Assimilation of surface-based boundary layer profiler observations during a cool-season weather event using an observing system simulation experiment. Part II: Forecast assessment

Monthly Weather Review

Volumn 139, Issue 8, 2011, Pages 2327-2346

  Hartung, Daniel C ^a   Otkin, Jason A ^a   Petersen, Ralph A ^a   Turner, David D ^b,c   Feltz, Wayne F ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b NATIONAL SEVERE STORMS LABORATORY   (United States)

^c UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Boundary layer; Data assimilation; Ensembles; Forecasting; Profilers; Winter cool season

Indexed keywords

ATMOSPHERIC ANALYSIS; ATMOSPHERIC EMITTED RADIANCE INTERFEROMETERS; DATA ASSIMILATION; DOPPLER WIND LIDAR; ENSEMBLES; EQUITABLE THREAT SCORE; GROUND-BASED REMOTE SENSING; HUMIDITY PROFILE; MESOSCALE; MICROWAVE RADIOMETERS; MOISTURE FLUX CONVERGENCES; MULTI SENSOR; OBJECT BASED; OBSERVING SYSTEM SIMULATION EXPERIMENTS; PROFILERS; PROFILING INSTRUMENT; RAMAN LIDAR; REMOTE SENSING SYSTEM; SHORT RANGE ENSEMBLE FORECASTS; SURFACE-BASED; THERMODYNAMIC STRUCTURES; VERIFICATION TOOLS; WEATHER EVENTS; WIND PROFILES; WINTER/COOL SEASON;

ATMOSPHERIC HUMIDITY; COMPUTER SIMULATION; DATA PROCESSING; EXPERIMENTS; FORECASTING; MICROWAVE DEVICES; MOISTURE; OPTICAL RADAR; RATING; REMOTE SENSING; WATER VAPOR;

ATMOSPHERIC BOUNDARY LAYER;

AIR TEMPERATURE; ATMOSPHERIC FORCING; ATMOSPHERIC MOISTURE; ATMOSPHERIC STRUCTURE; BOUNDARY LAYER; COMPUTER SIMULATION; COOLING; DATA ASSIMILATION; ENSEMBLE FORECASTING; LIDAR; RADIOMETER; RELATIVE HUMIDITY; WEATHER FORECASTING; WIND PROFILE; WINTER;

EID: 80052421209     PISSN: 00270644     EISSN: None     Source Type: Journal    
DOI: 10.1175/2011MWR3623.1     Document Type: Article

References (32)

1. 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.
2. AIRDAT, 2004:TAMDARsensor 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.]
3. Anderson, J., T. Hoar, K. Raeder, H. Liu, N. Collins, R. Torn, and A. Avellano, 2009: The Data Assimilation Research Testbed: Acommunity facility. Bull. Amer. Meteor. Soc., 90, 1283-1296.
4. 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.
5. 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.
6. 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.
7. Casati, B., and Coauthors, 2008: Forecast verification: Current status and future directions. Meteor. Appl., 15, 3-18.
8. 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.] 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.
9. 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.
10. Davis, C.,R.Bullock, and B.G. Brown, 2006b: Object-based verification of precipitation forecasts. Part II: Application to convective rain systems. Mon. Wea. Rev., 134, 1785-1795.
11. 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.
12. 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.
13. Ebert, E. E., 2009: Neighborhood verification: A strategy for rewarding close forecasts. Wea. Forecasting, 24, 1498-1510.
14. Ebert, E. E., and J. L. McBride, 2000: Verification of precipitation in weather systems: Determination of systematic errors. J. Hydrol., 239, 179-202.
15. 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.
16. Gallus, W. A., 2010: Application of object-based verification techniques to ensemble precipitation forecasts. Wea. Forecasting, 25, 144-158.
17. Gandin, L. S., and A. H. Murphy, 1992: Equitable skill scores for categorical forecasts. Mon. Wea. Rev., 120, 361-370.
18. 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.
19. Kuo, Y.-H., and Y.-R. Guo, 1989: Dynamic initialization using observations from a hypothetical network of profilers. Mon. Wea. Rev., 117, 1975-1998.
20. Kuo, Y.-H., E. G. Donall, and M. A. Shapiro, 1987: Feasibility of shortrange numerical weather prediction using observations from a network of wind profilers. Mon. Wea. Rev., 115, 2402-2427.
21. 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.
22. 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.
23. National Research Council, 2009: Observing Weather and Climate from the Ground Up:ANationwide Network of Networks. The National Academies Press, 250 pp. [Available online at http://www.nap.edu/catalog/12540.html.]
24. 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.
25. Rostkier-Edelstein, D., and J. P. Hacker, 2010: The roles of surfaceobservation ensemble assimilation and model complexity for nowcasting of PBL profiles: A factor separation analysis. Wea. Forecasting, 25, 1670-1690.
26. Schaefer, J. T., 1990: The critical success index as an indicator of warning skill. Wea. Forecasting, 5, 570-575.
27. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2005:Adescription of the Advanced Research WRF version 2. NCAR Tech. Note/TN-4681STR, 88 pp.
28. 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.
29. Stein, U., and P. Alpert, 1993: Factor separation in numerical simulations. J. Atmos. Sci., 50, 2107-2115.
30. 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.
31. Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 467 pp.
32. World Meteorological Organization, 2003: Aircraft Meteorological Data Relay (AMDAR) reference manual. WMO Publication 958,WorldMeteorological Organization, Geneva, Switzerland, 80 pp.