Analysis of Record Issues: Research Perspective John Horel noaa cooperative Institute for Regional Prediction


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Analysis of Record Issues: Research Perspective

  • John Horel

  • NOAA Cooperative Institute for Regional Prediction

  • Department of Meteorology

  • University of Utah

  • jhorel@met.utah.edu


Science, Technology, and Resources

  • To what extent can the needs and requirements for objective analyses be met given existing scientific understanding, technologies, and resources?

  • What are the critical scientific issues that must be faced in order to successfully develop quality analyses at high spatial/temporal resolution?



Data Assimilation vs. Objective Analysis

  • Data Assimilation

    • Determine best analysis from observations to minimize future model forecast errors


Objective Analysis



Analysis Strategies depend upon goals

  • Define microclimates?

    • Requires attention to details of geospatial information (e.g., minimize terrain smoothing)
  • Resolve mesoscale/synoptic-scale features?

    • Requires good prediction from previous analysis


High terrain (dark),Flat (tan),Valleys (light)



Is There One Answer?

  • Each analysis approach has strengths and weaknesses

  • What are the lessons that can be learned from all of the different analysis approaches?



What Are the Classes of Analyses?

  • Observational error assumed small: Empirical (regression, curve fitting, successive corrections, Barnes) & Nudging

  • Error covariances specified: Sequential (OI, Bratseth) & Variational (3DVAR, PSAS, 4DVAR)

  • Error covariances predicted: Extended Kalman filter, Ensemble Kalman filters



Empirical Methods

  • Observational error ignored

  • Cressman/Barnes

  • PRISM (OSU)

    • Background defined from geospatial information (elevation, slope)
    • Observations distance weighted
  • MatchObsAll (Boise WFO)

    • Spline fit to differences between background and observations






Match Obs All

  • Developed to meet critical needs of forecasters



Science, Technology, and Resources

  • To what extent can the needs and requirements for objective analyses be met given existing scientific understanding, technologies, and resources?

  • What are the critical scientific issues that must be faced in order to successfully develop quality analyses at high spatial/temporal resolution?



Selected Issues for AOR

    • What’s the best way to utilize the available surface observations?
    • Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena?
    • Nocturnal radiational inversions are difficult to analyze in basins/valleys.
    • Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?


Are All Surface Observations Equally Good?

  • All measurements have errors (random and systematic)

  • Errors arise from many factors:

    • Siting (obstacles, surface characteristics)
    • Exposure to environmental conditions (e.g., temperature sensor heating/cooling by radiation, conduction or reflection)
    • Sampling strategies
    • Maintenance standards
    • Metadata errors (incorrect location, elevation)


Using Surface Observations in AORs

  • Advocate using all available surface observations subject to some healthy caution

  • Observing needs and sampling strategies vary (air quality, fire weather, road weather, COOP)

  • Station siting results from pragmatic tradeoffs: power, communication, obstacles, access

  • Accurate metadata are critical

    • Geospatial information must be utilized: terrain, exposure, land use, soil, vegetation type
    • Sensor type, installation, and maintenance
  • Quality control procedures applied to data are very important

  • Observations can be tagged with differing levels of uncertainty



Selected Issues for AOR

    • What’s the best way to utilize the available surface observations?
    • Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena?
    • Nocturnal radiational inversions are difficult to analyze in basins/valleys.
    • Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?


Resolution Issues

  • High resolution analysis based upon coarse background field and sparse data is simply downscaling/regressing to specified grid terrain

  • High resolution analysis adds value if:

  • To what extent can a single deterministic analysis be derived given the spatial variability at sub-grid scales and the temporal variability within 1 hour?



Selected Issues for AOR

    • What’s the best way to utilize the available surface observations?
    • Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena?
    • Nocturnal radiational inversions are difficult to analyze in basins/valleys.
    • Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?


Selected Issues for AOR

    • What’s the best way to utilize the available surface observations?
    • Scales of severe weather phenomena are usually small. What are appropriate horizontal and temporal scales for the analysis to resolve such phenomena?
    • Nocturnal radiational inversions are difficult to analyze in basins/valleys.
    • Vertical decoupling from ambient flow of surface wind during night is difficult to analyze. Which is better guidance: match locally light surface winds or focus upon synoptic-scale forcing?


RUC SLP & MesoWest Observations 12Z 10 Oct. 2003



Temperature and Wind RUC Analysis: 12 Z 10 Oct. 2003



Temperature and Wind ADAS Analysis: 12 Z 10 Oct. 2003



NDFD 12 H Forecast: VT 12Z 10 Oct.



Science, Technology, and Resources

  • To what extent can the needs and requirements for objective analyses be met given existing scientific understanding, technologies, and resources?

  • What are the critical scientific issues that must be faced in order to successfully develop quality analyses at high spatial/temporal resolution?



RUC Temperature Decorrelation DJF 2003-2004



ADAS: ARPS Data Assimilation System

  • ADAS is run in near-real time to create analyses of temperature, relative humidity, and wind over the western U. S. (Lazarus et al. 2002 WAF)

  • Analyses on NWS GFE grid at 5 km spacing in the West

  • Test runs made for lower 48 state NDFD grid at 5 km spacing

  • Typically > 2000 surface temperature and wind observations available via MesoWest for analysis (5500 for lower 48)

  • The 20km Rapid Update Cycle (RUC; Benjamin et al. 2002) is used for the background field

  • Background and terrain fields help to build spatial & temporal consistency in the surface fields

  • Efficiency of ADAS code improved significantly

  • Anisotropic weighting for terrain and coasts added (Myrick et al. 2004)

  • Current ADAS analyses are a compromise solution; suffer from many fundamental problems due to nature of optimum interpolation approach



RUC Temp. Analysis 12UTC 18 March 2004



ADAS Temp. Analysis 12UTC 18 March 2004



MesoWest

  • MesoWest: Cooperative sharing of current weather information around the nation

  • Real-time and retrospective access to weather information through state-of-the-art database http://www.met.utah. edu/mesowest

  • Distributing environmental information to government agencies and the public for protection of life and property

  • Horel et al. (2002) Bull. Amer. Meteor. Soc. February 2002



Nudging

  • Requires empirically determined time constants to relax model towards observations

  • Observational uncertainty ignored

  • The NCAR/ATEC Real-Time Four-Dimensional Data Assimilation and Forecast (RTFDDA) System: Basics, operation and future development Yubao Liu. NCAR/RAP

  • An Automated Humvee-Operated Meteorological Nowcast/Prediction System for the U. S. Army (MMS-Profiler) David Stauffer, Aijun Deng, Annette Gibbs, Glenn Hunter, George Young, Anthony Schroeder and Nelson Seaman http://www.met.psu.edu/dept/research/



Sequential/Variational

  • Sequential: find the optimal weights that minimizes the analysis error covariance matrix

  • Variational: find the optimal analysis that minimizes a scalar cost function

  • MSAS and RSAS Surface Analysis Systems. Patricia A. Miller and Michael F. Barth (NOAA Forecast Systems Laboratory)

  • Analysis of Record. Geoff DiMego

  • An FSL-RUC/RR proposal for the Analysis of Record. Stan Benjamin, Dezso Devenyi, Steve Weygandt, John Brown



Kalman Filters

  • Estimate forecast error covariance

  • Assimilation of Fixed Screen-Height Observations in a Parameterized PBL. Joshua Hacker NCAR

  • Ensemble Filters for Data Assimilation: Flexible, Powerful, and Ready for Prime-Time? Jeff Anderson. NCAR

  • Toward a Real-time Mesoscale Ensemble Kalman Filter. Greg Hakim. U. Washington

  • A New Approach for Mesoscale Surface Analysis: The Space-Time Mesoscale Analysis System. John McGinley, Steven Koch, Yuanfu Xie, Ning Wang, Patricia Miller, and Steve Albers



Upper Level Ridging and Surface Cold Pools: 14 January 2004





Sensitivity of OI/3DVar Solutions to Specification of Error Covariance





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