Automated weather warning proposals based on post-processed numerical weather forecasts Guido...

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Automated weather warning proposals based on post-processed numerical weather forecasts

Guido Schröder, Bernhard Reichert, Dirk HeizenrederGuido Schröder, Bernhard Reichert, Dirk Heizenreder

Deutscher Wetterdienst, Offenbach am Main, GermanyDeutscher Wetterdienst, Offenbach am Main, Germany

19 August 2014, WWOSC, Montreal, Canada19 August 2014, WWOSC, Montreal, Canada

The simplified warning process

ObservationsSynop, Radar, etc.

Numerical ModelsGME, COSMO-DE-EPS, IFS, etc

Other forecast productsPost-processed model forecasts, NowCastMIX, etc.

WarningsForecasters

Guido Schröder, DWD, Germany

The simplified warning process with AutoWARN

ObservationsSynop, Radar, etc.

Numerical ModelsGME, COSMO-DE-EPS, IFS, etc.

Other forecast productsPost-processed model forecasts, NowCastMIX, etc.

ModelMIXStatistical Post-processing

Automated warnings

Forecastersintegrate automated warnings

Warnings

1) On the characteristics of weather warnings

2) Automatic generation of warning proposals

3) Verification with station observations

4) Summary and outlook

Guido Schröder, DWD, Germany 4

Manual generation of warnings – Example for gust warnings (winter storm Xaver 2013)

- Bft 7 Bft 8,9 Bft 10 Bft 11 Bft 12> Bft 12

Station observations

Height levels: A - > 0m B - > 200m C - > 400m D - > 600m E - > 800m F - > 1000m G - > 1500m H - > 2000m

Good weather warnings are:

accurate

significant

meteorologically consistent

overwarn rather than underwarn

homogeneous

simple

Manually generated warnings – here for Bft 8-9 only

Using a DWD layer within NinJo the forecasters define (draw) polygons for which a specific warning is valid for a given time.

Station observations

Using a DWD layer within NinJo the forecasters define (draw) polygons for which a specific warning is valid for a given time.

All manually generated gust warnings

Manually generated warnings – here for Bft 8-9 only

Automatically generated warning proposals

To support the manual warning generation warning proposals are generated automatically based on post-processed numerical model forecasts.

11Guido Schröder, DWD, Germany

For mountainous areas predefined polygons will be used to generate warning proposals for upper levels.

2a) Mountainous areas

2b) Lower levels – maximum event

Lower levels

The maximum gust forecast for the event is computed for lower levels on a 20kmx20km grid. In mountainous areas it is the wind speed forecast within the valleys.

The gridded data is smoothed with the constraint that the gusts can only increase. Overwarning is preferred to underwarning.

2c) Smoothing gridded data

Smoothing

2d) Generation of warning regions

Based on contour lines regions with the same maximum warning are extracted. Too small polygons are removed as they are insignificant.

Summary – temporal fragmentation

2e) Temporal fragmentation

Regions with similar starting and end time are clustered together. Stronger warnings are nested into weaker warnings. This way the warning regions are temporally split into fragments.

3) Verification result for Oct 13-Feb 14

Forecasters Automated system

Each horizontal bar (normalized to the same width) corresponds to all observed events for a given wind speed category. The colors indicate which fraction of the observed events were observed correctly (green) or incorrectly (other colors). The numbers in each box is the number of actual events

underwarning overwarning underwarning overwarning

4) Summary

The increased amount of data in the warning process requires more and more automization

The automated system tries to generate warning proposals the same way the forecaster would do it. That implies

• Separate treatment of mountainous areas

• Maximum of the event: It is more important to get the location right than the timing

• Significance: Smoothing the gridded data

The automated warnings give slightly better verification results than the manually generated warnings

For low wind speeds (Bft 7) the automated system has the tendency to underwarn – the system needs to be optimized to generate automated warning at lower thresholds for Bft 7

4) Outlook

For wind gusts the AutoWARN system is already being tested

Tools needs to be developed to better integrate the warning proposals into the actual warnings

More research is needed in how to integrate several more numerical models (e.g. ICON, ICON-EPS, IFS-EPS) and data sources

Probabilities for the events to occur need to be issued along with the warnings

Appendix

Strategic Goals of DWD for the Weather Warning Service

DWD Strategy 2014-2020:

Development of a system for an optimal decision support in the warning process

Automated support for the warning service and for the production of customer specific warn products

Stepwise centralization of the warning service from current regional centers to the DWD headquarter in Offenbach

Development of AutoWARN: An automated decision support system with manual

monitoring and decision capabilities for the forecaster

COSMO-DE-EPS

Data sources for warnings generation

Numerical models (DWD-models GME and COSMO-DE-EPS, ECMWF-model IFS)

Statistically post-processed numerical models (ModelMIX, Hirsch et al., WWOSC 2014)

Observations (station data, radar observations)

ModelMIX

COSMO-DE-EPS

Automated warnings

All warnings issued

The actual warnings are issued on County (“Landkreis”) level by an automated system that takes the manually generated warnings as input.

B. Reichert, FE ZE, DWD

Manually EditedFuture Warn Status

Time intervalTime interval

Elevationinterval

Warning EventWarning Event

AttributesAttributes

Indicator: New Significant Warn Proposal

Automatic Warn Proposal

2a) Event extraction

The raw observed time series is inflated to reduce fluctuations. Between two adjacent local maxima the observations are artificially increased. This is achieved applying the above formula twice to the data.

Inflated observationRaw observation

The inflated wind speeds are converted into the event categories. Distinct maximum events are extracted. Only events with at least 3h duration are used for verification.

2a) Event extraction

Warning polygons for mountainous areas

1h) Merging of warning polygons for mountainous areas

The maximum event in the mountainous areas is determined. Redundant polygons are removed. The remaining polygons are spatially merged where possible.

Appendix Smoothing

Guido Schröder, FEZE-B, DWD 29

Filter for smoothing

Higher order filters (Shapiro 1971, also Schlünzen et al., 1996)

2112 410416

1 iiiii

fi uuuuuu

ui Non-filtered value at grid point i

Filtered value at grid point i

fi uuuu

111123 6154415664

1 iiiiiii

fi uuuuuuuu

3-Points

5-Points

7-Points

Dots are model data

The blue line is the filtered model data

Higher order filters (Shapiro 1971, also Schlünzen et al., 1996)

Filter for smoorhing

Multi-linear Regression for smoothing

Assuming model values at , these can be approximated using a multi-linear regression with the regression function

where . Here

where is generated as linear combination of with the coefficients

The error is minimized. The base functions can be fields that describe the main structures in the data. It is impossible to attain a perfect fit everywhere – this is why a local regression

is done – for each grid point with n surrounding grid points. For each grid point there are n values where k = 1, …,n . Of these a

weighted average is computed while taking into account the error corresponding to the regression function. The smoothed value is then

jj xfcxf )()(

ii xfye 22 )(

ii yxf ~)(

jff jc

)( ik xf

y33 )(

On this slide k and g are no exponents!

Multi-linear regression for smoothing

The example below uses the base functions

with n=11 The model data (dots) is discontinuous – the optimal regression curve is taking

that into account. Individual local regression curves (red) don‘t.

constxf )(1 xxf )(2

Example regression for i=41 Example regression for i=41

Schröder / FEZE-B – 10/2012

The example below uses the base functions

with n=11 The model data (dots) is discontinuous – the optimal regression curve is taking

that into account. Individual local regression curves (red) don‘t.

constxf )(1 xxf )(2

Example regression for i=41 All regressions for i=41

Dots are model data

The blue line is the smooth data

The green line is derived with the 7-point-filter

Quadratic programming for smoothing

Multi-linear regression does not preserve maxima – which could mean that extreme warnings are smoothed away – which is not acceptable

This problem can be solved by indroducing a constraint to the linear regression

This is a quadratic programming problem and can be solved with standard solvers

The above constraint ensures that all values can only increase. It is also possible to preserve only selected local maxima.

ii xfye 22 )( ii yxf )(j

jj xfcxf )()(

Multi-lineare regression for smoothingGlätter

If all values can only increase, this will lead to overwarning If only selected maxima are pteserved, the overwarned area is smaller

No constraint Values can only increase Only some maxima are preserved

Dots are model data

The blue line is the smooth data

The green line is derived with the 7-point-filter

Appendix temporal clustering

Guido Schröder, FEZE-B, DWD 39

Preperation for temporal fragmentation

1e) Temporal fragmentation

The polygons are fragmented with a regular hexagon grid.

For each polygon the starting and end time within each hexagon is computed.

Starting time

End time

1f) Temporal fragmentation

Clustering of hexagons with similar starting and end time

Regions with similar starting and end time are clustered together. Stronger warnings are nested into weaker warnings.

1g) Temporal fragmentation

Intersection with the original contour polygons

The hexagon clusters are smoothed and interseted with the original

1h) Temporal fragmentation

Automated weather warning proposals based on post-processed numerical weather forecasts Guido...

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