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1. In general, we recommend that the hourly (analysed) "2 metre temperature" be used to construct the minimum and maximum over longer periods, such as a day, rather than using the forecast parameters "Maximum temperature at 2 metres since previous post-processing" and "Minimum temperature at 2 metres since previous post-processing".
2. ERA5: compute pressure and geopotential on model levels, geopotential height and geometric height
3. ERA5: How to calculate wind speed and wind direction from u and v components of the wind?
4. Sea surface temperature and sea-ice cover (sea ice area fraction), see Table 2 above, are available at the usual times, eg hourly for the HRES, but their content is only updated once daily. However, for inland water bodies (lakes, reservoirs, rivers and coastal waters) the FLake model calculates the surface temperature (ie the lake mixed-layer temperature or lake ice temperature) and does include diurnal variations.
5. Mean rates/fluxes and accumulations at step=0 have values of zero because the length of the processing period is zero.
6. Convective Inhibition (CIN). A missing value is assigned to CIN for values of CIN > 1000 or where there is no cloud base. This can occur where convective available potential energy (CAPE) is low.

7. 
   

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   title ERA5: mixing CDS and MARS data
   In the ECMWF data archive (MARS), ERA5 data is archived on various native grids. For the CDS disks, ERA5 data have been interpolated and are stored on regular latitude/longitude grids. For more information, see Spatialgrid. Storing the data on these different grids can cause incompatibilities, particularly when comparing native spherical harmonic, pressure level, MARS data with CDS disk data on a third, coarse grid. Native spherical harmonic, pressure level parameters are comprised of: Geopotential, Temperature, U component of wind, V component of wind, Vertical velocity, Vorticity, Divergence and Relative humidity. When these parameters are retrieved from MARS and a coarse output grid is specified, the default behaviour is that the spherical harmonics are truncated to prevent aliasing on the output grid. The coarser the output grid, the more severe the truncation. This truncation removes the higher wavenumbers, making the data smoother. However, the CDS disk data has been simply interpolated to the third grid, without smoothing. This incompatibility is particularly relevant when comparing ERA5.1 data (which are only available from MARS - see DataorganisationandhowtodownloadERA5 - and only for 2000-2006) with ERA5 data on the CDS disks. The simplest means of minimising such incompatibilities is to retrieve the MARS data on the same grid as that used to store the ERA5 CDS disk data.

   
   

8. 
   

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   title ERA5: Land-sea mask for wave variables
   The land-sea mask in ERA5 is an invariant field. This parameter is the proportion of land, as opposed to ocean or inland waters (lakes, reservoirs, rivers and coastal waters), in a grid box. This parameter has values ranging between zero and one and is dimensionless. In cycles of the ECMWF Integrated Forecasting System (IFS) from CY41R1 (introduced in May 2015) onwards, grid boxes where this parameter has a value above 0.5 can be comprised of a mixture of land and inland water but not ocean. Grid boxes with a value of 0.5 and below can only be comprised of a water surface. In the latter case, the lake cover is used to determine how much of the water surface is ocean or inland water.  The ERA5 land-sea mask provided is not suitable for direct use with wave parameters, as the time variability of the sea-ice cover needs to be taken into account and wave parameters are undefined for non-sea points. In order to produce a land-sea mask for use with wave parameters, users need to download the following ERA5 data (for the required period): the model bathymetry (Model bathymetry. Fig 1) the sea-ice cover (Sea ice area fraction, Fig 2) and combine these data to produce the land-sea mask (Fig 3). See attached pictures: Fig 1: Model bathymetry                                                 Fig 2: Sea-ice cover                                                          Fig 3: Combined mask Note Please note that sea-ice cover is only updated once daily. Please see the Toolbox workflow below to see a possible way to proceed. The results is a carousel of land-sea mask for each time step requested: Code Block title Toolbox workflow collapse true import cdstoolbox as ct @ct.application(title='Download data') @ct.output.download() @ct.output.carousel() def download_application(): count = 0 years=['1980'] months = [ '01', #'02', '03', # '04', '05', '06', # '07', '08', '09', # '10', '11', '12' ] # For hourly data hourly=True # For monthly data monthly=True hourly = True monthly = False for yr in years: for mn in months: if hourly == True: mb,si = get_hourly_data(yr, mn) elif monthly == True: mb,si = get_monthly_data(yr, mn) print(mb) # Check values are >= 0.0 in the model bathymetry mask compare_ge_mb = ct.operator.ge(mb, 0.0) print(si) # Check values are > 0.5 in the sea ice mask compare_ge_si = ct.operator.gt(si, 0.500) # Invert model bathymetry mask new = ct.operator.add(compare_ge_mb, -1.0) new1 = ct.operator.mul(new, -1.0) # Add the Bathymetry Mask to the Sea Ice Mask new_all = ct.operator.add(compare_ge_si,new1) # Reset scale to land=1, ocean=0 new_all_final = ct.operator.ge(new_all, 1.0) print(new_all_final) if count == 0: combined_mask = new_all_final else: combined_mask = ct.cube.concat([combined_mask, new_all_final], dim = 'time') count = count + 1 renamed_data = ct.cdm.rename(combined_mask, "wavemask") new_data = ct.cdm.update_attributes(renamed_data, attrs={'long_name': 'Wave Land Sea Mask'}) combined_mask = new_data print("combined_mask") print(combined_mask) # Plot mask for first timestep fig_list = ct.cdsplot.geoseries(combined_mask) return combined_mask, fig_list def get_monthly_data(y,m): m,s = ct.catalogue.retrieve( 'reanalysis-era5-single-levels-monthly-means', { 'product_type': 'monthly_averaged_reanalysis', 'variable': [ 'model_bathymetry', 'sea_ice_cover', ], 'year': y, 'month': m, 'time': '00:00', } ) return m, s def get_hourly_data(y,m): m,s = ct.catalogue.retrieve( 'reanalysis-era5-single-levels', { 'product_type': 'reanalysis', 'variable': [ 'model_bathymetry', 'sea_ice_cover', ], 'year': y, 'month': m, 'day': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'time': [ '00:00', '01:00', '02:00', '03:00', '04:00', '05:00', '06:00', '07:00', '08:00', '09:00', '10:00', '11:00', '12:00', '13:00', '14:00', '15:00', '16:00', '17:00', '18:00', '19:00', '20:00', '21:00', '22:00', '23:00', ], } ) return m, s

   
   

9. 
   

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   title Altimeter wave parameters
   The following wave parameters are sparse observations, or quantities derived from the observations, that have been interpolated to the wave model grid and contain many missing values: altimeter_wave_height (140246) altimeter_corrected_wave_height (140247) altimeter_range_relative_correction (140248) These parameters are not available from the CDS disks but can be retrieved from MARS using the CDS API. For further guidelines, please see: Altimeter wave height in the Climate Data Store (CDS)

   
   

10. 
    

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    title Computation of near-surface humidity
    Near-surface humidity is not archived directly in ERA datasets, but the archive contains near-surface (2m from the surface) temperature (T) and dew point temperature (Td), and also surface pressure (sp), from which you can calculate specific and relative humidity at 2m. Specific humidity can be calculated using equations 7.4 and 7.5 from Part IV, Physical processes section (Chapter 7, section 7.2.1b) in the documentation of the IFS for CY41R2. Use the 2m dew point temperature and surface pressure (which is approximately equal to the pressure at 2m) in these equations. The constants in 7.4 are to be found in Chapter 12 (of Part IV: Physical processes) and the parameters in 7.5 should be set for saturation over water because the dew point temperature is being used. Relative humidity should be calculated from: RH = 100 * es(Td)/es(T)  Relative humidity can be calculated with respect to saturation over water, ice or mixed phase by defining es(T) with respect to saturation over water, ice or mixed phase (water and ice). The usual practice is to define near-surface relative humidity with respect to saturation over water. Note that in ERA5, the relative humidity on pressure levels has been calculated with respect to saturation over mixed phase.

    
    

11. 
    

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    title Computation of snow cover
    In the ECMWF model (IFS), snow is represented by an additional layer on top of the uppermost soil level. The whole grid box may not be covered in snow. The snow cover gives the fraction of the grid box that is covered in snow. For ERA5, the snow cover (SC) is computed using snow water equivalent (ie parameter SD (141.128)) as follows: Panel title ERA5 Snow cover formula snow_cover (SC) = min(1, (RW*SD/RSN) / 0.1 ) where RW is density of water equal to 1000 and RSN is density of snow (parameter 33.128). ERA5 physical depth of snow where there is snow cover is equal to RW*SD/(RSN*SC).

    
    

12. 
    

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    title "Forecast albedo" is only for diffuse radiation
    The parameter "Forecast albedo" is only for diffuse radiation and assuming a fixed spectrum of downward short-wave radiation at the surface. The true broadband, all-sky, surface albedo can be calculated from accumulated parameters: (SSRD-SSR)/SSRD where SSRD is parameter 169.128 and SSR is 176.128. This true surface albedo cannot be calculated at night when SSRD is zero. For more information, see Radiation quantities in the ECMWF model and MARS.

    
    

13. 
    

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    title Actual and potential evapotranspiration
    Actual evapotranspiration in the ERA5 single levels datasets is called "Evaporation" (param ID 182) and is the sum of the following four evaporation components (which are not available separately in ERA5 but only for ERA5-Land): Evaporation from bare soil Evaporation from open water surfaces excluding oceans Evaporation from the top of canopy Evaporation from vegetation transpiration For the ERA5 single levels datasets, actual evapotranspiration can be downloaded from the C3S Climate Data Store (CDS) under the category heading "Evaporation and Runoff", in the "Download data" tab. For details about the computation of actual evapotranspiration, please see Chapter 8 of Part IV : Physical processes, of the IFS documentation: ERA5 IFS cycle 41r2 The potential evapotranspiration in the ERA5 single levels CDS dataset is given by the parameter potential evaporation (pev).  Pev data can be downloaded from the CDS under the category heading "Evaporation and Runoff", in the "Download data" tab for the ERA5 single levels datasets. Note The definitions of potential and reference evapotranspiration may vary according to the scientific application and can have the same definition in some cases. Users should therefore ensure that the definition of this parameter is suitable for their application. Note Please note that based on ERA5 atmospheric forcing, other independent (offline) methods such us "Priesley-Taylor1 (1972) , Schmidt2 (1915) or de Bruin3 (2000)" can also be used to estimate Potential evapotranspiration. 1PRIESTLEY, C. H. B., & TAYLOR, R. J. (1972). On the Assessment of Surface Heat Flux and Evaporation Using Large-Scale Parameters, Monthly Weather Review, 100(2), 81-92. Retrieved Aug 27, 2021, from https://journals.ametsoc.org/view/journals/mwre/100/2/1520-0493_1972_100_0081_otaosh_2_3_co_2.xml  2Schmidt, W., 1915: Strahlung und Verdunstung an freien Wasserflächen; ein Beitrag zum Wärmehaushalt des Weltmeers und zum Wasserhaushalt der Erde (Radiation and evaporation over open water surfaces; a contribution to the heat budget of the world ocean and to the water budget of the earth). Ann. Hydro. Maritimen Meteor., 43, 111–124, 169–178. 3de Bruin, H. A. R., , and Stricker J. N. M. , 2000: Evaporation of grass under non-restricted soil moisture conditions. Hydrol. Sci. J., 45, 391–406, doi:10.1080/02626660009492337.

    
    

14. 
    

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    title "Evaporation" and "Instantaneous moisture flux"
    The "Instantaneous moisture flux" (units: kg m-2 s-1; paramId=232) incorporates the same processes as "Evaporation" (units: m of water equivalent; paramId=182), but the latter is accumulated over a particular time period (during the hour preceeding the validity date/time, in the ERA5 HRES), whereas the former is an instantaneous parameter. Note, the different units of these two parameters. For the atmosphere, these two parameters only involve water vapour. Cloud liquid does not sediment and the cloud ice sedimentation flux is included in the snowfall flux. Here are some further details about the processes in the "Instantaneous moisture flux" and "Evaporation": Surface characteristics Process from surface to atmosphere (defined to be negative) Process from atmosphere to surface (defined to be positive) Warm surface Evaporation from liquid water to water vapour Dew deposition from water vapour Cold vegetation surface Evaporation from liquid water to water vapour Dew deposition from water vapour Ice surface Sublimation from ice to water vapour Ice deposition from water vapour Snow surface Sublimation from snow to water vapour Snow deposition from water vapour

    
    

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1. ERA5T: from 1 September to 13 December 2021, the final ERA5 product is different to ERA5T due to the correction of the assimilation of incorrect snow observations in central Asia. Although the differences are mostly limited to that region and mainly to surface parameters, in particular snow depth and soil moisture and to a lesser extent 2m temperature and 2m dewpoint temperature, all the resulting reanalysis fields can differ over the whole globe but should be within their range of uncertainty (which is estimated by the ensemble spread and which can be large for some parameters). On the CDS disks, the initial, ERA5T, fields have been overwritten (with the usual 2-3 month delay), i.e., for these months, access to the original CDS disk, ERA5T product is not possible after it has been overwritten. Potentially incorrect snow observations have been assimilated in ERA5 up to this time, when the effects became noticeable. The quality control of snow observations has been improved in ERA5 from September 2021 and from 15 November 2021 in ERA5T.
2. ERA5 uncertainty: although small values of ensemble spread correctly mark more confident estimates than large values, numerical values are over confident. The spread does give an indication of the relative, random uncertainty in space and time.
3. ERA5 suffers from an overly strong equatorial mesospheric jet, particularly in the transition seasons.
4. From 2000 to 2006, ERA5 has a poor fit to radiosonde temperatures in the stratosphere, with a cold bias in the lower stratosphere. In addition, a warm bias higher up persists for much of the ERA5 period. The lower stratospheric cold bias was rectified in a re-run for the years 2000 to 2006, called ERA5.1, see "Resolved issues" below.
5. Discontinuities in ERA5: The historic ERA5 data was produced by running several parallel experiments, each for a different period, which were then spliced together to create the final product. This can create discontinuities at the transition points.
   
6. The analysed "2 metre temperature" can be larger than the forecast "Maximum temperature at 2 metres since previous post-processing".
7. The analysed 10 metre wind speed (derived from the 10 metre wind components) can be larger than the forecast "10 metre wind gust since previous post-processing".
8. ERA5 diurnal cycle for near surface winds: the hourly data reveals a mismatch in the analysed near surface wind speed between the end of one assimilation cycle and the beginning of the next (which occurs at 9:00 - 10:00 and 21:00 - 22:00 UTC). This problem mostly occurs in low latitude oceanic regions, though it can also be seen over Europe and the USA. We cannot rectify this problem in the analyses. The forecast near surface winds show much better agreement between the assimilation cycles, at least on average, so if this mismatch is problematic for a particular application, our advice would be to use the forecast winds. The forecast near surface winds are available from MARS, see the section, Data organisation and how to download ERA5.
   
9. ERA5 diurnal cycle for near surface temperature and humidity: some locations do suffer from a mismatch in the analysed values between the end of one assimilation cycle and the beginning of the next, in a similar fashion to that for the near surface winds (see above), but this problem is thought not to be so widespread as that for the near surface winds. The forecast values for near surface temperature and humidity are usually smoother than the analyses, but the forecast low level temperatures suffer from a cold bias over most parts of the globe. The forecast near surface temperature and humidity are available from MARS, see the section Data organisation and how to download ERA5.
10. ERA5: large 10m winds: up to a few times per year, the analysed low level winds, eg 10m winds, become very large in a particular location, which varies amongst a few apparently preferred locations. The largest values seen so far are about 300 ms^-1.
    
11. ERA5 rain bombs: up to a few times per year, the rainfall (precipitation) can become extremely large in small areas. This problem occurs mostly over Africa, in regions of high orography.
    
12. Large values of CAPE: occasionally, the Convective available potential energy in ERA5 is unrealistically large.
13. Ship tracks in the SST: prior to September 2007, in the period when HadISST2 was used, ship tracks can be visible in the SST.
14. Prior to 2014, the SST was not used over the Great Lakes to nudge the lake model. Consequently, the 2 metre temperature has an annual cycle that is too strong, with temperatures being too cold in winter and too warm in summer.
    
15. The Potential Evaporation field (pev, parameter Id 228251) is largely underestimated over deserts and high-forested areas. This is due to a bug in the code that does not allow transpiration to occur in the situation where there is no low vegetation.
16. Wave parameters (Table 7 above) for the three swell partitions: these parameters have been calculated incorrectly. The problem is most evident in the swell partition parameters involving the mean wave period: Mean wave period of first swell partition, Mean wave period of second swell partition and Mean wave period of third swell partition, where the periods are far too long.
17. Surface photosynthetically available radiation (PAR) is too low in the version (CY41R2) of the ECMWF Integrated Forecasting System (IFS) used to produce ERA5, so PAR and clear sky PAR have not been published in ERA5. There is a bug in the calculation of PAR, with it being taken from the wrong parts of the spectrum. The shortwave bands include 0.442-0.625 micron, 0.625-0.778 micron and 0.778-1.24 micron. PAR should be coded to be the sum of the radiation in the first of these bands and 0.42 of the second (to account for the fact that PAR is normally defined to stop at 0.7 microns). However, in CY41R2, PAR is in fact calculated from the sum of the second band plus 0.42 of the third. We will try to fix this in a future cycle.

18. 
    

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    title The instantaneous turbulent surface stress components (eastward and northward) and friction velocity tend to be too small
    The ERA5 analysed and forecast step=0, instantaneous surface stress components and surface roughness and the forecast step=0, friction velocity (friction velocity is not available from the analyses in ERA5) tend to suffer from values that are too low over the oceans. The analysis for such parameters is obtained by running the surface module to connect the surface with the model level analysed variables. However, at that stage, the surface aero-dynamical roughness length scale (z0) over the oceans is not initialised from its actual value but a constant value of 0.0001 is used instead. This initial value of z0 is needed to determine the initial value of u* and the surface stress based on solving for a simple logarithmic wind profile between the surface and the lowest model level. This initial u* is in turn used to determine an updated value of z0 based on the input Charnock parameter and then the value of the exchange coefficients needed to determine the output 10m winds (normal and neutral) and u* (see (3.91) to (3.94) with (3.26) in the IFS documentation). The surface stress is output as initialised. This initial value for z0 is generally too low ( by one order of magnitude or more): Over the oceans, for winds above few m/s, z0 is modelled using the Charnock relation: z0 ~ (alpha/g) u*2 where alpha is the Charnock parameter, g is gravity, and u* is the friction velocity with typical values of alpha ~ 0.018 g=9.81 u*2 = Cd U102 where Cd is the drag coefficient Cd ~ 0.008 + 0.0008 U10 for U10=10m/s =>  z0 ~ 0.003 As a consequence, the analysed instantaneous surface stress components will tend to be too low and even the updated value of z0 (surface roughness) will also tend to be too low. For forecast, instantaneous surface stress components, surface roughness and friction velocity, the same problem affects step 0. However, this problem will not affect the accumulated surface stress parameters (recall the accumulated parameters are produced by running short range forecasts), because the accumulation starts from the first time step (i.e. at time step 0 all accumulated variables are initialised to 0). This problem can easily be fixed, by using the initial value of Charnock that is available at the initial time. Note, in ERA5 the parameter for surface roughness is called "forecast surface roughness", even when it's analysed.

    
    

19. ERA5 forecast parameters are missing for the validity times of 1st January 1940 from 00 UTC to 06 UTC (except for forecast step=0). This problem occurs because the first forecast in ERA5 was initiated from 1st January 1940 at 06 UTC.

    
    

20. Maximum temperature at 2 metres since previous post-processing: in a small region over Peru, at 19 UTC, 2 August 2013, this forecast parameter exhibited erroneous values, which were greater than 50C. This occurrence is under investigation. Note, in general, we recommend that the hourly (analysed) "2 metre temperature" be used to construct the minimum and maximum over longer periods, such as a day.

21. 
    

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    title Four reasons why hourly data might not be consistent with their monthly mean
    The ERA5 monthly means are calculated from the hourly (3 hourly for the EDA) data, on the native grid (including spherical harmonics) from the GRIB data, in each production "stream" or experiment. This can give rise to inconsistencies between the sub-daily data and their monthly mean, particularly in the CDS. In general, the inconsistencies will be small. In the CDS, the ERA5 data (sub-daily and monthly mean) has been interpolated to a regular latitude/longitude grid. This interpolated sub-daily data will be slightly different to the native sub-daily data used in the production of the ERA5 monthly means. The netCDF data available in the CDS has been packed, see What are NetCDF files and how can I read them, which states "unpacked_data_value = (packed_data_value * scale_factor) + add_offset" and "packed_data_value = nint((unpacked_data_value - add_offset) / scale_factor)". This netCDF packing will change the sub-daily values slightly, compared with the native sub-daily data used in the production of the ERA5 monthly means. The GRIB data in the ERA5 monthly means (and sub-daily data) has been packed using a binning algorithm (which is different to the netCDF packing algorithm). Monthly means produced in other formats, such as netCDF, will differ from the ERA5 monthly means because of this packing. Finally, there is a further reason why monthly mean values might be different to the mean of the sub-daily values, which even occurs in MARS. This cause only affects forecast parameters (the CDS provides analysed parameters unless the parameter is only available from the forecasts), such as the Total precipitation, and only occurs sporadically. In order to speed up production, ERA5 is produced in several parallel "streams" or experiments, which are then spliced together to produce the final product. Consider, the "stream" change at the beginning of 2015. The ERA5 forecast monthly means for January 2015 have been produced from the sub-daily data from that "stream", the first few hours of which (up until 06 UTC on 1st January 2015) come from the 18 UTC forecast on 31 December 2014. However, the sub-daily forecast data published in ERA5, is based on the date of the start of the forecast, so these first few hours of 2015 originate from the "stream" that produced December 2014. These two "streams" are different experiments, with different data values. The resulting inconsistencies might be larger than for the other three causes, above, depending on how consistent the two streams are.

    
    

22. ERA5 sea-ice cover and 2 metre temperature: in the period 1979-1989, in a region just to the north of Greenland, the sea-ice cover outside of the melt season is too low and hence the 2 metre temperature is too high. For more information, see Section 3.5.4 of Low frequency variability and trends in surface air temperature and humidity from ERA5 and other datasets
23. ERA5 sea-ice cover is missing in the Caspian Sea from late 2007 to 2013, inclusive.
24. ERA5 sea-ice surface temperature (skin temperature) in the Arctic, during winter, can have a warm bias of 5K or more. This issue is most pronounced over thick snow-covered sea ice under cold clear-sky conditions, when the modelled conductive heat flux from the warm ocean underneath the ice and snow layer is too high. More information can be found in Batrak and Müller (2019) and Zampieri et al., (2023), the latter of which, also describes a method to improve on this bias.
25. Altimeter wave height observations have not been available for ERA5 in the following periods (since coverage began in mid-1991): early February 2021 to mid-January 2022; mid-October 2023 onwards.
26. ERA5 CDS: wind values are far too low on pressure levels at the poles in the CDS
27. Snow present in Iberia throughout 1978 due to assimilation of erroneous in situ snow data. This has an effect on 2m temperature, which shows negative anomalies of several degrees Celsius. There is no snow present in ERA5-Land, as this snow data is not assimilated. However, the 2m temperature anomaly is present, as the forcing comes from the erroneous ERA5 data. These figures show ERA monthly averaged 2m temperature (t2m) and snow depth (sd)  (38 to 43N, -8 to -6W), from 1940-2023, with 1978 highlighted in red. The same snow depth plot, limited to 1977-07 to 1979-04 shows more detail, with the period of erroneous snow depth in ERA5 extending from 1977-12 to 1979-03 in the monthly mean dataset.

Resolved issues

1. ERA5.1 is a re-run of ERA5, for the years 2000 to 2006 only, and was produced to improve upon the cold bias in the lower stratosphere seen in ERA5. 

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   title More information and details for downloading ERA5.1
   ERA5.1 is a re-run of ERA5 for the years 2000 to 2006 only. ERA5.1 was produced to improve upon the cold bias in the lower stratosphere exhibited by ERA5 during this period. Moreover, ERA5.1 analyses have a better representation of the following features: upper stratospheric temperature stratospheric humidity The lower and middle troposphere in ERA5.1 are similar to those in ERA5, as is the synoptic evolution in the extratropical stratosphere. For access to ERA5.1 data read Data organisation and how to download ERA5. The dataset is 'reanalysis-era5.1-complete' in the CDS API.

   
   

2. ERA5.1 CDS: If you retrieved ERA5.1 using the CDS API anytime before 20/05/2020 08:00 UTC, for any stream other than oper (i.e. streams: wave, enda, edmo, ewmo, edmm, ewmm, ewda, moda, wamd, mnth, wamo), you will need to request the data again. Prior to this date, stream oper would be delivered regardless of which stream was requested.
3. ERA5 CDS: incorrect values of U/V on pressure levels in the CDS
4. ERA5 CDS: Data corruption

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