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: 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 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

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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

Blue numbers are vector wind differences of observed winds minus model background

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

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25 Aircraft vs Sond GSI Draws to Temps between hPa # Aircraft >> # Sondes, thus warm aircraft data overwhelms the GSI/GFS system Aircraft Tdiff (obs-ges) Aircraft Tdiff (obs-anl)SOND Tdiff (obs-anl) SOND Tdiff (obs-ges)

26 AMDAR Versus Sond Counts hPa Aircraft Sonds Aircraft Sonds

27 Suru Saha’s 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