2018 EUMETSAT NWP SAF mesoscale wind data assimilation workshop

  • A EUMETSAT NWP SAF mesoscale wind data assimilation workshop was held on 18 September 2018 in Tallinn, Estonia.

    2018 sees an unprecedented increase in the number of wind-measuring satellites:

    • EUMETSAT’s MetOp-C will bring the 3rd ASCAT into orbit, where the three ASCATs will provide, for the first time, close to 100% wind coverage of the seas around 9:30 local time (LT), both in the morning and evening;
    • ESA’s Aeolus mission will, also for the first time, provide wind profiles from space;
    • The Chinese-French Ocean SATellite, CFOSAT, includes a rotating fan-beam wind scatterometer, SCAT, and combines it with a wave-measuring instrument, SWIM;
    • India will launch OceanSat-3 with on-board its well-proven wind scatterometer, providing ocean wind coverage at 12:00 and 00:00 local time;
    • China launched HY-2B, providing ocean wind coverage at 6:00 and 18:00 LT.

    Satellite observations have been fundamental in improving weather forecast skill over the past two decades on all terms, while few high-quality wind observations were present in the Global Observing System. To depict and initialize the flow on scales smaller than 500 km, wind observations are essential, however, and adding satellite wind observations for predicting dynamical weather has proven to be beneficial. But how are we going to exploit all these wind observations?

    The mesoscale wind data assimilation workshop addressed this question and raised expectations for the beneficial and practical use of these novel wind observing systems. It addressed the spatial scales observed and the errors in scatterometer winds. For satellite ocean winds it addressed the calibration wind reference at high and extreme winds, ambiguity removal, and a method to obtain weather-dependent error covariances, both for buoy, satellite and NWP data. For NWP models it provided examples of the spatial scales deterministically modelled, but also the spatial scales which are only realistically modelled and not well initialized. This sets the scene to discuss spatial representation errors, which often dominate wind measurement observation errors, be it from radiosondes, aircraft winds or wind scatterometers. The pros and cons of thinning, superobbing and “supermodding” have been discussed. The workshop also addressed well-known biases of weather models, but which violate the BLUE data assimilation principle: Best Linear Unbiased Estimate. While air-mass dependent bias correction schemes are fundamental to the current beneficial use of satellite observations, they are not in place for satellite winds. A way forward has been suggested and discussed with the attendees.

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