Contributors: S. Schimanke (SMHI), L. Isaksson (SMHI), L. Edvinsson (SMHI)
Issued by: S. Schimanke (SMHI)
Issued Date: 01/03/2022
Ref: C3S_322_Lot1.4.1.3_CERRA_data_user_guide – version 1
Official reference number service contract: 2017/C3S_322_Lot1_SMHI/SC2
1. History of modifications to this User Guide
2. Acronyms
3. Introduction
3.1. The service
The Copernicus European Regional ReAnalysis (CERRA) dataset has been produced under the framework of the Copernicus Climate Change Service, service contract C3S 322 Lot1. The dataset can be used in support of adaptation action and policy development as well as contribute to climate services, climate monitoring and research.
The service has been implemented in several steps. First, a reanalysis system developed in the EU-funded FP7 project called Uncertainties in Ensembles of Regional Reanalyses (UERRA; www.uerra.eu) was used to update the pre-existing UERRA-HARMONIE regional reanalysis for Europe dataset. The reanalysis system consists of two parts: a 3-dimensional atmospheric model version called UERRA-HARMONIE (11 km horizontal resolution and 65 vertical levels) and a 2-dimensional surface analysis, MESCAN-SURFEX (5.5 km horizontal resolution). Data are available for the period January 1961 to July 2019 and can be accessed through CDS (https://cds.climate.copernicus.eu). No further extension of these datasets is planned.
While extending in time the UERRA-HARMONIE dataset, an improved reanalysis systems have been set up within the service (see Figure 1). The new main reanalysis system is called CERRA, the 10-member ensemble of data assimilation system is called CERRA-EDA and the surface analysis is called CERRA-Land. This document aims to describe the data produced by the CERRA and CERRA-EDA systems, whereas CERRA-Land is described in a separate document. CERRA is fully operational and is used to produce a pan-European reanalysis dataset with very high horizontal resolution (5.5 km) forced by the ERA5 global reanalysis. CERRA-EDA has a lower horizontal resolution (11 km).
A comprehensive set of output parameters have been produced for soil, surface, height levels in the boundary layer for energy applications, conventional pressure levels and model levels. Also, a wide range of diagnostic fluxes, with hourly frequency, have been generated and made available through the CDS. Uncertainty of all output parameters can be estimated from the 10-member CERRA-EDA.
The production of CERRA data started at the end of 2019 by running spin-up simulations. At the beginning of 2020, real production started. Most data were produced until late summer 2021 followed by some corrections and updates afterwards.
Figure 1: The scheduled time line of production with the different reanalysis systems.
The CERRA data assimilation system is implemented and optimised for the European area with surrounding sea areas (see Figure 2) with a horizontal resolution of 5.5 km and 106 vertical levels.
Figure 2: Model domain highlighted with the topography of the CERRA system where ocean cells are masked in blue.
3.2. Principles of reanalysis systems
Atmospheric reanalysis is a method to reconstruct the past weather by combining historical observations (in situ, surface and satellite remote sensing), with a short-range forecasts from a numerical weather prediction model. It provides a physically and dynamically coherent description of the state of the atmosphere. The synthesis is accomplished by assimilating the observational data into a meteorological model and thereby forcing the model to reproduce the observations as closely as possible. The advantage of reanalysis is that they provide a multivariate, spatially complete, and coherent record of the atmospheric state – far more complete than any observational dataset is able to achieve.
The main advantages of reanalyses are (see Verver 2017):
- They provide regularly gridded data, even in places where there are no or few observations;
- They provide a coherent, complete set of variables describing the atmospheric state;
- They provide a reconstruction of the record of past weather since it is constrained by observations.
Weather forecasting is based on an analysis of the current state of the atmosphere and the surface of land and sea. The forecasts are made with mathematical and physical computer models starting from the analysis. The temperatures, winds, pressure, moisture, cloud contents and other variables are mapped at regular points in space and time (Fig. 3).
Figure 3: Schematic representation of a grid for model variables (surface pressure, temperature, u and v wind components and geopotential (Z), energy and specific moisture content (q)).
Reanalysis uses a weather forecasting model to create a 'first guess' (also called background) of the atmospheric state at a certain time. The first guess, a 3-h forecast in CERRA (6-h forecast for the CERRA-EDA), is then corrected on the basis of observations. This corrective step, referred to as 'data assimilation' (see Figure 4), requires statistical knowledge of the forecast errors and the observation errors which are prescribed for each type of observation and instrument. The observation errors include the measurement error and the representativity error. The procedure also uses physical and statistical relationships of the atmosphere when interpreting the observational data. The result of the data assimilation is called the analysis. By repeating this process for a number of time steps the analyses will contain a complete set of values describing the evolution of the atmosphere and the surface over time, also for locations where there are no observations.
This complete estimate of the atmospheric state over time can be of great value to users, for example in assessing impacts of past weather and climate related events, for statistics of the climate in a location or an area or for running other fine scale models or validating climate models.
An important difference between reanalyses and archived weather analyses from operational forecasting systems is that the entire reanalysis dataset is produced with the same, 'frozen', version of a data assimilation system – including the forecast model used – and is therefore not affected by changes in method.
Reanalysis systems differ in the set of observations that are assimilated, the model that is used, and the way the error statistics are estimated and corrections are applied. A variety of reanalysis methods exists as, for instance, the 4-dimensional variational analysis (4D-VAR), 3D-VAR (used for CERRA and schematically shown in Fig. 4), nudging, or optimal interpolation (OI).
Figure 4: Schematic showing the simulation of the atmospheric state (black line) in the reanalysis, which starts from the analysis (green dots) and resulting in the background (blue dots). Note that the background usually does not coincide with the true observed state of the atmosphere. The source of the figure is unknown.
3.3. The CERRA system components
In this section we will briefly mention the main features of the system. In general, CERRA consists of three components: (i) CERRA and CERRA-EDA, three-dimensional reanalysis systems, and (ii) CERRA-Land, a two-dimensional surface reanalysis system.
3.3.1. The CERRA system
The CERRA system is based on the HARMONIE-ALADIN data assimilation system which has been developed and used within the ACCORD consortium. It is implemented and optimized for the entire European area with surrounding sea areas (see Fig. 2) with a horizontal resolution of 5.5 km and 106 vertical levels. The system uses lateral boundaries conditions obtained from the ERA5 global reanalysis (Fig. 5). Also, the large scales in the regional system are constrained by data from the global reanalysis. The increase of resolution from the global reanalysis (RA) as well as the precursor regional reanalysis (RRA) is depicted in Figure 6.
Figure 5: Three different stages of RA: the global reanalyses from ERA5 are used as lateral boundary conditions for CERRA and CERRA-EDA reanalyses. Subsequently, short-forecast data from CERRA are used as background fields for the CERRA-Land surface reanalysis. As indicated by the vertical arrows, the amount of assimilated observations per area unit increases, in principle, from the global to the regional reanalysis as indicated by the arrows.
The CERRA system employs the 3D variational analysis (3D-VAR) method depicted schematically in Figure 4. At fixed points in time the model state is adjusted based on the observed state, taking into account the error statistics of both model and observations. The CERRA high-resolution system has been running with eight assimilation cycles per day performing analyses at 00, 03, 06, 09, 12, 15, 18 and 21 UTC. The forecasts lengths vary between 6 and 30 hours (see section 4.1.3 for more information) depending on the starting hour.
A flow-dependent background error covariance (B) matrix is estimated using a 10-member ensemble of data assimilation (EDA) system, which is briefly described in the next section.
Figure 6: Topography over parts of Norway, Sweden and Denmark in the reanalysis systems ERA5 (left), UERRA-HARMONIE (middle) and CERRA (right). Cells are marked as ocean cells where the land-sea-mask has values smaller than 0.3. Note that for UERRA-HARMONIE lakes are not presented in the land-sea-mask but they exist in the system.
3.3.2. The CERRA-EDA system
The main purpose of the CERRA-EDA system is to create an 'online', continuously serially updated flow-dependent background error covariance matrix for use in the CERRA high-resolution system. CERRA-EDA is set up for the same geographical region as CERRA high-resolution, has the same number of vertical levels, but a horizontal resolution of 11 km. The EDA system utilises lateral boundary conditions from ERA5 and the same type of observations, including satellite observations as CERRA high-resolution. CERRA-EDA is a ten member EDA system, where nine members differ through the perturbation of observations. But, one of the members is running with unperturbed observations forming the so called control member.
The CERRA-EDA system performs four analyses per day, at 00 UTC, 06 UTC, 12 UTC, and 18 UTC. Each analysis is used to start a six hour forecast.
More information on the CERRA-EDA system and especially regarding the construction of the background error covariance matrix can be found in El-Said et al. (2021).
3.3.3. The CERRA-Land system
The CERRA-Land analysis system uses the 2D-analysis system MESCAN and the land surface platform SURFEX to generate a coherent surface and soil analysis. The system combines CERRA forecast fields and additional surface observation (e.g. precipitation), to generate high-resolution (5.5 km) 2-dimensional analyses over Europe. MESCAN is a surface analysis system which uses an optimal interpolation algorithm for the analysis of 2m temperature and relative humidity and 24-h total accumulated precipitation (Soci et al., 2016). SURFEX is a land surface platform, which is driven by temperature, humidity, precipitation, wind and radiative fluxes (Bazile et al., 2017).
4. General guidelines for the usage of CERRA data
This section aims to summarise important features of the models that the user needs to be aware of when using the CERRA datasets. Although CERRA provides consistent and coherent dataset, there are weaknesses and limitations. Some of these are common for reanalyses in general, other are model/version dependent. The user should assess whether the data are fit for their specific purpose.
For CERRA, the user might choose among more than 50 parameters at different heights and time steps. CERRA-Land offers additional parameters for the surface and the soil. Information about all available parameters is given in Section 5.
4.1. Resolution in time and space
4.1.1. Horizontal resolution
As depicted in Figure 3, all parameters are computed in grid points. Having a horizontal grid spacing of 5.5 km, as for the CERRA system, implies that each grid point characterizes values for an area of roughly 30 km2 (5.5km*5.5km). This needs to be considered when for instance CERRA data are compared with observations. The CERRA-EDA system has a horizontal resolution of 11km, meaning that each grid point characterizes values for an area of 121 km2 (11km*11km).
4.1.2. Vertical resolution
As already mentioned in Section 3.3.1, the CERRA system has 106 levels in the vertical (also called model levels) from the surface up to 1 hPa. However, only a very restricted number of parameters is stored on model levels. The main reason for that is the amount of needed storage space, when all parameters would be stored for all levels. Moreover, the vertical model grid is on hybrid-sigma coordinates, which do not match with any standard pressure level. Hence, this makes it quiet complex to use. Information about the model levels is here.
Therefore, the major part of the data is post-processed and stored on 29 selected pressure levels between 1000 - 1 hPa with a higher number of levels at lower altitudes. In addition, some parameters are also stored on 11 height levels between 15-500m. One reason to provide atmospheric variables on height levels is for applications in the wind energy sector. The exact levels both for pressure and height are given in Section 5 in the corresponding tables.
The CERRA system also contains a soil model which has 3 layers in the vertical. The three layers represent approximately the surface, the soil at root depth and the deep soil. Due to the used force-restore scheme in the soil model it is not possible to relate the layers with a certain depth in metre. Users interested in soil parameters should consider to use the data from the CERRA-Land system.
The CERRA-Land soil model has 14 layers in the vertical, which range from the surface to a depth of 12m. The edges between different levels are at 0.01m, 0.04m, 0.1m, 0.2m, 0.4m, 0.6m, 0.8m, 1.0m, 1.5m, 2m, 3m, 5m, 8m and 12m. Values for a certain level reflect the mean value over the layer thickness.
4.1.3. Time resolution
In general, data are stored with hourly resolution for the CERRA dataset. However, for all time steps, users have different options to select from and this is not always an easy choice. The preferred selection might vary for different parameters and the application of the user, respectively.
Figure 7 gives an overview on available analysis and forecast times. First, there are the eight analyses at 00 UTC, 03 UTC, 06 UTC, 09 UTC, 12 UTC, 15 UTC, 18 UTC and 21 UTC highlighted in red. Analysis data at these hours are assumed to be of higher quality than the forecasts valid at the same hours as they are in general closer to observations. They are available only every third hour and not all parameters are available for the analyses (the available analysis parameters are listed in Section 5). The forecast model is then started from the analysis and the output is saved hourly for the first six hours as indicated in dark blue in Fig. 7. Whereas the forecasts initiated at 00 UTC and 12 UTC continue until the forecast hour 30, all forecasts initiated at other hours (e.g. 15 UTC) stop after 6 hours. However, note that the output frequency of the forecasts initiated at 00 and 12 UTC is three hourly after the first six forecast hours (see blue boxes in Fig. 7) are completed. Due to the forecast lengths, the forecasts are overlapping and for every hour of the day data might be chosen from forecasts initiated at different hours and eventually from the analysis. For instance, at 12 UTC, the users can choose between the analysis and four different forecasts.
Figure 7: This table illustrates how different forecasts overlap and which options users have at a certain hour of the day. The availability of data is illustrated for the example date 2009/12/10. The colour coding reflects analysis (red) and forecasts (blue). Moreover, different shades of blue correspond to frequency of the saved forecasts – hourly in dark blue and 3 hourly forecasts in blue.
Figure 7 illustrates the 10th of December as an example date to show the overlap of different forecast lengths. It can be noticed that at 12 UTC, for example, data are available from the analysis as well as the forecasts initiated at 09 UTC (three hour forecast), 06 UTC (six hour forecast), 00 UTC (twelve hour forecast), and 12 UTC of the previous day (24 hour forecast). At least two forecasts are available for every hour of the day.
Different forecasts lengths have different strengths and weaknesses. After the assimilation of observations (the analysis), the model state is not completely balanced dynamically, meaning that the model atmosphere might contain high-frequency waves. That might affect the quality of the forecasts closest to the analysis time if they are not rapidly dumped. On the other hand, longer forecasts might veer away from the real weather, e.g. due to shortcomings in the model parametrizations, the initial conditions and inaccurate boundary conditions. In general, it is not possible to give a general recommendation for which time steps parameters should be used and the users have to check on their own, which selection gives the best result in their application.
4.2. General limitations of reanalyses
Generally it is challenging for a reanalysis system to correctly reconstruct variables that are very variable in space and time, such as precipitation. For some applications, e.g. in hydrology, it is therefore quite common to correct the precipitation data for biases. Other variables, like surface temperature, are generally less variable in space and time and easier to reconstruct by the reanalysis system.
Similar to above, results in complex terrain, such as mountainous regions or coastal areas, are generally less reliable than results over a more homogeneous terrain. The models can hardly represent the strong local gradients (e.g. temperature gradients) that are usually driven by the complex terrain.
Figure 8 illustrates this behaviour. Here, we show locations in Sweden having the best (blue) and the worst (red) correlations between the UERRA 2m temperature and observational sites. A total of 853 measurement sites have been investigated. The Figure shows 50 locations each of highest and lowest correlation. Clearly, correlations are the lowest in the Swedish mountains and along the (east) coast.
Users need to be aware that the reanalysis provides gridded data where each grid point value describes an entire grid box area. That is in contrast to observations, which are usually point measurements. In case users need information with a higher horizontal resolution than provided by the CERRA systems, further downscaling (statistically or dynamically) needs to be considered. For instance, the correlations indicated in Fig. 8 increased when a linear interpolation from the four closest grid points to the observational site was applied than purely taken the values from the closest grid point.
Partially due to this it is more difficult for a reanalysis system to correctly capture absolute values of extremes than values closer to the mean. This is especially the case for precipitation extremes, where the reanalysis data are highly resolution dependent. This means, for example, that the number of days with precipitation over a certain absolute threshold value is likely to be less accurate than using a relative threshold such as a 95-percentile value. Also extremes on larger scales, like droughts and heatwaves, are better represented than extremes on smaller scales.
As mentioned earlier, the entire reanalysis dataset is produced with the same version of data assimilation system and forecast model and is therefore not affected by changes in method. But it is worth noting that, for example, the number and the quality of available observations change over time.
Figure 8: A validation of the UERRA 2m temperature data with Swedish observations. 50 places each with highest (blue) and lowest (red) correlation are shown out of 853 measurement sites included in the investigation.
4.3. FAQ
- What do you mean with near real-time?
The production of the CERRA data is delayed by 2-3 months with respect to real time. The delay is directly dependent on the availability of some reprocessed datasets (e.g. GNSS-RO). Note: at the time when the dataset was released, a contract for the near-real time update of CERRA dataset had not been concluded, and therefore the production was suspended; it will be resumed once the contract is signed. One should be aware that it takes several months to produce data that will bridge the gap between June 2021 (last available month) and the near-real time, and that only after filling the gap, the dataset will be updated with a delay of 2-3 months behind the real time. For instance, the release of data for January 2024 can be expected in April 2024.
- Can we use reanalysis data for local applications?
Reanalysis data are gridded products. The values represent a certain spatial scale which may be hard to compare to a point value that may be obtained from a station. The horizontal grid resolution of the reanalysis is 5.5 km and hence a grid box has an area of 30.25km2. Given values are usually a mean for the entire grid area whereas station data represent only a single point. Hence, the spatial scales of the data are not the same.
- Precipitation and snow are represented in the model output, but not rain?
The rain can be calculated as the amount of precipitation that is not snow, that is the total precipitation minus the precipitation as snow.
- Is the data free and how can it be accessed?
The CERRA reanalysis datasets are provided under Copernicus licence and are available from the CDS at https://cds.climate.copernicus.eu/.
- What is the projection used? Is there an EPSG code?
The used grids are in the Lambert Conformal Conic projection with parameters according to section 5.1.6 for CERRA. There are no defined EPSG codes for these specific projections, but there is a general definition of Lambert Conformal Conic with 2 standard parallels in EPSG:9802.
The coordinate systems are often problematic when handling model output, but a great improvement is that nowadays many available programs are able to handle GRIB files and especially the projection information that is embedded in GRIB files. For example, later versions of many GIS tools (e.g QGIS, ArcGIS) can open and georeference the grib files out of the box. Just drag and drop a GRIB file into the GIS tool and it should open up, see Fig. 9. This can be a good starting point for getting to know the grid and handle the projection.
Figure 9: A CERRA-Land GRIB file opened in QGIS. To the left in the Lambert Conformal Conic projection, to the right transformed to WGS 84.
5. Detailed data description and availability
5.1. CERRA
5.1.1. Parameters on single level
Metadata for CERRA surface parameters | |
Horizontal coverage | The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Herewith, it covers entire Europe. |
Horizontal resolution | 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members |
Vertical coverage | Each surface parameter is valid for one level in the vertical. There are four different (near) surface levels:
The cloud cover is provided for 3 atmospheric layers. |
Vertical resolution | single level |
Temporal coverage | 1984-09-01 00 UTC – 2021-06-30 21 UTC |
Temporal resolution | CERRA high-resolution reanalysis:
CERRA ensemble members:
|
Data type and format | Gridded data in GRIB2 |
Grid | Lambert conformal conic grid; 1069x1069 grid points for CERRA high-resolution reanalysis; 565x565 grid points for CERRA-EDA |
Table 1: Overview of the surface parameters
Name | Unit | GRIB code | Analysis 3 hourly | Forecast | Height | |
1. | 10m wind speed | m/s | 207 | yes | yes | 10m |
2. | 10m wind gust since previous post-processing | m/s | 49 | - | yes | 10m |
3. | 10m wind direction | degree of true North | 260260 | yes | yes | 10m |
4. | 2m relative humidity | % | 260242 | yes | yes | 2m |
5. | 2m temperature | K | 167 | yes | yes | 2m |
6. | Albedo | % | 260509 | yes | yes | surface |
7. | Evaporation | kg/m2 | 260259 | - | yes | surface |
8. | Total column integrated water vapour | kg/m2 | 260057 | yes | yes | vertically integrated above the surface |
9. | Total precipitation | kg/m2 | 228228 | - | yes | surface |
10. | Maximum 2m temperature since previous post-processing | K | 201 | - | yes | 2m |
11. | Minimum 2m temperature since previous post-processing | K | 202 | - | yes | 2m |
12. | Skin temperature | K | 235 | yes | yes | surface |
13. | Surface latent heat flux | J/m2 | 147 | - | yes | surface |
14. | Surface sensible heat flux | J/m2 | 146 | - | yes | surface |
15. | Time-integrated surface direct short-wave radiation | J/m2 | 260264 | - | yes | surface |
16. | Surface net solar radiation | J/m2 | 176 | - | yes | surface |
17. | Surface solar radiation downwards | J/m2 | 169 | - | yes | surface |
18. | Surface net thermal radiation | J/m2 | 177 | - | yes | surface |
19. | Surface thermal radiation downwards | J/m2 | 175 | - | yes | surface |
20. | Surface net solar radiation, clear sky | J/m2 | 210 | - | yes | surface |
21. | Surface net thermal radiation, clear sky | J/m2 | 211 | - | yes | surface |
22. | Momentum flux at the surface u-component | N/m2 | 235017 | - | yes | surface |
23. | Momentum flux at the surface v-component | N/m2 | 235018 | - | yes | surface |
24. | Mean sea level pressure | Pa | 151 | yes | yes | surface |
25. | Surface pressure | Pa | 134 | yes | yes | surface |
26. | High cloud cover | % | 3075 | yes | yes | above 5000m |
27. | Low cloud cover | % | 3073 | yes | yes | surface-2500m |
28. | Medium cloud cover | % | 3074 | yes | yes | 2500m-5000m |
29. | Total cloud cover | % | 228164 | yes | yes | above ground |
30. | Snow density | kg/m3 | 33 | yes | yes | surface |
31. | Snow depth | m | 3066 | yes | yes | surface |
32. | Snow depth water equivalent | kg/m2 | 228141 | yes | yes | surface |
33. | Snowfall water equivalent | kg/m2 | 228144 | - | yes | surface |
34. | Land-sea mask | dimensionless | 172 | yes | - | surface |
35. | Orography | m | 228002 | yes | - | surface |
36. | Surface roughness | m | 173 | yes | yes | surface |
| 37. | Soil temperature | K | 260360 | yes | yes | top layer of soil |
| 38. | Liquid Volumetric soil moisture (non-frozen) | m3/m3 | 260199 | yes | yes | top layer of soil |
| 39. | Volumetric soil moisture | m3/m3 | 260210 | yes | yes | top layer of soil |
5.1.1.1. 10m wind speed
The 10-metre (10m) wind speed is the wind speed valid for the grid area determined for a height of 10m above the surface. The parameter is given in m/s. It is computed from both the zonal (u) and the meridional (v) wind components by wind speed=u2+v2. The 10m wind speed is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.2. 10m wind gust since previous post-processing
The 10-metre (10m) wind gust is a diagnostic variable. The parametrization is based on wind speed and turbulence. A 3s wind gust is computed every time step and the maximum is kept since the last post-processing at the grid area. It is determined for a height of 10m above the surface. The parameter is given in m/s. The 10m wind gust is only available for the forecast time steps. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum wind speed, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum wind speed which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the maximum over a longer time period is saved. For instance, for the 15h forecast the maximum wind speed is identified within the period 12h – 15h since the last post-processing happened at 12h (12 hours after the onset of the forecast).
5.1.1.3. 10m wind direction
The 10-metre wind direction is the wind direction valid for the grid area determined for a height of 10m above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. The 10m wind direction is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.4. 2m relative humidity
The 2-metre (2m) relative humidity is the modelled air humidity valid for the grid area (approximately 5.5km*5.5km = 30.25km2) determined for a height of 2m above the surface. It expresses the relation between actual humidity and saturation humidity. The parameter is given in %. Values are in the interval [0,100]. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. 2m relative humidity is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.5. 2m temperature
The 2-metre temperature with the parameter name surface air temperature is the model temperature valid for the grid area determined for a height of 2m above the surface. Surface air temperature is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The parameter is given in Kelvin [K].
5.1.1.6. Albedo
The albedo is the amount of radiation which is reflected for the given grid area. It is determined for the surface to the atmosphere, both for ground and water surfaces. The parameter is given in %. Small values mean that large amounts of the radiation are absorbed whereas large values mean that more radiation is reflected. Albedo is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.7. Evaporation
Evaporation is the amount of moisture flux from the surface (ground and water) into the atmosphere. It is given as a mean for the grid area. The mean is a weighted average over all tile types present in the grid point. By model convention downward fluxes are positive. Hence, evaporation is represented by negative values and positive values represent condensation. Evaporation is only available for forecast time steps. It is an accumulated parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated evaporation over 24 hours. The parameter is given in kg/m2.
5.1.1.8. Total column integrated water vapour
The total column integrated water vapour is the vertically integrated water vapour (precipitable water content) valid for the grid area. It is vertically integrated from the surface to the top of the atmosphere. Total column water vapour is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The parameter is given in kg/m2.
5.1.1.9. Total precipitation
Total precipitation is the amount of precipitation falling onto the ground/water surface. It includes a few types of precipitation forms such as convective precipitation, large scale precipitation, liquid and solid precipitation. The amount is valid for the grid area and has the unit kg/m2. The total precipitation is available only for the forecast time steps. It is an accumulated parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated precipitation over 24 hours. The total amount of rain might be computed by subtracting the snowfall water equivalent from the total precipitation.
5.1.1.10. Maximum 2m temperature since previous post-processing
It is the maximum air temperature at the height of 2 m above the surface since the last post-processing. The maximum 2m temperature is only available for the forecast time steps. The value is the maximum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the maximum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the maximum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the maximum over a longer time period is saved. For instance, for the 15h forecast the maximum surface air temperature is identified within the period 12h – 15h since the last post-processing happened at 12h (12 hours after the onset of the forecast). The parameter is given in Kelvin [K].
5.1.1.11. Minimum 2m temperature since previous post-processing
It is the minimum air temperature at the height of 2 m above the surface since the last post-processing. The minimum 2m temperature is only available for the forecast time steps. The value is the minimum since the previous post-processing. For instance, for the first saved time step at forecast 1h it is the minimum surface air temperature, which occurred within the first hour of the forecast. For the second saved time step at forecast 2h, it is the minimum surface air temperature which happened in the second forecast hour, hence between fc1 and fc2. For longer forecasts, the output frequency is reduced. Hence, the minimum over a longer time period is saved. For instance, for the 15h forecast the minimum surface air temperature is identified within the period 12h – 15h since the last post-processing happened at 12-h (12 hours after the onset of the forecast). The parameter is given in Kelvin [K].
5.1.1.12. Skin temperature
The skin temperature is the model temperature valid for the grid area determined for the boundary surface to the atmosphere, both ground and water surfaces. Skin temperature is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The parameter is given in Kelvin [K].
5.1.1.13. Surface latent heat flux
The surface latent heat flux is the exchange of latent heat (due to phase transitions: evaporation, condensation) with the surface (ground and water) through turbulent diffusion. It is the mean for the grid area. By model convention downward fluxes are positive. Surface latent heat flux is only available for forecast time steps up to forecast hour six. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated heat flux over 24 hours. The parameter is given in J/m2.
5.1.1.14. Surface sensible heat flux
The surface sensible heat flux is the exchange of heat (no phase transition) with the surface (ground and water) through turbulent diffusion. It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface sensible heat flux is only available for forecast time steps up to forecast hour six. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated heat flux over 24 hours. The parameter is given in J/m2.
5.1.1.15. Time-integrated surface direct short-wave radiation
The surface direct short-wave radiation is the amount of direct solar radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface direct solar radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2.
5.1.1.16. Surface net solar radiation
The surface net solar radiation is the amount of solar (short-wave) radiation that is absorbed at the surface (ground and water). It is computed as
Surface net solar radiation = surface solar radiation downwards * (1 – albedo)
It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface net solar radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2.
5.1.1.17. Surface solar radiation downwards
The surface solar radiation downward is the amount of solar (short-wave) radiation reaching the surface (ground and water). It is given as a mean for the grid area. By model convention downward fluxes are positive. Surface solar radiation downwards is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2.
5.1.1.18. Surface net thermal radiation
The surface net thermal radiation is the difference between thermal (long-wave) downward and upward radiation at the surface (ground and water) of the Earth. Thermal radiation is emitted by the atmosphere, clouds and the surface of the Earth. It is the mean for the grid area. By model convention downward fluxes are positive. Surface net thermal radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2.
5.1.1.19. Surface thermal radiation downwards
The surface thermal radiation downward is the amount of thermal (long-wave) radiation reaching the surface (ground and water). It is given as a mean for the grid. By model convention downward fluxes are positive. Surface thermal radiation downwards is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2.
5.1.1.20. Surface net solar radiation, clear sky
This parameter is the amount of solar radiation from the Sun (also known as short-wave or direct radiation), which would be absorbed at the surface of the Earth assuming clear-sky (cloudless) conditions. It is computed as
Clear-sky net solar radiation = Clear-sky solar radiation downwards * (1 – albedo)
It is the mean for the grid area. By model convention downward fluxes are positive.
Clear-sky net solar radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2. To convert to watts per square metre (W/m2), the accumulated values should be divided by the accumulation period expressed in seconds.
5.1.1.21. Surface net thermal radiation, clear sky
The thermal radiation (also known as long-wave or terrestrial radiation) refers to radiation emitted by the atmosphere, clouds and the surface of the Earth. The surface net thermal radiation is the difference between downward and upward thermal radiation at the surface of the Earth, assuming clear-sky (cloudless) conditions. It is the mean for the grid area. By model convention downward fluxes are positive. Surface net thermal radiation is only available for forecast time steps. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated radiation over 24 hours. The parameter is given in J/m2. To convert to watts per square metre (W/m2), the accumulated values should be divided by the accumulation period expressed in seconds.
5.1.1.22. Momentum flux at the surface u-component
Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in an eastward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the eastward (westward) direction. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated momentum fluxes over 24 hours.
It is the mean for the grid. The parameter is given in N m-2 s.
5.1.1.23. Momentum flux at the surface v-component
Air flowing over a surface exerts a stress that transfers momentum to the surface and slows the wind. Here, the parameter is the sum of all surface stress components, in a northward direction. Momentum flux components are associated to orographic gravity waves, the turbulent interactions between the atmosphere and the surface, and to turbulent orographic form drag. For instance, the turbulent interactions between the atmosphere and the surface are due to the roughness of the surface. Positive (negative) values denote stress in the northward (southward) direction. It is an accumulated (time-integrated) parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated momentum fluxes over 24 hours. It is the mean for the grid area. The parameter is given in N m-2 s.
5.1.1.24. Mean sea level pressure
The mean sea level pressure is the air pressure reduced to mean sea level valid for the grid area. The parameter is given in Pascal [Pa]. Mean sea level pressure is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.25. Surface pressure
The surface pressure is the air pressure at the surface (ground and water) valid for the grid area. The parameter is given in Pascal [Pa]. Mean sea level pressure is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.26. High cloud cover
The high cloud cover is the percentage of sky covert with clouds in high altitude. It is valid for the grid area and high refers to height above 5000m. The parameter is given in %. High cloud cover is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.27. Low cloud cover
The low cloud cover is the percentage of sky covert with clouds in low altitude. It is valid for the grid area and low altitude refers to heights below 2500m. The parameter is given in %. Low cloud cover is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.28. Medium cloud cover
The medium cloud cover is the percentage of sky covert with clouds in medium altitude. It is valid for the grid area, and medium altitude refers to heights between 2500m through 5000m. The parameter is given in %. Medium cloud cover is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.29. Total cloud cover
Total cloud cover is the percentage of sky covert with clouds. It is valid for the grid area, and clouds at any height above the surface are considered. The parameter is given in %. Total cloud cover is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.30. Snow density
Snow density is the snow mass per unit of volume. Hence, the parameter is given in kg/m3. It is given as the mean for the grid area. Grid points without snow have missing values. Snow density is available for the analysis and the forecast time steps up to forecast hour six. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.31. Snow depth
Snow depth is the average snow height for the grid area. Snow depth is given in metre [m]. Snow depth is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.32. Snow depth water equivalent
Snow depth water equivalent expresses the snow depth in kg of snow over one square metre [kg/m2]. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid area. Snow depth water equivalent is available for the analysis and the forecast time steps. For the forecast, the value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.1.33. Snowfall water equivalent
Snowfall water equivalent expresses the snowfall in kg of snow over one square metre [kg/m2]. The unit corresponds to 1 mm of water equivalent. It is given as the mean for the grid. Snowfall water equivalent is only available for the forecast time steps. It is an accumulated parameter meaning that it is accumulated from the beginning of the forecast. For instance, the 24-h forecast has the accumulated snowfall water equivalent over 24 hours.
5.1.1.34. Land-Sea mask
The land-sea mask is a field that contains, for every grid, the proportion of land in the grid box. The parameter is dimensionless and the values are between 0 (sea) and 1 (land). The land-sea mask is constant in time and the field is available for every analysis.
5.1.1.35. Orography
The orography is the height of the terrain with respect to the model defined globe. Each grid point has one value representing the mean over the grid point domain. The orography is given as geopotential height in metre [m]. The orography is constant in time and the field is available for every analysis.
5.1.1.36. Surface roughness
The surface roughness describes the aerodynamic roughness length (over land). Each grid point has one value representing the mean over the grid point. The surface roughness is given in metre [m]. The effective surface roughness is depending on the orographic component (constant part), the snow depth, the evolution of the Leaf Area Index and the fraction of vegetation, which is different for each month. Surface roughness is available for the analysis and the forecast time steps.
5.1.1.37. Soil temperature
The soil temperature is the model temperature valid for the grid area at the corresponding soil layer. The parameter is given in Kelvin [K]. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data.
5.1.1.38. Liquid Volumetric soil moisture (non-frozen)
The liquid volumetric soil water is the amount of non-frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is given in m3/m3. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data.
5.1.1.39. Volumetric soil moisture
The volumetric soil moisture is the sum of the liquid and frozen water in a cubic metre soil valid for the grid area in the corresponding soil layer. The parameter is given in m3/m3. The parameter is available for analysis and forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The soil model has three layers but only data for the top layer, closest to the surface, are provided. Deeper layers are affected by spin-up effects at the seams of the production streams. Users interested in soil parameters are recommended to use CERRA-Land data.
5.1.2. Parameters on height levels
Metadata | |
Horizontal coverage | The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Herewith, it covers entire Europe. |
Horizontal resolution | 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members |
Vertical coverage | 11 height levels (from 15m up to 500m) |
Vertical levels | 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500m |
Temporal coverage | 1984-09-01 00 UTC – 2021-06-30 21 UTC |
Temporal resolution | CERRA high-resolution reanalysis:
CERRA ensemble members:
|
Data type and format | Gridded data in GRIB2 |
Grid | Lambert conformal conic grid; 1069x1069 grid points for CERRA high-resolution reanalysis; 565x565 grid points for CERRA-EDA |
Table 2: Overview of the parameters on height levels
Parameter | Unit | GRIB code | Analysis 3 hourly | forecast | |
1. | Wind speed | m/s | 10 | yes | yes |
2. | Wind direction | degree of true North | 3031 | yes | yes |
3. | Pressure | Pa | 54 | yes | yes |
4. | Relative humidity | % | 157 | yes | yes |
5. | Temperature | K | 130 | yes | yes |
6. | Specific cloud liquid water content | kg/kg | 246 | - | yes |
7. | Specific cloud ice water content | kg/kg | 247 | - | yes |
8. | Specific rain water content | kg/kg | 75 | - | yes |
9. | Specific snow water content | kg/kg | 76 | - | yes |
10. | Turbulent kinetic energy | J/kg | 260155 | - | yes |
5.1.2.1. Wind speed
Wind speed is the wind speed valid for the grid area determined for a certain height (15m-500m) above the surface. The parameter is given in m/s. It is computed from both the zonal (u) and the meridional (v) wind components by wind speed=u2+v2. The wind speed is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.2. Wind direction
The wind direction is the wind direction valid for the grid area determined for a certain height (15m-500m) above the surface. The parameter is given in degrees ranging from 0-360. Here, 0° means a northerly wind and 90° indicates an easterly wind. The wind direction is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.3. Pressure
The pressure is the air pressure at a certain height (15m-500m) above the surface valid for the grid area. The parameter is given in Pascal \[Pa\]. The pressure is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.4. Relative humidity
The relative humidity is the modelled humidity valid for the grid area determined at a certain height (15m-500m) above the surface. The parameter is given % ranging from 0-100. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Surface air relative humidity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.5. Temperature
The temperature is the air temperature valid for the grid area determined at a certain height (15m-500m) above the surface. The parameter is given in Kelvin [K]. The temperature is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.6. Specific cloud liquid water content
Specific cloud liquid water content is the grid-box mean liquid water content (mass of condensate / mass of moist air) on a height level. It is given in kg/kg. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.7. Specific cloud ice water content
Specific cloud ice water content is the grid-box mean ice water content (mass of condensate / mass of moist air) on a height level. It is given in kg/kg. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.8. Specific rain water content
The mass of water that is of raindrop size and so can fall to the surface as precipitation. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.9. Specific snow water content
The mass of snow (aggregated ice crystals) that can fall to the surface as precipitation. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.2.10. Turbulent kinetic energy
The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a certain height (15m-500m) above the surface and is valid for the grid area. It is given in J/kg. The turbulent kinetic energy is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3. Parameters on pressure levels
Metadata | |
Horizontal coverage | The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Herewith, it covers entire Europe. |
Horizontal resolution | 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members |
Vertical coverage | From 1000 hPa to 1 hPa |
| Vertical levels | 29 pressure levels (1000, 975, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, 10, 7, 5, 3, 2, 1) |
Temporal coverage | 1984-09-01 00 UTC – 2021-06-30 21 UTC |
Temporal resolution | CERRA high-resolution reanalysis:
CERRA ensemble members:
|
Data type and format | Gridded data in GRIB2 |
Grid | Lambert conformal conic grid; 1069x1069 grid points for CERRA high-resolution reanalysis; 565x565 grid points for CERRA-EDA |
Table 3: Overview of the parameters on pressure levels
Parameter | Unit | GRIB code | Analysis 3 hourly | forecast | |
1. | Cloud cover | % | 260257 | - | yes |
2. | Specific cloud liquid water content | kg/kg | 246 | - | yes |
3. | Specific cloud ice water content | kg/kg | 247 | - | yes |
4. | Specific rain water content | kg/kg | 75 | - | yes |
5. | Specific snow water content | kg/kg | 76 | - | yes |
6. | Turbulent kinetic energy | J/kg | 260155 | - | yes |
7. | Relative humidity | % | 157 | yes | yes |
8. | Temperature | K | 130 | yes | yes |
9. | U-component of wind | m/s | 131 | yes | yes |
10. | V-component of wind | m/s | 132 | yes | yes |
11. | Geopotential | m2/s2 | 129 | yes | yes |
5.1.3.1. Cloud cover
Cloud cover is the percentage of sky covert with clouds. It is valid for the grid at the corresponding height. The parameter is given in %. Total cloud cover is only available for the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3.2. Specific cloud liquid water content
The specific cloud liquid water content is the grid-box mean mass of condensate / mass of moist air on a pressure level. The parameter is given in kg/kg. Specific cloud liquid water content is only available for the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3.3. Specific cloud ice water content
The specific cloud ice water content is the grid-box mean mass of condensate / mass of moist air on a pressure level. The parameter is given in kg/kg. Specific cloud ice water content is only available for the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3.4. Specific rain water content
The mass of water that is of raindrop size and so can fall to the surface as precipitation. The quantity is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3.5. Specific snow water content
The mass of snow (aggregated ice crystals) that can fall to the surface as precipitation. The mass is expressed in kilograms per kilogram of the total mass of moist air. The 'total mass of moist air' is the sum of the dry air, water vapour, cloud liquid, cloud ice, rain and falling snow. This parameter represents the average value for a grid box. Clouds contain a continuum of different sized water droplets and ice particles. The parameter is only available for forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3.6. Turbulent kinetic energy
The turbulent kinetic energy is the mean kinetic energy per unit mass associated with eddies in turbulent flow. This parameter describes the turbulent kinetic energy at a pressure level and it is valid for the grid area. It is is given in J/kg.The turbulent kinetic energy is only available the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.3.7. Relative humidity
The relative humidity is the modelled humidity valid for the grid area at the corresponding height. The parameter is given in % ranging from 0-100. 0% means that the air is totally dry whereas 100% indicates that the air is saturated with water vapour. The saturation is defined with respect to saturation of the mixed phase, i.e. with respect to saturation over ice below -23°C and with respect to saturation over water above 0°C. In the regime in between a quadratic interpolation is applied. Relative humidity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. Please check section 2.3, Model specific issues, when using this parameter.
5.1.3.8. Temperature
The temperature is the model temperature valid for the grid area at the corresponding height. Temperature is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The parameter is given in Kelvin [K].
5.1.3.9. U-component of wind
The U-component of wind or U-velocity is the zonal component of the wind valid for the grid area at the corresponding height. By model convention westerly wind (blowing from the west to the east) are positive. U-velocity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The parameter is given in m/s.
5.1.3.10. V-component of wind
The V-component of wind or V-velocity is the meridional component of the wind valid for the grid area at the corresponding height. By model convention southerly wind (blowing from the south to the north) are positive. V-velocity is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step. The parameter is given in m/s.
5.1.3.11. Geopotential
The geopotential is the potential energy of unit mass at this pressure level relative to the sea level. It is valid for the grid area and it is given in m2/s2. The geopotential is available for the analysis and the forecast time steps. The value is instantaneous meaning that it is valid for the last time step of the integration at the issued time step.
5.1.4. Parameters on model levels
Table 4: Overview of parameters on model levels
Metadata | |
Horizontal coverage | The model domain spans from northern Africa beyond the northern tip of Scandinavia. In the west it ranges far into the Atlantic Ocean and in the east it reaches to the Ural Mountains. Herewith, it covers entire Europe. |
Horizontal resolution | 5.5 km x 5.5 km for CERRA high-resolution reanalysis 11 km x 11 km for CERRA ensemble members |
Vertical coverage | From approximately 10m (model level 106) above the surface to a height of 1 hPa (model level 1) |
Vertical levels | 106 hybrid atmospheric model levels (106, 105, 104 ... 3, 2, 1) |
Temporal coverage | 1984-09-01 00 UTC – 2021-06-30 21 UTC |
Temporal resolution | CERRA high-resolution reanalysis: 3-hourly analyses at 00, 03, 06, 09, 12, 15, 18 and 21 UTC CERRA ensemble members: 6-hourly analyses at 00, 06, 12 and 18 UTC Note: forecast data are not saved for the parameters on model levels |
Data type and format | Gridded data in GRIB2 |
Grid | Lambert conformal conic grid; 1069x1069 grid points for CERRA high-resolution reanalysis; 565x565 grid points for CERRA-EDA |
Table 4: Overview of parameters on model levels
Parameter | Unit | GRIB code | Analysis 3 hourly | forecast | |
1. | Specific humidity | kg/kg | 133 | yes | - |
2. | Temperature | K | 130 | yes | - |
3. | U-velocity | m/s | 131 | yes | - |
4. | V-velocity | m/s | 132 | yes | - |
5.1.4.1. Specific humidity
The specific humidity is the mass of water vapour per unit mass of air valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. The parameter is given in kg/kg.
5.1.4.2. Temperature
The temperature is the model temperature valid for the grid area at the corresponding model level. Only analyses are stored for parameters on model levels. The parameter is given in Kelvin [K]. Temperature given in Kelvin can be converted to degrees Celsius (°C) by subtracting 273.15.
5.1.4.3. U-component of wind
The U-component of wind is the zonal component of the wind valid for the grid area at the corresponding model level. By model convention westerly wind (blowing from the west to the east) are positive. Only analyses are stored for parameters on model levels. The parameter is given in m/s.
5.1.4.4. V-component of wind
The V-component of wind is the meridional component of the wind valid for the grid area at the corresponding model level. By model convention southerly wind (blowing from the south to the north) are positive. Only analyses are stored for parameters on model levels. The parameter is given in m/s.
5.1.5. The CERRA grid description
Below are the essential parameters describing the grid and the used Lambert Conformal Conic projection. More information about the grid and coordinates can be found in the FAQ.
Number of points along x-axis: 1069
Number of points along y-axis: 1069
X-direction grid length: 5500 m
Y-direction grid length: 5500 m
Projection: Lambert Conformal Conic
Central meridian: 8
Standard parallel 1: 50
Standard parallel 2: 50
Latitude of origin: 50
Earth assumed spherical with radius: 6371229 m
Latitude and longitude of the corner grid points in decimal degrees | ||
Grid point | Latitude | Longitude |
Upper-left | 63.7695 | -58.1051 |
Upper-right | 63.7695 | 74.1051 |
Lower-right | 20.2923 | 33.4859 |
Lower-left | 20.2923 | -17.4859 |
5.2. CERRA-EDA
CERRA-EDA comprises the same domain as CERRA and has exactly the same set of parameters as the high-resolution CERRA dataset. It differs only in the horizontal resolution, which is 11km as well as in the number of available time steps. CERRA-EDA has four analyses per day, at 00, 06, 12 and 18 UTC. Starting from the analyses, forecasts are run for six hours. Forecast fields are saved with hourly resolution.
Metadata for CERRA-EDA parameters | |
Horizontal coverage | Same as CERRA. |
Horizontal resolution | 11 km x 11 km |
Vertical coverage | Same as for CERRA parameters. |
Vertical resolution | Same as for CERRA-parameters. |
Temporal coverage | Same as for CERRA. |
Temporal resolution | Analyses are available at 00, 06, 12, and 18 UTC. |
Data type and format | Gridded data in GRIB2 |
Grid | Lambert conformal conic grid with 565x565 grid points |
5.2.1. The CERRA-EDA grid description
Below are the essential parameters describing the CERRA-EDA grid and the used Lambert Conformal Conic projection. More information about the grid and coordinates can be found in the FAQ in section 2.3.
Number of points along x-axis: 565
Number of points along y-axis: 565
X-direction grid length: 11000 m
Y-direction grid length: 11000 m
Projection: Lambert Conformal Conic
Central meridian: 8
Standard parallel 1: 48
Standard parallel 2: 48
Latitude of origin: 48
Earth assumed spherical with radius: 6371229 m
Latitude and longitude of the corner grid points in decimal degrees | ||
Grid point | Latitude | Longitude |
Upper-left | 63.4028 | -60.4047 |
Upper-right | 63.4028 | 76.4047 |
Lower-right | 17.6121 | 34.3203 |
Lower-left | 17.6121 | -18.3203 |
6. Known issues
6.1. Wrong metadata for the 2m maximum and minimum temperature
The metadata for the forecast step range is incorrect in the GRIB2 files for the maximum 2m temperature since previous post-processing and the minimum 2m temperature since previous post-processing. The correct step range is given in the table below.
Step range in the metadata | Correct step range |
|---|---|
0-1 | 0-1 |
| 0-2 | 0-2 |
| 0-3 | 0-3 |
| 0-4 | 3-4 |
| 0-5 | 3-5 |
| 0-6 | 3-6 |
| 0-9 | 6-9 |
| 0-12 | 9-12 |
| 0-15 | 12-15 |
| 0-18 | 15-18 |
| 0-21 | 18-21 |
| 0-24 | 21-24 |
| 0-27 | 24-27 |
| 0-30 | 27-30 |
6.2. Minor data assimilation issues (issues when some observations were not available)
Observational data is the backbone information source of a reanalysis system. However, during the course of reanalysis, for various technical reasons, some of the data streams got left out. In many cases this is only discovered after the reanalysis has been run. It can also occur that instances in which some radiance data, that should have been blacklisted, are used. Whenever it is possible and the impact is judged to be of significance, the affected periods have been rerun. However, reruns have not been performed for periods with only minor issues in the observational data streams. This has been decided largely based on experiences from verification and real time monitoring. If the impact has been judged to be relatively insignificant, and not to affect the overall consistence and integrity of the CERRA or CERRA-EDA reanalysis, reruns have not been performed.
For completeness, a list of the occurrences of missing observational data and affected periods are listed below.
| CERRA | |
|---|---|
| Affected period | Description |
| 2020-10-01 to 2021-06-30 | Fewer SYNOP data than usual. About 1850 instead of 2100 stations. |
| 2021-02-01 to 2021-03-31 | Only few AMV data assimilated in this period. |
2020-04-01 to 2021-03-31 2019-01-01 to 2019-04-09 | Only very few ocean buoy observations. |
2019-01-01 to 2019-03-09 | Data assimilation used a slightly degraded B-matrix. The climatological part of the B-matrix was shifted by two months, i.e. the November climatology was used instead of the January climatology. |
| CERRA-EDA | |
|---|---|
| Affected period | Description |
2021-04-01 to 2021-04-30 | IASI missing for both METOP-A and METOP-B |
2019-01-01 to 2019-05-31 | No AMV included. Only very few ocean buoy observations. |
2019-01-01 to 2019-01-02 | No additional local observations included for Greenland, Iceland, Norway, Sweden, Finland, and France. |
| 2016-10-03 | The 18UTC cycle was ran without TEMP and PILOT data. |
1984-09-01 to present | No MSU data. |
6.3. Missing forecast parameters
| CERRA | ||
| Parameter | Forecast Time and Date | Forecast lead time |
10m wind gust since previous post-processing | 00 UTC, 1 April 2021 | +2h; +4h to +30h |
| Minimum 2m temperature since previous post-processing | 00 UTC, 1 April 2021 | +6h |
Maximum 2m temperature since previous post-processing | 00 UTC, 1 April 2021 | +6h |
| CERRA-EDA | ||
| Parameter | Forecast Time and Date | Forecast lead time |
10m wind gust since previous post-processing | 00 UTC, 1 April 2021 06 UTC, 1 April 2021 | +2h; +4h to +6h +2h; +4h to +6h |
| Minimum 2m temperature since previous post-processing | 00 UTC, 1 April 2021 06 UTC, 1 April 2021 | +6h +6h |
Maximum 2m temperature since previous post-processing | 00 UTC, 1 April 2021 06 UTC, 1 April 2021 | +6h +6h |
7. References
- Bazile E, R. Abida, A. Verelle, P. Le Moigne and C. Szczypta (2017): MESCAN-SURFEX surface analysis, deliverable D2.8 of the UERRA project, http://www.uerra.eu/publications/deliverable-reports.html
- El-Said A., P. Brousseau, M. Ridal and R. Randriamampianina (2021): A new temporally flow-dependent EDA estimating background errors in the new Copernicus European Regional Re-Analysis (CERRA), Earth and Space Science Open Archive, pp. 28, doi 10.1002/essoar.10507207.1, https://doi.org/10.1002/essoar.10507207.1
- Niermann D. et al. (2017): Scientific report on assessment of regional analysis against independent data sets, deliverable D3.6 of the UERRA project, http://www.uerra.eu/publications/deliverable-reports.html
- Ridal M., S. Schimanke and S. Hopsch (2018): Documentation of the RRA system: UERRA (C3S deliverable D322_Lot1.1.1.2, Documenting the UERRA system)
- Soci C., E. Bazile, F. Besson and T. Landelius (2016). High-resolution precipitation re-analysis system for climatological purposes. Tellus A, Dynamic Meteorology and Oceanography, 68:1, DOI: 10.3402/tellusa.v68.29879
- Verver Gé (2017): User Guidance, deliverable D8.4 of the UERRA project, http://www.uerra.eu/publications/deliverable-reports.html
