Difference between revisions of "Weather Monitoring"

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(Aggregation to regions)
(Aggregation to regions)
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==Aggregation to regions==
 
==Aggregation to regions==
[[File:Aggregation_to_regions.jpg|Example of different administrative levels in combination with landcover type 'arable land' on 25 x 25 km grid. Each grid cell (weighted by the area covered with 'arable land') contributes to the aggregated value of the region it is contained by.|thumb|300px]]
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[[File:Aggregation_to_regions.jpg|Example of different administrative levels in combination with landcover type 'arable land' on 25 x 25 km grid. Each grid cell (weighted by the area covered with 'arable land') contributes to the aggregated value of the region it is contained by.|thumb|250px]]
The primary results of interpolating observed stations weather and downscalled forecast weather is grid weather (25 x 25 km resolution). To answer questions like:
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The primary results of the interpolating 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?''''
 
''''What was the average temperature in France during the last week for locations where winter wheat is grown?''''
  
the grid weather data are aggregated to different levels of administrative regions  and different levels of climatic zones ('agri-environmental regions') while weighting each gridcells for there area covered by a certain crop or landcover type.
 
  
Aggregation types:
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the grid weather data are aggregated to different levels of administrative regions and different levels of climatic zones ('agri-environmental regions') while weighting each gridcells for there area covered by a certain crop or landcover type.
* Four levels of administrative regions for 12 crops and 7 landcover types
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* Two levels of climate zones  for 7 landcover types.
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 +
In total grid weather is aggregated to 90 combinations:
 +
* 4 levels of administrative regions for 12 crops and 7 landcover types
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* 2 levels of climate zones  for 7 landcover types.
  
 
{{Scientific_box|[[Aggregation of weather indicators]]}}
 
{{Scientific_box|[[Aggregation of weather indicators]]}}

Revision as of 16:16, 30 September 2010

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General description

File:Architecture weather monitoring.jpg
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 handle 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 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.


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

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.

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File:Interpolation of observed weather.jpg
Interpolation of observed weather

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

5 products from ECMWF are loaded into the system:

  • Analysis model
  • Deterministic forecast
  • Ensemble Prediction System
  • Monthly forecast
  • Seasonal forecast

The 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 forecast results.

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

The acquired weather parameters are:

  • mean temperature
  • maximum temperature
  • minimum temperature
  • dewpoint temperature
  • precipitation
  • global radiation
  • snow depth
  • wind speed

The HIS model data are stored as archive and used for:

  • (model based) long term average weather
  • (model based) crop simulation until the current day.

The most recent available forecast data of the other models (OPE, EPS, MON, SEA) are used to finish crop simulation as far into the future as the forecast model allows.

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External links:

Downscaling of forecasted weather to 25x25km grid

Example of source grid (black dots) and target grid (gray lines)

The data of different forecast models ('source') are acquired in different spatial resolutions (sea 'Acquisition of observed weather'). The downscalling procedure converts these data into a 25x25 km ('target') resolution which can be used in the crop simulation module.


In the first part of the downscalling method parameter values for a target gridcell are derived from the 4 closest source gridcells where closer source gridcells have a higher weight (inverse distance weighting).


In the second part of the downscalling a correction is made for systematic bias (systematic deviations between estimated downscalled source data and observed target data). For instance: temperature is correlated with height. The heigt values in the source grid are different from the height values in the target grid (they sample different locations). Therefor downscalled temperature will be biased depending upon the height differences.

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External links: Inverse Distance Weighting (IDW)

Aggregation to regions

Example of different administrative levels in combination with landcover type 'arable land' on 25 x 25 km grid. Each grid cell (weighted by the area covered with 'arable land') contributes to the aggregated value of the region it is contained by.

The primary results of the interpolating 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?'


the grid weather data are aggregated to different levels of administrative regions and different levels of climatic zones ('agri-environmental regions') while weighting each gridcells for there area covered by a certain crop or landcover type.


In total grid weather is aggregated to 90 combinations:

  • 4 levels of administrative regions for 12 crops and 7 landcover types
  • 2 levels of climate zones for 7 landcover types.

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Climatology

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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