Editorial Type:
Article
Article Type:
Research Article

Assimilation of Surface-Based Boundary Layer Profiler Observations during a Cool-Season Weather Event Using an Observing System Simulation Experiment. Part II: Forecast Assessment

Daniel C. Hartung Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Daniel C. Hartung in
Current site
Google Scholar
PubMed Close
,
Jason A. Otkin Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Jason A. Otkin in
Current site
Google Scholar
PubMed Close
,
Ralph A. Petersen Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Ralph A. Petersen in
Current site
Google Scholar
PubMed Close
,
David D. Turner NOAA/National Severe Storms Laboratory, Norman, Oklahoma, and Department of Atmospheric and Oceanic Sciences, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by David D. Turner in
Current site
Google Scholar
PubMed Close
, and
Wayne F. Feltz Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

Search for other papers by Wayne F. Feltz in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Aug 2011
DOI:
https://doi.org/10.1175/2011MWR3623.1
Page(s):
2327–2346
Restricted access

Abstract

In this study, atmospheric analyses obtained through assimilation of temperature, water vapor, and wind profiles from a potential network of ground-based remote sensing boundary layer profiling instruments were used to generate short-range ensemble forecasts for each assimilation experiment performed in Part I. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). Overall, the results show that the most accurate forecasts were achieved when mass (temperature and humidity profiles from the RAM, MWR, and/or AERI) and momentum (wind profiles from the DWL) observations were assimilated simultaneously, which is consistent with the main conclusion from Part I. For instance, the improved wind and moisture analyses obtained through assimilation of these observations contributed to more accurate forecasts of moisture flux convergence and the intensity and location of accumulated precipitation (ACPC) due to improved dynamical forcing and mesoscale boundary layer thermodynamic structure. An object-based verification tool was also used to assess the skill of the ACPC forecasts. Overall, total interest values for ACPC matched objects, along with traditional forecast skill statistics like the equitable threat score and critical success index, were most improved in the multisensor assimilation cases.

Corresponding author address: Daniel C. Hartung, Cooperative Institute for Meteorological Satellite Studies/Space Science and Engineering Center, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706.E-mail: daniel.hartung@ssec.wisc.edu

Abstract

In this study, atmospheric analyses obtained through assimilation of temperature, water vapor, and wind profiles from a potential network of ground-based remote sensing boundary layer profiling instruments were used to generate short-range ensemble forecasts for each assimilation experiment performed in Part I. Remote sensing systems evaluated during this study include the Doppler wind lidar (DWL), Raman lidar (RAM), microwave radiometer (MWR), and the Atmospheric Emitted Radiance Interferometer (AERI). Overall, the results show that the most accurate forecasts were achieved when mass (temperature and humidity profiles from the RAM, MWR, and/or AERI) and momentum (wind profiles from the DWL) observations were assimilated simultaneously, which is consistent with the main conclusion from Part I. For instance, the improved wind and moisture analyses obtained through assimilation of these observations contributed to more accurate forecasts of moisture flux convergence and the intensity and location of accumulated precipitation (ACPC) due to improved dynamical forcing and mesoscale boundary layer thermodynamic structure. An object-based verification tool was also used to assess the skill of the ACPC forecasts. Overall, total interest values for ACPC matched objects, along with traditional forecast skill statistics like the equitable threat score and critical success index, were most improved in the multisensor assimilation cases.

Corresponding author address: Daniel C. Hartung, Cooperative Institute for Meteorological Satellite Studies/Space Science and Engineering Center, University of Wisconsin—Madison, 1225 W. Dayton St., Madison, WI 53706.E-mail: daniel.hartung@ssec.wisc.edu
Save
Cover Monthly Weather Review
Monthly Weather Review

  • Ahjevych, D., E. Gilleland, B. Brown, and E. Ebert, 2009: Application of spatial verification methods to idealized and NWP gridded precipitation forecasts. Wea. Forecasting, 24, 1485–1497.

    • Search Google Scholar
    • Export Citation

  • AIRDAT, 2004: TAMDAR sensor and system overview: Airborne real time atmospheric data collection and reporting system. AIRDAT, LLC, Morrisville, NC, 10 pp. [Available online at http://www.airdat.com/tamdar/tech_docs/TAMDAR%20Overview%20101904.pdf.]

    • Search Google Scholar
    • Export Citation

  • Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: A community facility. Bull. Amer. Meteor. Soc., 90, 1283–1296.

    • Search Google Scholar
    • Export Citation

  • Baldwin, M. E., and J. S. Kain, 2006: Sensitivity of several performance measures to displacement error, bias, and event frequency. Wea. Forecasting, 21, 636–648.

    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., K. A. Brewster, R. Brümmer, B. F. Jewett, T. W. Schlatter, T. L. Smith, and P. A. Stamus, 1991: An isentropic 3-hourly data assimilation system using ACARS aircraft observations. Mon. Wea. Rev., 119, 888–906.

    • Search Google Scholar
    • Export Citation

  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. E. Schwartz, and T. W. Schlatter, 2010: Relative short-range forecast impact from aircraft, profiler, radiosonde, VAD, GPS-PW, METAR, and mesonet observations via the RUC hourly assimilation cycle. Mon. Wea. Rev., 138, 1319–1343.

    • Search Google Scholar
    • Export Citation

  • Casati, B., and Coauthors, 2008: Forecast verification: Current status and future directions. Meteor. Appl., 15, 3–18.

  • Chapman, M., R. Bullock, B. G. Brown, C. A. Davis, K. W. Manning, R. Morss, and A. Takacs, 2004: An object-oriented approach to the verification of quantitative precipitation forecasts: Part II—Examples. Preprints, 17th Conf. on Probability and Statistics in the Atmospheric Sciences/20th Conf. on Weather Analysis and Forecasting/16th Conf. on Numerical Weather Prediction, Seattle, WA, Amer. Meteor. Soc., J12.5. [Available online at http://ams.confex.com/ams/pdfpapers/70881.pdf.]

    • Search Google Scholar
    • Export Citation

  • Davis, C., B. Brown, and R. Bullock, 2006a: Object-based verification of precipitation forecasts. Part I: Methodology and application to mesoscale rain areas. Mon. Wea. Rev., 134, 1772–1784.

    • Search Google Scholar
    • Export Citation

  • Davis, C., B. Brown, and R. Bullock, 2006b: Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Wea. Rev., 134, 1785–1795.

    • Search Google Scholar
    • Export Citation

  • Davis, C., B. G. Brown, R. Bullock, and J. Halley Gotway, 2009: The Method for Object-based Diagnostic Evaluation (MODE) applied to numerical forecasts from the 2005 NSSL/SPC Spring program. Wea. Forecasting, 24, 1252–1267.

    • Search Google Scholar
    • Export Citation

  • Donaldson, R. J., R. M. Dyer, and R. M. Kraus, 1975: An objective evaluator of techniques for predicting severe weather events. Preprints, Ninth Conf. on Severe Local Storms, Norman, OK, Amer. Meteor. Soc., 321–326.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 1498–1510.

  • Ebert, E. E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179–202.

    • Search Google Scholar
    • Export Citation

  • Ebert, E. E., and W. A. Gallus Jr., 2009: Toward better understanding of the contiguous rain area (CRA) method for spatial forecast verification. Wea. Forecasting, 24, 1401–1415.

    • Search Google Scholar
    • Export Citation

  • Gallus, W. A., 2010: Application of object-based verification techniques to ensemble precipitation forecasts. Wea. Forecasting, 25, 144–158.

    • Search Google Scholar
    • Export Citation

  • Gandin, L. S., and A. H. Murphy, 1992: Equitable skill scores for categorical forecasts. Mon. Wea. Rev., 120, 361–370.

  • Gilleland, E., D. Ahijevych, B. G. Brown, B. Casati, and E. E. Ebert, 2009: Intercomparison of spatial forecast verification methods. Wea. Forecasting, 24, 1416–1430.

    • Search Google Scholar
    • Export Citation

  • Kuo, Y.-H., and Y.-R. Guo, 1989: Dynamic initialization using observations from a hypothetical network of profilers. Mon. Wea. Rev., 117, 1975–1998.

    • Search Google Scholar
    • Export Citation

  • Kuo, Y.-H., E. G. Donall, and M. A. Shapiro, 1987: Feasibility of short-range numerical weather prediction using observations from a network of wind profilers. Mon. Wea. Rev., 115, 2402–2427.

    • Search Google Scholar
    • Export Citation

  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407–430.

    • Search Google Scholar
    • Export Citation

  • Moninger, W. R., S. G. Benjamin, B. D. Jamison, T. W. Schlatter, T. L. Smith, and E. J. Szoke, 2010: Evaluation of regional aircraft observations using TAMDAR. Wea. Forecasting, 25, 627–645.

    • Search Google Scholar
    • Export Citation

  • National Research Council, 2009: Observing Weather and Climate from the Ground Up: A Nationwide Network of Networks. The National Academies Press, 250 pp. [Available online at http://www.nap.edu/catalog/12540.html.]

    • Search Google Scholar
    • Export Citation

  • Otkin, J. A., D. C. Hartung, D. D. Turner, R. A. Petersen, W. F. Feltz, and E. Janzon, 2011: Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part I: Analysis impact. Mon. Wea. Rev., 139, 2309–2326.

    • Search Google Scholar
    • Export Citation

  • Rostkier-Edelstein, D., and J. P. Hacker, 2010: The roles of surface-observation ensemble assimilation and model complexity for nowcasting of PBL profiles: A factor separation analysis. Wea. Forecasting, 25, 1670–1690.

    • Search Google Scholar
    • Export Citation

  • Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570–575.

  • Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005: A description of the Advanced Research WRF version 2. NCAR Tech. Note/TN-468+STR, 88 pp.

    • Search Google Scholar
    • Export Citation

  • Smith, T. L., S. G. Benjamin, S. I. Gutman, and S. Sahm, 2007: Short-range forecast impact from assimilation of GPS-IPW observations into the Rapid Update Cycle. Mon. Wea. Rev., 135, 2914–2930.

    • Search Google Scholar
    • Export Citation

  • Stein, U., and P. Alpert, 1993: Factor separation in numerical simulations. J. Atmos. Sci., 50, 2107–2115.

  • Weisman, M. L., C. Davis, W. Wang, K. W. Manning, and J. B. Klemp, 2008: Experiences with 0–36-h explicit convective forecasts with the WRF-ARW model. Wea. Forecasting, 23, 407–437.

    • Search Google Scholar
    • Export Citation

  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.

  • World Meteorological Organization, 2003: Aircraft Meteorological Data Relay (AMDAR) reference manual. WMO Publication 958, World Meteorological Organization, Geneva, Switzerland, 80 pp.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 374 94 13
PDF Downloads 260 49 4