1. Storm detection, tracking and prediction
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Mikko Hiirsalmi
VTT
MMEA 5th year autumn result seminar
Syke, Mechelininkatu 34 a (Muuttohaukka h118)
Wed, :30-12:30

2. Motivation To be able to warn citizens on approaching storm fronts.
Driver travel planning assistance
Alerts for a desired location.
Etc.
What is needed to build such warning systems?
Storm detection
tracking
trajectory nowcasting

3. How?
Storms are detected from rain intensity radar images supplied by FMI.
Typically new images arrive every 5 minutes.
Accessible via the open data interface (see FMI open data:  ).
Analysis is based on
basic radar image analysis and
detecting temporal storm tracks of detected storm areas and
forecasting of the future trajectories.
Storm detection and tracking is based on FMI’s legacy Matlab program which has been tailored to also produce future trajectory forecasts.
In MMEA,
the Matlab program is run as an Octave program and
the analysis chain is embedded as a MMEA ComputationService polling newest radar images from FMI and performing the analysis tasks with Octave and
feeding the results forward via ESB bus as MMEA messages to future warning systems.
The integration is done by Okko Kauhanen from UEF and
an example of a storm aware driver routing assistance system is being drafted by Markus Stocker from UEF.

4. Detection of storm polygons

5. On the detection process
Basic image analysis techniques include
suitable thresholding on the radar images to eliminate noise and low rain intensities.
Image is binarized and morphological closure operation is used to remove small holes in the image and
storm cell detection is done by contour function in Matlab.
After this storm cells are represented as polygonal areas.
Then temporal clustering is used to group storm cell polygons to larger clusters and to associate clusters from successive temporal images together.
Storm tracks are formed by detecting the found dependencies and represented as tree structures of cluster histories.
Previously separate storm cells may
merge together at later times,
may disappear or
may split to multiple separate cells.
Also new storm cells may emerge.

6. Storm tracking visualization
History of storm tracks
Cluster centroids visualized over time
Splits
Merges
Steady movement
Storm cell
mergers marked with green
splits with magenta
Default colour is red
Difficult to tell the temporal order of tracks in this figure.

7. On the forecasting process
The future paths of the centroids of the detected storm cells are being forecast by fitting a prediction model on the detected track observations and forecasting future positions for the tracks with the estimated model.
Weighted linear regression is being used.
one gives a larger weight to the newer observations and therefore adopts faster to changes in the storm direction or speed.

8. Example of forecasts

9. Uncertainty of the forecasts
Accuracy needed for reliable warnings, but still open.
Storms are rapidly changing.
Detecting areas with heavy rain and grouping these into larger areas.
May result into rather large areas at times.
Also many small areas.
Temporal tracking occasionally causes noise in predictions
As small areas may be submerged into bigger areas causing large velocities on cluster centroids at those times.
Only forecasting the progression of the cluster centroids.
The area and shape are supposed to stay similar to the last observed cluster.

10. Storm prediction awareness with Wavellite/Sense

11. Potential for fusion of other types of observations
Thunderstorm data from FMI
Where and when flashes occur?
Citizen observations of storm data observations.
How this data co-occurs with the storm radar observations.

12. Thanks!
Any questions?

13. TECHNOLOGY FOR BUSINESS
Final slide is optional in VTT’s internal presentations