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.: /2311 Fax: Internet:

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

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

Introduction  Mean absolute error of the 2m temperature forecast during Febr at the station Vienna-Hohe Warte

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

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

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

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

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

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. ALADIN INCA downward shift along gradient above PBL

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.

An Example of INCA Temperature Analysis

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

An Example of INCA Wind Analysis before relaxation algorithmafter relaxation algorithm

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

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)

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

INCA- Precipitation Analysis

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.

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: inversion Cold air pool ALADIN - topography INCA-topography

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.

What‘s new?  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

INCA Forecasts  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

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.

INCA-Cloudiness Forecasts  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.

INCA-Precipitation Forecast  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)

INCA-Precipitation Forecast 1 t2=6 h+48 h 0 Forecast Time +31 bis +43 ht1=2 h +00 h-15 min ALADIN ECMWF ANALYSIS NOW- CASTING weighting

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