# Presentation: 095275

## Comparison of Aircraft and Radiosonde Data with Implications for Bias Correction Dr. Bradley Ballish NCEP/NCO/PMB21 November 2006 

*“**Where America’s Climate and Weather Services Begin”*
* *

## 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**

## 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**

## 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/paper_103076.htm](http://ams.confex.com/ams/Annual2006/techprogram/paper_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**

## 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**

## Monthly Average Temperature Biases 300 to 200 hPa 00Z

## Monthly Average Temperature Biases 300 to 200 hPa 12Z

## Slide 8

## Slide 9

## Saha Web Site

**It is useful to look at temperature bias profiles in Suru Saha’s web site **_[http://](http://wwwt.emc.ncep.noaa.gov/gmb/ssaha/)__[wwwt.emc.ncep.noaa.gov/gmb/ssaha](http://wwwt.emc.ncep.noaa.gov/gmb/ssaha/)__[/](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**

## Slide 11

## 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**

## Monthly Average Temperature Differences Versus the Guess 00Z 250 hPa all Sondes

## Monthly Average Temperature Differences Versus the Guess 12Z 250 hPa all Sondes

## Radiation Correction

- Stopped on
- Chinese Sondes

## Slide 16

## 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**

## Aircraft Temperature Biases by Aircraft Types 300 hPa and up all Times of Day 

## Aircraft Temperature Biases by Aircraft Types 300 hPa and up all Times of Day 

## 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**

## May be due to

- 2 subgroups

## 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**

## Slide 23

## Slide 24

## 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**

## Slide 26

## Slide 27

## Slide 28

## 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: &Time=1.5 hours, &P=25 hPa, &Dist=200Km**

## Slide 30

## 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**

## Slide 32

## Slide 33

## 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**

## Slide 35

## Slide 36

## 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**

## Slide 38

## 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**

## Slide 40

## 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**

## Slide 42

## 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**

## Slide 44

## 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**

## 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**

## Slide 47

## Slide 48

## Slide 49

## Skewt Plot for Site 72476 12Z 17 Jan 2006

- Trop

## Slide 51

## Slide 52

## Slide 53

## 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**

## Draft Plan for Bias CorrectionTesting

**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**

## 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**

## 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**

## Extra Examples Follow

## Slide 59

## Slide 60

## Slide 61

## Slide 62

## Aircraft Temperature Biases 250 +/- 25 hPa 00Zon 2.5 by 2.5 degree grid January 2005  

## Radiosonde Temperature Biases 250 +/- 25 hPa 00ZJanuary 2005  

## Average Analysis minus Guess Temperature 250 hPa January 2005  

## Aircraft Temperature Biases 250 +/- 25 hPa 00Zon 2.5 by 2.5 degree grid July 2005  

## Radiosonde Temperature Biases 250 +/- 25 hPa 00ZJuly 2005  

## Average Analysis minus Guess Temperature 250 hPa July 2005  

## 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**

## Slide 70

## Slide 71