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For most of the variables the shortest forecast data are recommended to use. In general the data quality decreases with forecast length. On the other hand, for variables that are affected by spin-up effects - that is the model needs to run for a certain number of hours before these variables have an optimal quality - the longer forecasts can be better to use. The cloud and precipitation variables are directly affected by spin-up. For time integrated quantities such as theseprecipitation, accumulation over 12 hours between +6 and +18 h forecasts are recommended. Likewise 24 hour accumulation can be obtained as the difference between +30 hour and +6 hour forecasts. However, if accuracy in timing of precipitation events is of very high importance in the application, an option could be to combine hourly forecasts from each of the 8 analysis times (00, 03, 06, 09, 12, 15, 18, 21 UTC) for lead times 1, 2 and 3, which then will be slightly affected by spin-up. It is not possible to make general recommendations on this issue. Users , therefore users are advised to check this for themselves.make their own choices based on the general guidelines described here. (On spin-up of precipitation, see also here.)

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table1 table1

Table 1: Overview of single level variables. Some variables have not yet been uploaded to CDS, these are marked with * and unfortunately not be included in the first batch of released reanalysis data. Parameters labelled with TBD ("To Be Determined") do not yet have short name and GRIB code definitions. Most static fields (except land-sea mask and orography) marked with * * are available only as NetCDF-files below. 

The static fields marked as * * above are available as NetCDF files for the West and East domain respectively here: fractions.west.nc and fractions.east.nc     

Soil level variables

Soil level variables are given for two model depths, where the first depth is the soil surface and the second depth is the so-called root depth. The root depth varies with the cover type climatology. Please note that the soil level variables are accommodated in the Arctic Regional Reanalysis single level variables catalogue entry.

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table2 table2

Table 2: Overview of soil level variables

Model level variables

Model level variables are output at 65 hybrid model levels of the HARMONIE-AROME model. These follow the surface at the lowest levels and are gradually evolved into pure pressure levels at the highest levels. These are the levels at which the model computations are done. The height level and pressure level variables are interpolated from these data.

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Table 3: Overview of model level variables

Pressure level variables

Pressure level variables are interpolated to 23 specific pressure levels: 1000, 950, 925, 875, 850, 800, 750, 700, 600, 500, 400, 300, 200, 100, 70, 50, 30, 20 and 10 hPa. Thus, they are on isobaric surfaces.

Output of diagnostic variables at pressure levels are available in three hourly intervals at 00, 03, 06, 09, 12, 15, 18 and 21 UTC.

Long forecasts are available from the forecasts initiated at 00 and 12 UTC. Long forecasts include forecast lengths of 1, 2, 3, 4, 5, 6, 9, 12, 15, 18, 21, 24 and 30 hours.

Short forecasts of 1, 2 and 3 hours are made for the forecasts initiated at 03, 06, 09, 15, 18 and 21 UTC.

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table4 table4

Table 4: Overview of pressure level variables

Height level variables

Height level variables are interpolated to 11 specific height levels: 15, 30, 50, 75, 100, 150, 200, 250, 300, 400 and 500 metres above the surface.

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Table 5: Overview of height level variables

Details about the data fields

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The snow variables are given as instantaneous values from the most recent model time step relative to the output time. The snow density output unit is kg/m³, the snow water equivalent (SWE) output is in units of kg/m², and the snow fraction output has fractional units in the range 0-1. The snow depth can be derived from these variables.

Sea and sea

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ice variables

For the sea the sea surface temperature is is output in units of K. For areas partially or completely covered with sea - ice, the following variables are output:   Sea - ice area fraction [-], upper layer sea ice temperature [K], sea - ice thickness [m], and sea - ice snow thickness  [m]. Here the sea-. For sea ice thickness please note that the routine that computes this variable does not reproduce the evolution of ice thickness with all its complexity. Rather this variable should be treated as a rough estimate in order to get reasonable estimations for the energy fluxes. The sea ice fraction and sea surface temperatures are in fact interpolated input data and are only updated once every day. During the course of a forecast they are kept constant. All other sea and sea - ice variables are given as instantaneous values from the most recent model time step relative to the output time.

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The model level variables are computed at the full model levels, and are given as instantaneous values from the most recent model time step relative to the output time. There are 65 vertical model levels in HARMONIE-AROME.  These full model levels are hybrid-sigma coordinates that are counted from the model top toward the surface. They go from being pure pressure levels, i.e. levels with constant pressure starting at 10 hPa, 30 hPa, etc. to being relative to the surface topography in height. Level 64 is at approximately 30 m height and level 65 is at approximately 12 m height above the surface. For a more detailed description of the vertical model layers, see Annex 8.3. The following thermodynamic variables are the output variables at model levels: Temperature [K], u-component of wind [m/s], v-component of wind [m/s], turbulent kinetic energy [J/kg]. Here turbulent kinetic energy is the mean kinetic energy per unit mass from eddies in turbulent flow. Note that the HARMONIE-AROME weather forecasting model with 2.5 x 2.5 km² resolution does not explicitly resolve this turbulent energy. The u- and v- wind components follow the direction of the Lambertian model grid with the u-component being directed 90 degrees clockwise relative to the v-component. From these model level wind components, the model level wind speed and wind direction relative north can be calculated with Equations 3 and 4. The grid rotation angle 𝛼 can be computed with this script: https://github.com/metno/NWPdocs/wiki/From-x-y-wind-to-wind-direction.

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Static fields are output variables that do not change depending on the model initial time or the forecast length (in other words they are time-independent). These include the land-sea mask, that is the fraction of land in a given model grid box of 2.5 x 2.5 km² in units of %, and the orography in units of m. For each model grid box in HARMONIE-AROME There are two more orography-related static parameters: subgrid orography average slope and subgrid orography standard deviation. For each model grid box in HARMONIE-AROME 4 tile fractions are defined in units of fraction. These are: The fraction of sea, the fraction of inland water (lakes and rivers), the fraction of urban areas, and the fraction of nature, i.e. land areas that are not inland water or urban. The fraction of glaciers is also output. This is assumed to be a constant field with glacier extents representative of the middle of the full reanalysis period (19971991-2021). Glacier extent in remote Arctic locations is not available as accurately mapped yearly datasets. The official maps are outdated due to major calving events in the recent decades. HARMONIE-AROME has not yet been designed to deal with changing land-sea masks or other surface classifications. Thus, these are static fields.

What are the uncertainties of the data fields?

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table6 table6

Table 6: Variables and levels for which field uncertainty estimates are provided. (Due to technical issues, there is a difference in parameter availability between the East and West domain.)

In section 6.5 we present uncertainties measured as statistics of actual deviations from observations (also known as verification statistics). Note that observations are a reference not identical to the actual truth, as they also will have uncertainties as well as representativeness issues. This verification statistics is provided for a set of near-surface quantities which are covered by the meteorological observation network, including 2m temperature, 10m winds and precipitation.

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Table 7: Climatological analysis error standard deviation for surface pressure and mean sea level pressure for the CARRA-East domain.

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Figure 2: Climatological analysis error standard deviation for u-component of wind: summer statistics (left), winter statistics (right) as function of standard vertical pressure levels for the CARRA-East domain.

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Table 8: Climatological analysis error standard deviation for u wind component for the CARRA-East domain. 

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table9 table9

Table 9: Climatological analysis error standard deviation for v wind component, summer period for the CARRA-East domain. 

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table10 table10

Table 10: Climatological analysis error standard deviation temperature for the CARRA-East domain.

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table11 table11

Table 11: Climatological analysis error standard deviation geopotential for the CARRA-East domain. 

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table12 table12

Table 12: Climatological analysis error standard deviation relative humidity for the CARRA-East domain.

Uncertainties for the CARRA-West model domain

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Table 13: Climatological analysis error standard deviation for surface pressure and mean sea level pressure for the CARRA-West domain.

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Figure 7: Climatological analysis error standard deviation for u-component of wind (left plot) and for v-component of wind (right plot) as function of standard vertical pressure levels for the CARRA-West domain.

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table14 table14

Table 14: Climatological analysis error standard deviation for u-component of wind for the CARRA-West domain. 

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table15 table15

Table 15: Climatological analysis error standard deviation for v-component of wind for the CARRA-West domain. 

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Figure 8: Climatological analysis error standard deviation for temperature (left plot) and specific humidity (right plot) as a function of standard pressure levels for the CARRA-West domain.

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table16 table16

Table 16: Climatological analysis error standard deviation for temperature for the CARRA-West domain.

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Table 17: Climatological analysis error standard deviation for specific humidity for the CARRA-West domain.

Questions & Answers on field uncertainty estimates

For information and "questions and answers" (Q&A) on the uncertainty estimates for the ERA5 host reanalysis used on the lateral boundaries of this Arctic Regional Reanalysis, see this link:
ERA5: uncertainty estimation

The field uncertainty estimates provided here apply a different approach. The below Q&A is similar to the Q&A for the global reanalysis, adapted to apply for the uncertainty estimates provided here (for the Arctic reanalysis).

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Since the regional reanalysis is run nested into the ERA5 global reanalysis, it is affected by the known issues of ERA5. In addition to those issues, we have found that ERA5 uses incorrect glacier masks for most of the glaciers in the regional Arctic reanalysis domain, and the glaciers in ERA5 always have an analysis albedo of 0.85. This is wrong, since for instance exposed glacier ice albedos during summer are unaccounted for. These areas affect the general circulation and thermodynamic state in ERA5 and can affect the quality of the Arctic regional reanalysis.
Additionally, the Arctic reanalysis has the following known issues:

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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.

 Definition of the 65 vertical layer structure in HARMONIE 

The CARRA vertical coordinate system is a terrain-following hybrid vertical coordinate, which means that it is terrain following at the bottom and pressure based on the top of the atmosphere. It has the advantage to describe the surface terrain properly, but also benefitting the advantage of having the pressure coordinate at the top of the atmosphere.

CARRA uses 65 model levels (level 65 is the surface and level 1 is the top of the atmosphere), which is further splitted into the so called half levels. CARRA has 66 half levels and the pressure of each half level can be obtained by the following formula:

P (k+1/2) = A (k+1/2) + B (k+1/2) * Ps

where k=0.... 65, Ps is surface pressure and the A and B coefficients (see below) valid at each half level. 

The full model level pressure [1, 2, ...65] is defined as the mean of the pressure of each pair of neighbouring half levels [0,5, 1.5, ...65.5]. The model variables are defined in the full model levels. 

The A and B coefficients are listed below (from the top to the bottom).

'AHALF'=>'0.00000000, 2000.00000000, 4000.21287319, 6002.09662113, 7911.25838577, 9633.01049417,11169.37146237, 12522.57753978, 13695.00149653, 14689.11546998, 15507.49052823, 16154.69697732, 16632.12471208, 16940.14949960,  17082.34869816, 17065.28164099, 16898.18367797, 16592.58939571,  16161.90395878,  15620.94340550,  14985.46502362, 14271.70773051, 13495.95994372, 12674.16909910, 11821.60314859, 10952.57042620, 10080.20053763, 9216.28565403, 8371.17893039, 7553.74479607, 6771.35457397, 6029.92021691, 5333.95880836, 4686.68074804, 4090.09511346, 3545.12645110, 3051.73811264, 2609.05813936, 2215.50455766, 1868.90774223, 1566.62821060, 1305.66882073, 1081.85503306, 890.47596795, 727.74548529, 590.17748096, 474.58767980, 378.08857614, 298.07947335, 232.23312781, 178.48015386, 134.99207440, 100.16369201, 72.59529482, 51.07508967, 34.56216490, 22.17022046, 13.15225964, 6.88641310, 2.86306141, 0.67344356, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000

'BHALF'=>'0.00000000,  0.00000000,  0.00000000, 0.00000000, 0.00095468, 0.00382570, 0.00862327, 0.01535782, 0.02404046, 0.03468314, 0.04729839, 0.06195102, 0.07868187, 0.09744325, 0.11815586, 0.14071098, 0.16497348, 0.19078554,   0.21797086, 0.24633925, 0.27569119, 0.30582244, 0.33652825, 0.36760726, 0.39886479, 0.43011564, 0.46118624, 0.49191624, 0.52215946, 0.55178443, 0.58067442, 0.60872709, 0.63585388, 0.66197911, 0.68703898, 0.71098036, 0.73375964, 0.5534143, 0.77569737, 0.79480486, 0.81264598, 0.82920633, 0.84454000, 0.85875505, 0.87191802, 0.88409276, 0.89534045,  0.90571965, 0.91528643, 0.92409452, 0.93219549, 0.93963895, 0.94647277, 0.95274328, 0.95849551, 0.96377340, 0.96862008, 0.97307803, 0.97718944, 0.98099640, 0.98454132, 0.98786727, 0.99102462, 0.99406510, 0.99703923, 1.00000000

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