The Use of Digital Warping of Microwave Integrated Water Vapor Imagery to Improve Forecasts of Marine Extratropical Cyclones

G. David Alexander Universities Space Research Association, Microwave Sensors Branch, NASA/Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by G. David Alexander in
Current site
Google Scholar
PubMed
Close
,
James A. Weinman Microwave Sensors Branch, NASA/Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by James A. Weinman in
Current site
Google Scholar
PubMed
Close
, and
J. L. Schols General Sciences Corporation, Laurel, Maryland

Search for other papers by J. L. Schols in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A technique is described in which forecasts of the locations of features associated with marine cyclones may be improved through the use of microwave integrated water vapor (IWV) imagery and image warping of forecast mesoscale model fields. Here, image warping is used to optimally match mesoscale model output to observations of IWV measured by microwave sensors. In the mesoscale model simulations presented here (one of the March 1993 “superstorm,” one of a rapidly deepening cyclone observed in the North Atlantic in February 1992, and one of the ERICA IOP 4 cyclone), the Pennsylvania State University–National Center for Atmospheric Research MM5 model is initialized from the standard National Meteorological Center (recently renamed the National Centers for Environmental Prediction) operational analysis. The simulations are then run until a time at which a Special Sensor Microwave/Imager (SSM/I) overpass occurs. For each simulation, the forecast pattern of IWV is then compared to the field shown in the SSM/I image. In all three cases, the MM5 moves the cyclones too slowly, and therefore places distinguishing features in the forecast IWV fields significantly upstream of their locations as revealed in the microwave imagery. To rectify these errors, the grid on which the source image (forecast field) is defined is then warped to match the target image (remotely observed IWV field) by choosing pairs of tie points corresponding to similar features in the two images. The values of all model moisture variables at all vertical levels are then carried to the new warped grid points and interpolated back to the original model grid. Model integration then proceeds with the new model fields. The model results at a subsequent time after the warping is applied are then compared with simultaneous model results in simulations in which no warping was applied as well as with model simulations in which a standard nudging technique is applied. Warping results in improved forecasts of cyclone minimum sea level pressure, tracks, and IWV fields over both the control simulations and the nudged simulations.

Corresponding author address: James A. Weinman, NASA/Goddard Space Flight Center, Code 975, Greenbelt, MD 20771.

Email: weinman@sensor.gsfc.nasa.gov

Abstract

A technique is described in which forecasts of the locations of features associated with marine cyclones may be improved through the use of microwave integrated water vapor (IWV) imagery and image warping of forecast mesoscale model fields. Here, image warping is used to optimally match mesoscale model output to observations of IWV measured by microwave sensors. In the mesoscale model simulations presented here (one of the March 1993 “superstorm,” one of a rapidly deepening cyclone observed in the North Atlantic in February 1992, and one of the ERICA IOP 4 cyclone), the Pennsylvania State University–National Center for Atmospheric Research MM5 model is initialized from the standard National Meteorological Center (recently renamed the National Centers for Environmental Prediction) operational analysis. The simulations are then run until a time at which a Special Sensor Microwave/Imager (SSM/I) overpass occurs. For each simulation, the forecast pattern of IWV is then compared to the field shown in the SSM/I image. In all three cases, the MM5 moves the cyclones too slowly, and therefore places distinguishing features in the forecast IWV fields significantly upstream of their locations as revealed in the microwave imagery. To rectify these errors, the grid on which the source image (forecast field) is defined is then warped to match the target image (remotely observed IWV field) by choosing pairs of tie points corresponding to similar features in the two images. The values of all model moisture variables at all vertical levels are then carried to the new warped grid points and interpolated back to the original model grid. Model integration then proceeds with the new model fields. The model results at a subsequent time after the warping is applied are then compared with simultaneous model results in simulations in which no warping was applied as well as with model simulations in which a standard nudging technique is applied. Warping results in improved forecasts of cyclone minimum sea level pressure, tracks, and IWV fields over both the control simulations and the nudged simulations.

Corresponding author address: James A. Weinman, NASA/Goddard Space Flight Center, Code 975, Greenbelt, MD 20771.

Email: weinman@sensor.gsfc.nasa.gov

Save
  • Alishouse, J. C., S. Snyder, J. Vongsathorn, and R. R. Ferraro, 1990:Determination of oceanic total precipitable water from the SSM/I. IEEE Trans. Geosci. Remote Sens.,28, 811–816.

  • Bougeault, P., 1983: A non-reflective upper boundary condition for limited-height hydrostatic models. Mon. Wea. Rev.,111, 420–429.

  • Chang, S.-W., R. J. Alliss, S. Raman, and J.-J. Shi, 1993: SSM/I observations of ERICA IOP-4 marine cyclone: A comparison with in-situ observations and model simulation. Mon. Wea. Rev.,121, 2452–2464.

  • Davies, H. C., and R. E. Turner, 1977: Updating prediction models by dynamical relaxation: An examination of the technique. Quart. J. Roy. Meteor. Soc.,103, 225–245.

  • Deblonde, G., L. Garand, P. Gauthier, and C. Grasotti, 1995: Assimilation of SSM/I and GOES humidity retrievals with a one-dimensional variational analysis scheme. J. Appl. Meteor.,34, 1536–1550.

  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci.,46, 3077–3107.

  • ——, 1993: A non-hydrostatic version of the Penn State/NCAR mesoscale model: Validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev.,121, 1493–1513.

  • Ferraro, R. R., F. Weng, N. C. Grody, and A. Basist, 1996: An eight-year (1987–1994) series of rainfall, clouds, water vapor, snow cover, and sea ice derived from SSM/I measurements. Bull. Amer. Meteor. Soc.,77, 891–905.

  • Forbes, G. S., R. M. Blackall, and P. L. Taylor, 1993: ‘Blizzard of the century’—The storm of 12–14 March 1993 over the eastern United States. Meteor. Mag.,122, 153–162.

  • Grell, G. A., 1993: Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Wea. Rev.,121, 764–787.

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

  • ——, Z. Liu, J.-F. Louis, and C. Grassotti, 1995: Distortion representation of forecast errors. Mon. Wea. Rev.,123, 2758–2770.

  • Hollinger, J., J. Peirce, and G. Poe, 1990: Validation for the Special Sensor Microwave/Imager, IEEE Trans. Geosci. Remote Sens.,28, 781–790.

  • Hord, R. M., 1982: Digital Image Processing of Remotely Sensed Data. Academic Press, 256 pp.

  • Kain, J. S., and J. M. Fritsch, 1990: A one-dimensional entraining/detraining plume model and its application in convective parameterization. J. Atmos. Sci.,47, 2784–2802.

  • Katsaros, K. B., I. Bhatti, L. A. McMurdie, and G. W. Petty, 1989: Identification of atmospheric fronts over the ocean with microwave measurements of water vapor and rain. Wea. Forecasting,4, 449–460.

  • Klemp, J. B., and D. R. Durran, 1983: An upper boundary condition permitting internal gravity wave radiation in numerical mesoscale models. Mon. Wea. Rev.,111, 430–444.

  • Kocin, P. J., P. N. Schumacher, R. F. Morales Jr., and L. W. Uccellini, 1995: Overview of the 12–14 March 1993 superstorm. Bull. Amer. Meteor. Soc.,76, 165–199.

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

  • Kuo, H.-L., 1974: Further studies of the influence of cumulus convection on large-scale flow. J. Atmos. Sci.,31, 1232–1240.

  • Kuo, Y.-H., Y.-R. Guo, and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale numerical model. Mon. Wea. Rev.,121, 1215–1238.

  • Lawson, R. P., R. E. Stewart, J. W. Stapp, and G. A. Issac, 1993: Aircraft observations of the origin and growth of very large snowflakes, Geophys. Res. Lett.,20, 53–56.

  • ——, L. J. Angus, and R. E. Stewart, 1998: Observations and numerical simulations of the origin and development of very large snowflakes. J. Atmos. Sci., in press.

  • Liu, C.-H., R. M. Waikimoto, and F. Roux, 1997: Observations of mesoscale circulations within extratropical cyclones over the North Atlantic Ocean during ERICA. Mon. Wea. Rev.,125, 341–364.

  • Manobianco, J., S. Koch, V. M. Karyampudi, and A. J. Negri, 1994:The impact of assimilating satellite-derived precipitation rates on numerical simulations of the ERICA IOP4 cyclone. Mon. Wea. Rev.,122, 341–365.

  • Olson, W. S., C. D. Kummerow, G. M. Heymsfield, and L. Giglio, 1996: A method for combined passive–active microwave retrievals of cloud and precipitation profiles. J. Appl. Meteor.,35, 1763–1789.

  • Prabhakara, C. H., H. D. Chang, and A. T. C. Chang, 1982: Remote sensing of precipitable water over oceans from Nimbus-7 microwave measurements. J. Appl. Meteor.,21, 59–68.

  • Pratt, W. K., 1991: Digital Image Processing. 2d ed. John Wiley and Sons.

  • Reed, R. J., Y.-H. Kuo, and S. Low-Nam, 1994: An adiabatic simulation of the ERICA IOP 4 storm: An example of quasi-ideal frontal cyclone development. Mon. Wea. Rev.,122, 2688–2708.

  • Schluessel, P., and W. J. Emery, 1990: Atmospheric water vapor over oceans from SSM/I measurements. Int. J. Remote Sens.,11, 753–766.

  • Stauffer, D. R., and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev.,118, 1250–1277.

  • Stewart, R. E., R. W. Crawford and N. R. Donaldson, 1990: Precipitation characteristics within severe Canadian east coast winter storms. Atmos. Res.,25, 293–316.

  • Zhang, D.-L., and R. A. Anthes, 1982: A high-resolution model of the planetary boundary layer—Sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor.,21, 1594–1609.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 102 50 2
PDF Downloads 54 30 2