American Meteorological Society Demo

to a single standard. To minimize this bias we have developed an empirical correction to the 85–92-GHz horizontally polarized brightness temperatures based on histogram matching (Bovik 2000). In its conventional application, a histogram-matching algorithm adjusts the brightness values of a source image to give it the same brightness value histogram of a target image. This application is preferable to a pixel-topixel comparison between different instruments’ imagery because the time differences between observations from each orbital platform leads to significant spatial offsets in the features being compared. The TMI imagery served as the calibration standard because TRMM is the only satellite to regularly cross tracks with the other satellites, and also because its footprint size is between the large and small extremes. Calibration datasets for each imager were selected from six to eight scenes from three tropical cyclones with well-developed eyes. In each scene, a source image matched with a TMI image with a time gap 2 h or less. Both images were interpolated to the storm-centered, polar grid format used in the morphed display. This ensured that the final, interpolated images from the respective instruments would show a minimum amount of bias. Pixels were weighted by inverse distance from the center in order to emphasize the importance of matching the brightness temperatures in the eyewall. The resulting adjustments (Fig. A1) show an overall negative adjustment to SSM/I brightness temperatures and an overall positive adjustment to AMSR-E and SSMI/S brightness temperatures in the scattering range ( ~270 K), SSM/I brightness temperatures are adjusted slightly upward, AMSR-E brightness temperatures are downward, and SSMI/S temperatures are basically unaffected. We hasten to add that a robust normalization of raw brightness temperatures (rather than interpolated values) would yield somewhat different results from those shown here. This is because our normalization gives added weight to the eyewall, and also because matching histograms of interpolated images does not produce the same results as with noninterpolated imagery. Therefore, the adjustments shown here should not be used directly in other applications. REFERENCES Bovik, A. C., 2000: Basic gray-level image processing. Handbook of Image and Video Processing, A. C. Bovik, Ed., Academic Press, 21–36. Cecil, D. J., and E. J. Zipser, 1999: Relationships between tropical cyclone intensity and satellite-based indicators of inner core convection: 86-GHz icescattering signature and lightning. Mon. Wea. Rev., 127, 103–123. Corbosiero, K. L., and J. Molinari, 2002: The effects of vertical wind shear on the distribution of convection in tropical cyclones. Mon. Wea. Rev., 130, 2110–2123. Fortner, L. E., 1958: Typhoon Sarah, 1956. Bull. Amer. Meteor. Soc., 39, 633–639. Germann, U., and I. Zawadzki, 2002: Scale-dependence of the predictability of precipitation from continental radar images. Part I: Description of the methodology. Mon. Wea. Rev., 130, 2859–2873. Grody, N. C., 1993: Remote sensing of the atmosphere from satellites using microwave radiometry. Atmospheric Remote Sensing by Microwave Radiometry, M. A. Janssen, Ed., John Wiley, 259–334. Grose, A., E. A. Smith, H.-S. Chung, M.-L. Ou, B.-J. Sohn, and F. J. Turk, 2002: Possibilities and limitations for quantitative precipitation forecasts using nowcasting methods with infrared geosynchronous satellite imagery. J. Appl. Meteor., 41, 763–785. Hawkins, J. D., T. F. Lee, J. Turk, C. Sampson, J. Kent, and K. Richardson, 2001: Real-time Internet distribution of satellite products for tropical cyclone reconnaissance. Bull. Amer. Meteor. Soc., 82, 567–578. Hohti, H., J. Koistinen, P. Nurmi, E. Saltikoff, and K. Holmund, 2000: Precipitation nowcasting using radar-derived atmospheric motion vectors. Phys. Chem. Earth B, 25, 1323–1327. Holland, G. J., 1980: An analytic model of the wind and pressure profiles in hurricanes. Mon. Wea. Rev., 108, 1212–1218. Houze, R. A., and Coauthors, 2006: The hurricane rainband and intensity change experiment: Observations and modeling of hurricanes Katrina, Ophelia, and Rita. Bull. Amer. Meteor. Soc., 87, 1503–1521. Jones, T. A., D. Cecil, and M. DeMaria, 2006: Passivemicrowave-enhanced statistical hurricane intensity prediction scheme. Wea. Forecasting, 21, 613–635. Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503. Kummerow, C., W. Barnes, T. Kozu, J. Shiue, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. J. Atmos. Oceanic Technol., 15, 809–817. Lee, T. F., F. J. Turk, J. Hawkins, and K. Richardson, 2002: Interpretation of TRMM TMI images of tropical cyclones. Earth Interactions, 6. [Available online at http://EarthInteractions.org.] 1198 | AUGUST 2007 http://EarthInteractions.org

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