SISU. It enables a great many of customers to get a huge amount of customized products.

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Anne-Mari Mattila
7 vuotta sitten
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1 SISU The SISU goal is to build an efficient weather production system encompassing observations, numerical forecast models, forecasters, databases, visualization tools and end products. The production process is reliable and thorough. It enables a great many of customers to get a huge amount of customized products. 1

2 SISU core themes Customers get more better products Forecasters concentrate on meteorology Services use FMI know how to the fullest 2

3 SISU timetable SISU, part I test system, 15 months, started 2005/2 Planning System building SISU, part II testing the process, 12 months Testing forecast office Process benchmarking Modification and deployment planning SISU, part III system deployment, 6-12 months Forecaster training System is ready for use in the beginning of

4 Test products Flight route forecast, SWC Army helicopters Plowing mission forecast Finnish Road Enterprises winter road maintenance Trajectory tool Radiation Security disaster control 4

5 Freely chosen flight route forecast

6 SWC automatically on any time step 6

Snow plowing mission forecast Preliminary mission assessment and situation reaction Develop ways to make fast precipitation and -type analysis

7 Snow plowing mission forecast Preliminary mission assessment and situation reaction Develop ways to make fast precipitation and -type analysis and extrapolation forecasts Time untill plowing is needed Test postproduction model coupling and take in account sub grid phenomena in product 7

8 Trajectory tool Frequent ability to fast calculate air parcel trajectories away and toward any given location upon request. Test abilities: fast calculation forecast uncertainty high input data resolutions real time and archive data use remote service to customers over the internet 8

9 The clue to all products Quality information in 4D grids 9

10 New weather service process Sääpalveluprosessin tietovirrat Yksikäsitteisen tiedon tietovaraston Database user käyttöliittymä interface warranty Takuu Web web jakelu dist Tuotantotyöasema production WS Weather Säätuotantotiimi production team Tietovarsto database Manuaalisesti manually produced tuotettu information säätieto Tiedon Datarefining jalostus product Tuote general Suuri yleisö public Ohjaustieto guidance Automaattituotanto Automatic production companies Yritykset Hirlam Ecmwf Metman raw Raaka säätieto information Katselutyöasema Visualization WS Viranomaiset government Gts Customer Asiakaspalvelutiimi service team Katselutyöasema Visualization WS 10

11 New weather service process Sääpalveluprosessin tietovirrat Yksikäsitteisen tiedon tietovaraston Database user käyttöliittymä interface warranty Takuu Web web jakelu dist Tuotantotyöasema production WS Weather Säätuotantotiimi production team Tietovarsto database Manuaalisesti manually produced tuotettu information säätieto Tiedon Datarefining jalostus product Tuote general Suuri yleisö public Ohjaustieto guidance Automatic Automaattituotanto production companies Yritykset Hirlam Ecmwf Metman raw Raaka säätieto information Katselutyöasema Visualization WS Viranomaiset government Gts Customer Asiakaspalvelutiimi service team Katselutyöasema Visualization WS 11

12 OBSERVATIONS SYNOPTIC ANALYSIS SYNOPT. INTERPRETATION MESO SC INTERPRETATION Forecasters process NUMERICAL MODELS SYNOPTIC INTERPRETATION PROBABILITY DISTRIBUTION Conceptual Model Meteorological Objects T2M ABL AND SURFACE PARAMETERS TUULI T SURF TASA- PAINO- RAIN PILVI HUMI FOG TILA DITY VISI ICE BILITY MISSING PHENOMENA SYSTEMATIC ERROR CORRECTION PARAMETER CORRECTION SUBGRID PHENOMENA ANALYSIS GRIDS FORECAST COMBINATION FROM DIFFERENT RANGES LONG RANGE ( h) EXTRAPOLATION NOWCASTING FORECAST (0 6 h) SHORT RANGE (6 48 h) 12

13 Observation and model data interpretation Synoptic and meso scale conceptual models Important for automatic production (especially text products) Possible to go from over generalized forecasts to more precision Generally human based information made ready for machine use In the future conceptual models have to include output definitions (cloud structure, text description, etc) Totally new models to grasp all weather phenomena 13

14 Conceptual models to be used in testing Synoptic scale Meso scale Tropopause jet and height Low and high pressures Temperature anomalies Fronts and ridges Conveyor belts Low troposphere jet Convective rain Convergence Humidity gradient Winds Radar objects First CMs are the top level main models of a decision tree structure. Later subtypes will be added. In the end over 100 different CMs will be needed. 14

15 New weather service process Sääpalveluprosessin tietovirrat Yksikäsitteisen tiedon tietovaraston Database user käyttöliittymä interface warranty Takuu Web web jakelu dist Tuotantotyöasema production WS Weather Säätuotantotiimi production team Tietovarsto database Manuaalisesti manually produced tuotettu information säätieto Tiedon Datarefining jalostus product Tuote general Suuri yleisö public Ohjaustieto guidance Automaattituotanto Automatic production companies Yritykset Hirlam Ecmwf Metman raw Raaka säätieto information Katselutyöasema Visualization WS Viranomaiset government Gts Customer Asiakaspalvelutiimi service team Katselutyöasema Visualization WS 15

16 Customer products Products are made on demand Data retrievals deliver specific product needs (lots of small retrievals = fast) Partly data is preprocessed for performance Finnish Meteorological ML xml documents usable from many programming languages through http or language specific interfaces Centralized in house GIS information and standard interface to outside provider DB centralized product specifications (data, layout, style, customer info) Customer automatically also gets product derived from analysis for comparison = product specific verification 16

17 Product specs DB XML definition stored in SQL DB including Parameters, objects, probability data, sub grid info needed Processing needed Layout, style (defaults or custom) Media Customer info Usage logs (for smart data preprocessing) Editable by Visualization WS 17

18 Conclusion FMI is aiming high First versions will only achieve parts of our goals, but the system will evolve in time to exceed our wishes Thank you 18

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