Difference between revisions of "Weather Monitoring"

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==General description==
 
==General description==
 
[[File:Flowchart_mcyfs_modules_weather.jpg|thumb|right|200px|The role of weather monitoring within the MCYFS]]
 
[[File:Flowchart_mcyfs_modules_weather.jpg|thumb|right|200px|The role of weather monitoring within the MCYFS]]
The weather monitoring module is one of the five modules of the MCYFS and can be split in 4 procedures.
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The weather monitoring module is one of the five modules of the MCYFS and can be split in four procedures.
 
#[[#Acquisition|Acquisition]]
 
#[[#Acquisition|Acquisition]]
 
#[[#Interpolation|Interpolation]]
 
#[[#Interpolation|Interpolation]]
 
#[[#Aggregation|Aggregation]]
 
#[[#Aggregation|Aggregation]]
#[[#Climatology (long term average weather)|Calculate climatology]]
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#[[#Climatology and analysis|Climatology and analysis]]
  
  
The output of the weather monitoring module is used in two ways. In the first place as input for the [[Crop Simulation|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.  
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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.
  
 
==Acquisition==
 
==Acquisition==
 
[[File:Weather_station.jpg|thumb|right|200px|Weather Station, Garreg Fawr, Aberdaron]]
 
[[File:Weather_station.jpg|thumb|right|200px|Weather Station, Garreg Fawr, Aberdaron]]
 
====Observed weather====
 
====Observed weather====
Every day the raw data of at least 3000 stations that regularly collect and supply one or more {{Gloshint|Indicator|A physical property that can be quantified by measurement. |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.
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Each 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 3- or 6-hourly data are then converted to daily values that fit in an uniform station weather database. Some variables, required for the crop simulation module, are not (or not regularly) observed. Such variables, for example solar radiation or evapotranspiration, are derived from the measured data and also added to the database.
  
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.
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[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
 
 
{{Scientific_box_2|[[Meteorological data from ground stations]]}}
 
  
 
====Forecasted weather====
 
====Forecasted weather====
[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
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In addition to observed weather data also weather forecast data are loaded into the system so that crop development and biomass accumulation can be simulated into the future (see [[Crop Simulation|crop simulation module]]), reaching closer to the end of the crop season and therefore advancing crop yield forecasts along the season (see [[Yield Forecasting|yield forecasting module]]).
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|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|yield forecasting module]]).
 
  
6 products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
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Six data products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
 
*ERA-Interim (ERA)
 
*ERA-Interim (ERA)
 
*Analysis model (HIS)
 
*Analysis model (HIS)
 
*Deterministic forecast model (OPE)
 
*Deterministic forecast model (OPE)
*Ensemble Prediction System (EPS)
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*Ensemble Prediction System (ENS)
*Monthly forecast model (MON)
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*Monthly forecast model (ENSEXT)
*Seasonal forecast model (SEA)
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*Seasonal forecast model (SEAS)
  
 +
These products have a different number of forecast days (forecast depth) and a varying number of possible weather 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 such as evapotranspiration are calculated from these variables within the MCYFS.
  
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.
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{{Scientific_box_2|
 
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*[[Meteorological data from ground stations]]
{{Scientific_box_2|[[Meteorological data from ECMWF models]]}}
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*[[Meteorological data from ECMWF models]]
 +
}}
  
 
==Interpolation==
 
==Interpolation==
 +
[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
 
====Observed weather====
 
====Observed weather====
[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
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Observed weather, aquired from weather stations, have an irregular distribution and density in space. Data of a single weather 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). This is called the regular climatic grid. The interpolation is managed by a sub-system called {{Hint|CGMS|Crop Growth Monitoring System}}.
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. {{Gloshint|Interpolation||interpolation}} 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 {{Hint|CGMS|Crop Growth Monitoring System}}.
 
  
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]].
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[[File:Downscalling_eps_mon.jpg‎|Interpolation from 0.5 x 0.5 degrees grid to  25 x 25 km regular climate grid.|thumb|200px]]
 
 
{{Scientific_box_2|[[Interpolation of observed weather]]}}
 
  
 
====Forecasted weather====
 
====Forecasted weather====
[[File:Downscalling_eps_mon.jpg‎|Interpolation from 0.5 x 0.5 degrees grid to  25 x 25 km regular climate grid.|thumb|200px]]
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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 the 'source' grid to the 'target' grid: the regular climatic grid of 25 by 25 km. This specific interpolation procedure is also called "downscaling", because usually it converts a low resolution source data into higher resolution target data.
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.
 
  
{{Scientific_box_2|[[Interpolation of forecasted weather]]}}
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{{Scientific_box_2|
 +
*[[Interpolation of observed weather]]
 +
*[[Interpolation of forecasted weather]]
 +
}}
  
 
==Aggregation==
 
==Aggregation==
[[File:Aggregation_to_regions.jpg|Example of 4 different administrative levels in combination with landcover type 'arable land' on 25 x 25 km grid.|thumb|200px]]
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[[File:Aggregation_to_regions.jpg|Example of four different administrative levels in combination with landcover type 'arable land' on the 25 km grid.|thumb|200px]]
The primary results of the interpolated observed stations weather and downscalled forecast weather is grid weather. To answer questions like:
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The interpolation procedure generates 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 northern 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?''''
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gridded weather data are aggregated to different types of regions. Observed and forecast gridded weather data are aggregated to different levels of administrative regions for a number of landcover types. This aggregation is based on a weight of each grid cell for the area covered by the selected landcover type.
  
 +
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.
  
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:
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{{Scientific_box_2|[[Aggregation of weather indicators]]}}
* 4 levels of administrative regions for 12 crops and 7 landcover types
 
* 2 levels of agri-environmental regions for 7 landcover types.
 
  
 +
==Climatology and analysis==
 +
[[File:Climatology.jpg|Long-term average air temperature over period January-June on a 25 km resolution.|thumb|200px]]
 +
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.
  
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:
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Long-term average daily values are calculated over the years in the archive for all spatial resolutions defined in each regional window. Two periods are considered: 1975-last year and 1995-last year.
*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 crop and 7 landcovers) and threshold (0%, 5%).
 
 
 
{{Scientific_box_2|[[Aggregation of weather indicators]]}}
 
  
==Climatology (long term average weather)==
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In addition to 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.
[[File:Climatology.png|Example of long term average interpolated observed temperature over the first six months on a 25 x 25 km resolution.|thumb|200px]]
 
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 values are calculated for observed weather and forecasted weather (HIS model only) of different resolutions (25 x 25 km regular climate grid, 4 administrative regions and 2 agri-environmental regions).
 
  
 
{{Scientific_box_2|
 
{{Scientific_box_2|
 
* [[Calculation of Climatology]]
 
* [[Calculation of Climatology]]
 
* [[Analysis of weather indicators]]
 
* [[Analysis of weather indicators]]
* [[Time series analysis at station, grid and regional level]]
 
 
}}
 
}}
  
[[Category:MCYFS modules]]
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[[Category:MCYFS introduction]]
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[[Category:Weather Monitoring]]

Latest revision as of 13:51, 13 August 2018



General description

The role of weather monitoring within the MCYFS

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

  1. Acquisition
  2. Interpolation
  3. Aggregation
  4. Climatology and analysis


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.

Acquisition

Weather Station, Garreg Fawr, Aberdaron

Observed weather

Each 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 3- or 6-hourly data are then converted to daily values that fit in an uniform station weather database. Some variables, required for the crop simulation module, are not (or not regularly) observed. Such variables, for example solar radiation or evapotranspiration, are derived from the measured data and also added to the database.

Supercomputer at ECMWF

Forecasted weather

In addition to observed weather data also weather forecast data 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 and therefore advancing crop yield forecasts along the season (see yield forecasting module).

Six data products from ECMWF are loaded into the system:

  • 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 weather 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 such as evapotranspiration are calculated from these variables within the MCYFS.


Interpolation

Interpolation from weather stations to 25 x 25 km regular climate grid.

Observed weather

Observed weather, aquired from weather stations, have an irregular distribution and density in space. Data of a single weather 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). This is called the regular climatic grid. The interpolation is managed by a sub-system called CGMS.

Interpolation from 0.5 x 0.5 degrees grid to 25 x 25 km regular climate grid.

Forecasted weather

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 the 'source' grid to the 'target' grid: the regular climatic grid of 25 by 25 km. This specific interpolation procedure is also called "downscaling", because usually it converts a low resolution source data into higher resolution target data.


Aggregation

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

The interpolation procedure generates 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 northern France during the last week for locations where winter wheat is grown?'

gridded weather data are aggregated to different types of regions. Observed and forecast gridded weather data are aggregated to different levels of administrative regions for a number of landcover types. This aggregation is based on a weight of each grid cell for the area covered by the selected landcover type.

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.


Climatology and analysis

Long-term average air temperature over period January-June on a 25 km resolution.

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. Two periods are considered: 1975-last year and 1995-last year.

In addition to 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.