Natural Hazards and Earth System Science www.nat-hazards-earth-syst-sci.net 1561-8633 1684-9981 10 1 2010 10.5194/nhess-10-121-2010 http://www.nat-hazards-earth-syst-sci.net/10/121/2010/ http://www.nat-hazards-earth-syst-sci.net/10/121/2010/nhess-10-121-2010.html http://www.nat-hazards-earth-syst-sci.net/10/121/2010/nhess-10-121-2010.pdf 121 132 2010-01-22 Water vapour distribution at urban scale using high-resolution numerical weather model and spaceborne SAR interferometric data E. Pichelli emanuela.pichelli@aquila.infn.it R. Ferretti D. Cimini D. Perissin M. Montopoli F. S. Marzano N. Pierdicca Department Physics, University of L'Aquila/CETEMPS, L'Aquila, Italy Institute of Space and Earth Information Science, Chinese University of Hong Kong, Hong Kong, China Department Electronic Engineering, Sapienza University of Rome, Rome, Italy Department of Electrical and Information Engineering, University of L'Aquila, L'Aquila, Italy The local distribution of water vapour in the urban area of Rome has been studied using both a high resolution mesoscale model (MM5) and Earth Remote Sensing-1 (ERS-1) satellite radar data. Interferometric Synthetic Aperture Radar (InSAR) techniques, after the removal of all other geometric effects, estimate excess path length variation between two different SAR acquisitions (Atmospheric Phase Screen: APS). APS are strictly related to the variations of the water vapour content along the radar line of sight. To the aim of assessing the MM5 ability to reproduce the gross features of the Integrated Water Vapour (IWV) spatial distribution, as a first step ECMWF IWV has been used as benchmark against which the high resolution MM5 model and InSAR APS maps have been compared. As a following step, the high resolution IWV MM5 maps have been compared with both InSAR and surface meteorological data. The results show that the high resolution IWV model maps compare well with the InSAR ones. Support to this finding is obtained by semivariogram analysis that clearly shows good agreement beside from a model bias. Collier, C. G.: The impact of urban areas on weather, Q. J. Roy. Meteor. Soc., 132, 1–25, 2006. Dandou, A., Tombrou, M., Akylas, E., Soulakellis, N., and Bossioli, E.: Development and evaluation of an urban parameterization scheme in the Penn State/NCAR Mesoscale Model (MM5), J. Geophys. Res., 110, D10102, doi:10.1029/2004JD005192, 2005. Davis, J. L., Herring, L. T. A., Shapiro, I. I., Rogers, A. E., and Elgered, G.: Geodesy by radio interferometry: Effects of atmospheric modelling errors on estimates of baseline length, Radio Sci., 20, 1593–1607, 1985. Dudhia, J.: A nonhydrostatic version of the Penn State-NCAR Mesoscale Model: validation tests and simulation of an atlantic cyclone and cold front, Mon. Weather Rev., 129, 1493–-1513, 1993. Ferretti, A., Prati, C., and Rocca, F.: Permanent Scatterers in SAR Interferometry, IEEE TGARS, 39(1), 8–20, doi:10.1109/36.898661, 2001. Ferretti, A., Colesanti, C., Perissin, D., Prati, C., and Rocca, F.: Evaluating the effect of the observation time on the distibution of SAR Permanent Scatterers, Proceedings of FRINGE03, Frascati, Italy, December 2003a. Ferretti, R., Mastrantonio, G., Argentini, S., Santoleri, L., and Viola, A.: A model-aided investigation of winter thermally driven circulation in the Italian Tyrrhenian coast for a case study, J. Geophys. Res., 108(D24), 4777–4792, 2003b. Ferretti, R. and Raffaele, F.: Impact of the urban modification on the circulation of the Milan urban area. Part I: Model simulations and observations, Q. J. Roy. Meteor. Soc., submitted, 2009. Foster, J., Brooks, B., Cherubini, T., Shacat, C., Businger, S., and Werner, C. L.: Mitigating atmospheric noise for InSAR using a high resolution weather model, Geophys. Res. Lett., 33, L16304, doi:10.1029/2006GL026781, 2006. Hanssen, R. F., Weckwerth, T. M., Zebker, H. A., and Klees, R.: High-resolution water vapour mapping from interferometric radar measurements, Science, 283, 1297–1299, 1999. Hong, S.-Y. and Pan, H.-L.: Nonlocal boundary layer vertical diffusion in medium-range forecast model, Mon. Weather Rev., 124, 2322–2339, 1996. Kain, J. S. and Fritsch, J. M.: Convective parameterization for mesoscale models: the Kain-Fritsch scheme, Meteor. Mon., 24(46), 165–170, 1993. Kain, J. S.: The Kain-Fritsch convective parameterization: an update, J. Appl. Meteorol., 43(1), 170–181, 2004. Kuo, Y.-H., Guo, Y.-R., and Westwater, E.: Assimilation of precipitable water measurement into a mesoscale numerical model, Mon. Weather Rev., 121, 1215–1238, 1993. Kuo, Y.-H., Zou, X., and Guo, Y.-R.: Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part I: Moisture retrieval and sensitivity experiments, Mon. Weather Rev., 124, 122–147, 1996. Onn, F. and Zebker, H. A.: Correction for interferometric synthetic aperture radar atmospheric phase artifacts using time series of zenith wet delay observations from a GPS network, J. Geophys. Res., 111, B09102, doi:10.1029/2005JB004012, 2006. Reisner, J., Rasmussen, R., and Bruintjes, R.: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model, Q. J. Roy. Meteor. Soc., 124, 1071–1107, 1998. Rotunno, R. and Ferretti, R.: Mechanisms of intense Alpine rainfall, J. Atmos. Sci., 58, 1732–1749, 2001. Rotunno, R. and Ferretti, R.: Orographyc analysis of rainfall in MAP cases IOP2B and IOP8, Q. J. Roy. Meteor. Soc., 129B, 373–390, 2003. Solheim, F. S., Vivekanandan, J., Ware, R., and Rocken, C.: Propagation delays induced in GPS signals by dry air, water vapour, hydrometeors, and other particulates, J. Geophys. Res., 104(D8), 9663–9670, 1999. Stephens, G. L.: The parametrization of radiation for numerical Weather prediction and climate models, Mon. Weather Rev., 112, 826–867, 1984. Webley, P. W., Bingley, R. M., Dodson, A. H., Wadge, G., Waugh, S. J., and James, I. N.: Atmospheric water vapour correction to InSAR surface motion measurements on mountains: results from a dense GPS network on Mount Etna, Phys. Chem. Earth 27, 363–370, 2002.