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

Analysis of Record Issues: Research Perspective

• John Horel

• NOAA Cooperative Institute for Regional Prediction

• Department of Meteorology

• University of Utah

• [e-mail]

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:

• Quality data sources are available at high resolution
• AND/OR a quality background field is available at high resolution

• 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