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

Internet: http://www.zamg.ac.at

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. 2003 at the station 11035-Vienna-Hohe Warte

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Prognosezeit (h)

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Persistenz

Klimatologie, adaptiert

ALADIN

General Characteristics

Analysis and forecast fields with a high temporal and spatial resolution: Dt=1h (15min), Dx=1km

Surface stations Radar/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 WIND9,6km/h wind field

Interpolation & Modification Corrected 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 algorithm after relaxation algorithm

INCA- Cloudiness Analysis:

TAWES datainsolation 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

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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

.Vdt

d

t

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.

Temperaturprognose 2-17 Nov 2004, alle Stationen

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Prognosezeit (h)

ALADIN

INCA

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

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t2=6 h +48 h

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Forecast Time

+31 bis +43 ht1=2 h

+00 h-15 min

ALADIN

ECMWF

ANALYSIS

NOW-CASTING

weighting

CineSat

Pmsl; Fronts/ IR10.8

CineSat

Pmsl; Fronts; Synthetic Sat

CineSat

Pmsl; ATP500; Fronts