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?

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

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

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)

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)

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

sampling total

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

Data quality: impact on uncertainty

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)

Some examples

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.

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

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