Difference between revisions of "Weather Monitoring"

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{{Non expert}}
 
==General description==
 
==General description==
[[File:architecture_weather_monitoring.jpg|thumb|right|300px
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[[File:Flowchart_mcyfs_modules_weather.jpg|thumb|right|200px|The role of weather monitoring within the MCYFS]]
|General layout of the weather monitoring components in MCYFS]]
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The weather monitoring module is one of the five modules of the MCYFS and can be split in four procedures.
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#[[#Acquisition|Acquisition]]
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#[[#Interpolation|Interpolation]]
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#[[#Aggregation|Aggregation]]
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#[[#Climatology and analysis|Climatology and analysis]]
  
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.
 
  
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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 module contains 5 procedures. The first procedure handles data [[#Acquisition of observed weather|acquisition]] of observed weather. The second procedure [[#Interpolation of observed weather to 25x25km grid|interpolates]] the cleaned station weather to a 25x25km grid. A third and fourth procedure handle the [[#Acquisition of forecasted weather|acquisition]] and [[#Downscaling of forecasted weather to 25x25km grid|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 [[#Aggregation to regions|aggregated]] to regions in a fifth procedure.
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==Acquisition==
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[[File:Weather_station.jpg|thumb|right|200px|Weather Station, Garreg Fawr, Aberdaron]]
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====Observed weather====
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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.
  
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[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
  
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|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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====Forecasted weather====
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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]]).
  
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Six data products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
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*ERA-Interim (ERA)
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*Analysis model (HIS)
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*Deterministic forecast model (OPE)
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*Ensemble Prediction System (ENS)
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*Monthly forecast model (ENSEXT)
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*Seasonal forecast model (SEAS)
  
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.
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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.
  
'''External links:'''
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{{Scientific_box_2|
*[http://www.ecmwf.int/ European Centre for Medium-Range Weather Forecasts (ECMWF)]
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*[[Meteorological data from ground stations]]
 
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*[[Meteorological data from ECMWF models]]
==Acquisition of observed weather==
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}}
[[file:acquisition_of_observed_weather.jpg|thumb|right|300px|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.
 
 
 
{{Scientific_box|[[Meteorological data from ground stations]]}}
 
  
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:
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==Interpolation==
* Calculated radiation at surface
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[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
* Transpiration of water surface
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====Observed weather====
* Transpiration of wet bare soil
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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}}.
* Evapotranspiration Evapotranspiration
 
  
{{Scientific_box|[[Calculation of advanced parameters]]}}
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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]]
  
==Interpolation of observed weather to 25x25km grid==
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====Forecasted weather====
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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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.
  
{{analyst_box|
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{{Scientific_box_2|
* Database structure: [[Appendix 4: CGMS DB description#Appendix 4|Appendix 4 (CGMS DB description)]]
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*[[Interpolation of observed weather]]
* Table descriptions: [[Appendix 5: CGMS tables#Appendix 5|Appendix 5 (CGMS tables)]].
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*[[Interpolation of forecasted weather]]
* Description of software procedures: [[Appendix 3: Overview of the software#Appendix 3|Appendix 3 (Overview of the software)]].
 
 
}}
 
}}
  
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==Aggregation==
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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]]
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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.:
  
[[file:interpolation_of_observed_weather.jpg|thumb|right|300px|Interpolation of observed weather]]
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''''What was the average temperature in northern France during the last week for locations where winter wheat is grown?''''
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
 
 
 
{{Scientific_box|[[Interpolation method onto regular climatic grid]]}}
 
 
 
==Acquisition of forecasted weather==
 
 
 
Different forecast products from ECMWF are loaded into the system:
 
 
 
{|class="wikitable"
 
!Model !! Forecast days !! Members !! Spatial resolution !! Delivery
 
|-
 
|Analysis model || 1 || 1 || 0.25° x 0.25° || Daily (10.30 hr)
 
|-
 
|Deterministic forecast || 10 || 1 || 0.25° x 0.25° || Daily (12.00 hr)
 
|-
 
|Ensemble Prediction System || 15 || 51 || 0.5° x 0.5° || Daily (14.00 hr)
 
|-
 
|Monthly forecast || 32 || 50 || 0.5° x 0.5° || Every Friday (03.00 hr)
 
|-
 
|Seasonal forecast || 170 || 40 || 0.25° x 0.25° || Every 15th of the month (14.00 hr)
 
|}
 
 
 
{{Scientific_box|[[Meteorological data from ECMWF models]]}}
 
 
 
==Downscaling of forecasted weather to 25x25km grid==
 
  
Inverse Distance Weighting (IDW) is used to convert the data to a 25x25km grid. Only the Analysis model is archived. All other forecasts are overwritten whenever a new one is available. In this way an archive is build up with daily model weather for each grid cell going back to 1989.
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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.  
  
{{Scientific_box|[[Processing ECMWF model data]]}}
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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 to regions==
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{{Scientific_box_2|[[Aggregation of weather indicators]]}}
''''What was the average temperature in France during the last week?''''
 
To answer a question like this, grid weather data is aggregated to regions:
 
* Four levels of administrative regions
 
* Two levels of climate zones called: 'agri-environmental regions'
 
  
''''What was the average temperature in France during the last week where winter wheat is grown?''''
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==Climatology and analysis==
To answer this question crop specific aggregations are calculated. [[Appendix 12: Crop masks|Crop masks]] are used to decide which grid cells should be taken into account.
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[[File:Climatology.jpg|Long-term average air temperature over period January-June on a 25 km resolution.|thumb|200px]]
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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.  
  
{{Scientific_box|[[Aggregation of weather indicators]]}}
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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.
  
==Climatology==
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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.
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.
 
  
{{Scientific_box|
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{{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.