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Bias Correction of In Situ Observations Elizabeth Kent, David Berry, Peter Taylor, Margaret Yelland and Ben Moat National Oceanography Centre, Southampton,

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Presentation on theme: "Bias Correction of In Situ Observations Elizabeth Kent, David Berry, Peter Taylor, Margaret Yelland and Ben Moat National Oceanography Centre, Southampton,"— Presentation transcript:

1 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)

2 Outline oSources of bias oIdentification of bias Using high-quality data (examples: OWS air temperature, RV winds) Using biased data (example: VOS SST) Using comparison standards oModel output (examples: VSOP-NA, VOSClim and MetDB) oSatellite retrievals (example: scatterometer winds) Modelling (example: airflow distortion) 3-way intercomparisons (example sea surface heights) oMetadata oConclusions

3 Some sources of bias oWinds Airflow distortion Height adjustment Anemometer calibration Visual/anemometer inhomogeneity oSST Variations in measurement depth (definition of SST) Sensor calibration/drift Heating/cooling of local environment Mixing of surface layer by platform

4 Some sources of bias …. continued oAir Temperature Radiative heating Height adjustment Sensor calibration/drift Sensor ventilation oHumidity As air temperature plus drying of wet bulb Salt contamination oPressure Sensor calibration/drift Wind effects Sealed bridge (air conditioning).

5 Identification of bias: Using high quality data oAir temperature data from OWS L used to derived correction for radiative heating. oComparison of well- exposed and poorly- exposed sensors used to quantify biases. oBiases 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, )

6 Identification of bias: Using high quality data oComparison of Research Vessel winds and pressures with reanalysis output reveals bias in NCEP1. oNCEP1 winds are too low, except at very low wind speeds and the bias increases with increasing wind speed. oPressure gradients are underestimated in NCEP1 oSee Smith et al., 2001: Quantifying Uncertainties in NCEP Reanalyses Using High-Quality Research Vessel Observations, J. Climate., 14,

7 Identification of bias: Using biased data oWe do not need unbiased data to reveal biases, but we must understand the error structure of the data. oA comparison of bucket and engine intake SST revealed biases in both sources of data. oKent, and Kaplan, 2006: Toward Estimating Climatic Trends in SST, Part 3: Systematic Biases. J. Atmos. Ocean. Tech., 23,

8 Identification of bias: Using biased data oComparison of sonic anemometers on either side of the RRS Discovery foremast. oThe effects of flow distortion by the foremast platform and extension are seen in the ratio of the two sensors. oWe should be able to choose the least-biased sensor as a function of relative wind direction. oYelland et al., 2002: CFD model estimates of the airflow over research ships and the impact on momentum flux measurements. J. Atmos. Oceanic Tech.,19,

9 Identification of bias: Comparison standards oIt it not necessary that the standard used is bias free. oUseful to compare observations made by different methods. oExamples using model output: VSOP-NA VOSClim Met Office GTS Monitoring reports

10 Identification of bias: Intercomparison oNeed to remove large scale biases in AVHRR satellite SST using in situ data. oBut buoy and ship SST have relative bias too. oBiases are estimated by using Empirical Orthogonal Teleconnections (EOT). oPlots show the estimated bias in Pathfinder AVHRR SST for , before (top) and after (bottom) bias correction using these techniques. oSmith and Reynolds, 2005, Journal of Climate, 18,

11 Identification of bias: Comparison Standards oSatellite retrievals can also be used to compare distributed in situ reports. oThe number of co- locations is often small. oAgain need to consider both random and systematic errors in both data sources. oKent, Taylor and Challenor, 1998: A Comparison of Ship and Scatterometer-Derived Wind Speed Data. Int.J.Remote Sensing, 19(17),

12 Identification of Bias: Modelling airflow

13 Identification of Bias: Laboratory measurements oBuckets used to sample the sea water for SST measurements are insulated to reduce heat exchange with the atmosphere. oHowever analysis suggested that heat exchange could be detected. oLaboratory measurements of the temperature of warm water in the bucket over time showed that this was likely.

14 Identification of bias: Triple co-locations oGiven 3 independent estimates of the same quantity (here sea surface height from a model and 2 satellites) can calculate the errors in each. oIf the errors are not independent (e.g. errors in tide correction to satellite estimates) this can be accounted for. oCannot exclude the possibility of bias common to all. oTokmakian, and Challenor, 2000, Ocean Modelling., 1, or Caires and Sterl, Geophys. Res., 2003, C108, 3, 3098, doi: /2002JC

15 Metadata oMetadata is necessary to analyse bias and apply corrections Measurement heights Measurement methods Instrument and calibration information Instrument siting oMetadata 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 oPragmatic decisions - if you ask for too much you can end up with nothing. oProxy and implied metadata can be useful.

16 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,

17 Metadata: Proxies July : Air Temperature Differences from Local Mean Normalised by Local Standard Deviation

18 Conclusions oA range of methods is available to quantify bias. oNeed to consider biases in all data sources. oRandom errors can appear systematic if not handled correctly. oMetadata are vital in the quantification and correction of bias. oSometimes we may be able to deduce metadata from the characteristics of the data themselves. oBias correction can rehabilitate data for a wide range of applications.


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