Difference between revisions of "Meteorological data from ECMWF models"

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{{Scientific}}
 
{{Scientific}}
 
==General description==
 
==General description==
The {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} is one of the world's leading numerical modeling centres. It operates various global circulation models in various forecast depths. To evaluate the initial state of the atmosphere the models integrate observations from ground stations, radiosondes, satellites and many other sources. Special techniques bring these observations in balance with the meteorological equations to form a physically valid state of the atmosphere.
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The {{Gloshint|ECMWF|European Centre for Medium-Range Weather Forecasts. |ECMWF}} is one of the world's leading numerical modeling centres. It operates a set of global models and of data assimilation systems for the dynamics, thermodynamics and composition of the Earth's fluid envelope and interacting parts of the Earth-system. The data assimilation systems bring observations from ground stations, radiosondes, satellites and many other sources in balance with the meteorological equations to form a physically valid state of the atmosphere. These data is used as initial condition for the various forecast model sets.
  
 
In order to extend the period of analysis and to better perform the crop monitoring and yield forecasting, weather forecasts are integrated in the MCYFS. These data permit to have important information on the evolution of the main meteorological phenomena at mesoscale.
 
In order to extend the period of analysis and to better perform the crop monitoring and yield forecasting, weather forecasts are integrated in the MCYFS. These data permit to have important information on the evolution of the main meteorological phenomena at mesoscale.
  
The ECMWF's assimilation data is used to produce meteorological and derived agro-meteorological parameters that are visualized in dynamic maps and graphs by the [[Software Tools#Marsop3 viewer|MARS viewer]] and static maps [[Analysis of weather indicators#Static maps (Quick-looks)|quick-looks]]. Data from the ECMWF's Ensemble Prediction System (EPS), Monthly forecast model (MON) and Seasonal forecast model (SEA) have multiple forecast results. Small perturbations of the initial state are used to produce respectively 51, 50 and 50 different model runs.
+
The ECMWF's model results are used to produce meteorological and derived agro-meteorological parameters that are visualized in dynamic maps and graphs by the [[Software Tools#Marsop3 viewer|MARS viewer]] and static maps [[Analysis of weather indicators#Static maps (Quick-looks)|quick-looks]].  
 +
 
 +
Data from the ECMWF's Ensemble Prediction System (EPS), Monthly forecast model (MON) and Seasonal forecast model (SEA) have multiple forecast results. As the atmosphere is a chaotic system where small differences in the initial conditions can lead to in huge differences in the resulting forecasts in 1992 ECMWF introduced an ensemble prediction system, providing information on the uncertainty of a weather forecast. Small perturbations of the initial state are used to produce (nowadays) 50 different initial conditions. Together with the unpertubated control run this results in an ensemble of 51 model results.
  
 
Before ECMWF forecasted weather data can be ingested in the MCYFS, the data have to be preprocessed in order to get the appropriate resolutions in time and space.
 
Before ECMWF forecasted weather data can be ingested in the MCYFS, the data have to be preprocessed in order to get the appropriate resolutions in time and space.
[[File:Flowchart_ecmwf_preprocessing_steps.jpg|link=|frame|preprocessing of ECMWF data|none]]
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[[File:ProcessFlowECMWF.gif|link=|frame|Pre-Processing of ECMWF model data in Marsop-3|none]]
  
 
==Data acquisition from ECMWF==
 
==Data acquisition from ECMWF==
The data is delivered by ECMWF in FM-92 {{Gloshint|GRIB|GRIdded Binary. |GRIB}} format which is specified in WMO Publication 306 Manual on Codes. For the “Global Window” land and sea points are received. Filtering on land points takes place during decoding from GRIB format.
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The data for surface and pressure levels is delivered by ECMWF in FM-92 {{Gloshint|GRIB|GRIdded Binary. |GRIB}} format which is specified in WMO Publication 306 Manual on Codes.  
  
6 products of the ECMWF are ingested into the MCYFS:
+
6 products of the ECMWF model set are ingested into the MCYFS:
 
{|class="wikitable"
 
{|class="wikitable"
!Model !! Abbreviation !!Number of forecast days !! Members !! Gaussian grid* !! Horizontal model resolution* !! Acquired resolution** !! Delivery
+
!Model set with ECMWF's abbrevation !! Abbreviation within Marsop-3 !!Number of forecast days !! Members !! Gaussian/Spectral grid* !! Horizontal model resolution* !! Acquired resolution** !! Emission of data files and maps
 
|-
 
|-
|ERA-Interim**** || ERA || 1 || 1 || N128 ||~80km || 0.75° x 0.75° || Once
+
|ERA-Interim**** || ERA || 1 || 1 || N128/T255 ||~80km || 0.75° x 0.75° || Once
 
|-
 
|-
|Analysis || OPE || 1 || 1 || N640 || ~16km || 0.25° x 0.25° || Daily (10.30 hr)
+
|Analysis HRES || OPE || 1 || 1 || N640/T1279 || ~16km || 0.25° x 0.25° || Daily (10.30 hr)
 
|-
 
|-
|Deterministic forecast || OPE ||10 || 1 || N640 || ~16km || 0.25° x 0.25° || Daily (12.00 hr)
+
|Deterministic forecast HRES || OPE ||10 || 1 || N640/T1279 || ~16km || 0.25° x 0.25° || Daily (12.00 hr)
 
|-
 
|-
|Ensemble Prediction System || ENS ||15 || 51 || N320 / N160*** || ~30km / ~60km*** || 0.5° x 0.5° || Daily (14.00 hr)
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|Ensemble Prediction System ENS || ENS ||15 || 50+1 || N320 - N160 / T639 - T319 *** || ~30km / ~60km*** || 0.5° x 0.5° || Daily (14.00 hr)
 
|-
 
|-
|Monthly forecast model || MON ||32 || 50 || N320 / N160*** || ~30km / ~60km*** || 0.5° x 0.5° || Every Friday (03.00 hr)
+
|Monthly forecast model ENS extended || MON ||32 || 50+1 || N320 - N160 / T639 - T319 *** || ~30km / ~60km*** || 0.5° x 0.5° || Every Friday (03.00 hr)
 
|-
 
|-
|Seasonal forecast model || SEA ||183 || 50 || N128 || ~80km || 0.75° x 0.75° || Every 8th of the month (14.00 hr)
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|Seasonal forecast model SEAS || SEA ||183 || 50+1 || N128/T255 || ~80km || 0.75° x 0.75° || Every 8th of the month (14.00 hr)
 
|}
 
|}
<nowiki>*</nowiki> resolution 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}}. Grid names start with 'N' followed by number of lines by which latitude is divided.<br>  
+
<nowiki>*</nowiki> resolution in which the model simulates the weather indicators (state: March 2014). Depending on the variable ECMWF uses either a {{Gloshint|Reduced Gaussian grid||Reduced Gaussian grid}} or a spectral model. The Gaussian grid names start with 'N' followed by number of lines by which latitude is divided. The spectral grids are named for the particular wave number where the spherical harmonic expansion is truncated, eg T1279 identifies truncation at wave number 1279. The results are made available by ECWMF on Gaussian or on corresponding regular lat-lon-grids.<br>  
 
<nowiki>**</nowiki> resolution in which the simulated indicators are acquired and loaded into the MCYFS. The simulated indicators are distributed over the earth using a {{Gloshint|WGS84|World Geodetic System, revision 1984|WGS84}} coordinate system.<br>
 
<nowiki>**</nowiki> resolution in which the simulated indicators are acquired and loaded into the MCYFS. The 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 on a N320 grid (~30km horizontal resolution). The remaining days on a N160 grid (~60km horizontal resolution).<br>
 
<nowiki>***</nowiki> The first 10 days are simulated on a N320 grid (~30km horizontal resolution). The remaining days on a N160 grid (~60km horizontal resolution).<br>
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The data of other models are replaced when a more recent data set comes available (OPE, EPS, MON and SEA). Therefore only the ERA-Interim, extended with HIS data is used to calculate [[#Climatology (long term average weather)|climatology]].
 
The data of other models are replaced when a more recent data set comes available (OPE, EPS, MON and SEA). Therefore only the ERA-Interim, extended with HIS data is used to calculate [[#Climatology (long term average weather)|climatology]].
  
For the OPE and EPS models the ECMWF model is run twice per day based on 00 and 12 UTC observations. As the delivery needs to take place until 15.00 hours of each day in standard situation the 00 UTC model run can be used. Sometimes, the model production is delayed so that as fallback the 12 UTC model data of the previous day is taken into account.
+
For the OPE and EPS models the ECMWF model is run twice per day based on 00 and 12 UTC observations. As the delivery needs to take place until 15.00 hours of each day in standard situation the 00 UTC model run can be used. Sometimes, the model emission is delayed so that as fallback the 12 UTC model data of the previous day is taken into account.
  
 
==Spatial representation==
 
==Spatial representation==
The ECMWF models run on Gaussian grids with different resolutions. The central MCYFS database however requires the initial data in a specific grid resolution with regular latitudes and longitudes. Therefore conversions are needed.
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The ECMWF models run on Gaussian grids, for certain parameters and model levels on spectral grids, with different resolutions. The central MCYFS database however requires the initial data in a specific grid resolution with regular latitudes and longitudes. Therefore conversions are needed.
  
 
====OPE====
 
====OPE====
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==Aggregation to daily data==
 
==Aggregation to daily data==
First of all an aggregation of 3-, 6- and 12-hourly data to daily data are calculated. Algorithms were developed in the ASEMARS project and differ per ECMWF model. The algorithms are presented in the box below.
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First of all an aggregation of 3-, 6- and 12-hourly data to daily data is calculated. Algorithms were developed in the ASEMARS project and differ per ECMWF model. The algorithms are presented in the box below.
  
 
{|class="collapsing_table collapsible collapsed"
 
{|class="collapsing_table collapsible collapsed"
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====Aggregation areas====
 
====Aggregation areas====
[[File:Map_world_zones.jpg|thumb|right|250px|Aggregation areas West, Central and East on world map.]]
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[[File:map_world_zones.jpg||none|250px|Aggregation areas West, Central and East on world map.]]<br>
 
To consider the earth's different times zones aggregation rules for 3 different areas (East, Central, West) have been defined. The aggregation rules for the model data refer to the report schedule of synoptical weather stations (e.g. maximum air temperature in Europe and Africa refers to the period between 06 and 18 UTC of the corresponding day).
 
To consider the earth's different times zones aggregation rules for 3 different areas (East, Central, West) have been defined. The aggregation rules for the model data refer to the report schedule of synoptical weather stations (e.g. maximum air temperature in Europe and Africa refers to the period between 06 and 18 UTC of the corresponding day).
{|class="wikitable"
 
!Region !! West!!Centre!!East
 
|-
 
|Longitude||180W - 30W||30W - 60E||60E - 180E
 
|-
 
|Precipitation||12-12UTC||06-06UTC||00-245UTC
 
|-
 
|Maximum temperature||12-24UTC||06-18UTC||00-12UTC
 
|-
 
|Minimum temperature||00-12UTC||18-06UTC||12-24UTC
 
|-
 
|Others||06-06UTC||00-24UTC||18-18UTC
 
|}
 
====HIS====
 
The temporal resolution is 3-hourly.
 
{|class="wikitable"
 
!Indicator !! Derivation Rule
 
|-
 
|Minimum temperature ||Minimum(MN2T6<sub>00</sub>, MN2T6<sub>06</sub>)
 
|-
 
|Maximum temperature ||Maximum(MX2T6<sub>12</sub>, MX2T6<sub>18</sub>)
 
|-
 
|Average temperature ||(2T<sub>03</sub> + 2T<sub>06</sub> + 2T<sub>09</sub> + 2T<sub>12</sub> + 2T<sub>15</sub> + 2T<sub>18</sub> + 2T<sub>21</sub> + 2T<sub>24</sub>) / 8
 
|-
 
|Precipitation||TP<sub>24</sub> – TP<sub>00</sub>
 
|-
 
|Wind speed||(√(10U<sub>03</sub><sup>2</sup> + 10V<sub>03</sub><sup>2</sup>) + √(10U<sub>06</sub><sup>2</sup> + 10V<sub>06</sub><sup>2</sup>) + √(10U<sub>09</sub><sup>2</sup> + 10V<sub>09</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>15</sub><sup>2</sup> + 10V<sub>15</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>) + √(10U<sub>21</sub><sup>2</sup> + 10V<sub>21</sub><sup>2</sup>) + √(10U<sub>24</sub><sup>2</sup> + 10V<sub>24</sub><sup>2</sup>)) / 8
 
|-
 
|Dewpoint temperature||(2D<sub>03</sub> + 2D<sub>06</sub> + 2D<sub>09</sub> + 2D<sub>12</sub> + 2D<sub>15</sub> + 2D<sub>18</sub> + 2D<sub>21</sub> + 2D<sub>24</sub>) / 8
 
|-
 
|Global radiation||SSRD<sub>24</sub> – SSRD<sub>00</sub>
 
|-
 
|Snow depth||SD<sub>06</sub>
 
|}
 
  
====OPE====
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The following table summarized the deviation rules for the different aggregation zones and data sets.<br>
The temporal resolution is 3-hourly until 72h and 6-hourly afterwards. Algorithms for T2M, FFM and TD change at time step after FP +72.
+
p = previous day, f = following day<br>
 +
Temporal resolution of HIS is 3-hourly. Temporal resolution of OPE is 3-hourly for the first 72 hours and 6-hourly afterwards. Thus algorithms for air temperature, dew point and wind speed of the OPE data set change when the aggregation includes forecast time step +72h. Temporal resolution of MON and SEA is 6-hourly.
 +
 
 
{|class="wikitable"
 
{|class="wikitable"
!Indicator !! Derivation Rule
+
!Region !! West (only HIS, OPE)!!Centre!!East (only HIS, OPE)
 
|-
 
|-
|Minimum temperature ||Minimum(MN2T6<sub>00</sub>, MN2T6<sub>06</sub>)
+
|Longitude||180W - 30W ||30W - 60E||60E - 180E
 
|-
 
|-
|Maximum temperature ||Maximum(MX2T6<sub>12</sub>, MX2T6<sub>18</sub>)
+
|Precipitation HIS, OPE, ENS, MON, SEA, ERA||TP<sub>12</sub> – TP<sub>12f</sub>||TP<sub>06</sub> – TP<sub>06f</sub>||TP<sub>00</sub> – TP<sub>24</sub>
 
|-
 
|-
|Average temperature ||'''First 72 hours:''' (2T<sub>03</sub> + 2T<sub>06</sub> + 2T<sub>09</sub> + 2T<sub>12</sub> + 2T<sub>15</sub> + 2T<sub>18</sub> + 2T<sub>21</sub> + 2T<sub>24</sub>) / 8<br>
+
|Average temperature HIS, OPE until +72h, ENS until +72h, ERA|| (2T<sub>09</sub> + 2T<sub>12</sub> + 2T<sub>15</sub> + 2T<sub>18</sub> + 2T<sub>21</sub> + 2T<sub>24</sub> + 2T<sub>03f</sub> + 2T<sub>06f</sub>) / 8 || (2T<sub>03</sub> + 2T<sub>06</sub> + 2T<sub>09</sub> + 2T<sub>12</sub> + 2T<sub>15</sub> + 2T<sub>18</sub> + 2T<sub>21</sub> + 2T<sub>24</sub>) / 8 || (2T<sub>21p</sub> + 2T<sub>00</sub> + 2T<sub>03</sub> + 2T<sub>06</sub> + 2T<sub>09</sub> + 2T<sub>12</sub> + 2T<sub>15</sub> + 2T<sub>18</sub>) / 8
'''After 72 hours:''' (2T<sub>06</sub> + 2T<sub>12</sub> + 2T<sub>18</sub> + 2T<sub>24</sub>) / 4
 
 
|-
 
|-
|Precipitation||TP<sub>24</sub> – TP<sub>00</sub>
+
|Average temperature OPE after +72h, ENS after +72h, MON, SEA|| (2T<sub>12</sub> + 2T<sub>18</sub> + 2T<sub>24</sub> + 2T<sub>06f</sub>) / 4 || (2T<sub>06</sub> + 2T<sub>12</sub> + 2T<sub>18</sub> + 2T<sub>24</sub>) / 4 || (2T<sub>00</sub> + 2T<sub>06</sub> + 2T<sub>12</sub> + 2T<sub>18</sub>) / 4
 
|-
 
|-
|Wind speed||'''First 72 hours:''' (√(10U<sub>03</sub><sup>2</sup> + 10V<sub>03</sub><sup>2</sup>) + √(2U<sub>06</sub><sup>2</sup> + 2V<sub>06</sub><sup>2</sup>) + √(2U<sub>09</sub><sup>2</sup> + 2V<sub>09</sub><sup>2</sup>) + √(2U<sub>12</sub><sup>2</sup> + 2V<sub>12</sub><sup>2</sup>) + √(2U<sub>15</sub><sup>2</sup> + 2V<sub>15</sub><sup>2</sup>) + √(2U<sub>18</sub><sup>2</sup> + 2V<sub>18</sub><sup>2</sup>) + √(2U<sub>21</sub><sup>2</sup> + 2V<sub>21</sub><sup>2</sup>) + √(2U<sub>24</sub><sup>2</sup> + 2V<sub>24</sub><sup>2</sup>)) / 8<br>
+
|Dew point HIS, OPE until +72h, ENS until +72h, ERA|| (2D<sub>09</sub> + 2D<sub>12</sub> + 2D<sub>15</sub> + 2D<sub>18</sub> + 2D<sub>21</sub> + 2D<sub>24</sub> + 2D<sub>03f</sub> + 2D<sub>06f</sub>) / 8 || (2D<sub>03</sub> + 2D<sub>06</sub> + 2D<sub>09</sub> + 2D<sub>12</sub> + 2D<sub>15</sub> + 2D<sub>18</sub> + 2D<sub>21</sub> + 2D<sub>24</sub>) / 8 || (2D<sub>21p</sub> + 2D<sub>00</sub> + 2D<sub>03</sub> + 2D<sub>06</sub> + 2D<sub>09</sub> + 2D<sub>12</sub> + 2D<sub>15</sub> + 2D<sub>18</sub>) / 8
'''After 72 hours:''' (√(2U<sub>06</sub><sup>2</sup> + 2V<sub>06</sub><sup>2</sup>) + √(2U<sub>12</sub><sup>2</sup> + 2V<sub>12</sub><sup>2</sup>) + √(2U<sub>18</sub><sup>2</sup> + 2V<sub>18</sub><sup>2</sup>) + √(2U<sub>24</sub><sup>2</sup> + 2V<sub>24</sub><sup>2</sup>)) / 4
 
 
|-
 
|-
|Dewpoint temperature||'''First 72 hours:''' (2D<sub>03</sub> + 2D<sub>06</sub> + 2D<sub>09</sub> + 2D<sub>12</sub> + 2D<sub>15</sub> + 2D<sub>18</sub> + 2D<sub>21</sub> + 2D<sub>24</sub>) / 8<br>
+
|Dew point OPE after +72h, ENS after +72h, MON, SEA|| (2D<sub>12</sub> + 2D<sub>18</sub> + 2D<sub>24</sub> + 2D<sub>06f</sub>) / 4 || (2D<sub>06</sub> + 2D<sub>12</sub> + 2D<sub>18</sub> + 2D<sub>24</sub>) / 4 || (2D<sub>00</sub> + 2D<sub>06</sub> + 2D<sub>12</sub> + 2D<sub>18</sub>) / 4
'''After 72 hours:''' (2D<sub>06</sub> + 2D<sub>12</sub> + 2D<sub>18</sub> + 2D<sub>24</sub>) / 4
 
 
|-
 
|-
|Global radiation||SSRD<sub>24</sub> – SSRD<sub>00</sub>
+
|Maximum temperature HIS, OPE, ENS, MON, ERA||Maximum(MX2T6<sub>18</sub>, MX2T6<sub>24</sub>)||Maximum(MX2T6<sub>12</sub>, MX2T6<sub>18</sub>)||Maximum(MX2T6<sub>06</sub>, MX2T6<sub>12</sub>)
 
|-
 
|-
|Snow depth||SD<sub>06</sub>
+
|Maximum temperature SEA||-||MX2T24<sub>24</sub>||-
|}
 
 
 
====ERA, EPS, MON and SEA====
 
The temporal resolution is 6-hourly.
 
{|class="wikitable"
 
!Indicator !! Derivation Rule
 
 
|-
 
|-
|Minimum temperature ||Minimum(MN2T6<sub>00</sub>, MN2T6<sub>06</sub>)
+
|Minimum temperature HIS, OPE, ENS, MON, ERA||Minimum(MN2T6<sub>06</sub>, MN2T6<sub>12</sub>||Minimum(MN2T6<sub>00</sub>, MN2T6<sub>06</sub>||Minimum(MN2T6<sub>18p</sub>, MN2T6<sub>00</sub>)
 
|-
 
|-
|Maximum temperature ||Maximum(MX2T6<sub>12</sub>, MX2T6<sub>18</sub>)
+
|Minimum temperature SEA||-||MN2T24<sub>24</sub>||-
 
|-
 
|-
|Average temperature ||(2T<sub>06</sub> + 2T<sub>12</sub> + 2T<sub>18</sub> + 2T<sub>24</sub>) / 4
+
|Wind speed HIS, OPE until +72h, ENS until +72h, ERA||(√(10U<sub>09</sub><sup>2</sup> + 10V<sub>09</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>15</sub><sup>2</sup> + 10V<sub>15</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>) + √(10U<sub>21</sub><sup>2</sup> + 10V<sub>21</sub><sup>2</sup>) + √(10U<sub>24</sub><sup>2</sup> + 10V<sub>24</sub><sup>2</sup>) + √(10U<sub>03f</sub><sup>2</sup> + 10V<sub>03f</sub><sup>2</sup>) + √(10U<sub>06f</sub><sup>2</sup> + 10V<sub>06f</sub><sup>2</sup>)) / 8||(√(10U<sub>03</sub><sup>2</sup> + 10V<sub>03</sub><sup>2</sup>) + √(10U<sub>06</sub><sup>2</sup> + 10V<sub>06</sub><sup>2</sup>) + √(10U<sub>09</sub><sup>2</sup> + 10V<sub>09</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>15</sub><sup>2</sup> + 10V<sub>15</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>) + √(10U<sub>21</sub><sup>2</sup> + 10V<sub>21</sub><sup>2</sup>) + √(10U<sub>24</sub><sup>2</sup> + 10V<sub>24</sub><sup>2</sup>)) / 8||(√(10U<sub>21f</sub><sup>2</sup> + 10V<sub>21f</sub><sup>2</sup>) + √(10U<sub>00</sub><sup>2</sup> + 10V<sub>00</sub><sup>2</sup>) + √(10U<sub>03</sub><sup>2</sup> + 10V<sub>03</sub><sup>2</sup>) + √(10U<sub>06</sub><sup>2</sup> + 10V<sub>06</sub><sup>2</sup>) + √(10U<sub>09</sub><sup>2</sup> + 10V<sub>09</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>15</sub><sup>2</sup> + 10V<sub>15</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>)) / 8
 
|-
 
|-
|Precipitation||TP<sub>24</sub> – TP<sub>00</sub>
+
|Wind speed OPE after +72h, ENS after +72h, MON, SEA||(√(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>) + √(10U<sub>24</sub><sup>2</sup> + 10V<sub>24</sub><sup>2</sup>) + √(10U<sub>06f</sub><sup>2</sup> + 10V<sub>06f</sub><sup>2</sup>)) / 4||(√(10U<sub>06</sub><sup>2</sup> + 10V<sub>06</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>) + √(10U<sub>24</sub><sup>2</sup> + 10V<sub>24</sub><sup>2</sup>)) / 4||(√(10U<sub>00</sub><sup>2</sup> + 10V<sub>00</sub><sup>2</sup>) + √(10U<sub>06</sub><sup>2</sup> + 10V<sub>06</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>)) / 4
 
|-
 
|-
|Wind speed||(√(10U<sub>06</sub><sup>2</sup> + 10V<sub>06</sub><sup>2</sup>) + √(10U<sub>12</sub><sup>2</sup> + 10V<sub>12</sub><sup>2</sup>) + √(10U<sub>18</sub><sup>2</sup> + 10V<sub>18</sub><sup>2</sup>) + √(10U<sub>24</sub><sup>2</sup> + 10V<sub>24</sub><sup>2</sup>)) / 4
+
|Global radiation HIS, OPE, ENS, MON, SEA, ERA||SSRD<sub>06f</sub> – SSRD<sub>06</sub>||SSRD<sub>24</sub> – SSRD<sub>00</sub>||SSRD<sub>18</sub> – SSRD<sub>18p</sub>
 
|-
 
|-
|Dewpoint temperature||(2D<sub>06</sub> + 2D<sub>12</sub> + 2D<sub>18</sub> + 2D<sub>24</sub>) / 4
+
|Snow depth HIS, OPE, ENS, MON, SEA, ERA||SD<sub>12</sub>||SD<sub>06</sub>||SD<sub>00</sub>
|-
 
|Global radiation||SSRD<sub>24</sub> – SSRD<sub>00</sub>
 
|-
 
|Snow depth||SD<sub>06</sub>
 
|-
 
|Cloud*||(TCC<sub>06</sub>+2*TCC<sub>12</sub>+TCC<sub>18</sub>) / 4
 
 
|}
 
|}
<nowiki>*</nowiki> Only EPS model
 
 
|}
 
|}
  
Line 534: Line 488:
 
|SSRD (Global radiation)|| 0, when < 0
 
|SSRD (Global radiation)|| 0, when < 0
 
|}
 
|}
 +
|}
 +
 +
 +
 +
In parallel daily, decadal and monthly aggregates of the analysis and deterministic forecast (HIS, OPE) is provided as csv to JRC and Vito.
 +
{|class="collapsing_table collapsible collapsed"
 +
!csv format description and deliverables
 +
|-
 +
|
 +
{|class="wikitable"
 +
!column!! Daily HIS, OPE, ERA !! Decadal HIS, OPE, ERA !! Monthly HIS, OPE, ERA
 +
|-
 +
|1||lat||lat||lat
 +
|-
 +
|2||long||long||long
 +
|-
 +
|3||YYYYMMDD||YYYY||YYYY
 +
|-
 +
|4||tav||MM||MM
 +
|-
 +
|5||tmax||dekad (1 or 2 or 3)||dekad (0)
 +
|-
 +
|6||tmin||tav||tav
 +
|-
 +
|7||rrr||tmax||tmax
 +
|-
 +
|8||E0||tmin||tmin
 +
|-
 +
|9||ES0||rrr||rrr
 +
|-
 +
|10||ET0||E0||E0
 +
|-
 +
|11||rad||ES0||ES0
 +
|-
 +
|12||sdav||ET0||ET0
 +
|-
 +
|13||cwb||rad||rad
 +
|-
 +
|14||tav_sum||sdav||sdav
 +
|-
 +
|15||ffav||cwb||cwb
 +
|-
 +
|16||vapav||tav_sum||tav_sum
 +
|-
 +
|17||||ffav||ffav
 +
|-
 +
|18||||vapav||vapav
 +
|-
 +
|19||||sdmax||sdmax
 +
|-
 +
|20||||sdmin||sdmin
 +
|}
 +
 +
{|class="wikitable"
 +
!abbreviation!! unit !! meaning
 +
|-
 +
|lat||Deg.decDeg||latitude
 +
|-
 +
|long||Deg.decDeg||longitude
 +
|-
 +
|tav||°C||average temperature
 +
|-
 +
|tmin||°C||minimum temperature
 +
|-
 +
|tmax||°C||maximum temperature<br>
 +
|-
 +
|rrr||mm = liters/m2||precipitation; sum over day, dekad or month
 +
|-
 +
|E0||mm = liters/m2||evapotranspiration open water; sum over day, dekad or month
 +
|-
 +
|ES0||mm = liters/m2||evapotranspiration bare soil; sum over day, dekad or month
 +
|-
 +
|ET0||mm = liters/m2||evapotranspiration Penman-Monteith; sum over day, dekad or month
 +
|-
 +
|rad||kJ/m2||global radiation; sum over day, dekad or month
 +
|-
 +
|sdav||cm||average snow depth (water equivalent)
 +
|-
 +
|sdmax||cm||maximum snow depth (water equivalent)
 +
|-
 +
|cwb||mm = liters/m2||climatic water balance (rrr - ET0); sum over day, dekad or month
 +
|-
 +
|tav_sum||°C||mean temperature; sum over day, dekad ormonth
 +
|-
 +
|ffav||m/s||average wind speed
 +
|-
 +
|vapav||hPa||average water vapour pressure
 +
|-
 +
|YYYY||||year
 +
|-
 +
|MM||||month
 +
|-
 +
|DD||||day
 +
|}
 +
 +
{|class="wikitable"
 +
!model set!! destination !! naming
 +
|-
 +
|Daily analysis||JRC (ECMWF/fine_global/daily/analysis/YYYY)||Meteodata_world_[day as YYYYmmdd].csv.gz
 +
|-
 +
|Daily analysis||Alterra (marsop/meteodata/global)||Meteodata_world_[day as YYYYmmdd].csv.gz
 +
|-
 +
|Daily analysis||Vito (MARSOP3/Global)||YYYYmmdd.csv
 +
|-
 +
|Daily forecast||JRC FOOD-SEC (ECMWF/file_global/daily/forecast/YYYY)||Meteodata_world_[day as YYYYmmdd].csv.gz
 +
|-
 +
|10-daily analysis||JRC FOOD-SEC (ECMWF/fine_global/ten-daily/analysis/YYYY)||Meteodata_world_[YYYYmm_dec[1 or 2 or 3].csv.gz
 +
|-
 +
|10-daily analysis||Alterra (GWSI_MARSOP3)||GLD_OPE_dekad_[end of decad as YYYYmmdd]_00.csv.gz
 +
|-
 +
|10-daily forecast||JRC FOOD-SEC (ECMWF/fine_global/ten-daily/forecast/YYYY)||Meteodata_world_[YYYYmm_dec[1 or 2 or 3].csv.gz
 +
|-
 +
|monthly analysis||JRC FOOD-SEC (ECMWF/fine_global/ten-daily/analysis/YYYY)||Meteodata_world_[YYYYmm]_month.csv.gz
 +
|}
 +
 
|}
 
|}
  
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|Parameter||Operational ECMWF||EPS ECMWF||calculated Operational||calculated EPS||Number of Maps||||||||
 
|Parameter||Operational ECMWF||EPS ECMWF||calculated Operational||calculated EPS||Number of Maps||||||||
 
|-
 
|-
|Sea-level pressure||10+anim.||10+anim.||||||22||SLP||ENS-SLP||||
+
|Sea-level pressure||10+anim.||15+anim.||||||22||SLP||ENS-SLP||||
 
|-
 
|-
|GPH 500hPa||10+anim.||10+anim.||||||22||GPH500||ENS-GPH500||||
+
|GPH 500hPa||10+anim.||15+anim.||||||22||GPH500||ENS-GPH500||||
 
|-
 
|-
|GPH 850 hPa||10+anim.||10+anim.||||||22||GPH850||ENS-GPH850||||
+
|GPH 850 hPa||10+anim.||15+anim.||||||22||GPH850||ENS-GPH850||||
 
|-
 
|-
 
|Tmin-24h||||||10+anim.||15+anim.||27||||||TMIN||ENS-TMIN
 
|Tmin-24h||||||10+anim.||15+anim.||27||||||TMIN||ENS-TMIN

Revision as of 14:14, 11 March 2014



General description

The ECMWF is one of the world's leading numerical modeling centres. It operates a set of global models and of data assimilation systems for the dynamics, thermodynamics and composition of the Earth's fluid envelope and interacting parts of the Earth-system. The data assimilation systems bring observations from ground stations, radiosondes, satellites and many other sources in balance with the meteorological equations to form a physically valid state of the atmosphere. These data is used as initial condition for the various forecast model sets.

In order to extend the period of analysis and to better perform the crop monitoring and yield forecasting, weather forecasts are integrated in the MCYFS. These data permit to have important information on the evolution of the main meteorological phenomena at mesoscale.

The ECMWF's model results are used to produce meteorological and derived agro-meteorological parameters that are visualized in dynamic maps and graphs by the MARS viewer and static maps quick-looks.

Data from the ECMWF's Ensemble Prediction System (EPS), Monthly forecast model (MON) and Seasonal forecast model (SEA) have multiple forecast results. As the atmosphere is a chaotic system where small differences in the initial conditions can lead to in huge differences in the resulting forecasts in 1992 ECMWF introduced an ensemble prediction system, providing information on the uncertainty of a weather forecast. Small perturbations of the initial state are used to produce (nowadays) 50 different initial conditions. Together with the unpertubated control run this results in an ensemble of 51 model results.

Before ECMWF forecasted weather data can be ingested in the MCYFS, the data have to be preprocessed in order to get the appropriate resolutions in time and space.

Pre-Processing of ECMWF model data in Marsop-3

Data acquisition from ECMWF

The data for surface and pressure levels is delivered by ECMWF in FM-92 GRIB format which is specified in WMO Publication 306 Manual on Codes.

6 products of the ECMWF model set are ingested into the MCYFS:

Model set with ECMWF's abbrevation Abbreviation within Marsop-3 Number of forecast days Members Gaussian/Spectral grid* Horizontal model resolution* Acquired resolution** Emission of data files and maps
ERA-Interim**** ERA 1 1 N128/T255 ~80km 0.75° x 0.75° Once
Analysis HRES OPE 1 1 N640/T1279 ~16km 0.25° x 0.25° Daily (10.30 hr)
Deterministic forecast HRES OPE 10 1 N640/T1279 ~16km 0.25° x 0.25° Daily (12.00 hr)
Ensemble Prediction System ENS ENS 15 50+1 N320 - N160 / T639 - T319 *** ~30km / ~60km*** 0.5° x 0.5° Daily (14.00 hr)
Monthly forecast model ENS extended MON 32 50+1 N320 - N160 / T639 - T319 *** ~30km / ~60km*** 0.5° x 0.5° Every Friday (03.00 hr)
Seasonal forecast model SEAS SEA 183 50+1 N128/T255 ~80km 0.75° x 0.75° Every 8th of the month (14.00 hr)

* resolution in which the model simulates the weather indicators (state: March 2014). Depending on the variable ECMWF uses either a Reduced Gaussian grid or a spectral model. The Gaussian grid names start with 'N' followed by number of lines by which latitude is divided. The spectral grids are named for the particular wave number where the spherical harmonic expansion is truncated, eg T1279 identifies truncation at wave number 1279. The results are made available by ECWMF on Gaussian or on corresponding regular lat-lon-grids.
** resolution in which the simulated indicators are acquired and loaded into the MCYFS. The simulated indicators are distributed over the earth using a WGS84 coordinate system.
*** The first 10 days are simulated on a N320 grid (~30km horizontal resolution). The remaining days on a N160 grid (~60km horizontal resolution).
**** In more detail: ECWMF runs ERA-Interim on IFS Version Cy31r1

ERA-Interim is only used as archive containing daily data covering the period January 1989 - March 2013. From the OPE model the forecast for the current day (analysis) is processed and added to the archive assuming this is the best estimator for weather indicators of that day. Sometimes the term HIS is used to refer to the archive composed by: 1) ERA-interim and 2) the first day of each daily issue of the OPE model.

The data of other models are replaced when a more recent data set comes available (OPE, EPS, MON and SEA). Therefore only the ERA-Interim, extended with HIS data is used to calculate climatology.

For the OPE and EPS models the ECMWF model is run twice per day based on 00 and 12 UTC observations. As the delivery needs to take place until 15.00 hours of each day in standard situation the 00 UTC model run can be used. Sometimes, the model emission is delayed so that as fallback the 12 UTC model data of the previous day is taken into account.

Spatial representation

The ECMWF models run on Gaussian grids, for certain parameters and model levels on spectral grids, with different resolutions. The central MCYFS database however requires the initial data in a specific grid resolution with regular latitudes and longitudes. Therefore conversions are needed.

OPE

The Deterministic forecast model and Analysis model (OPE) produce forecast weather for grid cells on a Gaussian N640 reduced grid (~16x~16km). The resolution is converted to a Gaussian N400 reduced grid (~25x~25km) and after this to a regular 0.25 x 0.25 degrees latitude longitude grid (OPE grid). For the OPE grid two height models are kept. First a height model calculated in the same way as the data sets: first aggregation on the Gaussian grid from a Gaussian N640 reduced grid (~16x~16km) to a Gaussian N400 reduced grid (~25x~25km) and next a conversion from the Gaussian grid to the OPE grid. In addition the height model of a previous version of OPE model (prior to January 2010) is available. The previous OPE version was run on a Gaussian N400 reduced grid (~25x~25km) and the related height model was directly converted into the OPE grid. The grid description is stored in table GRID_<MODEL>.

Black dots: Gaussian N640 reduced grid (~16x~16km) to regular 0.25 x 0.25 degrees latitude longitude. Gray lines: 25x25km climate grid.

EPS & MON

The first 10 forecast days (Leg A) of the Ensemble Prediction System and Monthly forecast are modelled for grid cells on a Gaussian N320 reduced grid (~30x~30km). Because the modelling of the remaining days (Leg B and C) is on the Gaussian N160 reduced grid (~60x~60km) it is not possible to switch for the whole forecast depth (EPS: 15 days and MON: 32 days) to a finer resolution. It means that the data of first 10 days must be aggregated. First the resolution is reduced to a Gaussian N200 reduced grid (~50x~50km) and finally converted to a regular 0.5 x 0.5 degrees latitude longitude grid. The height model of the latter grid is calculated in the same way as the data sets: first aggregation on the Gaussian grid from N320 (~30x~30km) to N200 (~50x~50km) and next a conversion from the Gaussian grid N200 to the regular 0.5 x 0.5 degrees latitude longitude grid.

Black dots: Gaussian N320 reduced grid (~30x~30km) to regular 0.5 x 0.5 degrees latitude longitude. Gray lines: 25x25km climate grid.

After the first 10 day, the resolution of the models for the remaining forecast days (Leg B and C) is at a Gaussian N160 reduced grid (~60x~60km). The results are directly converted into a regular 0.5 x 0.5 degrees latitude longitude grid.

Black dots: Gaussian N160 reduced grid (~60x~60km) to regular 0.5 x 0.5 degrees latitude longitude. Gray lines: 25x25km climate grid.

The grid description is stored in table GRID_<MODEL>.

SEA

All forecast days of the Seasonal forecast are calculated for a Gaussian N128 reduced grid (~80x~80km). The results are directly converted into a regular 0.75 x 0.75 degrees latitude longitude grid. The grid description is stored in table GRID_<MODEL>.
Black dots: Gaussian N128 reduced grid (~80x~80km) to regular 0.75 x 0.75 degrees latitude longitude. Gray lines: 25x25km climate grid.

ERA

The ERA data are calculated for a Gaussian N128 reduced grid (~80x~80km). The results are directly converted into a regular 0.75 x 0.75 degrees latitude longitude grid. The grid description is stored in table ECMWF_ERA_GRID_GLD (linked to view ECMWF_ERA_GRID).
Black dots: Gaussian N128 reduced grid (~80x~80km) to regular 0.75 x 0.75 degrees latitude longitude. Gray lines: 25x25km climate grid.

Decoding and extraction of GRIB data

Data is delivered in GRIB format and hence data is first decoded. In previous years the program ‘wgrib’ has been used which is capable of extracting GRIB content into ASCII files for further processing. Recently ECMWF has released version 1.2.0 of their GRIB API which is the successor of GRIBEX. While GRIBEX was used within FORTRAN programs the new GRIB API is designed for programs written in the C programming language.

During decoding additional indicators required by JRC and partners are calculated. This include aggregation to daily data, calculation of derived indicators and calculation of extreme weather events.

Aggregation to daily data

First of all an aggregation of 3-, 6- and 12-hourly data to daily data is calculated. Algorithms were developed in the ASEMARS project and differ per ECMWF model. The algorithms are presented in the box below.

Calculation of advanced parameters

Not all indicators can be retrieved directly from the models. These include:

  • Evapotranspiration
  • Transpiration of water surface
  • Transpiration of wet bare soil
  • Climate water balance
  • Vapour pressure
  • Snow height

Evapotranspiration

In general, the evapotranspiration from a reference surface, the so-called reference crop evapotranspiration or reference evapotranspiration can be described by the FAO‑Penman-Monteith (Allen et all., 1998).

Evapotranspiration from a wet bare soil surface (ES0) and from a crop canopy (ET0) is calculated with the well-known Penman formula (Penman, 1948). In general, the evapotranspiration from a water surface can be described by the Penman formula. Only the albedo and surface roughness differs for these two types of evapotranspiration as explained below.

The net absorbed radiation depends on incoming global radiation, net outgoing long-wave radiation, the latent heat and the reflection coefficient of the considered surface (albedo). For ET0, ES0, and ET0 albedo values of 0.05, 0.15 and 0.20 are used respectively. The evaporative demand is determined by humidity, wind speed and surface roughness. For a free water surface and for the wet bare soil (E0, ES0) a surface roughness value of 0.5 is used. For a more detailed description of the underlying formulae we refer to Supit et al. (1994) and van der Goot (1997).

Climatic water balance

Climatic water balance is calculated based on evapotranspiration calculated through the equation of Penman-Monteith and the total precipitation of a day.


Snow height

The snow height (thickness of the snow layer) is derived from snow depth (water equivalent) and snow density.


Calculation of extreme weather events

For the static map production over Europe it is necessary to derive additional parameters out of the raw data set. This especially concerns probabilities and aggregated counts of number of days where a special condition is met. Some of the probabilities to be mapped are available directly from ECMWF. Other probabilities need to be derived from individual ensemble runs. In this case it is checked for how many of the ensemble members a certain condition applies (e.g. TempMin < 0°C). The probability of the event is the fraction of ensemble members forecasting it against the total number of ensemble members. The operational run and the ensemble control run are treated like any other ensemble member.

Aggregation to 10-daily and monthly data

After each 10-day period and at the end of each month aggregation for this 10-day/month period takes place. Additionally a forecast of the next dekad, basing on aggregated forecasts for the next 10 days (resp. 8/9/11 days for the last dekad) is delivered. The daily data are aggregated from days to dekads by taking the average of mean temperature, maximum temperature, minimum temperature, snow depth and the sum of precipitation, ET0 and global radiation.

Additionally for the map production the number of occurrences of certain events (such as frost, hot or rainy days) is counted.


Extraction of data into files

After processing data are exported as data files and static maps that can be distributed to users and other MCYFS processes.

A simple file naming scheme was adopted with the general format: <ROI>_<model_code>_<timestep>_<yyyy><mm><dd>_<member>.dat

In which:

  • ROI = region (GLD, EUR, ASI)
  • model_code = ECMWF model (ERA, OPE, EPS, MON or SEA)
  • timestep = temporal resolution of data: day, dekad, month
  • yyyy = the year (four digits),
  • mm = the month number (two digits),
  • dd = the day in the month (two digits)
  • member = the member number (two digits)

Note the OPE and EPS start with member number 0 while the MON and SEA start with member number 1. The date in the filename links to the forecast day = 0 (FORECAST_OFFSET = 0).


An example of a file name for each of the 4 models is:

  • EUR_OPE_day_20100715_00.dat OPE data for July 15, 2010 (only member 00 allowed)
  • EUR_EPS_day_20100704_35.dat EPS data for July 4, 2010, member 35
  • EUR_MON_day_20100702_32.dat MON data for Friday July 2, 2010, member 32
  • EUR_SEA_day_20100601_34.dat SEA data for June 1*, 2007, member 34

* Model runs the 8th but has a hindcast of 8 days


Note the OPE and EPS start with member number 0 while the MON and SEA start with member number 1. The date in the filename links to the forecast day = 0 (FORECAST_OFFSET = 0).


An input file basically contains the following structure:

  • A header providing geo referencing information
  • Blocks of data for the first forecast date (for each variable)
  • Blocks of data for the second forecast date (for each variable)
  • etc.

For simplification purposes, below a simple example is given with a detailed explanation.



The data files are loaded in the tables WEATHER_<MODEL>_GRID_RAW where <MODEL> is to be replaced by the abbreviation of one of the five ECMWF products (HIS, OPE, EPS, MON or SEA). In case of ERA data are stored in table ECMWF_ERA_DATA. During loading two actions are executed:

  • unit conversion
  • plausible range checks



In parallel daily, decadal and monthly aggregates of the analysis and deterministic forecast (HIS, OPE) is provided as csv to JRC and Vito.

Extraction of data into maps

The static maps are exported as flat images or animated images with full layout and directly made available to analysts that use them during analysis of weather indicators. The geographic extent of the static maps is defined by the upper-left corner at 75° North/25° West and the lower-right corner 20° North/70° East. This production line includes GrADS mapping software which is able to create maps directly from GRIB files. For the weekly and monthly maps the absolute difference to long-term average values are calculated.

Static map of maximum temperature on 7-feb-2011 with a spring breeze in Central and Western Europe.
Animated Rainfall, emitted 20-dec-2010 with the forecasted precipitation of low "Petra". Petra's snow masses retarded travel in many regions of Europe before Christmas 2010. Large animation