Applying Statistical Tool CLIPER in Forecasting Visibility at Airports

Transcription

1 Applying Statistical Tool CLIPER in Forecasting Visibility at Airports Jadran Jurković 1, Marko Zoldoš 1, Igor Kos 1, Josip Juras 2, Zoran Pasarić 2 1 Croatia Control ltd 2 University of Zagreb, Dept. Of Geophys.

2 Contents Introduction to fog forecasting Previous research How the model works Results Conclusion

3 Introduction Croatia Control Ltd air traffic services (ATS), communication, navigation and surveillance services (CNS), aeronautical information services (AIS), aeronautical meteorological services (MET) Croatia Climatology (Zoldoš and Jurković 2016) LDZA 60 fog events/yr

4 Fog Fog challenging Formed in conditions in BL Varieties of observed types typical Radiation fog occasional events all daylong or several days Event ends during night Intermittent event Cloud base lowering Fog with snow on ground Maritime fog Complex terrain, nonhomogeneous surface, ground state

5 Aviation Fog or low ceiling conditions can reduce/prevent air traffic More frequently compared to other WX hazards Technology helps, but not for all Forecast for aviation Area forecasts Single point forecast (TAF, TREND) Hourly +2h, +24h Onset and dissipation Criteria for visibility 150,350,600,800,1500,3000,5000m Operational desirable accuracy

6 Fog forecast Operational model ECMWF, ALADIN Post processing 1d models Statistical tools Climatology, Fuzzy logic, NN CLIPER

7 CLIPER CLImatology and PERsistence Input : Present visibility (e.g. METAR report) Cumulative Correlation coefficient between hours Output: Visibility +9h + spread Suggested also as reference forecast for verification (Pasarić and Juras 2006)

8 How the model works First-order autocorrelation process combination of climatology and persistence (CLIPER) Climatology by itself pretty useless Current visibility = 100m But combining it with persistence might provide some useful results y t 0 = y t ρy 0 (Gringorten, 1971) 1 ρ2 The calculated forecast is actually the probability that visibility will exceed/not exceed a certain value. The normalized visibility y is the parameter that represents the climatology, and the correlation between the values of y at different time steps (ρ) represents the climatological persistence.

9 How the model works Data used: half-hourly METAR reports from Jan 1, 1994 to Dec 31, 2016 Hourly correlation coefficients are calculated for each month from normalized frequencies of visibility, and stored in dana files (Python was used here) Input data: month [1-12], UTC time (HHMM) and current visibility [m] The correlation coefficients are then used to forecast visibility for each successive hour, 9 hours in advance In addition to the median forecast of visibility, 50% and 80% confidence intervals are calculated as well Poor man s ensemble (PME), visibility nomogram for current month Results are visualised on the Fog Panel

10 Visibility distribution CLIMATOLOGY (median) PERsistence( anomaly)

11 CLIPER

12 FOG PANEL

13 FOG ONSET FOG DISIPATION Examples :00 UTC :00 UTC :00 UTC :00 UTC

14 Conclusion Fog forecast for aviation is very challenging Statistical tool CLIPER works well in typical climatological (radiation) fog events During morning during dissipation of fog evening situations, when fog is expected to form, are poorly forecast, except in some cases when visibility is already quite low (<5 km) - then PERsistence is better Fog panel - relates observed state comparing to climatology Presented to forecasters and now operational

15 Thank you