...
- Single level data: https://cds.climate.copernicus.eu/datasets/reanalysis-carra-single-levels
- Pressure level data: https://cds.climate.copernicus.eu/datasets/reanalysis-carra-pressure-levels
- Height level data: https://cds.climate.copernicus.eu/datasets/reanalysis-carra-height-levels
- Model level data: https://cds.climate.copernicus.eu/datasets/reanalysis-carra-model-levels
The dataset covers the period September 1990 to present which includes the latest WMO reference climate period 1991-2020. New data is added on a monthly basis (at the end of the month) with 2-3 months delay with respect to real time (this means that at the end of month N data is added for the month N-2).
The data are freely available; the only requirement is to register as a CDS user. There are several options to download and visualize the data. An introduction to the legacy CDS and the legacy CDS Toolbox is available in the Copernicus User Learning Services (registration is needed) at at https://uls.climate.copernicus.eu/group/learning/browse-lessons?packageId=1148.
...
Pressure level variables are interpolated to 23 specific pressure levels: 1000, 950, 925, 900, 875, 850, 825, 800, 750, 700, 600, 500, 400, 300, 250, 200,150, 100, 70, 50, 30, 20 and 10 hPa. Thus, they are on isobaric surfaces.
...
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, 27 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.
...
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, 27 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.
...
| Anchor | ||
|---|---|---|
|
For the CARRA-East domain, there are typical winter (January) and summer (July) uncertainty estimates provided. There are no monthly values for other months provided for the uncertainties. Users can expect that values for the intermediate months will lie between these values. There is no general recipe for deriving monthly estimates for each month, but common sense should prevail and of course, the specific user need should be applied. One solution is to do a temporal interpolation between the winter and summer uncertainty values, which at least would provide a better approximation for the actual month, although the method has its limitations.
For the larger CARRA-West domain we have not noticed such seasonal dependency for the error standard deviation. The reason is different geographical locations location of these domains in relation with respect to the jet - stream and its associated with its different climatology. Therefore it is proposed to use this single value for the entire year. Still, it must be remembered that the uncertainties represent climatological overall values and will vary depending on the weather situation.
| Anchor | ||
|---|---|---|
|
The profile figures provided above provide uncertainty indications for winter and summer values (for the CARRA-East domain) as well as indicating the differences between the two reanalysis domains. The estimates we provided are considered as overall estimates representing an average over the 24 years entire reanalysis period. It will not capture any evolution in time of the uncertainties throughout the period. We do expect a slight decrease of the uncertainties in time throughout the reanalysis period, so that recent periods have slightly smaller uncertainties than older periods.
...
(9) What are the uncertainties of the reanalysis output parameters not provided estimates for here?
The method and data we have had available for uncertainty estimation is only for a limited set of the output parameters available from the regional reanalysis. For some of the other variables verification statistics from comparison of reanalysis with observations, provided below, provides and that gives information on the uncertainties. This includes such commonly used parameters such as 2-metre temperatures, 10-metre winds and precipitation.
For the remaining output parameters, we do not have uncertainty estimates from this methodology. Here uncertainties need to be allowed for in a combination with common sense when using the data.
(10) How do the uncertainties in CARRA compare to the uncertainties in ERA5? Can we use ERA5 uncertainties for variables, where such estimate is not provided for CARRA?
Uncertainty information from ERA5 has some relevance for CARRA. ERA5 data is used on the lateral boundaries of CARRA, and an algorithm "mixes in" some of the large spatial scales from ERA5 in CARRA, so information and inaccuracies from ERA5 will be inherited by CARRA. Still, CARRA adds value and can improve accuracy and correct errors by employing higher resolution, more local observations and describing several surface aspects in more detail. ERA5 provides extended uncertainty information relative to CARRA by (1) providing it for more physical quantities than CARRA, and (2) also provide uncertainty estimates which vary with time, space and weather situation rather than overall average uncertainties for each vertical level. Overall, verification statistics indicates (see section 6.5) that at least for the near-surface variables of the reanalysis, CARRA will be more accurate than ERA5.
...
To supplement this we provide in the following a short description of the service as well as a description of basic principles of data assimilation in numerical weather prediction aimed at a non-specialist audience.
The service
The C3S_322_Lot2 contract of the Copernicus Climate Change Service produces and delivers a regional reanalysis (RRA) for the Arctic including long-term datasets of Essential Climate Variables (ECVs) for the 24 year period from 1997 to 2021a period from September 1990 to present. The data is updated on a monthly basis with 2-3 months of delay with respect to the real time (it means that at the end of month N, new data is added for the month N-2). The model domains are shown in Figure 1. The modelling system has a resolution of 2.5x2.5 km2 and 65 vertical levels in the atmosphere. The produced datasets are freely available and can be used by anyone who wishes detailed long-term atmospheric data, for instance to study typical ranges of meteorological variables, as a reference for climate model runs, or to investigate highly resolved changes in the ECVs during the reanalysis period. An extended model version for the entire Arctic (pan-Arctic) area is run for a period of 1 year as a proof-of-concept.
The reanalysis model is the weather forecasting model HARMONIE-AROME cy40h1.1.1, which has been enhanced with more Arctic input data, and more extensive surface and atmospheric data assimilation. Data assimilation is explained further in section 7.2. Additionally, the model formulation has been improved with a specific focus on processes essential in the Arctic.
...