warnmos – a mos-based weather warning system...11th ems & 10th ecam, 12-16 september, berlin goal...

WarnMOS – A MOS-based weather warning system

Sebastian Trepte

Deutscher Wetterdienst (DWD)Offenbach am Main, Germany

11th EMS Annual Meeting10th European Conference on Applications of Meteoro logy (ECAM)12 – 16 September 2011Berlin, Germany

11th EMS & 10th ECAM, 12-16 September, Berlin

Overview

Goal of the application

Model Output Statistics and some specific characteristics

Features of WarnMOS

Weather warning elements

Mapping on the 1x1km²-grid

Forecast examples and comparisons

Outlook and ongoing work


Goal of WarnMOS

• Generation of an automatic weather warning guidance for Germanybased on the DWD warning criteria

• Derived from numerical model data, SYNOP station data, DWD radar network, and lightning observation data

• All forecast elements should be available hourly on the nowcast and the very short-term scale


Model Output Statistics (MOS)

• Develops relationship equations between observed and modelforecast weather elements (using multiple linear regression)

• Combination of different independent data sources

• Use of extrapolation/persistence and model forecasts (NWP) as predictors

• Takes advantages of predictor variables in NWP output notaccessible as observations

• Bias correction

Kind of forecasts: deterministic, probabilistic and derived elements


MOS specifics (1): Classification of the SYNOP station s

WarnMOS domain

0 – 200Coast

> 1000F

800 – 1000E

600 – 800D

400 – 600C

200 – 400B

0 – 200A south

0 – 200A middle

0 – 200A north

Elevation (m)Elevation Class


MOS specifics (2): Multi-Station Equations

▪ ▪

▪

▪ ▪

▪ ▪

“region“

▪ SYNOP stationin elevation class

MOS regression equation(Multi-Station)

WarnMOS domain

county

●

grid point

●


MOS (3): Estimation of the observation predictorOperational mode

▪ ▪

▪ ▪ ▪

x▪

▪

▪

■ station

x county centre / grid point

5 nearest stations

weighted meandepending on horizontal distance and difference of the elevations


Features of WarnMOS

• MOS code by Knüpffer & Haalman (2007), modified by DWD• Input: GME and IFS model forecasts (two runs per day)

260 SYNOP observations (hourly)radar reflectivity, lightning observations (last 15 min)

• 6 years of historical data for the MOS development• Advection of SYNOP, radar and lightning observations (trajectories)• Running every 15 min• 74h, 24h, and 6h forecasts in 1h and 3h intervals• Raw output: deterministic and probabilistic predictands• Transformation of point probabilities to area probabilities (counties)

depending on area size and spatial autocorrelation function of the predictand (Taubenheim, 1969)

• Condensed output: warning elements (probabilities of exceedance)


Condensing of the forecasts

There are 177 WarnMOS predictands!Predictands are valid for different reference periods: 1, 3, 6, 12, 24, 48 hours

→ Reduction of the number of predictands to 27 warning elementsAggregation according to the DWD warning criteriaWeighting of the reference periods depending on forecast valid time


Forecasted warning elements

Wind gust speed greater than 27, 33, 47, 53, 63, 75 kts

Thunderstorm 3 categories

Heavy rainfall 2 categoriesContinuous rainfall 3 categories

Snowfall light, moderate, heavySnowdrift 2 categoriesThaw heavy

SlicknessBlack ice 2 categories

Frost 2 categories

Fog Visibility below 150 m


Forecasts for German counties

Example:Probability ofthunderstorm+12h forecast

The orangecolour indicatesthe 50% threshold


Forecasts on the fine mesh

Goal: Provide warnings on a 1x1 km² grid for combination with otherhigh-resolution grid-based forecast products (→ DWD AutoWARN)

Grid configuration corresponds to the radar product grid at the DWD→ adequate grid for verification

Grid is independent from a county reconfiguration

Forecasts on a 2D grid can be visualized easier than having the 3th dimension of the elevation classes


Forecasts on the fine mesh

Best way: direct calculation of MOS forecasts on the high-resolution grid

Problem: computing time much longer than forecast run interval

Solution:(1) Forecast calculation stepcalculation of forecasts on a coarse 20 x 20 km² grid and all elevation classes occurring within a grid cell

(2) Mapping on the fine mesh- the elevation of the point in the fine mesh appoints the elevation class- the nearest neighbour point in the coarse grid having the same

elevation class defines the forecast value on the fine mesh

Target grid covers all elevation classes but is only two-dimensional


Forecast example on counties

Probability ofrainfall > 25mm/12h+4h forecast



Forecast example on high-resolution grid

Probability ofrainfall > 25mm/12h+4h forecast

The light greencolour indicatesthe 50% threshold


Forecast example on counties

Probability ofmax wind gust > 27 kts+5h



Forecast example on high-resolution grid

Probability ofmax wind gust > 27 kts+5h

The light greencolour indicatesthe 50% threshold


Outlook and ongoing work

• Evaluation of WarnMOS on the fine mesh

• Spatial interpolation of the MOS forecasts on the fine mesh

• Integration into the DWD AutoWARN-ModelMIX concept

• Talk by C. Primo (AM5): “On the choice of thresholds to give warnings”


Thank you for your attention!

warnmos – a mos-based weather warning system...11th ems & 10th ecam, 12-16 september, berlin goal...

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