• Adler, R. F., A. J. Negri, P. R. Keehn, and I. M. Hakkarinen, 1993: Estimation of monthly rainfall over Japan and surrounding waters from a combination of low-orbit microwave and geosynchronous IR data. J. Appl. Meteor.,32, 335–356.

  • Alishouse, J. C., R. R. Ferraro, and J. V. Fiore, 1990: Inference of oceanic rainfall properties from the Nimbus-7 SMMR. J. Appl. Meteor.,29, 551–560.

  • Arkin, P. A., 1979: The relationship between fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Mon. Wea. Rev.,107, 1382–1387.

  • Austin, P. M., 1987: Relation between measured radar reflectivity and surface rainfall. Mon. Wea. Rev.,115, 1053–1070.

  • Barrett, E. C., and D. W. Martin, 1981: The Use of Satellite Data in Rainfall Monitoring. Academic Press, 340 pp.

  • Dennis, J. E., D. M. Gay, and R. E. Welsch, 1981: An adaptive nonlinear least-squares algorithm. ACM Trans. Math. Software,7, 348–383.

  • Ferraro, R. R., and G. F. Marks, 1995: The development of SSM/I rain-rate retrieval algorithms using ground-based radar measurements. J. Atmos. Oceanic Technol.,12, 755–770.

  • Ferriday, J. G., and C. D. Kummerow, 1992: Estimating instantaneous horizontal rainfall variability from space. Proc. Specialist Meeting on Microwave Radiometry and Remote Sensing Applications, Boulder, CO, NOAA, 284–288. [Available from NOAA, Boulder, CO 80301.].

  • ——, and S. K. Avery, 1994: Passive microwave remote sensing of rainfall with SSM/I: Algorithm development and implementation. J. Appl. Meteor.,33, 1587–1596.

  • Groisman, P. Y., and D. R. Legates, 1994: The accuracy of United States precipitation data. Bull. Amer. Meteor. Soc.,75, 215–227.

  • Hoffman, R. N., and C. Grassotti, 1996: A technique for assimilating SSM/I observations of marine atmospheric storms. J. Appl. Meteor.,35, 1177–1188.

  • Hollinger, J., R. Lo, and G. Poe, 1987: Special sensor microwave imager user’s guide. Naval Research Laboratory, Washington, DC, 120 pp.

  • Kummerow, C., and L. Giglio, 1994a: A passive microwave technique for estimating rainfall and vertical structure information from space. Part I: Algorithm description. J. Appl. Meteor.,33, 3–18.

  • ——, and ——, 1994b: A passive microwave technique for estimating rainfall and vertical structure information from space. Part II: Applications to SSM/I data. J. Appl. Meteor.,33, 19–34.

  • Rosenfeld, D., D. Atlas, D. B. Wolff, and E. Amitai, 1992: Beamwidth effects on ZR relations and area-integrated rainfall. J. Appl. Meteor.,31, 454–464.

  • Short, D. A., and G. R. North, 1990: The beam filling error in the Nimbus-5 electronically scanning microwave radiometer observations of Global Atlantic Tropical Experimental rainfall. J. Geophys. Res.,95, 2187–2193.

  • Simpson, J. A., R. F. Adler, and G. R. North, 1988: A proposed Tropical Rainfall Measuring Mission (TRMM) satellite. Bull. Amer. Meteor. Soc.,69, 278–295.

  • Smith, D. M., D. R. Kniveton, and E. C. Barrett, 1998: A statistical modeling approach to passive microwave rainfall retrieval. J. Appl. Meteor.,37, 135–154.

  • Smith, E., J. E. Lamm, R. E. Adler, J. Alishouse and K. Aonashi, 1998: Results of WetNet PIP-2 project. J. Atmos. Sci.,55, 1483–1536.

  • Spencer, R. W., M. H. Goodman, and R. E. Hood, 1989: Precipitation retrieval over land and ocean with the SSM/I: Identification and characteristics of the scattering signal. J. Atmos. Oceanic Technol.,6, 254–273.

  • Wentz, F. J., 1993: User’s manual SSM/I antenna temperature tapes, Revision 2. Tech. Rep. 120193, 14 pp. plus appendixes. [Available from Remote Sensing Systems, Santa Rosa, CA 95404.].

  • Williamson, D. L., and P. J. Rasch, 1989: Two-dimensional semi-Lagrangian transport with shape preserving interpolation. Mon. Wea. Rev.,117, 102–129.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 187 32 3
PDF Downloads 62 35 1

Calibration and Alignment

View More View Less
  • 1 Atmospheric and Environmental Research, Inc., Cambridge, Massachusetts
Restricted access

Abstract

Discrepancies between estimates of rainfall from ground-based radar and satellite observing systems can be attributed to either calibration differences or to geolocation and sampling differences. These latter include differences due to radar or satellite misregistration, differences in observation times, or variations in instrument and retrieval algorithm sensitivities. A new methodology has been developed and tested for integrating radar- and satellite-based estimates of precipitation using a feature calibration and alignment (FCA) technique. The parameters describing the calibration and alignment are found using a variational approach, and are composed of displacement and amplitude adjustments to the satellite rainfall retrievals, which minimize the differences with respect to the radar data and satisfy additional smoothness and magnitude constraints. In this approach the amplitude component represents a calibration of the satellite estimate to the radar, whereas the displacement components correct temporal and/or geolocation differences between the radar and satellite data.

The method has been tested on a number of cases of the NASA WetNet PIP-2 dataset. These data consist of coincident estimates of rainfall by ground-based radar and the DMSP SSM/I. Sensitivity tests were conducted to tune the parameters of the algorithm. Results indicate the effectiveness of the technique in minimizing the discrepancies between radar and satellite observations of rainfall for a variety of rainfall events ranging from midlatitude frontal precipitation to heavy convection associated with a tropical cyclone (Hurricane Andrew). A remaining issue to be resolved is the incorporation of knowledge about location dependencies in the errors of the radar and microwave estimates.

Once the satellite data have been adjusted to match the radar observations, the two independent estimates (radar and adjusted SSM/I rain rates) may be blended to improve the overall depiction of the rainfall event in a single analysis. The FCA technique also has potential applications in 1) the development of satellite rainfall retrieval algorithms that may be tuned to radar rain rates and 2) error assessment of rainfall predictions using radar or satellite rain rates as verification.

Corresponding author address: Ross N. Hoffman, Atmospheric and Environmental Research, Inc., 840 Memorial Drive, Cambridge, MA 02139.

rhoffman@aer.com

Abstract

Discrepancies between estimates of rainfall from ground-based radar and satellite observing systems can be attributed to either calibration differences or to geolocation and sampling differences. These latter include differences due to radar or satellite misregistration, differences in observation times, or variations in instrument and retrieval algorithm sensitivities. A new methodology has been developed and tested for integrating radar- and satellite-based estimates of precipitation using a feature calibration and alignment (FCA) technique. The parameters describing the calibration and alignment are found using a variational approach, and are composed of displacement and amplitude adjustments to the satellite rainfall retrievals, which minimize the differences with respect to the radar data and satisfy additional smoothness and magnitude constraints. In this approach the amplitude component represents a calibration of the satellite estimate to the radar, whereas the displacement components correct temporal and/or geolocation differences between the radar and satellite data.

The method has been tested on a number of cases of the NASA WetNet PIP-2 dataset. These data consist of coincident estimates of rainfall by ground-based radar and the DMSP SSM/I. Sensitivity tests were conducted to tune the parameters of the algorithm. Results indicate the effectiveness of the technique in minimizing the discrepancies between radar and satellite observations of rainfall for a variety of rainfall events ranging from midlatitude frontal precipitation to heavy convection associated with a tropical cyclone (Hurricane Andrew). A remaining issue to be resolved is the incorporation of knowledge about location dependencies in the errors of the radar and microwave estimates.

Once the satellite data have been adjusted to match the radar observations, the two independent estimates (radar and adjusted SSM/I rain rates) may be blended to improve the overall depiction of the rainfall event in a single analysis. The FCA technique also has potential applications in 1) the development of satellite rainfall retrieval algorithms that may be tuned to radar rain rates and 2) error assessment of rainfall predictions using radar or satellite rain rates as verification.

Corresponding author address: Ross N. Hoffman, Atmospheric and Environmental Research, Inc., 840 Memorial Drive, Cambridge, MA 02139.

rhoffman@aer.com

Save