Difference between revisions of "Weather Monitoring"

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(Interpolation of observed weather)
(General description)
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==General description==
 
==General description==
 
[[File:architecture_weather_monitoring.jpg|thumb|right|300px|General layout of the weather monitoring components in MCYFS]]
 
[[File:architecture_weather_monitoring.jpg|thumb|right|300px|General layout of the weather monitoring components in MCYFS]]
The weather monitoring module is one of the five modules of the MCYFS and can be split in 5 procedures. The first two handle '''(1)''': [[#Acquisition of observed weather|acquisition of observed weather]] and '''(2)''': [[#Acquisition of forecasted weather|acquisition of forecasted weather]]. After acquisition the data are converted to regular distrubuted values in procedure '''(3)''': [[#Interpolation of observed weather|interpolation of observed weather]] and '''(4)''': [[#Interpolation of forecasted weather|interpolation of forecasted weather]]. Finally the interpolated weather from observations and forecasts is '''(5)''': [[#Aggregation to regions|aggregated]] to regions.
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The weather monitoring module is one of the five modules of the MCYFS and can be split in 5 procedures.
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#[[#Acquisition of observed weather|Acquisition of observed weather]]
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#[[#Acquisition of forecasted weather|Acquisition of forecasted weather]]
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#[[#Interpolation of observed weather|Interpolation of observed weather]]
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#[[#Interpolation of forecasted weather|Interpolation of forecasted weather]]
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#[[#Aggregation to regions|Aggregated to regions]]
  
  

Revision as of 15:52, 28 October 2010



General description

File:Architecture weather monitoring.jpg
General layout of the weather monitoring components in MCYFS

The weather monitoring module is one of the five modules of the MCYFS and can be split in 5 procedures.

  1. Acquisition of observed weather
  2. Acquisition of forecasted weather
  3. Interpolation of observed weather
  4. Interpolation of forecasted weather
  5. Aggregated to regions


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. For this purpose the weather data are also input for calculating climatology.

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 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. Basic indicators 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 indicator values are checked for errors such as temperatures that are too low or indicator values that don't change over time. Errors are corrected and the data are converted to daily values that fit in a uniform station weather database.

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Some indicators that are needed in the crop simulation module are not sufficiently measured by weather stations. These advanced indicators are calculated from basic indicators:

  • Calculated radiation at surface
  • Transpiration of water surface
  • Transpiration of wet bare soil
  • Evapotranspiration

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Acquisition of forecasted weather

Instead of loading observed weather data in the system also weather forecasts can be loaded. This has the advantage that crop yield can be simulated into the future (see module Crop Simulation). In turn this simulations (future) crop yield that is closer to the end-of-the crop season can be used to make Yield Forecasts (see module Yield Forecasting).

5 weather forecast products from ECMWF are loaded into the system:

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

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 forecast results.

Model Abbreviation Forecast days Members Horizontal model resolution* Delivery resolution** Delivery
Analysis model HIS 1 1 ~16km 0.25° x 0.25° Daily (10.30 hr)
Deterministic forecast OPE 10 1 ~16km 0.25° x 0.25° Daily (12.00 hr)
Ensemble Prediction System EPS 15 51 ~30km / ~60km*** 0.5° x 0.5° Daily (14.00 hr)
Monthly forecast MON 32 50 ~30km / ~60km*** 0.5° x 0.5° Every Friday (03.00 hr)
Seasonal forecast SEA 170 40 ~150km 0.25° x 0.25° Every 15th of the month (14.00 hr)

* Reduced Gaussian grid
** WGS84
*** First 10 days with ~30km resolution, remaining days on ~60km resolution


The acquired basic indicators are:

  • Precipitation
  • Temperature (daily maximum, minimum and average)
  • Dewpoint temperature
  • Global radiation
  • Snow depth
  • Wind speed

Advanced indicators that are calculated from basis indicators:

  • Vapour pressure
  • Transpiration of water surface
  • Transpiration of wet bare soil
  • Evapotranspiration


Only the HIS data are stored as archive and used for calculating climatolgy. The data of other models is replaced when a more recent data set becomes available (OPE, EPS, MON, SEA).

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Interpolation of observed weather

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. interpolation is one of the methods to do this. This procedure converts iregular distrubuted station data to regular distributed data. The regular distribution is organized in a regular grid with side by side grid cells of 25 kilometer wide and 25 kilometer long that cover the European continent. This is called the regular climatic grid.


The interpolation is managed by a sub-system called CGMS.

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More information


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

Current year
In the current year (near real time) for every day and each grid cell up to the 4 most suitable stations are selected. This is based on comparison of the station locations and altitudes and the grid cell location and altitude. The suitable stations are used to interpolate the weather data to that specific grid cell for that specific day. The suitable stations can differ between days and even between indicators. In case no suitable stations are found, a long term average value is substituted to ensure spatial and temporal continuity.


Previous years (the archive)
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.


With the grid weather database available, other weather indicators can be derived:

  • average day temperature
  • climatic water balance
  • long term average indicators (climatolgy)


Some of the weather indicators are defined over an arbitrary period and can only be calculated on the fly with special tools such as the viewer:

  • sum, max, min and average of an indicator
  • 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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Interpolation of forecasted weather

Example of 0.25 x 0.25 degrees source grid (black dots) and 25 x 25 km target grid (gray lines)

The data of different forecast models ('source') are acquired in different spatial resolutions and projections (sea 'Acquisition of observed weather'). The data have a regular distribution that is different from the regular distribution used in the MCYFS. Therefore the data are interpolated by a procedure from the 'source' grid to the 'target' grid that is equal to the climate grid. This interpolation is also called downscalling.


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, even if corrections are applied for height differences.

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Aggregation to regions

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

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 agri-environmental regions while weighting each gridcells for the area covered by a certain crop or landcover type. Observed grid weather and forecasted grid weather are both aggregated to (90 combinations each):

  • 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 forecasted grid weather is restricted to the HIS model, OPE model and the median of all EPS member values per forecast day.


All 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.

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Climatology (long term average weather)

Climatology is essential to understand how current weather conditions relate to the normal situation. Long term average values are available for observed weather and forecasted weather (HIS model only) for different resolutions (25 x 25 km grid, administrative regions and agri-environmental regions).

The following parameters are available:

  • Precipitation
  • Temperature (daily maximum, minimum and average)
  • Dewpoint temperature (forecasted weather only)
  • Vapour pressure
  • Wind speed
  • Snow depth
  • Calculated radiation at surface (observed), global radiation (forecasted)
  • Transpiration of water surface
  • Transpiration of wet bare soiles
  • Evapotranspiration
  • fraction of days wit daily maximum temperature greater than 25 degrees celsius
  • fraction of days wit daily maximum temperature greater than 30 degrees celsius
  • fraction of days wit daily maximum temperature greater than 35 degrees celsius
  • fraction of days wit daily maximum temperature less than 0 degrees celsius
  • fraction of days wit daily maximum temperature less than minus 8 degrees celsius
  • fraction of days wit daily maximum temperature less than minus 10 degrees celsius
  • fraction of days wit daily maximum temperature less than minus 18 degrees celsius
  • fraction of days wit daily maximum temperature less than minus 20 degrees celsius
  • fraction of days wit rainfall greater than 5 mm
  • fraction of days wit rainfall greater than 10 mm
  • fraction of days wit rainfall greater than 15 mm
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