semianalytical cloud retrieval algorithm using backscattered radiation in 0.4–2.4 Am spectral region. J Geophys Res 108:4-1–4-19
Kurino T (1997) A satellite infrared technique for estimating ‘deep/shallow’ precipitation. Adv Space Res 19:511–514
Lang P (1997) Niederschlagsquantifizierung auf der Basis von Radardaten. Promet 26:22–31
Lensky IM, Rosenfeld D (2003a) A night-rain delineation algorithm for infrared satellite data based on microphysical considerations. J Appl Meteorol 42:1218–1226
Lensky IM, Rosenfeld D (2003b) Satellite-based insights into precipitation formation processes in continental and maritime convective clouds at nighttime. J Appl Meteorol 42:1227–1233
Levizzani V (2003) Satellite rainfall estimations: new perspectives for meteorology and climate from the EURAINSAT project. Ann Geophys 46:363–372
Levizzani V, Schmetz J, Lutz HJ, Kerkmann J, Alberoni PP, Cervino M (2001) Precipitation estimations from geostationary orbit and prospects for Meteosat Second Generation. J Meteor Appl 8:23–41
Mason I (1982) A model for assessment of weather forecasts. Aust Met Mag 30:291–303
Nakajima T, King M (1990) Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: theory. J Atmos Sci 47:1878–1893
Nakajima TY, Nakajima T (1995) Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX regions. J Atmos Sci 52:4043–4059
Nauss T, Kokhanovsky AA (2006) Discriminating raining from non-raining clouds at mid-latitudes using multispectral satellite data. Atmos Chem Phys 6:5031–5036
Nauss T, Kokhanovsky AA (2007) Assignment of rainfall confidence values using multispectral satellite data at mid-latitudes: First results. Adv Geosci 10:99–102
Platnick S, King MD, Ackerman SA, Menzel WP, Baum BA, Riedi JC, Frey RA (2003) The MODIS cloud products: algorithms and examples from Terra. IEEE Trans Geosci Remote Sens 41:459–473
Porcù F, Capacci D (2007) Seasonal sensitivity of a VIS-NIRIR rain-no rain classifier. Meteor Atmos Phys 101:147–157
Reudenbach C, Heinemann G, Heuel E, Bendix J, Winiger M (2001) Investigation of summertime convective rainfall in Western Europe based on a synergy of remote sensing data and numerical models. Meteorol Atmos Phys 76:23–41
Reudenbach C, Nauss T, Cermak J, Dobbermann M, Bendix J, Theissen W, Scheidgen P, Harmann O (2004) An integrated receiving and processing unit for MSG, NOAA and Terra/Aqua data. In: Proceedings of EUMETSAT 2003 conference, pp 291–297
Riedl J (1986) Radar-Flächenniederschlagsmessung. Promet 16:20–23
Roebeling PD, Feijt AJ, Stammes P (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. J Geophys Res 111:D20210. doi:10.1029/2005JD006990
Roebeling RA, Deneke HM, Feijt AJ (2008) Validation of cloud liquid water path retrievals from SEVIRI using one year of CloudNET observations. J Appl Meteor Climatol 47:206–222
Seltmann J (1997) Radarforschung im DWD: Vom Scan zum Produkt. Promet 26:32–42
Stanski HR, Wilson LJ, Burrows WR (1989) Survey of common verification methods in meteorology. World Weather Watch Technical Report 8, WMO/TD 358, WMO, Genf, 114 pp
Thies B, Nauss T, Bendix J (2008a) Discriminating raining from non-raining cloud areas at mid-latitudes using Meteosat Second Generation SEVIRI day-time data. Atmos Chem Phys 8:1–9
Thies B, Nauss T, Bendix J (2008b) Delineation of raining from non-raining clouds during nighttime using Meteosat-8 data. Meteorol Appl 15:219–230
Turk J, Bauer P (2006) The International Precipitation Working Group and its role in the improvement of quantitative precipitation measurements. Bull Am Meteorol Soc 87:643–647
Vicente GA, Davenport JC, Scofield RA (2002) The role of orographic and parallax corrections on real time high resolution satellite rainfall rate distribution. Int J Remote Sens 23:221–230
Wetterzentrale (2007a) Archiv der stündlichen Wettermeldungen. Online available at: http://www.wetterzentrale.de/topkarten/tkdwdar2.htm
Wetterzentrale (2007b) Archiv der Europablitzkarten. Online available at: http://www.wetterzentrale.de/topkarten/tkbeoblar.htm
WWRP/WGNE Joint Working Group on Verification (2007) Forecast verification—issues, methods and FAQ. Online available at: http://www.bom.gov.au/bmrc/wefor/staff/eee/verif/verif_web_page.html
Acknowledgments
The authors are grateful to the German Weather Service (DWD) for providing the ground-based precipitation dataset. The current study was funded by the German Ministry of Research and Education (BMBF) in the framework of GLOWA-Danube project (G-D/2004/TP-10, precipitation/remote sensing), as well as by the German Research Council DFG (BE 1780/18-1) within the SORT project. Furthermore, the authors would like to thank the anonymous reviewers for valuable remarks and comments which helped to improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Thies, B., Turek, A., Nauss, T. et al. Weather type dependent quality assessment of a satellite-based rainfall detection scheme for the mid-latitudes. Meteorol Atmos Phys 107, 81–89 (2010). https://doi.org/10.1007/s00703-010-0076-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00703-010-0076-x