The weather monitoring module is one of the five modules of the MCYFS and can be split in 4 procedures.
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 behaviors and evaluate the effects of weather on crops yields. Secondly as weather indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc.
Every day the raw data of at least 3000 stations that regularly collect and supply one or more 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.
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 make Yield Forecasts (see yield forecasting module).
- ERA-Interim (ERA)
- Analysis model (HIS)
- Deterministic forecast model (OPE)
- Ensemble Prediction System (EPS)
- Monthly forecast model (MON)
- Seasonal forecast model (SEA)
Some of these products have a different number of forecast days 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 retreaved from the models. Others have to be calculated from basis indicators within the MCYFS.
Observed weather is aquired from weather stations that have an iragular 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. is one of the methods to do this. In the MCYFS this procedure is used to converts iregular 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 .
With the grid weather database available, other weather indicators like average day temperature, climatic water balance and long term averages indicators can be precalculated. 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.
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 'source' grid to a 'target' grid that is equal to the climate grid. This specific interpolation procedure is also called 'downscalling' because for some models it converts lower resolution (source) data into higher resolution (target) data.
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 weighting each gridcells for the area covered by a certain crop or 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 12 crops and 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. All these combinations are aggregated a second time with the difference that only gridcells are taken into account that have at least 5 % coverage of a certain crop or landcover type. For example:
- administrative region: 'country' level
- landcover type: 'arable land'
- threshold: 5%
In this combination all grid cells that have at least 5% coverage of landcover type 'arable land' contribute to the aggregated values for 'countries' they are contained by. The contribution of each grid cell that passes the threshold of 5% is weighted by the area covered with landcover type 'arable land'. Grid cells with less than 5% coverage are not taken into account.
Altogether the aggregation procedure results in many (720) combinations of weather type (Observed, HIS, OPE, EPS), regions (4 administrative levels, 2 agri-environmental levels), landcover type (12 crops and 7 landcovers) and threshold (0%, 5%).
Aggregation of weather indicators
Climatology (long term average weather)
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.