Weather Monitoring

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General description

The role of weather monitoring within the MCYFS

The weather monitoring module is one of the five modules of the MCYFS and can be split in 4 procedures.

  1. Acquisition
  2. Interpolation
  3. Aggregation
  4. Climatology and analysis

The output of the weather monitoring module is used in two ways. In the first place as input for the crop simulation module to simulate crops behavior and evaluate the effect of weather on crops yield. Secondly as weather indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc.


Weather Station, Garreg Fawr, Aberdaron

Observed weather

Every day the raw data of at least 3000 stations that regularly collect and supply one or more indicators are acquired and added to the raw station weather database. For the year 2010 over 3400 stations have contributions to the database. Over time another 2800 stations have a usefull archive.

Most basic indicators like precipitation, temperature and windspeed can be directly retrieved from weather stations. All incoming data is checked for errors such as temperatures that are too low or too high. Errors are corrected and the data are converted to daily values that fit in a uniform station weather database. Some indicators that are needed in the crop simulation module are not sufficiently measured by weather stations. These indicators like solar radiation and evapotranspiration are calculated from basic indicators and is managed by a sub-system called CGMS.

Supercomputer at ECMWF

Forecasted weather

Instead of loading observed weather data in the system also weather forecasts are loaded. This has the advantage that crop yield can be simulated into the future (see crop simulation module) which is closer to the end of the crop season (compared to using observed weather) and can be used to advance Yield Forecasts (see yield forecasting module).

6 products from ECMWF are loaded into the system:

  • ERA-Interim (ERA)
  • Analysis model (HIS)
  • Deterministic forecast model (OPE)
  • Ensemble Prediction System (EPS)
  • Monthly forecast model (MON)
  • Seasonal forecast model (SEA)

These products have a different number of forecast days (forecast depth) and a varying number of possible results called 'members'. Different members can thought of as model runs with a slightly different initialization and thus slightly different results. Similar to observed weather, basic indicators like precipitation, temperature and solar radiation are directly retrieved from the models. Others have to be calculated from basis indicators within the MCYFS.


Interpolation from weather stations to 25 x 25 km regular climate grid.

Observed weather

Observed weather is aquired from weather stations that have an irregular distribution over Europe. Weather station data of a single station is only representative for the location of that station. To construct weather data for locations inbetween stations a conversion is needed. Interpolation (constructing new data points within the range of a discrete set of known data points) is one of the methods to do this. In the MCYFS this procedure is used to convert irregular distrubuted station data to regular distributed data. The regular distribution is organized as a grid with side by side grid cells of 25 kilometer wide and 25 kilometer long that cover the European continent and is called the regular climatic grid. The interpolation is managed by a sub-system called CGMS.

With the grid weather database available, other weather indicators like average day temperature, climatic water balance and long term averages indicators can be calculated. Other weather indicators are defined over an arbitrary period and can only be calculated on the fly with special tools such as the Marsop3 viewer.

Interpolation from 0.5 x 0.5 degrees grid to 25 x 25 km regular climate grid.

Forecasted weather

The data of different forecast models ('source') are acquired in different spatial resolutions and projections. They have a regular distribution but it is different from the regular distribution used in the MCYFS. Therefore the data are interpolated from a 'source' grid to a 'target' grid that is equal to the climate grid. This specific interpolation procedure is also called 'downscaling' because for some models it converts lower resolution (source) data into higher resolution (target) data.


Example of 4 different administrative levels in combination with landcover type 'arable land' on 25 x 25 km grid.

The primary results of the interpolated observed stations weather and downscalled forecast weather is grid weather. To answer questions like:

'What was the average temperature in France during the last week for locations where winter wheat is grown?'

grid weather data are aggregated to different types of regions while weighing each gridcell for the area covered by a certain landcover type. Observed grid weather and forecasted grid weather of the ECMWF HIS, OPE and EPS models are aggregated to:

  • 4 levels of administrative regions for 7 landcover types
  • 2 levels of agri-environmental regions for 7 landcover types

The aggregation of the EPS model is restricted to the median of all EPS member values per forecast day.

Altogether the aggregation procedure results in many (168) aggregated weather data sets based on the type (Observed, HIS, OPE, EPS), regions (4 administrative levels, 2 agri-environmental levels) and landcover types (7 landcovers).

Climatology and analysis

Long term average temperature over period Januari-June on a 25 x 25 km resolution.

Within the MCYFS climatology is considered as long term average values of weather indicators. It is essential to understand how current weather conditions relate to the normal situation.

Long term average daily values are calculated over the period 1975 until last year. The averages are calculated for observed weather and the HIS model of forecasted weather, each in 7 resolutions (25 x 25 km climate grid, 4 levels of administrative regions and 2 levels of agri-environmental regions).

Besides averages of the basic indicators (such as daily precipitation, daily temperature and daily solar radiation) additional statistics are calculated. This makes it possible to compare extreme weather event of the current year with extreme weather events in the past. For instance compare the number of hot days of last month with the average number of hot days in all according historic months.