Analysis of Select Data Biases in North America

Dr. Bradley BallishNCEP/NCO/PMB

October 2008 JAG/ODAA Meeting

“Where America’s Climate and Weather Services Begin”

Overview• Introduction• Temperature observation count comparison• Temperature bias comparison• Wind speed bias comparison• Profiler wind speed bias after using GSI determined

pressures• Proposed aircraft temperature bias corrections• Temperature bias vs Phase of Flight (POF) for

Canadian AMDAR data• Speed bias vs POF for Canadian AMDAR data• Relative humidity bias comparison• Future work• Summary

Introduction• There are large numbers of new aircraft observations

(temperatures, winds and moisture) over the North American (NA) continent, but these data exhibit considerable biases (observation minus NCEP background (guess)) that need to be corrected

• Ballish and Kumar (BAMS, Dec. 2008) show the need for aircraft temperature bias corrections and analyze different methods and issues

• Temperature and speed biases can vary with the aircraft POF as well as with aircraft type

• As far as known, no center is applying aircraft bias corrections

• Impact tests adding TAMDAR and Canadian AMDAR data did not show positive impact, but some of these new data types have large biases that need correction

Temperature Observation Count Comparison

• 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

• 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

Average Temperature Counts per Run NA Area June 2008

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Counts

CRJDHC-8TAMDARACARSSondes

Sondes have low counts relativeto large ACARS counts

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 sondes, ACARS, TAMDAR and two types of Canadian AMDAR are shown

• Clearly the aircraft temperatures are generally warmer than those from sondes (as found for ACARS and AMDAR, BK(2008))

• The DHC-8 aircraft have the warmest bias

Temperature Biases Versus Guess NA Area June 2008

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Temperature Bias in Degrees

CRJDHC-8TAMDARACARSSondes

Sondes are coldcompared toaircraft

Wind Speed Bias Comparison

• The next slide compares the average wind speed bias of different types of observations to the nearest mandatory pressure level per GDAS model run in June 2008

• Biases for sondes, profilers, ACARS, TAMDAR and two types of Canadian AMDAR are shown

• The profilers show negative speed biases due to using a standard atmosphere to obtain their pressures – this is corrected in the GSI

• For some aircraft, the speed biases vary much with the POF, see later slides

Speed Biases Versus Guess NA Area June 2008

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Speed Bias in m/sec

CRJDHC-8TAMDARACARSPROFSondes

Profiler lowspeed bias is fixedin GSI

Profiler Wind Speed Bias After Using GSI Determined Pressures

• The next slide compares the average wind speed bias of profiler data versus the guess using a standard atmosphere to derive pressure versus GSI derived pressures

• The GSI derived pressures are clearly better• The profilers with GSI derived pressures

show some small bias due to attempts at ground clutter and bird migration removal at the sites

Profiler Speed Biases NA Area 1-15 September 2008

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Speed Bias in m/sec

GSISTDA

Proposed Aircraft Temperature Bias Corrections

• BK(2008) studied aircraft temperature biases and has 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

Proposed ACARS Temperature Bias Corrections January 2007

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Most correctionsare negative

Proposed NUS-AMDAR Temperature Bias Corrections January 2007

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Temperature Bias vs POF for Canadian AMDAR Data

• In the following slide, the temperature biases for Canadian AMDAR type CRJ are shown vs the POF

• This aircraft type has generally warm biases that vary with the POF

• In the following slide, the same is shown for Canadian aircraft type DHC-8

• Here the biases vary considerably with the POF

Temperature Biases Versus Guess CRJ Aircraft NA Area June 2008

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Temperature Bias in Degrees C

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Temperature Biases Versus Guess DHC-8 Aircraft NA Area June 2008

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Temperature Bias in Degrees C

TOTALLEVELDESCENTASCENT

Ascent vsdescent is large

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 following slide, the same is shown for Canadian aircraft type DHC-8

• Here the speed biases vary even more with the POF

Speed Biases Versus Guess CRJ Aircraft NA Area June 2008

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Speed Bias in m/sec

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Ascent vs descent is large

Wind Speed Biases for Aircraft Type DHC-8 June 2008 NA Area

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Speed Bias in m/sec

TOTALLEVELDESCENTASCENT

Ascent vs descent is very large

Relative Humidity Bias Comparison

• The next three slides show counts of moisture observations, relative humidity biases and RMS differences versus the guess for the North American area in June 2008 for sondes, ACARS and TAMDAR data

• The TAMDAR data (at this time) is mainly in the mid west, yet have higher counts and very good stats versus the guess

Average Moisture Observation Counts per Run NA Area June 2008

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Counts

TAMDARACARSSondes

TAMDAR has large counts, butare just in mid-west only

Relative Humidity Biases Versus Guess NA Area June 2008

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RH Bias in %

TAMDARACARSSondes

TAMDAR biases maybe better than sondes

RMS Relative Humidity Differences Versus Guess NA Area June 2008

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RMS Difference in %

TAMDARACARSSondes

Aircraft RMS valueswould be lower withtime interpolation ofbackground

Future Work

• An algorithm needs to be developed, refined, tested and made operational to make bias corrections of aircraft data

• BK(2008) point out that there are problems to address such as large background temperature errors around the tropopause and inadequate time interpolation of the background

• International cooperation is needed to have the aircraft type as part of the raw aircraft data

Summary

• The aircraft data over the North American continent need temperature and wind bias corrections

• More work in this area and testing (impact studies) are needed to be able to make such corrections operational

• The TAMDAR data have excellent moisture stats and the UPS ACARS data are almost as good

• Care must be taken on how observational data are used, quality controlled and bias corrected to use them optimally