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NextGen FAB Progress and Plans
Steve Albers, Isidora Jankov, Zoltan Toth, Scott Gregory, Kirk Holub, Yuanfu Xie, Paula McCaslin
NOAA/ESRL/GSD Forecast Application Branch
Updated July 18 2013, 0000UTC
Presentation Outline
LAPS Overview
Recent Progress (Year 1 – AIV Validation)
Future Plans (Years 2,3 – Model Bias Correction)
Role of LAPS in RUA?
What is Local Analysis and Prediction System (LAPS) -- Variational LAPS?
LAPS• Observation oriented analysis• Efficient and fine resolution analysis, short latency• Portability and ease of use• Multiscale analysis• Hot-start analysis• Cloud analysis• Good performance in verification of real time forecastMoving LAPS toward variational LAPS• Gradually merging LAPS processes into a unified variational system
• Possessing the above traditional LAPS features• Providing spatial consistent analysis• Using CRTM for assimilation satellite data (AMSU under testing)• Terrain-following coordinate variational analysis is being tested
LAPS MotivationHigh Resolution (500m – 20km), rapid update (10-60min), local to global
Highly portable system Collaboration with user community - about 150 world wide
Federal Gov’t – NWS, RSA, PADS, FAA, DHS, SOS State Gov’t – California Dept of Water Resoures International – Finnish Met. Inst., China Heavy Rain Inst. Private Sector – Toyota, WDT
Wide variety of data sources:
OAR/ESRL/GSD/Forecast Applications Branch *
Presentation Outline
LAPS Overview
Recent Progress (Year 1 – AIV Validation)
Future Plans (Years 2,3 – Model Bias Correction)
Role of LAPS in RUA?
AIV Validation Progress
Real-time statistics comparing LAPS with observations available
• Analyses compared with mostly dependent observations
• Forecasts compared with independent observations
• State variables (wind, temperature, humidity, precipitation)
• Surface and aggregated 3-D variables
• Available on-line at laps.noaa.gov/verif/
Cloud / Reflectivity / Precip Type (1km 15-min analysis)
DIA
Obstructions to visibility along approach paths
*
AIV Validation Progress
Statistics of analyzed and forecast AIVs being investigated• One approach is using IR (11 micron) satellite to help verify clouds
• Compare gridded forecast and observed/analyzed brightness temp
• Verifying both forecasts and analyses
• Compare forecast (or analyzed) cloud ceiling with METARs
• Presently done qualitatively (with overlays of data)• Consider doing quantitatively, possibly collaborating with
verification group in ACE
Observed & Forecast IR Satellite Brightness Temp HWT 3km Domain 25 Jun 2013 0400 - 0600Z
• Simulated VIS also available (derived from cloud amount)• Forecasters are naturally familiar with satellite images• Used for objective cloud forecast verification
OBS Forecast
AIV Validation Progress
Precipitation related AIVs
• Threat Score (ETS, Bias) calculated for radar reflectivity thresholds
• Threat Score (ETS, Bias) calculated for precipitation amount
HWT 1km V-LAPS0-3 h Composite Reflectivity Verification
Higher ETS (best at short lead time)Compare WRF initialization schemes, work with DTC?
Var. LAPS Initialization
Cloud Analysis Independent ValidationAll-sky Imager• Compare LAPS simulated all-sky analyses (or forecasts) to actual all-sky imagery
• Validates quality of analyses (or forecasts) of clouds / visibility obstructions
Courtesy: Longmont
Astronomical Society
All-Sky Camera
Sun Glare
Cloud Analysis Independent Validation
All-sky Imager• This example has more clouds with high opacity
• Validation leads to improvements (e.g. parallax correction, thin cirrus)
• Can be extended to airplane point of view
Courtesy: Longmont Astronomical Society
Sun Glare
Presentation Outline
LAPS Overview
Recent Progress (Year 1 – AIV Validation)
Future Plans (Years 2,3 – Model Bias Correction)
Role of LAPS in RUA?
Statistical Post-processing of Ensemble Forecasts for Aviation Applications
Premise:• Statistically corrected ensemble forecasts will provide ultimate 6D datacube from which all forecast information, including covariability across variables, space, and time will be derivable
• Current State
• NAEFS - North American Ensemble Forecast System• global ensemble data, 1x1 degree resolution
• LAMP- http://www.nws.noaa.gov/mdl/lamp/
• Processed at obs sites, spread to grid
• No systematic processing of AIVs yet
• Objective
• Develop methods and test them in collaboration with EMC & MDL
Statistical Post-processing of Ensemble Forecasts for Aviation Applications
Produce ensemble of statistically bias corrected and calibrated 3-D AIV and other variables
• Why GSD/FAB?
• Combination of expertise in these areas
• Statistical post-processing
• Data assimilation
• Numerical Weather Prediction
•Proven record of collaboration
• Involvement in DTC
• Collaboration planned with EMC/NCEP & MDL (K. Gilbert et al)
Statistical Post-processing of Ensemble Forecasts for Aviation Applications
Produce ensemble of statistically bias corrected and calibrated 3-D AIV and other variables
• Gridded NWP analyses checked with observations used as "truth"
• Assess systematic errors in ensemble mean and spread
Data
Analysis
• Use variational version of 3-D LAPS analysis
• Installed in AWIPS-II and used operationally by the WFOs
Ensemble
• ExREF (Experimental Regional Ensemble Forecast System)
• 9-km experimental ensemble developed among GSD, HMT, EMC
• Used experimentally by NWS/WR, WPC
• Goal is to transfer new methods to EMC for operational SREF use
Choice of Variables / Methods
Model Prognostic Variables and Derived Variables• All will be bias corrected • AIVs derived from bias corrected prognostic variables• Will test if these AIVs are well calibrated• Bias correction represents new capability for NCEP
3-D Cloud Liquid, Cloud Ice, Precipitating Hydrometeors• Prognostic variable to be calibrated• Derived variables include cloud base, visibility• Determine ratio of ensemble spread and mean error • This spread correction method considered by EMC for NAEFS use
3-D Winds
Bayesian Methods (in FY`15)
Bayesian Processor of Ensemble (BPE)• Developed by R. Krzystofowicz et al for statistical AIV correction
Advantages• Proper treatment of non-Gaussian variables• More advanced methods to correct 2nd and higher moments of forecast distribution• Uses analyzed climatological distribution in correction process • Fuses predictive information from latest obs and/or analysis into correction process
BPE method will be implemented and tested with EMC• Transferrable to NCEP operations
Presentation Outline
LAPS Overview
Recent Progress (Year 1)
Future Plans (Years 2,3)
Role of LAPS in RUA?
ROLE OF LAPS IN RUA - PLUSES1) Very frequent update (10-15 mins, can be 5 mins)
2) Run at 1 km resolution (see eg HWT real time experiments)
3) Can be run either 2D or 3D
4) Uses multitude of observations
5) Uses multi-radar mosaicing, reflectivity, cloud liquid/ice, lightning, etc
6) LAPS executes operationally on AWIPS & AWIPS2 - can be ported to NCEP? What are criteria?
7) Variational LAPS - state of the art DA, with following innovations: multiscale, control variables, obs preconditioning, etc.
8) Used both as real time analysis for situational awareness & for initializing NWP WOF models (see, e.g., HWT)
ROLE OF LAPS IN RUA - NEGATIVEVariational LAPS meets most if not all criteria by Jason except:
• Not "GSI-based", not in "GSI framework"o GSI is not flexible or modular, unyieldy for development
E.g., LAPS multiscale and control variable choices very difficult to implement in GSI
• What does this criterion cover? o What warrants this? GSI has been used at NCEP for 20+ yrs?
• What criteria we think should be considered primarily?o Performance
E.g., Reflectivity ETS - LAPS competitive with persistence in 0-3 hrs
o Speed LAPS 18 times faster than GSI on same grid etc
o Modularity Both GSI and LAPS has work to do
o Other considerations? Please share
OUR VISION - NOAA DA REPOSITORY• NOAA's DA scheme 5-10 yrs from now will not be like current
GSI or LAPSo Will have components from both and other systems
• Create NOAA DA repositoryo Bring GSI, LAPS, and selected other NOAA DA systems onto
common platform (eg, DA systems at NSSL, AOML) Modularize each Test exchanging components to find optimal configuration for
each applicationo Engage DTC - difficult undertaking
Define goals and rules of engagement
• Accelerating NOAA's DA development that willo Set the foundation for development of NOAA's next
generation DA system(s)o Be configurable from common repository
PROPOSED LAPS WORK FOR RUA• Compare 2D RTMA with 2D variational LAPS
o Subjectivelyo Objectively against dependent / independent observations
• What additional, not listed features are desired of RUA? LAPS can focus on and add those
• If LAPS is deemed "not implementable" at NCEPo Fix shortcomings
Less costly than adding special LAPS features into GSI?
• Add other desired features into LAPS such aso Visual / quantitative products for
Visibility Particles
NextGen FAB Team Members
Steve Albers - FAB contact, DA, Verification
Scott Gregory - Ensemble Statistics
Isidora Jankov - Ensemble Statistics
Kirk Holub - AIV Verification
Paula McCaslin - AIV Verification, Visualization
Zoltan Toth - Project Guidance
Yuanfu Xie - Data Assimilation
LAPS System Overview
Data Ingest
Intermediatedata files
GSI
ENSEMBLE FORECAST MODEL
Verification
Analysis Scheme
Downscaling can work as a stand alone module
from background → GSI or other
applications such as Fire wx.
Downscaling is also an integral part of variational LAPS
(aka. STMAS).
Data Background (or cycled forecast)Observations
Standalone downscaling
module
Traditional LAPS
Variational LAPS (with downscaling)
Model prep
Transition from Traditional to Fully Variational LAPS
state vars, wind (u,v) clouds / precip
balance and constraintsin multi-scale variational
analysis
Windanalysis
Temp/Ht analysis
Humidity analysis
Cloud analysis
balance
Traditional LAPS analysis: Wind, Temp, Humidity, Cloud, Balance
Ultimately
Temporary hybrid system: Traditional LAPS cloud analysis and
balance
NumericalForecast
model
Large Scale Model First Guess
Cycling Option var. LAPS