AMDAR Quality Assurance

1

AMDAR Quality Assurance Bradley
Ballish NOAA/NWS/NCEP/NCO/PMB SSMC2/Silver
Spring 23 March, 2009


2
Outline

• Monthly reports
• Examples of data quality control (QC) problems
• Comparison of some aircraft temperatures, wind
  and moisture data in North American area
• Proposed aircraft temperature bias corrections
  and related issues
• Summary

3
Regular Monthly AMDAR Reports

• Based on a WMO meeting at the ECMWF in June 2002,
  NCEP prepares monthly aircraft monitoring reports
  at website
• http//www.ncep.noaa.gov/pmb/qap/amdar/
• These standard monthly reports are not frequent
  enough in time, do not have track-checking or
  stuck data summaries and do not have accent and
  descent statistics in most parts
• The WMO Integrated Global Observing System
  (WIGOS) Pilot Project for AMDAR suggests regional
  centers QC AMDAR data before transmission on the
  GTS
• This will require much more frequent updates than
  monthly reports

4
Japanese Data in Monthly Reports

• In the NCEP AMDAR report for February 2009, the
  Japanese data looked good
• Of 274 Japanese aircraft reporting data, only 7
  had suspect temperatures
• Units JP9Z4U44, JP9Z4Y4X, JP9Z4Y79, JP9Z4YVV,
  JP9Z5859, JP9Z585Z and JP9Z5Y79 had warm biases
• No units had suspect winds!
• There were about 100 minor track-check errors,
  see example on next page

5
Track-Check Error Example

• Aircraft Data for Unit JP9Z58XZ
• For 00Z 16 March 2009
• Time-Days Lat Lon Press
• 16.07153 27.40 125.00 196.8
• 16.07639 34.82 140.37 461.7
• 16.07708 29.53 127.97 196.8
• 16.11944 34.75 140.28 435.2
• 16.12083 32.05 132.23 196.8
• 16.14306 35.27 140.70 558.1
• 16.14444 35.48 140.78 609.7
• 16.22917 35.40 139.90 290.1
• 16.26806 34.32 133.58 300.9
• 16.28750 33.52 130.43 300.9

Locations and pressures are changing too fast
with time but all data are close to
model background All raw data received at NCEP
have only header KAWN US Air Force not RJTD as
expected Additional examples can be provided
6
Aircraft Monitoring Example

• On 9 August 2006, aircraft EU3102 started to show
  a large temperature bias from 300 hPa up compared
  to the background
• The spurious bias was so large that few spurious
  temperatures passed QC
• The bias was so large that the aircraft was
  probably wasting fuel
• If the airlines could check a website with this
  information, such problems could be found and
  fixed much sooner

7
(No Transcript)
8
Aircraft Track-check Example

• On 11 August 2006, aircraft AFZA01 was flying
  from the southeast to northwest with roughly
  several minutes between reports
• Three groups of reports are shown, with groups 1
  and 3 with correct locations and group 2 with all
  reports about 12 degrees too far north
• The blue numbers are vector wind differences to
  the guess, with group 3 having large differences
  that all passed QC
• Flying from the end of group 1 to the start of
  group 2 is an impossible distance in several
  minutes
• This is a tough example for current QC codes to
  correctly process as group2 can track-check with
  itself
• This problem with South African aircraft has
  lasted over a year
• Examples of solo track-check errors are common

9
2
Blue numbers are vector wind differences of
observed winds minus model background
3
1
10
Aircraft Temperature Observation Count
Comparison for NA area

• An impact test adding TAMDAR and Canadian AMDAR
  data at NCEP did not have positive impact, so
  here we examine this data
• The next slide compares the average number of
  different types of temperature counts to the
  nearest mandatory pressure level per GDAS model
  run in June 2008 for North America (NA)
• Counts for Radiosondes, ACARS, TAMDAR and two
  types of Canadian AMDAR are compared
• Wind observation counts (not shown) were found to
  be nearly identical to temperature counts
• Clearly the aircraft counts out number those from
  sondes
• The two main types of Canadian aircraft are
  labeled CRJ and DHC-8

11
Sondes have low counts relative to large ACARS
counts
12
Temperature Bias Comparison

• The next slide compares the average temperature
  bias of different types of observations to the
  nearest mandatory pressure level per GDAS model
  run in June 2008
• Biases for sonds, ACARS, TAMDAR and two types of
  Canadian AMDAR are shown
• Clearly the aircraft temperatures are generally
  warmer than those from sonds (as found for ACARS
  and AMDAR, Ballish and Kumar (BAMS, Nov 2008))
• The DHC-8 aircraft have the warmest bias

13
Sonds are cold compared to aircraft
14
Temperature Bias vs POF for Canadian AMDAR Data

• In the following slide, the temperature biases
  for Canadian AMDAR type DHC-8 are shown vs the
  phase of flight (POF)
• This aircraft type has generally warm biases that
  vary with the POF
• Here the biases vary considerably with the POF

15
Ascent vs descent is large
16
Speed Bias vs POF for Canadian AMDAR Data

• In the following slide, the wind speed biases for
  Canadian AMDAR type CRJ are shown vs the POF
• This aircraft type has speed biases that vary
  considerably with the POF
• In the second following slide, the same is shown
  for Canadian aircraft type DHC-8
• Here the speed biases vary even more with the POF
• At the WIGOS February 2009 meeting, it was noted
  that the CANADIAN AMDAR data are less accurate in
  high latitudes due to using magnetic, rather than
  GPS navigation

17
Ascent vs descent is large
18
Ascent vs descent is very large
19
Relative Humidity Bias Comparison

• The next two slides show counts of moisture
  observations and relative humidity biases
  differences versus the guess for the North
  American area in June 2008 for sonds, ACARS and
  TAMDAR data
• The TAMDAR data (at this time) are mainly in the
  mid west, yet have higher counts and very good
  stats versus the guess

20
TAMDAR has large counts, but are just in mid-west
only
21
TAMDAR biases may be better than sonds
22
Proposed Aircraft Temperature Bias Corrections

• Ballish and Kumar BAMS(Nov 2008) studied aircraft
  temperature biases and proposed bias corrections
  shown in the next slide for January 2007 for the
  15 aircraft types with the largest counts
• In the following slide, the same is shown for non
  US AMDAR types
• This study did not include TAMDAR or Canadian
  AMDAR types

23
Most corrections are negative
24
(No Transcript)
25
Aircraft vs Sond GSI Draws to Temps between
200-300 hPa
SOND Tdiff (obs-ges)
Aircraft Tdiff (obs-ges)
Aircraft Tdiff (obs-anl)
SOND Tdiff (obs-anl)
Aircraft Sondes, thus warm aircraft data
overwhelms the GSI/GFS system
26
AMDAR Versus Sond Counts 300-200 hPa
Aircraft
Aircraft
Sonds
Sonds
27
Suru Sahas website displays model fits to RAOBS
in North America showing the GFS analysis and
guess maintain a warm bias throughout most of the
troposphere that may be related to large numbers
of aircraft with warm biases
28
Model Climate Impact from Aircraft Warm
Temperatures

• The next slide courtesy of Dick Dee of ECMWF
  shows the increase in the number of aircraft
  reports versus time in the ECMWF reanalysis
• The temperature bias of the ECMWF analysis and
  background seem to be affected by the large
  increase in the number of aircraft temperatures
  along with other factors
• The NCEP GSI may have more bias impact as it does
  not thin aircraft data and its satellite radiance
  bias corrections are anchored to the analysis as
  truth as opposed to radiosondes as truth

29
Model Climate Bias Impact From Warm Aircraft
Temperatures
Global-mean departures of analysis (blue) and
background (red) from radiosonde temperatures (K)
at 200hPa, and number of obs/day (x10-4, green)
Global-mean departures of analysis (blue) and
background (red) from aircraft temperatures (K)
at 200hPa, and number of obs/day (x10-4, green)
30
Summary

• The standard monthly AMDAR reports are useful but
  do not contain enough information on aircraft
  data quality
• In part due to the WIGOS project, more frequent
  and complete quality information will be needed
• Improvements are needed in the aircraft
  track-checking
• The TAMDAR data appear to be of useful quality,
  especially the moisture
• The Canadian AMDAR data show considerable bias
  differences with the aircraft phase of flight and
  will need more effort to assimilate them well
• There is evidence that large numbers of
  relatively warm aircraft temperatures are
  impacting model analysis bias
• Improvements are needed in the bias correcting
  and or use of aircraft temperatures, winds and
  moisture
• NCEP and the ECMWF are both planning to perform
  aircraft temperature bias corrections
• It is likely that 4DVAR assimilation is needed to
  get maximum impact of aircraft data due to their
  reporting at off times