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Climate quality data and datasets from VOS and VOSClim

Climate quality data and datasets from VOS and VOSClim. Elizabeth Kent and David Berry National Oceanography Centre, Southampton. Outline. The requirement for climate quality data What are we collecting now? How best to improve the datasets? How does VOSClim help?.

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Climate quality data and datasets from VOS and VOSClim

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  1. Climate quality data and datasets from VOS and VOSClim Elizabeth Kent and David Berry National Oceanography Centre, Southampton

  2. Outline • The requirement for climate quality data • What are we collecting now? • How best to improve the datasets? • How does VOSClim help?

  3. The requirement for climate-quality data • GCOS implementation plan • Climate datasets (e.g. Hadley Centre, NOAA) • Satellite bias adjustment • Flux datasets (includes visual observations of cloud and weather codes) • SURFA NWP flux validation project • NWP/reanalysis validation • Satellite cal/val

  4. What are we collecting now? • Difficult to assess adequacy, need to know: • Number of observations • Distribution of sampling in space and time • Platform information and number of reports from each platform • Natural variability • Autocorrelation time and space scales • Random uncertainty in observations (intra-platform uncertainty) • Bias uncertainty between observation types (inter-platform uncertainty) • Overall bias • User requirement: target and useable accuracies, time and space scales • Only the first 2 are easy to calculate

  5. How do we assess uncertainty? • Comparisons of co-located observations • Comparison with a common standard • Approach taken with VOSClim • Common standard is Met Office NWP model output • Also have co-located data and model output for all VOS, drifters and moored buoys • Need to partition uncertainty between model and forecast (very basic approach taken so far)

  6. What data do we need? • Lots of data in high variability regions • Smaller amounts of high quality data in lower variability regions • Sampling in space and time • Far apart to increase representivity • Co-locations to perform quality assurance • Data from lots of different platforms OR data from single platform with small bias • Identifiable platforms with metadata and quantified uncertainty • Sampling of the diurnal cycle • Either fully sampled or randomly sampled (to avoid aliasing)

  7. Uncertainty estimates: Air Temp, Feb 07 intra-platform (random) inter-platform (bias) sampling total

  8. What are the sources of uncertainty? • Sampling uncertainty • Need lots of data, appropriately arranged in space and time • Purely random errors • can be overcome with large data volumes • Biases between platforms • Can be overcome with data from a variety of sources • Need more research, and co-located data from different platforms • Overall bias • Hard to identify - need as many sources of data as possible

  9. Data quality: impact on uncertainty

  10. How does VOSClim help? • VOSClim ships overall are typically better than average • For each country VOSClim ships are typically better than the average for the country • Some exceptions, e.g. • UK VOSClim pressure data is worse than their VOS pressure data (but still better than the overall average)

  11. Some examples

  12. How does VOSClim help? • VOSClim shows that operators are aware of factors that indicate which ships provide the best data. • In what way are the VOSClim data better? • Data are very much more consistent among the VOSClim ships than among the VOS generally • Improvements in random uncertainty for an individual ship are less dramatic but still important • Does the improved monitoring for VOSClim help? • Not sure how to demonstrate this - depends on response to monitoring • Do the extra parameters in delayed mode help? • Pretty sure they will (based on previous VSOP-NA), but data availability until recently was not good • Do the photos help? • Yes, we have used them to relate air temperature sensor exposure to the characteristics of the data from the sensor.

  13. Future improvements • Data shown are as reported • Can apply height adjustments - should bring down inter-platform uncertainty • Can apply bias adjustments, e.g. for solar radiative heating of air temperature - should bring down random (intra-platform) uncertainty and also inter-platform uncertainty • Use delayed mode data and parameters • Should help to improve winds, temperature (and possibly humidity), and maybe SST • Improve partition of data and model uncertainty

  14. Conclusions • VOSClim data are better than average • Improvements are mainly in the consistency of the data • Many "good" ships aren't in VOSClim • A few "bad" ships are • Sampling uncertainty is still a major problem in many regions - we need more data (improved data quality doesn't really help here) • All VOS should report delayed mode parameters • Now have useful information which we can feed back to ship operators (how?) • With improved data flow and volumes we are now poised to exploit the information in the VOSClim dataset

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