New Applications for Forecasters
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Transcript of New Applications for Forecasters
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New Applications for Forecasters
EGOWS 2007
Heleen ter Pelkwijk
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The items of this presentation
• Rich Intranet Application• Dashboard• Product Generation• Application for the use of Conditional Climatology
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Rich Intranet Application
• Purpose:
Create an easily accessible operational intranet environment (weather items) for the forecasters.
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Rich Intranet Application
• Reason:
- More than half of the forecasters make frequent use of internet/intranet
- Researchers want to have an easily accessible outlet to show the forecasters their experimental products.
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Rich Intranet Application
• Method:- Creation of two intranet domains:
Proeftuin.knmi.nl (the playground for scientists)Oper.knmi.nl (the operational website)
- Creation of a combined menu (portal)
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Rich Intranet Application
• Method:
Definition of operational intranet system:Data streams, data processing and the server are
monitored and when needed repaired 24 X 7.
The experimental site has an operational server but no operational data!
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Rich Intranet Application
• Result:- The menu is created from a database- All information has a tag “oper” or “exper”- All information has its own unique product
number
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Result:
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Rich Intranet Application
• Conclusion:
Do we still need a dedicated meteorological workstation?
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The items of this presentation
• Rich Intranet Application• Dashboard• Product Generation• Application for the use of Conditional Climatology
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Dashboard
• Purpose:
- Dashboard = the interface of a monitoring tool- Dashboard gives a signal (trigger) in case a severe event occurs.
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Dashboard
• Reason:- The forecaster has to handle more and more data- The forecaster has to make the right decision as soon as possible
• Desired results:- Makes the forecaster aware of important
phenomenon as soon as possible- Quality and efficiency- Optimal man-machine mix
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Dashboard
• Method: Possible monitoring types- observations – products (standard/incidental)- observations – model- model – model
- model – product (difference can be a choice)- observation – observation- product – product (consistency)- Product – time (monitoring)
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RADAR
trigger module
trigger dashboard
detailed info (intranet)
product editor
Dashboard:
the conceptground obs.
model
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Dashboard
• Realised at this moment1. Observations – product
- accumulation of precipitation from the radar / limit for a weather alarm- thunderstorm data from “FLITS” / limit for a weather alarm- wind observations / limit for a weather alarm- snow and blowing snow / limit for a weather alarm
2. Model – product- EPS / limit for a weather alarm- EPS / warnings for customers
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Dashboard
• Result: What does it look like?
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Dashboard
• Result: What does it look like?
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Dashboard
• Problems:
- Not too many triggers!- How to keep it conveniently arranged
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Dashboard
• Solutions:
- The use of smart mechanisms to reduce the amount of triggers- Filter and sort possibilities- Link triggers to the content of the forecasts
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The items of this presentation
• Rich Intranet Application• Dashboard• Product Generation• Application for the use of Conditional Climatology
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Product Generation
• Purpose:
Reducing the “routine jobs” by automate part of the standard production.
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Product Generation
• Reason:- To make more efficient use of the time of the
forecaster. - To make more time for the important things
during the shift like advising customers and severe weather monitoring.
- To make it possible to work with weather dependent shifts…..more time for applied research.
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Product Generation
• Method:
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data
cust
omer
s
Forecaster:
1. Monitoring2. Monitoring and adapt automatic product generation3. Not automatic production4. Not automatic monitoring
objectivepost processing
automatic Product generation
automatic monitoring
The red data streams are not monitored by the forecasters!
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Basis data(Observations, models, statistic values)
Generation of:•Texts (retrieval / text generation)•pictures•triggers
auto
+ mets
Product parts(Text, pictures)
Products(product layout)
dashboard
Weather dependent loop
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Crossing of fixed
threshold value(s)?
Weather dependent control by the forecaster
no yes
Automatic distribution Trigger to the forecaster;
A check and possible adjustments.
Then distribution.
Extra attention when the product becomes more important
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Product Generation
• Goal:
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Precipitation forecast Zoommeer
Average of 3 stations: Vlissingen, Brasschaat (of Woensdrecht) en Gilze Rijen
Modified long term forecast
EPS output
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Available alternative – Amount of observed precipitation
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Available alternative – Amount of observed precipitation in detail
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Available alternative – precipitation forecast
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Glider forecast
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..already available within KNMI…
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..New ideas…
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..more ideas…
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Product Generation
• At this moment:
Pilot with the use of e-forms
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The items of this presentation
• Rich Intranet Application• Dashboard• Product Generation• Application for the use of Conditional Climatology
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Application for the use of Conditional Climatology• Purpose:
An application which makes it possible to give an quick overview of the change in, for instance the visibility, during the next 12 hours, based on climatology.
This systems works for fog/low visibility/low clouds
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Application for the use of Conditional Climatology• Reason:
Give the forecaster an easy access to recent climatological reference material (1977-2006) to help him/her to make the forecast.
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Application for the use of Conditional Climatology• Methode:
- conditionally- Use of classes (colours => autotaf)
Fog (in m) Cloud height (in ft)0-200 0-200200-500 200-500500-800 500-1000800-1500 1000-15001500-3000
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Application for the use of Conditional Climatology• Used:
- Hourly synops over the past 30 years- 20 stations
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Application for the use of Conditional Climatology
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Application for the use of Conditional Climatology
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Application for the use of Conditional Climatology