Skip to main content
Log in

Two-dimensional variational analysis of near-surface moisture from simulated radar refractivity-related phase change observations

  • Published:
Advances in Atmospheric Sciences Aims and scope Submit manuscript

Abstract

Because they are most sensitive to atmospheric moisture content, radar refractivity observations can provide high-resolution information about the highly variable low-level moisture field. In this study, simulated radar refractivity-related phase-change data were created using a radar simulator from realistic highresolution model simulation data for a dryline case. These data were analyzed using the 2DVAR system developed specifically for the phase-change data.

Two sets of experiments with the simulated observations were performed, one assuming a uniform target spacing of 250 m and one assuming nonuniform spacing between 250 m to 4 km. Several sources of observation error were considered, and their impacts were examined. They included errors due to ground target position uncertainty, typical random errors associated with radar measurements, and gross error due to phase wrapping. Without any additional information, the 2DVAR system was incapable of dealing with phase-wrapped data directly. When there was no phase wrapping in the data, the 2DVAR produced excellent analyses, even in the presence of both position uncertainty and random radar measurement errors. When a separate pre-processing step was applied to unwrap the phase-wrapped data, quality moisture analyses were again obtained, although the analyses were smoother due to the reduced effective resolution of the observations by interpolation and smoothing involved in the unwrapping procedure. The unwrapping procedure was effective even when significant differences existed between the analyzed state and the state at a reference time. The results affirm the promise of using radar refractivity phase-change measurements for near-surface moisture analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Anthes, R. A., and Coauthors, 2008: The COSMIC/FORMOSAT-3 Mission: Early results. Bull. Amer. Meteor. Soc., 89, 313–333.

    Article  Google Scholar 

  • Bean, B., R. and E. J. Dutton, 1968: Radio Meteorology. Dover Publications, 435pp.

  • Bodine, D., and Coauthors, 2011: Understanding radar refractivity: Sources of uncertainty. J. Atmos. Oceanic Technol., 50, 2543–2560.

    Google Scholar 

  • Brewster, K., 1996: Application of a Bratseth analysis scheme including Doppler radar data. Preprints, 15th Conf. Weather Analysis Forecasting, Norfolk, VA, Amer. Meteor. Soc., 92–95.

    Google Scholar 

  • Brock, F. V., K. C. Crawford, R. L. Elliott, G. W. Cuperus, S. J. Stadler, H. L. Johnson, and M. D. Eilts, 1995: The Oklahoma Mesonet: A technical overview. J. Atmos. Oceanic Technol., 12, 5–19.

    Article  Google Scholar 

  • Cheong, B. L., R. D. Palmer, C. Curtis, T.-Y. Yu, D. S. Zrnic, and D. Forsyth, 2008: Refractivity retrieval using the phased array radar: First results and potential for multi-mission operation. IEEE Trans. Geosci. Remote Sens., 46, 2527–2537.

    Article  Google Scholar 

  • Droegemeier, K. K., and Coauthors, 2000: Hydrological aspects of weather prediction and flood warnings: Report of the ninth prospectus development team of the U.S. weather research program. Bull. Amer. Meteor. Soc., 81, 2665–2680.

    Article  Google Scholar 

  • Emanuel, K., and Coauthors, 1995: Report of the first prospectus development team of the U.S. weather research program to NOAA and the NSF. Bull. Amer. Meteor. Soc., 76, 1194–1208.

    Google Scholar 

  • Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dynamics, 53, 343–367.

    Article  Google Scholar 

  • Evensen, G., 2006: Data Assimilation: The Ensemble Kalman Filter. Springer, 280pp.

  • Fabry, F., C. Frush, I. Zawadzki, and A. Kilambi, 1997: On the extraction of near-surface index of refraction using radar phase measurements from ground targets. J. Atmos. Oceanic Technol., 14, 978–987.

    Article  Google Scholar 

  • Fritsch, J. M., and Coauthors, 1998: Quantitative precipitation forecasting: Report of the eighth prospectus development team, U.S. Weather Research Program. Bull. Amer. Meteor. Soc., 79, 285–299.

    Google Scholar 

  • Harris, B. A., and G. Kelly, 2001: A satellite radiancebias correction scheme for data assimilation. Quart. J. Roy. Meteor. Soc., 127, 1453–1468.

    Article  Google Scholar 

  • Huang, X.-Y., 2000: Variational analysis using spatial filters. Mon. Wea. Rev., 128, 2588–2600.

    Article  Google Scholar 

  • Ide, K., P. Courtier, M. Ghil, and A. Lorenc, 1997: Unified notation for data assimilation: Operational, sequential and variational. J. Meteor. Soc. Japan, 75, 181–189.

    Google Scholar 

  • Kalnay, E., 2002: Atmospheric Modeling, Data Assimilation, and Predictability. Cambridge University Press, 341pp.

  • Liu, H., and M. Xue, 2006: Retrieval of moisture from slant-path water vapor observations of a hypothetical GPS network using a three-dimensional variational scheme with anisotropic background error. Mon. Wea. Rev., 134, 933–949.

    Article  Google Scholar 

  • Liu, H., M. Xue, R. J. Purser, and D. F. Parrish, 2007: Retrieval of moisture from simulated GPS slantpath water vapor observations using 3DVAR with anisotropic recursive filters. Mon. Wea. Rev., 135, 1506–1521.

    Article  Google Scholar 

  • Lord, S. J., E. Kalnay, R. Daley, G. D. Emmitt, and R. Atlas, 1997: Using OSSEs in the design of the future generation of integrated observing systems. Preprint, 1st Symposium on Integrated Observation Systems, Long Beach, CA, Amer. Meteor. Soc., 45–47.

    Google Scholar 

  • Lorenc, A. C., 1992: Iterative analysis using covariance functions and filters. Quart. J. Roy. Meteor. Soc., 118, 569–591.

    Google Scholar 

  • McPherson, R., E. Kalnay, and S. Lord, 1997: The potential role of GPS/MET observations in operational numerical weather prediction. The Global Positioning System for the Geosciences, NRC Rep. 9254. National Academy Press [Available online at http://www.nap.edu/catalog/9254.html], 111–113.

  • Purser, R. J., 1987: The filtering of meteorological fields. J. Climate Appl. Meteor., 26, 1764–1769.

    Article  Google Scholar 

  • Ware, R., and Coauthors, 1996: GPS sounding of the atmosphere from low earth orbit: Preliminary results. Bull. Amer. Meteor. Soc., 77, 19–40.

    Article  Google Scholar 

  • Weckwerth, T. M., and D. B. Parsons, 2006: A review of convection initiation and motivation for IHOP 2002. Mon. Wea. Rev., 134, 5–22.

    Article  Google Scholar 

  • Weckwerth, T. M., J. W. Wilson, and R. M. Wakimoto, 1996: Thermodynamic variability within the convective boundary layer due to horizontal convective rolls. Mon. Wea. Rev., 124, 769–784.

    Article  Google Scholar 

  • Weckwerth, T. M., and Coauthors, 2004: An overview of the International H2O Project (IHOP 2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253–277.

    Article  Google Scholar 

  • Weckwerth, T. M., C. R. Pettet, F. Fabry, S. Park, M. A. LeMone, and J. W. Wilson, 2005: Radar refractivity retrieval: Validation and application to short-term forecasting. J. Appl. Meteor., 44, 285–277.

    Article  Google Scholar 

  • Xue, M., and W. J. Martin, 2006a: A high-resolution modeling study of the 24 May 2002 case during IHOP. Part II: Horizontal convective rolls and convective initiation. Mon. Wea. Rev., 134, 172–191.

    Article  Google Scholar 

  • Xue, M., and W. J. Martin, 2006b: A high-resolution modeling study of the 24 May 2002 case during IHOP. Part I: Numerical simulation and general evolution of the dryline and convection. Mon. Wea. Rev., 134, 149–171.

    Article  Google Scholar 

  • Xue, M., D.-H. Wang, J.-D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139–170.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ming Xue.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shimose, Ki., Xue, M., Palmer, R.D. et al. Two-dimensional variational analysis of near-surface moisture from simulated radar refractivity-related phase change observations. Adv. Atmos. Sci. 30, 291–305 (2013). https://doi.org/10.1007/s00376-012-2087-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00376-012-2087-7

Key words

Navigation