Advanced Targeting and Observation Selection 6'2 New Start 75P02406 Rolf Langland, Code 7532

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A Pacific Predictability Experiment - Targeted
Observing Issues and Strategies
Rolf Langland Pacific Predictability
Meeting Seattle, WA June 6, 2005
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Eight years since FASTEX - first targeting field
program
FASTEX Targeting Flight Meteo France / NCAR /
NRL / NOAA Goose Bay, Canada 22 Feb 1997
IOP-18
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Previous Targeting Field Programs

• Winter storm targeting
• North Atlantic (FASTEX-1997, NA-TREC-2003)
• North Pacific (NORPEX-1998, WSR-1999-2005)
• Hurricane / tropical cyclone targeting
• North Atlantic (NOAA-HRD, 2000-2005)
• Western Pacific (DOTSTAR, 2003-2005)
• Participants Meteo France, ECMWF, UKMO, NRL,
  NCEP, NCAR, NOAA-AOC, NOAA-HRD, USAF Hurricane
  Hunters, NASA, CIMSS, MIT, Univ. of Miami, Penn
  State Univ., others

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Targeting Results

• Forecast Impact of Targeted Data (adding 10-50
  dropsondes at single assimilation times)
• Targeted data improves the average skill of
  short-range forecasts, by 1020 over
  localized verification regions maximum
  improvements up to 50 forecast error reduction
  in localized areas
• In all analysis / forecast systems, and for
  all targeting methodologies, it is found that
  20-30 of forecast cases are neutral or degraded
  by the addition of targeted data
• Impact per-observation of targeted dropsonde
  data is large, but total impact is generally
  limited by the relatively small amount of
  targeted data

Results based on published forecast impact
studies performed at NCEP, ECMWF, Meteo France,
UKMO, NRL
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Targeting Impact on Forecast Error (regional
verification area)
UPPER LIMIT SUGGESTED BY PREDICTABILITY STUDIES
Average reduction in 2-day forecast error
(percent)
NOAA-WSR-04
NORPEX -98
NA-TReC -03
Total number of satellite or in-situ data
assimilated per forecast case
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How to increase the beneficial impact of Targeted
Observing?
ECMWF need to observe much larger part of the
SV-targeting subspace NRL - use higher-density
of satellite data in target regions, observe more
frequently, observe larger region (requires
satellite data targeting) NCEP ?? UKMO ??
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Targeting a major winter storm forecast failure
SENSITIVITY OF 72H FORECAST ERROR TO 300mb U-WIND
FORECAST VERIFICATION AREA
OBSERVATION TARGETS
Langland et al. (MWR, 2002)
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Pacific origins of the 2000 E. Coast blizzard
21 Jan 00
22 Jan 00
23 Jan 00
24 Jan 00
25 Jan 00
26 Jan 00
250mb Daily-Mean Geopotential Height
Figure by Mel Shapiro
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Objectives for future targeting programs
Goal 1 Increase the average beneficial impact of
targeted data in deterministic and ensemble
forecasts Goal 2 Increase the percentage of
forecasts that are improved by targeted data

• More data in target sub-space (fully observe the
  sub-space and provide near-continuous
  observations)
• Improve targeting techniques
• Improve data assimilation procedures

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Pacific predictability questions --

• Are the analyses over the Pacific getting better
  ?
• How much of the uncertainty or error that exists
  in current analyses over the Pacific will reduced
  by anticipated hyper-spectral (and other)
  satellite observations that will be provided over
  the next five to ten years? How to extract
  maximum benefit for NWP from this vast amount of
  satellite data?
• - Vertical resolution of satellite data vs.
  that of model background
• - Bias correction ?
• - Observations in sensitive cloudy regions ?

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NAVDAS Observation Count 12 May 2005
All observation types - 00, 06, 12, 18 UTC
Includes AMSU-A, scatterometer, MODIS, geosat
winds, SSMI, raobs, land, ship, aircraft
data Does not includes HIRS, AIRS, GPS, or ozone
MAX SENSITIVITY
Number of obs within 5o x 5o lat-lon boxes
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Targeting Strategies

• How much benefit can we obtain by tuning the
  network of existing regular satellite and in-situ
  observations in a targeted sense?
• Targeted satellite data thinning
• Targeted satellite channel selection
• On-request feature-track wind data
• Increase observations from commercial aircraft
• On-request radiosondes at non-standard times

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What major scientific and technical objectives
can be addressed by a Pacific predictability
experiment?

• Use field program data set to improve impact of
  satellite data for NWP (mid-latitude and
  tropical)
• observation and background error
• bias correction calibration and validation
• data thinning channel selection
• on-request targeted satellite data
• Test viability of new in-situ observing systems
  for targeting driftsonde, aerosonde,
  rocketsonde, smart balloon, etc.

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1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
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Targeting Strategy
Satellite Observations
In-situ observations
Data Selection Thinning Procedures
Targeting Guidance
Data Assimilation
Rejected Data
Forecast Model
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Forecast and Analysis Procedure
Observation (y)
Data Assimilation System
Forecast Model
Forecast (xf)
Analysis (xa)
Background (xb)
Adjoint of Forecast and Analysis Procedure
Observation Sensitivity (?J/ ?y)
Adjoint of the Forecast Model Tangent Propagator
Adjoint of the Data Assimilation System
Gradient of Cost Function J (?J/ ?xf)
Analysis Sensitivity (?J/ ?xa)
Background Sensitivity (?J/ ?xb)
Observation Impact lty-H(xb)gt (?J/ ?y)
What is the impact of the observations on
measures of forecast error (J) ?
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New vs. Old Targeting Approach
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Large Impact of Observations in Cloudy Regions
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High Forecast Impact
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High Forecast Impact
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High Forecast Impact
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High Forecast Impact
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Med-Low Forecast Impact
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Med-Low Forecast Impact
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Med-Low Forecast Impact
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FIGURE IN EARLY VERSION OF THORPEX PLAN
(April 2000)
Coverage at 00UTC 11Feb 1999
Initial Launch Time 00 UTC 06 Feb 1999 13 launch
sites
Drift Level 100 mb
Launch Interval 12hr Dropsonde Interval 6hr
Example of Driftsonde sounding coverage at one
assimilation time after five days of deployment
from launch sites along the Asian Pacific rim
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Targeting Impact Percent of Improved Forecasts
NOAA-WSR-04
NORPEX -98
Percent of 2-day forecasts improved
NA-TReC -03
Total number of satellite or in-situ data
assimilated per forecast case
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PROPAGATION OF PACIFIC TARGETING SIGNAL KINETIC
ENERGY From 00UTC 20 Jan 2005 ( 7 days)
EUROPE
U.S.
CHINA
FROM S. MAJUMDAR
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Extended-duration targeting flow regime 1
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Research Tasks
OSEs (real data) test procedures for targeted
satellite data thinning and channel selection
OSSEs (synthetic data) test impact of future
satellite and in-situ observing systems
Evaluate impact of targeted feature-track geosat
wind data and other targeted satellite data -
Examine 3d-var, 4d-var deterministic, TIGGE,
various metrics and various forecast verification
areas Perform operational tests of
driftsonde, aerosonde, rocketsonde, smart
balloon, etc. for potential field program
applications
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Predictability Questions
- Where are the most critical analysis errors or
uncertainties over the Pacific? How well are
cloudy regions analyzed? - Is there a benefit
from using higher horizontal or vertical
resolution of satellite data in target areas? -
What is the realistic upper-limit of forecast
improvement that can be expected from targeted
observing in various situations? - What is the
potential benefit from observing larger sections
of the targeting subspace, instead of attempting
to survey the smaller-scale areas of maximum
sensitivity, which have been the primary focus of
previous field programs? How can this be
accomplished?
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Interpretation of previous targeting results

• Targeted observing has the potential for
  significant improvement to deterministic and
  ensemble forecasting
• Previous targeting field programs have achieved
  only a small fraction of this potential
  intermittent small sets of data (10-50
  dropsondes) have modest beneficial impact
• New and next-generation satellite data are the
  primary resource that can advance the impact of
  targeting
• In-situ targeted observations provide value in
  certain situations where satellite observations
  are insufficient (including cloudy areas)

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Observation Impactduring THORPEX NA-TReC
1Nov-31Dec 2003 global domain
18UTC
Does not include moisture observations or
rapid-scan satellite wind data