Comparison of Aircraft and Radiosonde Data with Implications for Bias Correction Dr' Bradley Ballish

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Comparison of Aircraft and Radiosonde Data with
Implications for Bias Correction Dr. Bradley
Ballish NCEP/NCO/PMB21 November 2006

• Where Americas Climate and Weather Services
  Begin

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Overview

• Introduction
• Temperature bias time series
• Saha web site
• Stages of bias differences
• Aircraft temperature bias factors
• Aircraft temperature scatter plot
• Possible subgroup evidence
• Aircraft to aircraft collocation stats
• Sonde aircraft collocation stats
• Vertical temperature bias profiles for different
  ACARS aircraft types

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Overview (Continued)

• Vertical temperature bias profiles for different
  AMDAR aircraft types
• Wind speed biases for ACARS and sonde data
• RMS wind differences for different ACARS types
• Wind speed biases for different ACARS types
• Wind speed biases for European AMDAR data
• Vertical interpolation experiments
• Bias correction issues
• Draft plan for bias correction testing
• Acknowledgements
• Summary
• Extra examples

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Introduction

• At the January 2006 AMS meeting, it was shown
  that aircraft classes (ACARS, AMDAR and AIREPS)
  are warm near the jet and sondes cold, see
  http//ams.confex.com/ams/Annual2006/techprogram/p
  aper_103076.htm for more information
• New information on temperatures is provided here
  along with wind statistics
• Analysis is made on the cause of some of the
  biases along with discussion on bias correction
  and possible improvement in model vertical
  resolution

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Temperature Bias Time Series

• The next 4 slides show temperature biases versus
  time for all non gross data for sondes, AIREPS,
  ACARS and AMDAR types from 300 to 200 hPa

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Monthly Average Temperature Biases 300 to 200
hPa 00Z
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Monthly Average Temperature Biases 300 to 200
hPa 12Z
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Saha Web Site

• It is useful to look at temperature bias profiles
  in Suru Sahas web site http//wwwt.emc.ncep.noaa.
  gov/gmb/ssaha/
• Note that the cold bias around 250 hPa has been
  there for a long time and is biggest in winter
  and varies with location
• The warm bias around 150 hPa is small as of June
  2005, which has implications for bias correction
• Bias correction could decrease beneficial
  analysis correction of guess
• An example from January and February 2005 is
  shown on the next slide, with stats for the
  analysis and 6, 24 and 48 hour forecasts

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Stages of Bias Differences

• The next 4 slides show stages of global biases
  versus passing radiosonde data as a function of
  time
• These include raw data minus guess (RAWMG) with
  no NCEP RADCOR, NCEP RADCOR corrected data minus
  guess (RADMG) and analysis minus guess (ANLMG)
• Note the improvement as of June 2005 when the
  Chinese RADCOR was corrected

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Monthly Average Temperature Differences Versus
the Guess 00Z 250 hPa all Sondes
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Monthly Average Temperature Differences Versus
the Guess 12Z 250 hPa all Sondes
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Radiation Correction Stopped on Chinese Sondes
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Aircraft Temperature Bias Factors

• Many factors affect aircraft biases
• These include aircraft type, influence of past
  data on the guess, airlines, pressure level,
  software, temperature sensors and Phase of Flight
  (POF)
• Specific aircraft type seems to be most important
  such as 767-432 versus 767-322
• The following 2 slides do not include units whose
  bias is beyond 3 STD from mean

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Aircraft Temperature Biases by Aircraft Types 300
hPa and up all Times of Day
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Aircraft Temperature Biases by Aircraft Types 300
hPa and up all Times of Day
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Aircraft Temperature Bias Scatter Plot

• The next slide shows a scatter diagram of
  aircraft bias counts to the tenth of a degree for
  July 2006 300 hPa and up
• Only aircraft with at least 50 observations in
  the month were used
• The warmer colors indicate more aircraft with
  that bias
• It is not obvious that any aircraft type shows a
  pattern of 2 or more bias subgroups yet two
  subgroups will later be shown to exist

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May be due to 2 subgroups
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Possible Subgroup Evidence

• The next 2 slides show temperature biases for 2
  aircraft types where missing and level POF data
  have unexpected very different biases
• These different biases may be due to software
  differences or some other unknown factor
• One may want to treat these subgroups separately

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Aircraft to Aircraft Collocation Stats

• Collocation stats are useful as they do not use
  the guess directly
• Aircraft to aircraft collocations stats (blue) on
  the next 2 slides are consistent with biases to
  the guess (brown)
• This consistency is similar for most types of
  aircraft
• Collocation limits are 1 hPa, 150 Km and 1 hour
  using non gross temperatures not on reject-list

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Sonde Aircraft Collocation Stats

• Sonde to aircraft collocations stats (blue) on
  the next slide are consistent with biases to the
  guess (brown)
• Similar results for other US sondes and times are
  not shown
• Radiosonde to aircraft collocations are derived
  using linear in log(P) interpolation with sonde
  data to the ACARS observation averaged at the
  nearest mandatory pressure level
• Only data passing QC are used
• Collocation limits Time1.5 hours, P25 hPa,
  Dist200Km

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Vertical Temperature Bias Profiles for Different
ACARS Aircraft Types

• The next 2 slides show vertical biases profiles
  for different ACARS aircraft types
• Note these slides include all POF in the stats
• Stats are interpolated to nearest mandatory
  pressure level for non gross data

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Vertical Temperature Bias Profiles for Different
AMDAR Aircraft Types

• Different types of aircraft have different total
  biases to the guess shown on next slide for
  winter 2006 (December 2005 through February 2006)
• Biases are shown for Australian (AU), Airbus
  A319-100 and Boeing 757 and 747-400 AMDAR types
• Stats are interpolated to nearest mandatory
  pressure level for non gross data

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Wind Speed Biases for ACARS and Sonde Data

• The next slide shows speed biases for January
  2006 versus the guess in the US area
• Note ACARS speed biases vary with the POF, with
  ascent low and descent higher
• POF L is level, A is ascent, D is descent and M
  is missing
• Stats are interpolated to nearest mandatory
  pressure level for non gross data

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RMS Wind Differences for Different ACARS Types

• The next slide shows RMS wind differences to the
  guess for different aircraft types for January
  2006 versus the guess near 250 hPa
• Note the MD-88 difference is typically large and
  others similar
• FSL has these MD-88 units on the reject-list for
  the FSL RUC we do not better to let analysis
  know they are less accurate
• Stats are interpolated to nearest mandatory
  pressure level for non gross data

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Wind Speed Biases for Different ACARS Types

• The next slide shows speed biases for January
  2006 versus the guess near 250 hPa
• Note ACARS speed biases vary some with the
  different types but have the same sign
• Stats are interpolated to nearest mandatory
  pressure level for non gross data

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Wind Speed Biases for European AMDAR Data

• The next slide shows speed biases for January
  2006 versus the guess for European AMDAR data
• Note AMDAR speed biases vary with the POF, with
  ascent low and descent higher
• POF L is level, A is ascent, D is descent and M
  is missing
• Stats are interpolated to nearest mandatory
  pressure level for non gross data

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Vertical Interpolation Experiments

• Vertical interpolation experiments were done
  using US sonde data over the 48 states
• Since radiosonde data temperatures and winds are
  roughly linear in log(p) between reported levels,
  use that as truth for tests
• Only data passing QC were used, and used linear
  in log(p) to interpolate to model sigma levels
• The model now has perfect values which are then
  interpolated linear in log(p) back to the
  observations which are taken as truth
• The resulting biases are similar to guess biases,
  see the next 4 slides

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Vertical Interpolation Experiments(Continued)

• Experiment I64 used the operational 64 sigma
  levels
• Experiment I72 has 72 levels with 8 extra levels
  from sigma levels .3297 to .1382
• NTRP shows operational guess biases with no
  tropopause data
• Tropopause data from 300 to 175 hPa have biases
  close to 2 degrees
• Adding more sigma levels decreases the
  interpolation error

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Trop
Skewt Plot for Site 72476 12Z 17 Jan 2006
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Bias Correction Issues

• Collocations with good sondes may be best for
  deriving bias corrections if possible will use
  guess differences if not
• Plan for low count problems
• Develop consistent procedures for outliers
• Plan to adjust to changes in biases
• Study optimal pressure categories
• Develop vertical interpolation of corrections
  question near ground
• Do we need better model vertical resolution?
• Testing

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Draft Plan for Bias CorrectionTesting

• Review comments from giving seminar
• Draft bias correction procedures
• Ask for feedback on procedures and revise
• Run experiments when time permits
• Evaluate experiments
• Take further actions as warranted

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Acknowledgements

• Thanks to Krishna Kumar for help in many areas
• Thanks to Jeff Stickland for many suggestions
• Thanks to Stewart Taylor for AMDAR types
• Thanks to Louis Krivanek of the FAA for help with
  aircraft types
• Thanks to John Ward for supporting the work

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Summary

• Aircraft data have been shown to have different
  biases and accuracy than sondes with stats
  varying primarily by aircraft type and POF
• Since the guess appears to have biases, bias
  correction of aircraft data will need to be
  anchored to sonde data such as by collocation or
  some other solution
• Bias correction has some problems, such as low
  data counts for some data, that need to be
  addressed
• Some bias in the model maybe improved by more
  model vertical resolution
• Tests need to be done to access the value of bias
  correction

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Extra Examples Follow
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Aircraft Temperature Biases 250 /- 25 hPa 00Zon
2.5 by 2.5 degree grid January 2005
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Radiosonde Temperature Biases 250 /- 25 hPa
00ZJanuary 2005
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Average Analysis minus Guess Temperature 250 hPa
January 2005
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Aircraft Temperature Biases 250 /- 25 hPa 00Zon
2.5 by 2.5 degree grid July 2005
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Radiosonde Temperature Biases 250 /- 25 hPa
00ZJuly 2005
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Average Analysis minus Guess Temperature 250 hPa
July 2005
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Temperature Bias Profiles can be Atypical

• The next slide shows temperature biases for
  Airbus type A318-100 data for winter 2006
  (December 2005 through February 2006)
• Note that the ascent mode (red) is atypically
  colder than the descent mode (blue)
• The following slide has counts for these data and
  shows an example of where low counts have to be
  dealt with

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