Correlation of precipitation estimates from spaceborne passive microwave sensors and weather radar imagery for BALTEX PIDCAP

This paper describes the evaluation of a combined radar and passive microwave dataset obtained during the PIDCAP study of the Baltic Sea Experiment (BALTEX), where three-dimensional volumes of data from the Gotland radar were obtained timed according to the overpasses of the DMSP-satellites F10 and F13. Both satellites are equipped with a Special Sensor Microwave/Imager (SSM/I), suitable for precipitation retrievals. We compare radar precipitation estimates, convolved to the native resolution of the SSM/I, at different altitudes with polarization and scattering indices ( S 85 ) derived from the SSM/I. For all 22 overpasses investigated here radar precipitation estimates at 3-4 km altitude correlate well with the SSM/I-derived S 85 (average correlation coefficient = 0.70). Although more directly linked to surface precipitation, polarization indices have been found to be less correlated with radar data, due to limitations inherent in the remote sensing of precipitation at higher latitudes. A stratification of the dataset into frontal and convective events revealed significant variations in these relationships for different types of precipitation events, thus reflecting different cloud microphysical processes associated with precipitation initialization. The relationship between S 85 and radar rain estimates at higher altitudes varies considerably for different convective and frontal events. The sensitivity of S 85 to radar-derived rain rate ranges from 3.1 K mm m 1 h m 1 for a strong convective event to about 25 K mm m 1 h m 1 for the frontal and about 70 mm m 1 h m 1 for the small-scale convective events. For extrapolated surface precipitation estimates, sensitivities decrease to 14 mm m 1 h m 1 and 25 mm m 1 h m 1 for frontal and small-scale convective precipitation, respectively.

[1]  H. Michael Goodman,et al.  Precipitation retrieval over land and ocean with the SSM/I - Identification and characteristics of the scattering signal , 1989 .

[2]  Chris G. Collier,et al.  Applications of weather radar systems: A guide to uses of radar data in meteorology and hydrology , 1989 .

[3]  Robert F. Adler,et al.  Estimation of Monthly Rainfall over Japan and Surrounding Waters from a Combination of Low-Orbit Microwave and Geosynchronous IR Data , 1993 .

[4]  Grant W. Petty,et al.  Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part I: Theoretical characteristics of normalized polarization and scattering indices , 1994 .

[5]  D. Rosenfeld,et al.  The Window Probability Matching Method for Rainfall Measurements with Radar , 1994 .

[6]  Grant W. Petty,et al.  The Sensitivity of Microwave Remote Sensing Observations of Precipitation to Ice Particle Size Distributions , 2001 .

[7]  Ralf Bennartz,et al.  On the Use of SSM/I Measurements in Coastal Regions , 1999 .

[8]  J P Hollinger,et al.  DMSP Special Sensor Microwave/Imager Calibration/Validation , 1991 .

[9]  Frank S. Marzano,et al.  Results of WetNet PIP-2 Project , 1998 .

[10]  N. Grody.  Classification of snow cover and precipitation using the special sensor microwave imager , 1991 .

[11]  R. C. Srivastava.  Applications of weather radar systems: A guide to uses of radar data in meteorology and hydrology: By C.G. Collier, Ellis Harwood, 1989 , 1992 .

[12]  Eyal Amitai,et al.  Relationships between Radar Properties at High Elevations and Surface Rain Rate: Potential Use for Spaceborne Rainfall Measurements , 1999 .

[13]  J. Klett,et al.  Microphysics of Clouds and Precipitation , 1978, Nature.

[14]  Jarmo Koistinen,et al.  Gauge-Radar network adjustment for the baltic sea experiment , 2000 .

[15]  G. Petty.  Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part II: Algorithm implementation , 1994 .