Difference between revisions of "Weather Monitoring"

From Agri4castWiki
Jump to: navigation, search
(Climatology and analysis)
(Climatology and analysis)
 
(21 intermediate revisions by 2 users not shown)
Line 3: Line 3:
 
==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.
+
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]]
Line 10: Line 10:
  
  
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 behavior and evaluate the effect of weather on crops yield. Secondly as weather indicators for a direct evaluation of alarming situations such as drought, extreme rainfall during sowing, flowering or harvest etc.  
+
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 raw data of at least 4200 stations that regularly collect and supply one or more {{Gloshint|Indicator|A physical property that can be quantified by measurement. |indicators}} are acquired over Europe 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. Recently also data of around 300 stations over China is collected and stored in separate instance.
+
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 and is managed by a sub-system called {{Hint|CGMS|Crop Growth Monitoring System}}.
 
  
 
[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
 
[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
  
 
====Forecasted weather====
 
====Forecasted weather====
Besides observed weather data also weather forecasts are loaded into the system. This has the advantage that crop development and biomass 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 advance Yield Forecasts (see [[Yield Forecasting|yield forecasting module]]).
+
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]]).
  
6 products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
+
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)
Line 33: Line 30:
 
*Seasonal forecast model (SEAS)
 
*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.
These products have a different number of forecast days (forecast depth) and a varying number of possible realizations 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 retrieved from the models. Others have to be calculated from basis indicators within the MCYFS.
 
  
 
{{Scientific_box_2|
 
{{Scientific_box_2|
Line 44: Line 40:
 
[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
 
[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
 
====Observed weather====
 
====Observed weather====
Observed weather is aquired from weather stations that have an irregular distribution over Europe and China. Weather station data of a single station is only representative for the location of that station. 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 kilometer wide and 25 kilometer long that cover the European and Chinese continent and is called the regular climatic grid. The interpolation is managed by a sub-system called {{Hint|CGMS|Crop Growth Monitoring System}}.
+
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}}.
  
 
[[File:Downscalling_eps_mon.jpg‎|Interpolation from 0.5 x 0.5 degrees grid to  25 x 25 km regular climate grid.|thumb|200px]]
 
[[File:Downscalling_eps_mon.jpg‎|Interpolation from 0.5 x 0.5 degrees grid to  25 x 25 km regular climate grid.|thumb|200px]]
  
 
====Forecasted weather====
 
====Forecasted weather====
The data of different weather forecast models 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 a 'source' grid to a 'target' grid that is equal to the climate grid. This specific interpolation procedure is also called 'downscaling' because for some models it converts lower resolution (source) data into higher resolution (target) data.
+
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.
  
 
{{Scientific_box_2|
 
{{Scientific_box_2|
Line 57: Line 53:
  
 
==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]]
+
[[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 downscaled forecast weather is grid weather. To answer questions like:
+
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 France during the last week for locations where winter wheat is grown?''''
 
  
 +
''''What was the average temperature in northern France during the last week for locations where winter wheat is grown?''''
  
grid weather data are aggregated to different types of regions while weighing each gridcell for the area covered by a certain landcover type. Observed grid weather and forecasted grid weather of the ECMWF ERA, HIS and OPE models are aggregated to different levels of administrative regions for a number of landcover types. Optionally the same type of aggregations is done for agri-environmental zones too.
+
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 type (Observed, ERA, HIS, OPE), regions and landcover types.
+
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.
  
 
{{Scientific_box_2|[[Aggregation of weather indicators]]}}
 
{{Scientific_box_2|[[Aggregation of weather indicators]]}}
  
 
==Climatology and analysis==
 
==Climatology and analysis==
[[File:Climatology.jpg|Long term average temperature over period Januari-June on a 25 x 25 km resolution.|thumb|200px]]
+
[[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 long term average values of weather indicators. It is essential to understand how current weather conditions relate to the normal situation.  
+
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.  
+
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.
  
Besides averages of the basic indicators (such as daily precipitation, daily temperature and daily solar radiation) 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). This makes it possible to compare extreme weather event of the current year with extreme weather events in the past. For instance compare the number of rainy days (more than 5 mm) of last month with the average number of rainy days (more than 5 mm) in all according historic months.
+
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.
  
 
{{Scientific_box_2|
 
{{Scientific_box_2|

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.