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

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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

Observed and Forecast IR Satellite Brightness Temp 23 Apr 2013 0Z

Observed and Analyzed Cloud Base Height 24 Apr 2013 18Z

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?

Transition to Rapid Updating Analysis

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WHAT IS RUA? Courtesy Jason Levit

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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

Thanks much !

Questions?

More info at http://laps.noaa.gov

Backup slides for additional information.

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

Example of Surface analysis Temp, wind 10 Apr 2013 15Z

Three-Dimensional Cloud Analysis

METAR

LAPS HOT-START INITIALIZATION

FH

FL

+ FIRST GUESS*

3-hr Diabatically (hot-start) initialized WRF-ARW forecast

Analysis

Cloud Analysis Flow Chart

Cloud Fraction 3-D Isosurface

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(From radars and model first guess)