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Adaptive targeting in OSSE

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Implement ET similarly as NCEP operational Ensemble forecast system Coding ... Next T00Z ET KF Ensemble Transform Kalman Filter for short ET KF ... – PowerPoint PPT presentation

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Title: Adaptive targeting in OSSE


1
Adaptive targeting in OSSE
Yucheng Song and Zoltan Toth
  • Outline
  • Adaptive observing / data processing
    techniques in OSSE
  • Addition to OSSE
  • Link with THORPEX
  • Link with T-PARC

2
(1)Adaptive data observing/processing techniques
in OSSE
  • Test methods/platforms/application in OSSE
    framework
  • Develop software into OSSE
  • Ensemble (T126 or T170) product generation in
    OSSE
  • ETKF targeting strategy (certain instruments)
  • Evaluate data impact by certain instruments like
    UAS, Doppler Wind Lidar

3
NCEP Operational GEFS
  • NAEFS (NCEP/GEFS)
  • 80 perturbations in cycling (see next slide)
  • Replaced previous 56 perturbations in ensemble
    transform (ET) cycling
  • 20 perturbed long forecasts (16-d) in each cycle
  • Replaced previous14 long forecasts in each cycle

4
6 hours ET cycle NCEP ensemble (ET)
Re-scaling
6hrs
Up to 16-d
Next T00Z
T00Z 80m
Re-scaling
T06Z 80m
Up to 16-d
Re-scaling
T12Z 80m
Up to 16-d
Re-scaling
T18Z 80m
Up to 16-d
5
Concept of ET KF
  • ET KF Ensemble Transform Kalman Filter for short
  • ET KF provides a framework for estimating the
    effect
  • of observations on forecast error covariance
  • ET KF uses ensemble transformation and a
    normalization
  • to obtain the prediction error covariance matrix
    associated
  • with a particular deployment of observational
    resources
  • Linearity is assumed for ensemble transformation

6
ET KF formulation
7
ET KF formulation
8
Targeting methods - ETKF
Dropsondes to be made by G-IV
Storm
The ETKF spotted the target area
Expected error reduction propagation
9
MAIN THEME
Study the lifecycle of perturbations as they
originate from the tropics, Asia, and/or the
polar front, travel through the Pacific
waveguide, and affect high impact wintertime
weather events over North America and the Arctic
Influence of tropical Flare-ups in western
Pacific (IR) on deep cyclogenesis in northeast
Pacific captured by Ensemble Transform targeting
method
10
Better adaptive strategy if implemented (examples)
The optimal sampling region located in the jet
core
11
(2)Additions to OSSE
  • Assess threat of high impact events based on
    ensemble automatically pick high impact events
    at 3-day leading time
  • Run ET/ET KF targeting for each high impact case
  • Dispatch observing systems/data processing
    resources (before and inside DA)
  • Wind Lidar, UAV etc.
  • Assimilate targeted data (carry out adaptive data
    processing)
  • Evaluation (EXAMPLES NEXT FROM WSR)

12
Impact of Data
Surface pressure
Precipitation
Contours are 1000mb geopotential height, shades
are differences in the fields between two
experiments
500mb height
250mb height
13
Forecast verification
500mb height
Sea Level Pressure
Red contours show forecast improvement due to WSR
dropsondes, blue contours show forecast
degradation
250mb height
14
Forecast Verification for Temperature
(Measure by root-mean-square errors)
10-20 RMS error reduction in Temperature
60 hr forecast is equivalent to 48hr forecast
RMS error reduction vs. forecast lead time
15
(3)Link with THOPREX
  • THORPEX A World Weather Research Program
    (WWRP)
  • Accelerate improvements in skill/utility of 1-14
    day weather forecasts
  • Long-term (10-yrs) global research program in
    areas of
  • Observing system, data assimilation, numerical
    modeling/ensemble, socioec. appl.
  • Strong link with operational Numerical Weather
    Prediction (NWP) centers
  • International program under WMO

16
THORPEX evaluation metrics (1)
  • Possible new probabilistic guidance products for
    high impact events
  • Hydrometeorology
  • Extreme hydro-meteorological events, incl. dry
    and wet spells (CONUS)
  • Quantitative extreme river flow forecasting
    (OCONUS)
  • Tropical / winter storm prediction
  • Extreme surface wind speed
  • Extreme precipitation (related to wet spells)
  • Storm surges
  • Aviation forecasting
  • Flight restriction
  • Icing, visibility, fog, clear air turbulence
  • Health and public safety
  • Hot and cold spells

17
THORPEX evaluation metrics (2)
  • Legacy NCEP internal probabilistic scores to
    assess long-term progress
  • General circulation
  • Probabilistic 1000 500mb height forecasts
  • Storm
  • Strike probability for track
  • Probability of intensity (central pressure or
    wind-based)

18
(4) T-PARC interestsGlobal optimal positioning
of observing systems in OSSEImprove forecast
accuracy
19
T-PARC PROPOSED OBSERVING PLATFORMS
Day 3-4 Radiosondes Russia
NA VR
Day 5-6 Radiosondes Tibet
CONUS VR
D 2-3 G-IV
D 1-2 C-130 UAS
D-1 UAS P-3
Day 3-4 GEMS Driftsondes Aerosondes
Extensive observational platforms during T-PARC
winter phase allow us to track the potential
storms and take additional observations as the
perturbation propagate downstream into Arctic and
US continents
20
Before and after field campaign
  • Nature is defined as a series of states
    corresponding to the real atmosphere
  • Generated by very high resolution model runs
    nudged by operational analysis (GDAS)
  • Advantages
  • Use T-PARC type OSE to calibrate OSSE system
    much easier to calibrate, community will be
    convinced if we can reproduce their OSE work
  • Retrospective work after T-PARC T-PARC represent
    only one configuration of global observing
    system, with OSSE such defined, many other
    configuration can be tested
  • This is an alternative

21
Advantages (more)
  • Ease of calibration (one-to-one comparison, can
    quantitatively evaluate osse system based on a
    SINGLE (or few) case(s), instead of requiring a
    large sample of cases
  • Close to realistic representation of model
    related uncertainty
  • No need to painstakingly evaluate or amend osse
    nature run
  • Can use humidity (cloud, moisture) observations
    from real world to decide if certain observations
    can be made or not in osse world - potentially a
    big contribution to making osse real life-like
  • Same nature can be redone with higher resolution
    or other type of model (using operational
    analysis as forcing) - direct comparison of
    different OSSE systems possible
  • Estimate how proposed new observing systems
    would help analysis/forecast for real life
    significant events (Katrina, etc)
  • Post field campaign analysis Add significant
    value by osse testing of alternative deployments
    (after calibration in which actual and simulated
    field phase observations are assimilated and
    their impacts are compared in both OSE and OSSE
    framework

22
Concern
  • Improved analysis might not mean improved
    forecast for individual cases
  • We think statistically it will improve forecasts

23
OSSE strategy
  • 1. Implement ET similarly as NCEP operational
    Ensemble forecast system
  • Coding development
  • Initial conditions (Data analysis
    from conventional data radiance data
    assimilation)
  • 2. Targeting strategy similarly as WSR
  • Identify typical storm cases in the Nature
    run
  • use targeting strategy to find sensitive
    areas to target
  • 1. Increase data resolution in sensitive
    areas (adaptive grid)
  • 2. Direct observation

24
T-PARC interests (Ideas can be tested in OSSE)
  • Rossby-wave plays a major role in the development
    of high impact weather events over North America
    and the Arctic on the 3-5 days forecast time
    scale
  • Additional remotely sensed and in situ data can
    complement the standard observational network in
    capturing critical processes in Rossby-wave
    initiation and propagation
  • Adaptive configuration of the observing network
    and data processing can significantly improve the
    quality of data assimilation and forecast
    products
  • Regime dependent planning/targeting
  • Case dependent targeting
  • New DA, modeling and ensemble methods can better
    capture and predict the initiation and
    propagation of Rossby-waves leading to high
    impact events
  • Forecast products, including those developed as
    part of the TPARC research, will have significant
    social and/or economic value

25
  • Sequence of analysis fields
  • Dynamically consistent NOT COMPLETELY
  • Lack of consistency interferes with forecast
    evaluation
  • Only analysis quality can be evaluated directly
  • NATURE MODEL CAN BE RUN ALONG WITH OSSE FCST
  • Dynamics/physics different from assimilating
    model MOST REALISTIC REPRESENTATION OF MODEL
    ERRORS?
  • PERFECT MODEL SCENARIO NOT POSSIBLE
  • Differences should correspond to difference
    between nature our models
  • No difference means perfect model assumption,
    THORPEX interest
  • Realistic - YES
  • Climate stats matching reality - YES
  • Moisture variables realistic so obs locations can
    be chosen realistically
  • YES
  • Same weather as in nature - YES
  • Allows direct comparison between OSSE OSE
    results for reliable calibration using small
    amount of data - YES
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