Bias Correction of In Situ Observations

Elizabeth Kent, David Berry, Peter Taylor, Margaret Yelland and Ben Moat

National Oceanography Centre, Southampton, UK

(with help from Peter Challenor, Dick Reynolds and Tom Smith)

Outline

o Sources of bias

o Identification of bias

• Using high-quality data (examples: OWS air temperature, RV winds)

• Using biased data (example: VOS SST)

• Using comparison standards

o Model output (examples: VSOP-NA, VOSClim and MetDB)

o Satellite retrievals (example: scatterometer winds)

• Modelling (example: airflow distortion)

• 3-way intercomparisons (example sea surface heights)

o Metadata

o Conclusions

Some sources of bias

o Winds

• Airflow distortion

• Height adjustment

• Anemometer calibration

• Visual/anemometer inhomogeneity

o SST

• Variations in measurement depth (definition of SST)

• Sensor calibration/drift

• Heating/cooling of local environment

• Mixing of surface layer by platform

Some sources of bias …. continued

o Air Temperature

• Radiative heating

• Height adjustment

• Sensor calibration/drift

• Sensor ventilation

o Humidity

• As air temperature plus drying of wet bulb

• Salt contamination

o Pressure

• Sensor calibration/drift

• Wind effects

• Sealed bridge (air conditioning).

Identification of bias: Using high quality data

o Air temperature data from OWS L used to derived correction for radiative heating.

o Comparison of well-exposed and poorly-exposed sensors used to quantify biases.

o Biases modelled with analytical model of heat budget (Berry et al., 2004: An analytical model of heating errors in marine air temperatures from ships J. Atmos. Ocean. Tech., 21,1198 - 1215.)

Identification of bias: Using high quality data

o Comparison of Research Vessel winds and pressures with reanalysis output reveals bias in NCEP1.

o NCEP1 winds are too low, except at very low wind speeds and the bias increases with increasing wind speed.

o Pressure gradients are underestimated in NCEP1

o See Smith et al., 2001: Quantifying Uncertainties in NCEP Reanalyses Using High-Quality Research Vessel Observations, J. Climate., 14,4062 - 4072.

Identification of bias: Using biased data

o We do not need unbiased data to reveal biases, but we must understand the error structure of the data.

o A comparison of bucket and engine intake SST revealed biases in both sources of data.

o Kent, and Kaplan, 2006: Toward Estimating Climatic Trends in SST, Part 3: Systematic Biases. J. Atmos. Ocean. Tech., 23, 487-500.

Identification of bias: Using biased data

o Comparison of sonic anemometers on either side of the RRS Discovery foremast.

o The effects of flow distortion by the foremast platform and extension are seen in the ratio of the two sensors.

o We should be able to choose the least-biased sensor as a function of relative wind direction.

o Yelland et al., 2002: CFD model estimates of the airflow over research ships and the impact on momentum flux measurements. J. Atmos. Oceanic Tech.,19,1477-1499.

Identification of bias: Comparison standards

o It it not necessary that the standard used is bias free.

o Useful to compare observations made by different methods.

o Examples using model output:

• VSOP-NA

• VOSClim

• Met Office GTS Monitoring reports

Identification of bias: Intercomparison

o Need to remove large scale biases in AVHRR satellite SST using in situ data.

o But buoy and ship SST have relative bias too.

o Biases are estimated by using Empirical Orthogonal Teleconnections (EOT).

o Plots show the estimated bias in Pathfinder AVHRR SST for 2003-2004, before (top) and after (bottom) bias correction using these techniques.

o Smith and Reynolds, 2005, Journal of Climate, 18, 2021-2036.

Identification of bias: Comparison Standards

o Satellite retrievals can also be used to compare distributed in situ reports.

o The number of co-locations is often small.

o Again need to consider both random and systematic errors in both data sources.

o Kent, Taylor and Challenor, 1998: A Comparison of Ship and Scatterometer-Derived Wind Speed Data. Int.J.Remote Sensing, 19(17), 3361-3381.

Identification of Bias: Modelling airflow

Identification of Bias: Laboratory measurements

o Buckets used to sample the sea water for SST measurements are insulated to reduce heat exchange with the atmosphere.

o However analysis suggested that heat exchange could be detected.

o Laboratory measurements of the temperature of warm water in the bucket over time showed that this was likely.

Identification of bias: Triple co-locations

o Given 3 independent estimates of the same quantity (here sea surface height from a model and 2 satellites) can calculate the errors in each.

o If the errors are not independent (e.g. errors in tide correction to satellite estimates) this can be accounted for.

o Cannot exclude the possibility of bias common to all.

o Tokmakian, and Challenor, 2000, Ocean Modelling., 1, 39-52 or Caires and Sterl, Geophys. Res., 2003, C108, 3, 3098, doi:10.1029/2002JC001491.

Metadata

o Metadata is necessary to analyse bias and apply corrections

• Measurement heights

• Measurement methods

• Instrument and calibration information

• Instrument siting

o Metadata availability is patchy

• VOS metadata is collected in Pub. 47 (1955-present)

• Historical ODAS metadata can be hard to get hold of

• Research Vessel metadata ranges from excellent to non-existent

o Pragmatic decisions - if you ask for too much you can end up with nothing.

o Proxy and implied metadata can be useful.

Metadata: Deduction from the data

See: Berry and Kent, 2005: The Effect of Instrument Exposure on Marine Air Temperatures: An Assessment Using VOSClim Data, Int. J. Climatol., 25, 1007-1022.

Metadata: Proxies

July 1980-1997: Air Temperature Differences from Local Mean Normalised by Local Standard Deviation

Conclusions

o A range of methods is available to quantify bias.

o Need to consider biases in all data sources.

o Random errors can appear systematic if not handled correctly.

o Metadata are vital in the quantification and correction of bias.

o Sometimes we may be able to deduce metadata from the characteristics of the data themselves.

o Bias correction can rehabilitate data for a wide range of applications.