Vaisala AviMet advanced multi-sensor algorithms for generation of unmanned airport METAR observation

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Wednesday, 5 February 2014: 2:15 PM
Georgia Ballroom 3 (The Georgia World Congress Center )
Juhani Polvinen, Vaisala, Vantaa, Finland; and N. Demetriades, A. Aura, and B. Techlovský

The combination of Vaisala's AviMet software, observation sensor/system products, and aviation research and development has been used to create a powerful offering for deploying unmanned observation auto METAR solutions at airports. Vaisala provides decades of ICAO and aviation expertise and has already deployed these types of solutions today. A fully unmanned airport observation auto METAR solution must include present weather information and address concerns related to the representativeness of in situ weather observations for characterizing the weather conditions at the airport. Vaisala has developed a suite of advanced multi-sensor present weather algorithms within the AviMet automated weather observing system. These algorithms use the weather information fused from several sensors at the airport. The multi-sensor algorithms amend and possibly correct the information from the present weather sensor(s) to facilitate more accurate present weather detection. A brief review of each of the following algorithms will be provided: 1. Cloud coverage 2. Cloud type (CB and TCU) automatic METAR presentation 3. Global coverage Thunderstorm algorithm 4. Freezing rain and freezing fog algorithm 5. Obscuration type algorithms for fog patches (BCFG) or partial fog (PRFG) 6. Prevailing visibility The Vaisala multi-sensor suite has gone through vigorous end-user testing in operational conditions in several European airports. During the comparison tests, the automatic METAR messages are compared to METAR messages generated by experienced human observers. The agreement between automatic and human-generated METAR messages was 95% for present weather observations (total of 6597 reports) and 94% for cloud amount. It should also be noted that in some conditions (e.g. during dark conditions at night) automatized observations can produce operationally more correct results than a human observer. The comparison tests show that the multi-sensor automatic weather observation algorithms can be used operationally and they even can offer additional benefits when compared to a human observer under certain conditions.