• Arnold, C. P., Jr., and C. H. Dey, 1986: Observing-systems simulation experiments: Past, present, and future. Bull. Amer. Meteor. Soc., 67, 687695, doi:10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2004a: An hourly assimilation–forecast cycle: The RUC. Mon. Wea. Rev., 132, 495518, doi:10.1175/1520-0493(2004)132<0495:AHACTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., B. E. Schwartz, E. J. Szoke, and S. E. Koch, 2004b: The value of wind profiler data in U.S. weather forecasting. Bull. Amer. Meteor. Soc., 85, 18711886, doi:10.1175/BAMS-85-12-1871.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., S. Weygandt, D. Devenyi, J. M. Brown, G. Manikin, T. L. Smith, and T. Smirnova, 2004c: Improved moisture and PBL initialization in the RUC using METAR data. 22nd Conf. on Severe Local Storms, Hyannis, MA, Amer. Meteor. Soc., 17.3. [Available online at https://ams.confex.com/ams/11aram22sls/techprogram/paper_82023.htm.]

  • Benjamin, S. G., B. D. Jamison, W. R. Moninger, S. R. Sahm, B. 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, 13191343, doi:10.1175/2009MWR3097.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Benjamin, S. G., and Coauthors, 2016: A North American hourly assimilation and model forecast cycle: The Rapid Refresh. Mon. Wea. Rev., 144, 16691694, doi:10.1175/MWR-D-15-0242.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bresky, W. C., J. M. Daniels, A. A. Bailey, and S. T. Wanzong, 2012: New methods toward minimizing the slow speed bias associated with atmospheric motion vectors. J. Appl. Meteor. Climatol., 51, 21372151, doi:10.1175/JAMC-D-11-0234.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cardinali, C., 2009: Monitoring the observation impact on the short-range forecast. Quart. J. Roy. Meteor. Soc., 135, 239258, doi:10.1002/qj.366.

  • Côté, J., M. M. Roch, A. Staniforth, and L. Fillion, 1993: A variable-resolution semi-Lagrangian finite-element global model of the shallow water equations, Mon. Wea. Rev., 121, 231243, doi:10.1175/1520-0493(1993)121<0231:AVRSLF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Daniels, T. S., W. R. Moninger, and R. D. Mamrosh, 2006: Tropospheric Airborne Meteorological Data Reporting (TAMDAR) overview. 10th Symp. on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, Atlanta, GA, Amer. Meteor. Soc., 9.1. [Available online at https://ams.confex.com/ams/Annual2006/techprogram/paper_104773.htm.]

  • Gutman, S. I., and S. G. Benjamin, 2001: The role of ground-based GPS meteorological observations in numerical weather prediction. GPS Solutions, 4, 1624, doi:10.1007/PL00012860.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hollingsworth, A., P. Lonnberg, L. Illari, K. Arpe, and A. J. Simmons, 1986: Monitoring of observation and analysis quality by a data assimilation system. Mon. Wea. Rev., 114, 861879, doi:10.1175/1520-0493(1986)114<0861:MOOAAQ>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, X.-Y., and P. Lynch, 1993: Diabatic digital-filtering initialization: Application to the HIRLAM model. Mon. Wea. Rev., 121, 589603, doi:10.1175/1520-0493(1993)121<0589:DDFIAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ingleby, B., 2015: Global assimilation of air temperature, humidity, wind and pressure from surface stations. Quart. J. Roy. Meteor. Soc., 141, 504517, doi:10.1002/qj.2372.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kalnay, E., S. J. Lord, and R. D. McPherson, 1998: Maturity of operational numerical weather prediction: Medium range. Bull. Amer. Meteor. Soc., 79, 27532769, doi:10.1175/1520-0477(1998)079<2753:MOONWP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kelleher, K. E., and Coauthors, 2007: Project CRAFT: A real-time delivery system for NEXRAD level II data via the Internet. Bull. Amer. Meteor. Soc., 88, 10451057, doi:10.1175/BAMS-88-7-1045.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleist, D. T., D. F. Parrish, J. C. Derber, R. Treadon, W.-S. Wu, and S. Lord, 2009: Introduction of the GSI into the NCEP Global Data Assimilation System. Wea. Forecasting, 24, 16911705, doi:10.1175/2009WAF2222201.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lakshmanan, V., J. Zhang, and K. Howard, 2010: A technique to censor biological echoes in radar reflectivity. J. Appl. Meteor. Climatol., 49, 453462, doi:10.1175/2009JAMC2255.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and N. L. Baker, 2004: Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus, 56, 189201, doi:10.3402/tellusa.v56i3.14413.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and Coauthors, 2016: Forecast Sensitivity–Observation Impact (FSOI) Inter-comparison Experiment. Third Int. Winds Workshop, Monterey, CA, NOAA–EUMETSAT–WMO. [Available online at http://cimss.ssec.wisc.edu/iwwg/iww13/talks/01_Monday/1650_IWW13_NRL_FSOI_Langland.pdf.]

  • Le Marshall, J., and Coauthors, 2007: The Joint Center for Satellite Data Assimilation. Bull. Amer. Meteor. Soc., 88, 329340, doi:10.1175/BAMS-88-3-329.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lhermitte, R. M., and D. Atlas, 1960: Precipitation motion by pulse Doppler. Preprints, Ninth Weather Radar Conf., Kansas City, MO, Amer. Meteor. Soc., 218–223.

  • Lin, H., S. S. Weygandt, S. G. Benjamin, and M. Hu, 2017: Satellite radiance data assimilation within the hourly updated Rapid Refresh. Wea. Forecasting, doi:10.1175/WAF-D-16-0215.1, in press.

    • Search Google Scholar
    • Export Citation
  • Lupu, C., C. Cardinali, and A. P. McNally, 2015: Adjoint-based forecast sensitivity applied to observation-error variance tuning. Quart. J. Roy. Meteor. Soc., 141, 31573165, doi:10.1002/qj.2599.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McMurdie, L., and C. Mass, 2004: Major numerical forecast failures over the northeast Pacific. Wea. Forecasting, 19, 338356, doi:10.1175/1520-0434(2004)019<0338:MNFFOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Minnis, P., and Coauthors, 2008: Near-real time cloud retrievals from operational and research meteorological satellites. Remote Sensing of Clouds and the Atmosphere XIII, R. H. Picard et al., Eds., International Society for Optical Engineering (SPIE Proceedings, Vol. 7107-2), 710703, doi:10.1117/12.800344.

    • Crossref
    • Export Citation
  • Minnis, P., and Coauthors, 2011: CERES edition-2 cloud property retrievals using TRMM VIRS and Terra and Aqua MODIS data—Part I: Algorithms. IEEE Trans. Geosci. Remote Sens., 49, 43744400, doi:10.1109/TGRS.2011.2144601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moninger, W. R., R. D. Mamrosh, and P. M. Pauley, 2003: Automated meteorological reports from commercial aircraft. Bull. Amer. Meteor. Soc., 84, 203216, doi:10.1175/BAMS-84-2-203.

    • Crossref
    • 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, 627645, doi:10.1175/2009WAF2222321.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NOAA, 2013: NOAA research program overview: Sandy supplemental. NOAA Rep., 2 pp. [Available online at http://research.noaa.gov/sites/oar/Documents/oarProgramOverview_SandySupplemental_CC.pdf.]

  • Peckham, S. E., T. G. Smirnova, S. G. Benjamin, J. M. Brown, and J. S. Kenyon, 2016: Implementation of a digital filter initialization in the WRF Model and its application in the Rapid Refresh. Mon. Wea. Rev., 144, 99106, doi:10.1175/MWR-D-15-0219.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., 2016: On the impacts and benefits of AMDAR observations in operational forecasting. Part I: A review of the impacts of automated aircraft wind and temperature reports. Bull. Amer. Meteor. Soc., 97, 585602, doi:10.1175/BAMS-D-14-00055.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Petersen, R. A., L. Cronce, R. Mamrosh, R. Baker, and P. Pauley, 2016: On the impact and future benefits of AMDAR observations in operational forecasting. Part II: Water vapor observations. Bull. Amer. Meteor. Soc., 97, 21172133, doi:10.1175/BAMS-D-14-00211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rogers, E., and Coauthors, 2009: The NCEP North American Mesoscale modeling system: Recent changes and future plans. 23rd Conf. on Weather Analysis and Forecasting/19th Conf. on Numerical Weather Prediction, Omaha, NE, Amer. Meteor. Soc., 2A4. [Available online at https://ams.confex.com/ams/23WAF19NWP/techprogram/paper_154114.htm.]

  • Ryzhkov, A., S. E. Giangrande, V. M. Melnikov, and T. J. Schuur, 2005: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 11381155, doi:10.1175/JTECH1772.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shao, H., and Coauthors, 2016: Bridging research to operations transitions: Status and plans of community GSI. Bull. Amer. Meteor. Soc., 97, 14271440, doi:10.1175/BAMS-D-13-00245.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shapiro, M., and A. Thorpe, 2004: THORPEX international science plan, version 3. WMO/TD-1246, WWRP/THORPEX 2, 55 pp. [Available online at www.wmo.int/pages/prog/arep/wwrp/new/documents/CD_ROM_international_science_plan_v3.pdf.]

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

    • Crossref
    • 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, 29142930, doi:10.1175/MWR3436.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tang, L., J. Zhang, C. Langston, J. Krause, K. Howard, and V. Lakshmanan, 2014: A physically based precipitation–nonprecipitation radar echo classifier using polarimetric and environmental data in a real-time national system. Wea. Forecasting, 29, 11061119, doi:10.1175/WAF-D-13-00072.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tollerud, E. I., and Coauthors, 2013: The DTC ensembles task: A new testing and evaluation facility for mesoscale ensembles. Bull. Amer. Meteor. Soc., 94, 321327, doi:10.1175/BAMS-D-11-00209.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86, 205223, doi:10.1175/BAMS-86-2-205.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, X., D. Parrish, D. Kleist, and J. Whitaker, 2013: GSI 3DVar-based ensemble–variational hybrid data assimilation for NCEP Global Forecast System: Single-resolution experiments. Mon. Wea. Rev., 141, 40984117, doi:10.1175/MWR-D-12-00141.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weatherhead, E. C., and Coauthors, 1998: Factors affection the detection of trends: Statistical considerations and applications to environmental data. J. Geophys. Res., 103, 17 14917 161, doi:10.1029/98JD00995.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weckwerth, T. M., 2000: The effect of small-scale moisture variability on thunderstorm initiation. Mon. Wea. Rev., 128, 40174030, doi:10.1175/1520-0493(2000)129<4017:TEOSSM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weckwerth, T. M., and Coauthors, 2004: An overview of the International H2O Project (IHOP_2002) and some preliminary highlights. Bull. Amer. Meteor. Soc., 85, 253277, doi:10.1175/BAMS-85-2-253.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., and T. M. Hamill, 2002: Ensemble data assimilation without perturbed observations. Mon. Wea. Rev., 130, 19131924, doi:10.1175/1520-0493(2002)130<1913:EDAWPO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whitaker, J. S., T. M. Hamill, X. Wei, Y. Song, and Z. Toth, 2008: Ensemble data assimilation with the NCEP Global Forecast System. Mon. Wea. Rev., 136, 463482, doi:10.1175/2007MWR2018.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilczak, J. M., and Coauthors, 1995: Contamination of wind profiler data by migrating birds: Characteristics of corrupted data and potential solutions. J. Atmos. Oceanic Technol., 12, 449467, doi:10.1175/1520-0426(1995)012<0449:COWPDB>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wolfe, D. E., and S. I. Gutman, 2000: Developing an operational, surface-based, GPS, water vapor observing system for NOAA: Network design and results. J. Atmos. Oceanic Technol., 17, 426440, doi:10.1175/1520-0426(2000)017<0426:DAOSBG>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WMO, 2016: USA AMDAR program—Smoothed monthly average of daily (aircraft) report totals. [Available online at https://www.wmo.int/pages/prog/www/GOS/ABO/data/statistics/aircraft_obs_cmc_mthly_ave_daily_reports_by_type.jpg.]

  • Wu, W.-S., R. J. Purser, and D. F. Parrish, 2002: Three-dimensional variational analysis with spatially inhomogeneous covariances. Mon. Wea. Rev., 130, 29052916, doi:10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhu, Y., and R. Gelaro, 2008: Observation sensitivity calculations using the adjoint of the Gridpoint Statistical Interpolation (GSI) analysis system. Mon. Wea. Rev., 136, 335351, doi:10.1175/MWR3525.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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Observation System Experiments with the Hourly Updating Rapid Refresh Model Using GSI Hybrid Ensemble–Variational Data Assimilation

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  • 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado, and NOAA/OAR/Earth System Research Laboratory/Global Systems Division, Boulder, Colorado
  • 2 NOAA/OAR/Earth System Research Laboratory/Global Systems Division, Boulder, Colorado
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Abstract

A set of observation system experiments (OSEs) over three seasons using the hourly updated Rapid Refresh (RAP) numerical weather prediction (NWP) assimilation–forecast system identifies the importance of the various components of the North American observing system for 3–12-h RAP forecasts. Aircraft observations emerge as the strongest-impact observation type for wind, relative humidity (RH), and temperature forecasts, permitting a 15%–30% reduction in 6-h forecast error in the troposphere and lower stratosphere. Major positive impacts are also seen from rawinsondes, GOES satellite cloud observations, and surface observations, with lesser but still significant impacts from GPS precipitable water (PW) observations, satellite atmospheric motion vectors (AMVs), and radar reflectivity observations. A separate experiment revealed that the aircraft-related RH forecast improvement was augmented by 50% due specifically to the addition of aircraft moisture observations. Additionally, observations from en route aircraft and those from ascending or descending aircraft contribute approximately equally to the overall forecast skill, with the strongest impacts in the respective layers of the observations. Initial results from these OSEs supported implementation of an improved assimilation configuration of boundary layer pseudoinnovations from surface observations, as well as allowing the assimilation of satellite AMVs over land. The breadth of these experiments over the three seasons suggests that observation impact results are applicable to general forecasting skill, not just classes of phenomena during limited time periods.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Eric James, eric.james@noaa.gov

Abstract

A set of observation system experiments (OSEs) over three seasons using the hourly updated Rapid Refresh (RAP) numerical weather prediction (NWP) assimilation–forecast system identifies the importance of the various components of the North American observing system for 3–12-h RAP forecasts. Aircraft observations emerge as the strongest-impact observation type for wind, relative humidity (RH), and temperature forecasts, permitting a 15%–30% reduction in 6-h forecast error in the troposphere and lower stratosphere. Major positive impacts are also seen from rawinsondes, GOES satellite cloud observations, and surface observations, with lesser but still significant impacts from GPS precipitable water (PW) observations, satellite atmospheric motion vectors (AMVs), and radar reflectivity observations. A separate experiment revealed that the aircraft-related RH forecast improvement was augmented by 50% due specifically to the addition of aircraft moisture observations. Additionally, observations from en route aircraft and those from ascending or descending aircraft contribute approximately equally to the overall forecast skill, with the strongest impacts in the respective layers of the observations. Initial results from these OSEs supported implementation of an improved assimilation configuration of boundary layer pseudoinnovations from surface observations, as well as allowing the assimilation of satellite AMVs over land. The breadth of these experiments over the three seasons suggests that observation impact results are applicable to general forecasting skill, not just classes of phenomena during limited time periods.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Eric James, eric.james@noaa.gov
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