Difference between revisions of "Weather Monitoring"
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The output of the weather monitoring module is used in two ways.
The output of the weather monitoring module is used in two ways.as input the [[Crop Simulation|crop simulation]] module to simulate crops and evaluate the effect of weather on crops yield.
Revision as of 14:43, 13 October 2017
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 yield.
Every day raw data of at least 4200 stations that regularly collect and supply one or more are acquired over Europe 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. Recently also data of around 300 stations over China is collected and stored in separate instance.
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.
Besides observed weather data also weather forecasts are loaded into the system. This has the advantage that crop development and biomass 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).
- 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 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.
Observed weather is aquired from weather stations that have an irregular distribution over Europe and China. Weather station data of a single station is only representative for the location of that station. 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 kilometer wide and 25 kilometer long that cover the European and Chinese continent and is called the regular climatic grid. The interpolation is managed by a sub-system called.
The data of different weather forecast models 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.
The primary results of the interpolated observed stations weather and downscaled 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 ERA, HIS and OPE models are aggregated to different levels of administrative regions for a number of landcover types. Optionally the same type of aggregations is done for agri-environmental zones too.
Altogether the aggregation procedure results in many aggregated weather data sets based on the type (Observed, ERA, HIS, OPE), regions and landcover types.
Aggregation of weather indicators
Climatology and analysis
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 years in the archive for all spatial resolutions defined in each regional window.
Besides averages of the basic indicators (such as daily precipitation, daily temperature and daily solar radiation) 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). 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 rainy days (more than 5 mm) of last month with the average number of rainy days (more than 5 mm) in all according historic months.