Difference between revisions of "Weather Monitoring"

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__NOTOC__
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{{Non expert}}
 
==General description==
 
==General description==
[[image:architecture_weather_monitoring.jpg|thumb|right|200px|General layout of the weather monitoring components in MCYFS]]
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[[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 main elements of the MCYFS. Main input into the module are weather station observations and forecast data.
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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 module contains 4 procedures. The first procedure checks incoming station weather (three-hourly, six-hourly, daily) of over 3000 European weather stations with the AMDAC software. The second procedure interpolates the cleaned station weather to a 25x25km grid that completely covers the European region. A third procedure downscales the forecast weather from [http://www.ecmwf.int/ ECMWF] to the same 25x25km grid. And finally all grid weather from observations and forecasts is aggregated to regions.
 
  
====Quality check====
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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 AMDAC software checks all incoming data for errors such as temperatures that are too low or too hight or values that don't change over time. The software automatically flags suspicious data. A meteorologist gives the final verdict based on station observations nearby.
 
  
Detailed information on other pages:
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==Acquisition==
* [[Meteorological data from ground stations]].
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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.
  
====Interpolation to 25x25km grid====
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[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
[[image:flowchart_weather_monitoring.jpg|thumb|right|200px|Overview of the weather monitoring components of the CGMS]]
 
The interpolation is managed by a sub-system called Crop Growth Monitoring System (CGMS). For each grid cell every day the most suitable stations are selected and used to interpolate a grid value. From day to day and from weather indicator to weather indicator different stations can be used. In this way an archive is build up with daily weather for each grid cell. The archive goes back to 1975.
 
  
All input data and output data of CGMS is stored in a [http://en.wikipedia.org/wiki/Relational_database relational database] of which the structure is presented in [[Appendix 4: CGMS DB description#Appendix 4|Appendix 4]]. Individual tables are described in [[Appendix 5: CGMS tables#Appendix 5|Appendix 5]]. Procedures may be stored as database objects, scripts or separate software packages. A detailed description of the software procedures can be found in [[Appendix 3: Overview of the software#Appendix 3|Appendix 3]].
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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]]).
  
Detailed information on other pages:
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Six data products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
*[[Interpolation method onto regular climatic grid]]
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*ERA-Interim (ERA)
*[[Calculation of advanced parameters]]
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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)
  
====Downscalling to 25x25km grid====
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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.
Different forecast products from [http://www.ecmwf.int/ ECMWF] are loaded into the system:
 
  
{|class="wikitable"
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{{Scientific_box_2|
!Model !! Forecast days !! Members !! Spatial resolution !! Delivery
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*[[Meteorological data from ground stations]]
|-
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*[[Meteorological data from ECMWF models]]
|Analysis model || 1 || 1 || 0.25° x 0.25° || Daily (10.30 hr)
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}}
|-
 
|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 || 1 || 0.25° x 0.25° || Every 15th of the month (14.00 hr)
 
|}
 
  
[http://en.wikipedia.org/wiki/Inverse_distance_weighting 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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==Interpolation==
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[[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====
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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}}.
  
Detailed information on other pages:
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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]]
*[[Meteorological indicators from ecmwf model]]
 
  
====Aggregation to regions====
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====Forecasted weather====
''What was the minimum temperature in france during the last week?'' To answer a question like this, grid weather data is aggregated to regions:
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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.
* Three levels of administrative regions [http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts_nomenclature/introduction NUTS regions]
 
* Two levels of agri-environmental regions ([[References|Metzger et. al., 2005]])
 
  
''What was the minimum temperature in france during the last week where winter wheat is grown?'' 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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{{Scientific_box_2|
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*[[Interpolation of observed weather]]
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*[[Interpolation of forecasted weather]]
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}}
  
Detailed information on other pages:
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==Aggregation==
*[[Analysis of weather indicators]]
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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]]
*[[Time series analysis at station, grid and regional level]]
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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.:
*[[Future developments: cgms numerical weather based]]
 
  
==Goals and assumptions==
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''''What was the average temperature in northern France during the last week for locations where winter wheat is grown?''''
Daily meteorological station data are used in two ways for crop yield evaluations. In the first place as input for the crop growth model WOFOST to simulate crops behaviors and evaluate the effects of weather on crops yields at European level (see [[Crop Simulation]]). 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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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.
  
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 25 by 25 km are chosen as the resolutions to estimate crop yields at European scale.
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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.
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{{Scientific_box_2|[[Aggregation of weather indicators]]}}
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==Climatology and analysis==
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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.  
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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.
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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.
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{{Scientific_box_2|
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* [[Calculation of Climatology]]
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* [[Analysis of weather indicators]]
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}}
 +
 
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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.