Wintry weather: improved nowcasting through data fusion

Transcription

1 Wintry weather: improved nowcasting through data fusion Arnold Tafferner, Felix Keis DLR Institut für Physik der Atmosphäre (IPA) Wetter&Fliegen Final Colloquium, MAC MUC, 15 March

2 Outline The problem Available observation MUC Available forecast data Winter weather objects Nowcasting concept 2

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4 Snow clearing and de-icing procedures 4

5 In-flight Icing 5

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9 Main objectives within project weather and flying Improve the winter weather nowcasting for Munich Airport: Icing conditions at the surface Aircraft icing on approach, take-off and at the ground Onset, duration, amount and type of precipitation Combine observation and forecast data to determine and nowcast wintry weather conditions 9

10 The meteorological problem Forecasts of snow fall are available days in advance However: Snow events often occur in the form of showers, the precipitation amount they bring and their timing is difficult to predict Small changes in temperature, humidity and cloud properties (aerosol, turbulence), especially aorund 0⁰C, can turn a harmless cloud into a hazardous cloud with supercooled droplets and freezing precipitation Nowcasting these subtle changes in cloud parameters and their effect on precipitation are difficult to predict Cloud composition, i.e. cloud droplet size distribution, ice cristal concentration, precipitation particles (snow, rain, graupel) and turbulent wind are not directly measured However, remote sensing observation instruments in combination with standard observations, high resolution forecasts and data fusion concepts offer the possibilty to better handle the situation 10

11 Schematic illustration of available data at Munich airport Winter Weather Object COSMOMUC T,RH profiles SYNOP METAR Micro Rain Radar Parsivel Disdrometer Glatteis- Frühwarnsystem TMA volume boundary AMDAR SWIS POLDIRAD COSMOMUC 11

12 Analysis of Winter Weather Conditions Approach: Development of an advanced version of ADWICE in order to combine (fuzzy logic) surface observations (SYNOP, GFS) with meteo profiles (AMDAR, COSMOMUC), and remote sensing measurements (POLDIRAD, MRR, PARSIVEL) in order to determine: Melting/freezing conditions (de-icing procedures) Aircraft icing on approach and take-off Precip. type (snow, rain, freezing rain) and amount (light, moderate, severe) 2500 AGL MRR rain snow Meteo profile SLD 0 C AMDAR SYNOP METAR + COSMOMUC Automatic sensors F U S I O N Ht H2 H1 Hb MUC 0 m MUC 12

13 Advanced Diagnosis and Warning system for aircraft ICing Environments ADWICE has been developed since 1998 in a joint co-operation between DLR, DWD and IMuK Hannover is based on a former NCAR-RAP algorithm (adopted for the European area, meanwhile considerably extended/changed) merges forecast model data with hourly observation and radar data 1st version has been run pre-operationally at DWD since nd version output used operationally References Tafferner et al: ADWICE The Advanced Diagnosis and Warning system for aircraft ICing Environments; Wea. & Forec. Vol.18, No.2, April 2003 Leifeld, 2003, Weiterentwicklung des Nowcastingsystems ADWICE zur Erkennung vereisungsgefährdeter Lufträume, DWD Internal report / PhD thesis University of Hannover Rosczyk, 2004, Evaluation of the Advanced Diagnosis and Warning system for aircraft ICing Environments (ADWICE) Methodical aspects, Final thesis, University of Hannover 13

14 The advantages of ADWICE ADWICE runs automatic forecasts and analyses of icing environments for aviation is laid out to detect all significant environments (SLD, moderate & severe icing) provides this icing information to forecasters, pilots Advantages for forecasters: checks all the data more carefully and faster than forecasters are able to do it filters out the significant information of all the data reduces the possibility of overlooking significant icing hazards Forecasters/meteorologists experiences and know how has been used to develop prognostic and diagnostic icing algorithms for ADWICE 14

15 ADWICE Prognostic Icing Product - PIP model data (COSMO-EU): temperature specific humidity parameterization of moist convection Identification of icing scenarios freezing convective stratiform general ADWICE PIP FL UTC 3D - Prognostic Icing Product (PIP) 15

16 Icing scenario freezing model sounding 0 C Search for precipitable cloud beneath cirrus layer (-50 C T) relative humidity (RH) 80%, z 3000m Check possible evaporation beneath precipitable cloud evaporation of precip. if RH < 80% and z > 3000m Melting layer with T > 0 C SLD Supercooled layer with SLD / FZRA T 0 C SEVERE ICING Layer above freezing (T > 0 C), no inflight icing 16

17 Icing scenario stratiform model sounding check if there is a precipitable cloud beneath cirrus layer: (-50 C T) RH 80%, z 3000m 0 C If precipitable cloud above than check the evaporation layer: evaporation of precipitation if RH < 80% and z > 3000m SLD Search for cloud top with significant gradient of humidity: RH 2.5 % / 100m, -12 C CTT 0 C Stratiform layer with SLD / FZDZ: -12 C T 0 C, RH 85% Layer above freezing (T > 0 C), no inflight icing 17

18 Icing scenarios convective and general convective SLD in large towering cumulus (TCU) parameterization of moist convection Z 3000m & -40 C T 0 C general Clouds containing supercooled liquid water temperature -20 C T 0 C relative humidity varies linear with temperature: rh x%, 63% x 82% 18

19 Use of local data at MUC: AMDAR data (+) No icing derived from AMDAR Icing scenario stratiform derived from model forecast Correction of ADWICE scenario by use of AMDAR 19

20 Nowcasting: Data Availability 1 hrly background VERA, SYNOP, COSMO-DE 1/2 hrly METAR 10 min POLDIRAD, MRR, PARSIVEL, AMDAR, GFS, COSMOMUC Use these data in fuzzy logic procedure with frequent update 2nd talk 20 Vortrag > Autor > Dokumentname > Datu

21 Nowcasting: COSMOMUC Output 1- hour assimilation/forecast cycle enables: Frequent output (10 min) Assimilation of most recent observations from Radar, SYNOP, AMDAR, SODAR/RASS Optimized lower boundary data: landuse, soil Small domain - fast execution time-lagged ensemble precip type and amount hydrometeor mixing ratio fields in vertical sections along and across glide path profiles ADWICE icing conditions 21

22 COSMOMUC (t a ) (t a +1h) (t a +2h) (t a ) local data real time local data COSMO-EU Initial & boundary Time DATA ASSIMILATION forecast O U T P U T DATA ASSIMILATION DATA ASSIMILATION forecast Boundary data from COSMO-EU Boundary plus assimilated data; Initial data: Restart or NewStart DATA ASSIMILATION forecast Nudging of local data 22

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24 ADWICE Diagnostic Icing Algorithm Example 20. Jan UTC - FL250 24

25 Integration of additional data at stations around MUC Radar COSMO AMDAR Classification of hydrometeors GFS POLDIRAD SWIS 25

26 Nowcasting Concept Analysis Use all available local data with high refresh rates Apply fuzzy logic (extended ADWICE) Trend Take into account changes of local measurements (2nd talk) Use forecast data of COSMOMUC for trend estimates Take into account diagnostics at surrounding stations: advection of upstream weather Nowcast Combine analysis with trend to estimate conditions up to 2 hours 26

27 Summary Local Met data at and around MUC together with remote sensing from polarimetric radar (POLDIRAD) have the potential to improve existing ADWICE diagnostics: hydrometeor type, drop size, precipitation rate, icing scenario, icing intensity First results indicate that ADWICE operated with COSMO-DE instead with COSMO-EU reduces overforecasting The time-lagged ensemble COSMOMUC is expected to provide more accurate forecasts of precipitation, especially as regards to timing of events Nowcasting concept has been developed: combination of trends of local observations with COSMOMUC forecasts and advection of upstream observations first results in 2nd talk 27