Qualitative weather radar mosaic in a multisensor rainfall monitoring approach

Abstract. A method is presented for integrating the information available in a limited area (corresponding to Tuscany in Italy) coming from satellite sensors, point measurement stations and ground-based radars. The objective is the exploitation of the complementary information provided by the variety of methods and instruments nowadays existing for measuring precipitation or precipitation-related parameters, in order to upgrade the capability of reconstructing weather phenomena of main interest. Ground- and satellite-based measurements, working locally or remotely, are jointly analyzed to evaluate how heterogeneous data can amplify the effectiveness of the measurements, when synergically analyzed, and this holds also when some of the available instruments essentially give just qualitative information. A way to synthesize the different information provided by various instruments is presented, assessing the quality of all the available observations. Namely, steps are described for the achievement of a mosaic of qualitative weather radars, and it is shown how the joint analysis of satellite, rain gauge and lightning observations can support a correct interpretation of precipitation phenomena. Finally, a logical scheme for data integration is presented and discussed.

[1]  W. Krajewski,et al.  Satellite estimation of precipitation over land , 1996 .

[2]  David R. Legates,et al.  The Accuracy of United States Precipitation Data , 1994 .

[3]  Soroosh Sorooshian,et al.  On the simulation of infiltration‐ and saturation‐excess runoff using radar‐based rainfall estimates: Effects of algorithm uncertainty and pixel aggregation , 1998 .

[4]  Ralph Ferraro,et al.  The Development of SSM/I Rain-Rate Retrieval Algorithms Using Ground-Based Radar Measurements , 1995 .

[5]  Dong-Jun Seo,et al.  An Intercomparison Study of NEXRAD Precipitation Estimates , 1996 .

[6]  V. Levizzani,et al.  Rainfall variability associated with the summer African monsoon: A satellite study , 2010 .

[7]  Ezio Todini,et al.  A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements , 2001 .

[8]  P. Fournier,et al.  Advantages and limitations of genomics in prokaryotic taxonomy. , 2013, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[9]  Witold F. Krajewski,et al.  Radar Beam Occultation Studies Using GIS and DEM Technology: An Example Study of Guam , 2004 .

[10]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[11]  J. Marshall,et al.  THE DISTRIBUTION OF RAINDROPS WITH SIZE , 1948 .

[12]  G. P. Cressman AN OPERATIONAL OBJECTIVE ANALYSIS SYSTEM , 1959 .

[13]  G. Pegram,et al.  Combining radar and rain gauge rainfall estimates using conditional merging , 2005 .

[14]  Soroosh Sorooshian,et al.  Estimating Rainfall Intensities from Weather Radar Data: The Scale-Dependency Problem , 2003 .

[15]  S. Sorooshian,et al.  Intercomparison of rain gauge, radar, and satellite-based precipitation estimates with emphasis on hydrologic forecasting , 2005 .

[16]  Chris Kidd,et al.  Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network , 2000 .