Difference between revisions of "Weather Monitoring"

From Agri4castWiki
Jump to: navigation, search
(Interpolation of forecasted weather)
(Climatology and analysis)
 
(149 intermediate revisions by 3 users not shown)
Line 2: Line 2:
 
{{Non expert}}
 
{{Non expert}}
 
==General description==
 
==General description==
[[File:architecture_weather_monitoring.jpg|thumb|right|300px|General layout of the weather monitoring components in 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 5 procedures.
+
The weather monitoring module is one of the five modules of the MCYFS and can be split in four procedures.
#[[#Acquisition of observed weather|Acquisition of observed weather]]
+
#[[#Acquisition|Acquisition]]
#[[#Acquisition of forecasted weather|Acquisition of forecasted weather]]
+
#[[#Interpolation|Interpolation]]
#[[#Interpolation of observed weather|Interpolation of observed weather]]
+
#[[#Aggregation|Aggregation]]
#[[#Interpolation of forecasted weather|Interpolation of forecasted weather]]
+
#[[#Climatology and analysis|Climatology and analysis]]
#[[#Aggregation to regions|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|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 (long term average weather)|climatology]].
+
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 of observed weather==
+
==Acquisition==
[[file:acquisition_of_observed_weather.jpg|thumb|right|300px|Acquisition of observed weather]]
+
[[File:Weather_station.jpg|thumb|right|200px|Weather Station, Garreg Fawr, Aberdaron]]
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. Basic indicators that are collected include:
+
====Observed weather====
* Precipitation (daily and 6-hourly)
+
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.
* Temperature (daily maximum, daily minimum and 3-hourly)
 
* Measured radiation
 
* Sunshine
 
* Cloud cover
 
* Vapour pressure
 
* Wind speed
 
* Snow depth
 
* Humidity (3-hourly)
 
  
 +
[[File:Supercomputers_at_ecmwf.jpg|thumb|right|200px|Supercomputer at ECMWF]]
  
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.
+
====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|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]]).
  
{{Scientific_box|[[Meteorological data from ground stations]]}}
+
Six data products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |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)
  
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:
+
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.
* Calculated radiation at surface
 
* Transpiration of water surface
 
* Transpiration of wet bare soil
 
* Evapotranspiration
 
  
{{Scientific_box|[[Calculation of advanced parameters]]}}
+
{{Scientific_box_2|
 
+
*[[Meteorological data from ground stations]]
==Acquisition of forecasted weather==
+
*[[Meteorological data from ECMWF models]]
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]]). This simulated crop yield is closer to the end of the crop season (compared to observation based simulations) and can be used to make Yield Forecasts (see module [[Yield Forecasting]]).
+
}}
 
 
5 weather forecast products from {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} are loaded into the system:
 
* Analysis model
 
* Deterministic forecast model
 
* Ensemble Prediction System
 
* Monthly forecast model
 
* Seasonal forecast model
 
 
 
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.
 
 
 
{|class="wikitable"
 
!Model !! Abbreviation !!Forecast days !! Members !! Horizontal model resolution* !! Acquired resolution** !! Delivery
 
|-
 
|Analysis model || HIS || current day || 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)
 
|}
 
<nowiki>*</nowiki> '''Horizontal model resolution''': resoltion in which the model simulates the weather indicators. The points for which the indicators are simulated are distributed over the earth using a {{Gloshint|Reduced Gaussian grid||Reduced Gaussian grid}}.<br>
 
<nowiki>**</nowiki> '''Acquired resolution''': resolution in which the simulated indicators are acquired en loaded into the MCYFS. Simulated indicators are distributed over the earth using a {{Gloshint|WGS84|World Geodetic System, revision 1984|WGS84}} coordinate system.<br>
 
<nowiki>***</nowiki> The first 10 days are simulated with a ~30km resolution. The remaining days on a ~60km resolution<br>
 
 
 
 
 
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 {{Gloshint|HIS||HIS}} data are stored as archive and used for calculating [[#Climatology (long term average weather)|climatolgy]]. The data of other models are replaced when a more recent data set becomes available ({{Gloshint|OPE||OPE}}, {{Gloshint|EPS||EPS}}, {{Gloshint|MON||MON}}, {{Gloshint|SEA||SEA}}).
 
  
{{Scientific_box|[[Meteorological data from ECMWF models]]}}
+
==Interpolation==
 +
[[File:Interpolating_observed_weather.jpg‎‎|Interpolation from weather stations to  25 x 25 km regular climate grid.|thumb|200px]]
 +
====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 {{Hint|CGMS|Crop Growth Monitoring System}}.
  
==Interpolation of observed 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]]
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.
 
  
 +
====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.
  
The interpolation is managed by a sub-system called {{Hint|CGMS|Crop Growth Monitoring System}}.
+
{{Scientific_box_2|
{{expert_box|
+
*[[Interpolation of observed weather]]
* Database structure: [[Appendix 4: CGMS DB description#Appendix 4|Appendix 4 (CGMS DB description)]]
+
*[[Interpolation of forecasted weather]]
* Table descriptions: [[Appendix 5: CGMS tables#Appendix 5|Appendix 5 (CGMS tables)]].
 
* Description of software procedures: [[Appendix 3: Overview of the software#Appendix 3|Appendix 3 (Overview of the software)]].
 
 
}}
 
}}
  
[[file:interpolation_of_observed_weather.jpg|thumb|right|300px|Interpolation of observed weather]]
+
==Aggregation==
'''Current year'''<br>
+
[[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]]
In the current year (and in near real time) for every day and each grid cell up to the 4 most suitable weather stations are selected. The selection is based on comparing the station locations and altitudes with the grid cell location and altitude. The suitable stations differ between grid cells. They also may differ between days and even between indicators. Only the suitable weather stations are used to interpolate the weather data to the specific grid cell on the specific day. In case no suitable stations are found, a long term average value is substituted to ensure spatial and temporal continuity.
+
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.:
 
 
 
 
'''Previous years (the archive)'''<br>
 
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 ([[#Climatology (long term average weather)|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 [[Marsop3 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
 
 
 
{{Scientific_box|[[Interpolation method onto regular climatic grid]]}}
 
 
 
==Interpolation of forecasted weather==
 
[[File:Downscalling_ope_his.jpg‎|Example of 0.25 x 0.25 degrees source grid (black dots) and 25 x 25 km target grid (gray lines)|thumb|200px]]
 
The data of different forecast models ('source') are acquired in different spatial resolutions and projections (sea '[[#Acquisition of observed weather|Acquisition of observed weather]]'). 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 'target' grid that is equal to the climate grid explained in [[#Interpolation of observed weather]]. This specific interpolation procedure is also called 'downscalling' because for the EPS, MON and SEA models it converts lower resolution source data into higher resolution data.
 
 
 
 
 
'''Downscalling'''<br>
 
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.
 
 
 
{{Scientific_box|[[Processing ECMWF model data]]}}
 
 
 
==Aggregation to regions==
 
[[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]]
 
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.
+
''''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.
  
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:
+
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.
*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.
+
{{Scientific_box_2|[[Aggregation of weather indicators]]}}
  
{{Scientific_box|[[Aggregation of weather indicators]]}}
+
==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.
  
==Climatology (long term average weather)==
+
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 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:
+
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.
* 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
 
  
{{Scientific_box|
+
{{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]]
+
[[Category:MCYFS introduction]]
 +
[[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.