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INCA- Integrated Nowcasting through Comprehensive Analysis by T. Haiden; A. Kann; K. Stadlbacher; G. Pistotnik; C. Wittm

INCA- Integrated Nowcasting through Comprehensive Analysis by T. Haiden; A. Kann; K. Stadlbacher; G. Pistotnik; C. Wittmann. Mag. Thomas Turecek Austrian Meteorological Service (ZAMG) Tel.: ++43 1 36026/2311 Fax: ++43 1 3602673 E-mail: thomas.turecek@zamg.ac.at

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INCA- Integrated Nowcasting through Comprehensive Analysis by T. Haiden; A. Kann; K. Stadlbacher; G. Pistotnik; C. Wittm

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  1. INCA- Integrated Nowcasting through Comprehensive Analysisby T. Haiden; A. Kann; K. Stadlbacher; G. Pistotnik; C. Wittmann Mag. Thomas Turecek Austrian Meteorological Service (ZAMG) Tel.: ++43 1 36026/2311 Fax: ++43 1 3602673 E-mail: thomas.turecek@zamg.ac.at Internet: http://www.zamg.ac.at

  2. Content • Introduction • Why do we need INCA? • General characteristics • Data sources and NWP-model output • INCA analysis system • INCA forecasting system • What‘s new? • Short Introduction in CineSat • some examples how to use the system

  3. Problems we have…. • In NWP products there are the same errors in the nowcasting range up to 6 hours occur as in the range up to 12 hours because of the model initialization. • The limitation of the horizontal resolution which does not allow to reproduce all of the small-scale phenomena which determine local conditions. • For temperature forecasts a simple persistence forecast or a forecast based on climatology can be better than NWP forecast for up to several hours. • As the NWP-models are weak prefering nowcasting, ZAMG is developing the observation-based analysis and forecasting system INCA. • →Integrated Nowcasting through Comprehensive Analysis

  4. Introduction • Mean absolute error of the 2m temperature forecast during Febr. 2003 at the station 11035-Vienna-Hohe Warte

  5. General Characteristics NWP Output Surface stations Radar/satellite imagery INCA Analysis and forecast fields with a high temporal and spatial resolution: Dt=1h (15min), Dx=1km Detailed topography

  6. Data Source- NWP-Model-Output • Three dimensional INCA analyses of temperature; humidity and wind are based on ALADIN output. • ALADIN is used because it`s a limited area model which has been run operationally at ZAMG since 1999 and its output fields are readily available. • Model characteristics (ALADIN): • Resolution 9,6km with 45 levels in the vertical • Parameter fields are 1-hourly • Forecast runs 4 times a day (00.06,12,18UTC) • 00,12 runs are integrated up to +72 hours • 06,18 runs are integrated up to +60 hours • Fields are available about 4 hours after analysis time • Parameter fields are: temperature, total and low level cloudiness, geopotential height, wind, humidity, precipitation

  7. Surface Station Observation • Most important data source for INCA system are surface stations • ZAMG runs a network of ~150 automated stations (TAWES) • About 200 hydrological Stations • Some SYNOP-stations from neighbouring countries • What data do we use? (measurements every once a minute) • 2m temperature • relative humidity • dew point • 10m wind speed/ direction • precipitation amount • duration of precipitation • insolation minutes

  8. Other Data • Radar data: • 4 radarstations (Vienna-Airport, near City of Salzburg, Patscherkofel mountain, Zirbitzkogel mountain) • measurements every 5 minutes • Satellite data • MSG • measurements every 15 minutes • Elevation data • dataset from the US Geological Survey • resolution: 930m in latitudinal direction • 630m in longitudinal direction

  9. INCA Data-Fields • 2-D Analysis und forecasts • Precipitation • Total Cloud-Cover • 3-D Analysis und forecasts • temperature • humidity • wind speed and direction • global radiation

  10. downward shift along gradient above PBL ALADIN INCA INCA-Analysis: Temperature • The 3D-Analysis of temperature starts with the ALADIN (bias-corrected) forecast as a first guess and is corrected based on differences between observation and forecast at surface station location. • Interpolation of ALADIN temperature field onto 3-D INCA grid • In Valley atmospheres not represented in the ALADIN forecast, the PBL temperature profile is shifted down to the valley floor surface, along gradient above the PBL.

  11. INCA-Analysis Temperature • Difference between ALADIN forecasts and observations • 3-D interpolation of the temperature differences • 2-D interpolation of the temperature differences of forecast errors within the surface layer (2m-temperature) • (Figure 1.) ( Figure 1.) Schematic depiction of the strength of influence of a station observation. The ratio of the horizontal to vertical distance of influence is determined by station distance and static stability.

  12. An Example of INCA Temperature Analysis

  13. INCA-Analysis: Wind The first guess: ALADIN WIND 9,6km/h wind field Interpolation&Modification Correctedbyobservations 1km wind field with div = 0 relaxation algorithm 1km topography data 1km INCA wind field with div ~ 0

  14. An Example of INCA Wind Analysis before relaxation algorithm after relaxation algorithm

  15. INCA- Cloudiness Analysis: MSG.satellite information TAWES data insolation per Minute in %

  16. INCA- Precipitation Analysis • The precipitation analysis is a synthesis of station interpolation and radar-data. • It‘s designed to combine the strength of both methods. • Radar: can detect precipitating cells that do not hit a station • Interpolation: provides a precipitation analysis in areas not accessible by the radar beam. • Aggregation of 5min radar to 15min amounts • Aggregation of 1min observations to 15min amounts • Correlation radar values/observed values through linear regression (10 surrounding stations)

  17. INCA- Precipitation Analysis • Interpolation of station data onto a regular 1x1km INCA grid using distance weighting. • Climatological scaling of radar data • Radar field is strongly range dependent so it must be scaled before it‘s used in the analysis. • First step is a climatological scaling • A climatological scaling factor RFJ(i,j) is calculated for every month • Re-scaling of radar data using the latest observation • cross validation

  18. INCA- Precipitation Analysis

  19. What‘s new? • Precipitation Type • For INCA precipitation type we use: • Temperature and humidity (wet-bulb temperature +1,4°C to locate the snowline). • INCA ground temperature (based on surface observations of +5cm temperature and -10cm soil temperature). • Precipitation analyis and forecast • To locate cold air-pools the ALADIN temperature is corrected with local stations.

  20. What‘s new • A better temperature-analysis in case of inversions • Before: • 3D + 2D correction, whereas 2D correction is done by horizontal interpolation (problems with mountains and valleys) • Now: • 2 D correction of the temperature only in valleys up to the inversion. • That means: Maximum correction in the valley. Minimum correction near the inversion. • So you get an inversion-factor IFAC: INCA-topography ALADIN - topography inversion Cold air pool 0 0.8 1.0 0 0

  21. What‘s new? • The 2D temperature correction is mulipyled with the IFAC. • In valleys or in lowlands the factor is nearly one • On mountainsides/ ridges the factor is near by 0.

  22. What‘snew? • global radiation forecast • diagnostic fields of convective parameters like • lifted condensation level • level of free convection • CAPE • CIN • showalter index • lifted index • icing potential • Wind Chill • operational verification of INCA

  23. INCAForecasts • Now: different methods of extrapolation in time for temperature/ humidity, wind, cloudiness and precipitation • In Future: it‘s planned to replace these methods by a unified nowcasting method based on error motion vectors. • The concept: It represents a framework for the unification of nowcasting procedures • Computation of motion vector based on cross-correlating consecutive field distributions

  24. INCA- temperature nowcasting • Much of the temperature error in the NWP forecasts is due to errors in the cloudiness and associated errors in the surface energy budget. • When mistakes of model cloudiness occur the predicted diurnial temperature amplitude is corrected by a factor taking into account the degree of the error of the cloudiness. • If there is no cloudiness forecast error, the predicted temperature change is equal to the one predicted by the NWP model.

  25. INCA-CloudinessForecasts • INCA nowcasting of cloudiness is based on cloud motion vectors derived from consecutive visible (during daytime) and infrared (during nighttime) satellite images. • During sunrise and sunset a time weighted combination of both vector field is used. • The nowcasting procedure of cloudiness is finalized by a consistency check with the nowcasting field precipitation.

  26. INCA-PrecipitationForecast • Based on two components • Observation based extrapolation based on motion vectors determined from previous analyses like • Radar motion Vectors • Cloud motion vectors • Water vapour motion vectors • INCA motion vectors. • A NWP-model forecast (output fields of ALADIN and ECMWF)

  27. INCA-PrecipitationForecast ANALYSIS 1 ECMWF weighting NOW-CASTING ALADIN 0 t1=2 h t2=6 h +31 bis +43 h +48 h Forecast Time -15 min +00 h

  28. CineSat Pmsl; Fronts/ IR10.8

  29. CineSat Pmsl; Fronts; Synthetic Sat

  30. CineSat Pmsl; ATP500; Fronts

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