Difference between revisions of "Weather Monitoring"
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The output of the weather monitoring module is used in two ways: firstly, to derive agro-meteorological indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc., and secondly, as input to the [[Crop Simulation|crop simulation]] module to simulate crops behaviour and to evaluate the effect of weather on crops
The output of the weather monitoring module is used in two ways: firstly, to derive agro-meteorological indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc., and secondly, as input to the [[Crop Simulation|crop simulation]] module to simulate crops behaviour and to evaluate the effect of weather on crops.
Revision as of 13:17, 13 August 2018
The weather monitoring module is one of the five modules of the MCYFS and can be split in four procedures.
The output of the weather monitoring module is used in two ways: firstly, to derive agro-meteorological indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc., and secondly, as input to the crop simulation module to simulate crops behaviour and to evaluate the effect of weather on crops.
Every day raw data of at least 4200 synoptic weather stations that regularly collect and supply one or more meteorological variables are acquired over Europe and its neighbourhood and are added as raw data to the station weather database. The variables collected include air temperature, precipitation, radiation, air humidity, and wind speed. All incoming data are checked for errors and dubious values, such as e.g. air temperatures outside a realistic range. The incoming data are then converted to daily values that fit in a uniform station weather database. Some variables required for the crop simulation module are not at weather stations, or not in sufficient density. Such variables, like solar radiation or evapotranspiration, are derived from the measured data and also added to the database.
Besides observed weather data also weather forecasts are loaded into the system so that crop development and biomass accumulation can be simulated into the future (see crop simulation module), reaching closer to the end of the crop season, compared to using observed weather, and advancing crop yield forecasts along the season (see yield forecasting module).
- ERA-Interim (ERA)
- Analysis model (HIS)
- Deterministic forecast model (OPE)
- Ensemble Prediction System (ENS)
- Monthly forecast model (ENSEXT)
- Seasonal forecast model (SEAS)
These products have a different number of forecast days (forecast depth) and a varying number of possible realizations called 'members'. Different members can be thought of as model runs with a slightly different initialization and thus slightly different results, but with equal validity. Similar to observed weather, meteorological variables such as air temperature, precipitation, and solar radiation are directly retrieved from the forecast data. Additional parameters are calculated from these variables within the MCYFS.
Observed weather is aquired from weather stations that have an irregular distribution and density in space, e.g. over Europe. Weather station data of a single station are representative for the location of that station only. To construct weather data for locations in between 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 distributed station data to regular distributed data. The regular distribution is organized as a grid with side by side grid cells of 25 kilometre width and 25 kilometre length that covers the entire region of interest (e.g. Europe) and is called the regular climatic grid. The interpolation is managed by a sub-system called.
Weather forecast data come already as spatial fields, not point data as observations, but in varying spatial resolutions and projections that are different from the regular climatic grid used in the MCYFS. Therefore, forecast data have to be interpolated from a 'source' grid to a 'target' grid. This specific interpolation procedure is also called "downscaling", because usually it converts a low resolution source data into higher resolution target data.
Resulting from the interpolation procedure are gridded datasets of observed and forecast weather. In order to describe weather at a spatial domain larger than the grid size and to answer questions like e.g.:
'What was the average temperature in France during the last week for locations where winter wheat is grown?'
gridded weather data are aggregated to different types of regions. This aggregation is based on a weight of each grid for the area covered by a certain landcover type. Observed and forecast grid weather data are aggregated to different levels of administrative regions for a number of landcover types. Optionally, the same type of aggregation is done for agri-environmental zones, too.
Altogether, the aggregation procedure results in many aggregated weather data sets based on the data type (e.g. observed, ERA-Interim, forecast), regions and landcover types.
Aggregation of weather indicators
Climatology and analysis
Within the MCYFS climatology is considered as the long-term average of weather indicators. The long-term averages - or 'normal' conditions - are essential to understand how current weather conditions relate to the situation that was 'normal' in the past.
Long-term average daily values are calculated over the years in the archive for all spatial resolutions defined in each regional window.
Besides averages of the basic indicators (such as precipitation or air temperature) additional statistics are calculated, for instance the probability of having a rainy day, defined as a day receiving more than a certain amount of rainfall (5, 10, 15 mm). It allows to compare extreme weather events of the current year to extremes of the past. For example, the number of rainy days (with more than 5 mm/d) of last month can be compared to the average number of rainy days of this month that occurred in the past.