Analysis of Record Issues:Analysis of Record Issues: Research Perspective Research Perspective

John HorelJohn HorelNOAA Cooperative Institute for Regional PredictionNOAA Cooperative Institute for Regional Prediction

Department of MeteorologyDepartment of MeteorologyUniversity of UtahUniversity of Utah

jhorel@met.utah.edujhorel@met.utah.edu

General reference:Atmospheric Modeling, Data Assimilation, and Predictability. Kalnay (2003)

Science, Technology, and ResourcesScience, 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 vs. Objective Analysis

• Data Assimilation– Determine best

analysis from observations to minimize future model forecast errors

• Objective Analysis– Determine best

analysis from observations subject to specified constraints

Objective AnalysisObjective Analysis

Analysis value = Background value + observation Correction

- A good analysis requires a good background field- Background fields can be supplied by a model forecast- Observation correction depends upon weighted differences between observations & background values at observation locations

-Critical parameters and assumptions:- magnitude and relationship (covariance) between observational errors- magnitude and relationship (covariance) between background/model errors

Analysis Strategies depend upon goalsAnalysis 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)High terrain (dark),Flat (tan),Valleys (light)

Microclimates: Diurnal Temperature RangeMicroclimates: Diurnal Temperature Range

Is There One Answer?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?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 MethodsEmpirical 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

Relevant PRISM DatasetsAvailable Now

http://www.ocs.oregonstate.edu/prism/US and W Canada mean monthly climate grids

•All 50 states, plus YT,BC,AB,SK,MB• Tmin, Tmax, Precip• 1961-90 (1971-2000 update for CONUS)• 4-km resolution

Sequential monthly climate grids: “Monthly version of Analysis of Record”

• Jan 1895 – present (ongoing project)• CONUS• Tmin, Tmax, Precip, Dew Pt• 4-km resolution• Current methodology uses 1961-90 mean monthly grids as predictors

Rain Shadows: 1961-90 Mean Annual PrecipitationOregon Cascades

Portland

Eugene

Sisters

Redmond

Bend

Mt. Hood

Mt. Jefferson

Three Sisters

N

350 mm/yr

2200 mm/yr

2500 mm/yr

Dominant PRISM KBSComponents

Elevation

Terrain orientation

Terrain profile

Moisture Regime

Match Obs AllMatch Obs All

• Developed to meet critical needs of forecasters

June 9 00Z- Dewpoint Idaho700 mb T RUC

Science, Technology, and ResourcesScience, 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 AORSelected 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?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 AORsUsing 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 AORSelected 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 IssuesResolution 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 AORSelected 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 AORSelected 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 &RUC SLP &MesoWestMesoWest

ObservationsObservations12Z 10 Oct. 12Z 10 Oct.

20032003Weak winds reflect local blocking and other terrain effects that result in decoupling surface winds from synoptic forcing

Temperature and Wind RUC Analysis: 12 Z 10 Oct. Temperature and Wind RUC Analysis: 12 Z 10 Oct. 20032003

Temperature (C) Vector Wind and Speed (m/s)

Analyzed strong pre/post frontal winds consistent withsynoptic-scale forcing

Temperature and Wind ADAS Analysis: 12 Z 10 Oct. Temperature and Wind ADAS Analysis: 12 Z 10 Oct. 20032003

Temperature (C) Vector Wind and Speed (m/s)

ADAS analysis, forced by local obs, weakens RUC winds: which is correct?

NDFD 12 H Forecast: VT 12Z 10 Oct.NDFD 12 H Forecast: VT 12Z 10 Oct.

NDFD Temperature NDFD Wind

Science, Technology, and ResourcesScience, 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 DecorrelationRUC Temperature DecorrelationDJF 2003-2004DJF 2003-2004

Cov

aria

nce

Distance (km)

ADAS: ARPS Data Assimilation ADAS: ARPS Data Assimilation SystemSystem

• 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 2004RUC Temp. Analysis 12UTC 18 March 2004

ADAS Temp. Analysis 12UTC 18 March 2004ADAS Temp. Analysis 12UTC 18 March 2004

Sensitivity to Obs. Errors

MesoWestMesoWest• 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

NudgingNudging

• 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/VariationalSequential/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 FiltersKalman 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:Upper Level Ridging and Surface Cold Pools: 14 January 2004 14 January 2004

NDFD 48 h forecast Analysis

Surface Cold Pool Event: 14 January 2004Surface Cold Pool Event: 14 January 2004

NDFD 48 h forecast ADAS Analysis

NDFD and ADAS DJF 2003-2004 seasonal means removed

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

Myrick et al. (2004)

Sample of 1000 analyses with random observations and background fields

Background errors strongly correlated

Background errors anticorrelated

Mean background, OI, 3DVAR, and Bratseth solutions for 1000 case sample

Myrick et al. 2004