analysis of record issues: research perspective john horel noaa cooperative institute for regional...
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![Page 1: Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649d805503460f94a64f26/html5/thumbnails/1.jpg)
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
[email protected]@met.utah.edu
General reference:Atmospheric Modeling, Data Assimilation, and Predictability. Kalnay (2003)
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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?
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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
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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
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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
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High terrain (dark),Flat (tan),Valleys (light)High terrain (dark),Flat (tan),Valleys (light)
Microclimates: Diurnal Temperature RangeMicroclimates: Diurnal Temperature Range
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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?
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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
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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
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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
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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
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Match Obs AllMatch Obs All
• Developed to meet critical needs of forecasters
June 9 00Z- Dewpoint Idaho700 mb T RUC
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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?
![Page 14: Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649d805503460f94a64f26/html5/thumbnails/14.jpg)
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?
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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)
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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
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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?
![Page 18: Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649d805503460f94a64f26/html5/thumbnails/18.jpg)
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?
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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?
![Page 20: Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649d805503460f94a64f26/html5/thumbnails/20.jpg)
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?
![Page 21: Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649d805503460f94a64f26/html5/thumbnails/21.jpg)
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
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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
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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?
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NDFD 12 H Forecast: VT 12Z 10 Oct.NDFD 12 H Forecast: VT 12Z 10 Oct.
NDFD Temperature NDFD Wind
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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?
![Page 26: Analysis of Record Issues: Research Perspective John Horel NOAA Cooperative Institute for Regional Prediction Department of Meteorology University of Utah](https://reader035.vdocuments.mx/reader035/viewer/2022062421/56649d805503460f94a64f26/html5/thumbnails/26.jpg)
RUC Temperature DecorrelationRUC Temperature DecorrelationDJF 2003-2004DJF 2003-2004
Cov
aria
nce
Distance (km)
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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
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RUC Temp. Analysis 12UTC 18 March 2004RUC Temp. Analysis 12UTC 18 March 2004
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ADAS Temp. Analysis 12UTC 18 March 2004ADAS Temp. Analysis 12UTC 18 March 2004
Sensitivity to Obs. Errors
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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
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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/
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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
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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
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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
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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
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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
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Mean background, OI, 3DVAR, and Bratseth solutions for 1000 case sample
Myrick et al. 2004