Difference between revisions of "Weather Monitoring"

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[[Category:MCYFS modules]]
[[Category:MCYFS modules]]
[[Category:popular style]]

Revision as of 21:27, 3 August 2010


General description

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General layout of the weather monitoring components in MCYFS

The weather monitoring module is one of the main elements of the MCYFS. Input data that go into the module are weather station observations and weather forecast data.

The module contains 5 procedures. The first procedure handles data acquisition of observed weather. The second procedure interpolates the cleaned station weather to a 25x25km grid. A third and fourth procedure handles the acquisition and downscaling of forecasted weather from European Centre for Medium-Range Weather Forecasts (ECMWF) to the same 25x25km grid. Finally the grid weather from observations and forecasts is aggregated to regions in a fifth procedure.

The output of the weather monitoring module is used in two ways for crop yield evaluations. In the first place as input for the crop growth module to simulate crops behaviors and evaluate the effects of weather on crops yields (see Crop Simulation). Secondly as weather indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc.

The crops behaviors are mainly influenced by the atmospheric conditions near the earth surface. Considering the data availability, resources and purpose of the system a time scale of one day and a spatial scale of 25x25km are chosen as the resolutions to estimate crop yields at European scale.

External links:

Acquisition of observed weather

File:Acquisition of observed weather.jpg
Acquisition of observed weather

Every day the raw data of at least 3000 stations that regularly collect one or more parameters and supply them in near real time are acquired and added to the raw station weather database. For the year 2010 over 3400 stations have contributed the database. Over time another 2800 stations have a usefull archive. Weather parameters that are collected include:

  • Precipitation (daily and 6-hourly)
  • Temperature (daily maximum, daily minimum and 3-hourly)
  • Measured radiation
  • Sunshine
  • Cloud cover
  • Vapour pressure
  • Wind speed
  • Snow depth
  • Humidity (3-hourly)

The archive data and near real time incoming parameters values are checked for errors such as temperatures that are too low or parameters values that don't change over time. Errors are corrected and the data are processed to daily parameters that fit in a uniform station weather database.

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Because some parameters that are needed in the crop simulation module are not sufficiently measured by weather stations some advanced parameters are calculated from basis parameters:

  • Calculated radiation at surface
  • Transpiration of water surface
  • Transpiration of wet bare soil
  • Evapotranspiration Evapotranspiration

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Interpolation of observed weather to 25x25km grid

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Interpolation of observed weather

The interpolation is managed by a sub-system called Crop Growth Monitoring System (CGMS). All input data and output data of CGMS is stored in a relational database of which the structure is presented in Appendix 4 (CGMS DB description). Individual tables are described in Appendix 5 (CGMS tables). Procedures may be stored as database objects, scripts or separate software packages. A detailed description of the software procedures can be found in Appendix 3 (Overview of the software).

For the current year (near real time interpolation) for each grid cell every day the most suitable stations are selected. This is based on the stations location and altitude comparison to the grid cell location and altitude. The data from the suitable stations are used to interpolate to a specific grid value for a specific day. Due to this procedure the suitable stations can differ between days and even between parameters. In case no suitable stations are found, a long term average value is substituted to ensure spatial and temporal continuity.

For interpolation of archive station weather each grid cell uses the same suitable stations for a complete year. For most recent years around 2700 stations have a sufficient temporal coverage (enough observations within one year). Only these are used in the interpolation procedure. In this way a grid weather archive is build up with daily weather for each grid cell going back to 1975.

Having the grid weather database other weather indicators can be derived:

  • average day temperature
  • climatic water balance

Some of the weather indicators are defined over an arbitrary period:

  • sum, max, min and average of a parameter
  • number of heat waves
  • longest heat wave period
  • number of hot day's
  • number of cold day's
  • number of day's with significant rainfall

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Acquisition of forecasted weather

Different forecast products from ECMWF are loaded into the system:

Model Forecast days Members Spatial resolution Delivery
Analysis model 1 1 0.25° x 0.25° Daily (10.30 hr)
Deterministic forecast 10 1 0.25° x 0.25° Daily (12.00 hr)
Ensemble Prediction System 15 51 0.5° x 0.5° Daily (14.00 hr)
Monthly forecast 32 50 0.5° x 0.5° Every Friday (03.00 hr)
Seasonal forecast 170 40 0.25° x 0.25° Every 15th of the month (14.00 hr)

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Downscaling of forecasted weather to 25x25km grid

Inverse Distance Weighting (IDW) is used to convert the data to a 25x25km grid. Only the Analysis model is archived. All other forecasts are overwritten whenever a new one is available. In this way an archive is build up with daily model weather for each grid cell going back to 1989.

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Aggregation to regions

'What was the average temperature in France during the last week?' To answer a question like this, grid weather data is aggregated to regions:

  • Four levels of administrative regions
  • Two levels of climate zones called: 'agri-environmental regions'

'What was the average temperature in France during the last week where winter wheat is grown?' To answer this question crop specific aggregations are calculated. Crop masks are used to decide which grid cells should be taken into account.

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Climatology is essential to understand how current weather conditions relate to the normal situation. Long term average values are available for the observed and forecasted weather at different spatial levels like the 25 km grid, administrative regions and agri-environmental regions.

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