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Domain | Number of tested month long Period | Conclusions | Decision | |
Test of grid types | West domain, | 2 | Quadratic grid slightly better than cubic on wind | Use quadratic grid for both domains weighing on both scores and computation efficiency |
East domain, | 4 | Quadratic grid | ||
Mixed phase microphysics option OCND2 | West domain | 4 | OCND2 option verify better on precipitation in spring and summer but slightly worse in MSLP | Use OCND2 option |
Large scale constraints | Both domains | 4 | Both LSMIX and Jk improves large scale scores; Jk needs further tuning | Use LSMIX |
Structure function derived with obs perturbation vs Brand | Both domains | 2 | Mixed results for most parameters, better on humidity for Brand | Use Brand |
Daily varying structure function | Both domains | 2 | Beneficial with interpolated structure function | Use interpolated daily-varying structure function |
Incremental Analysis Update | Both domains | 1 | Minor benefit. More tuning needed | Not ready for implementation yet |
Non-GTS surface data from national or climatological network | Both domains | 2 | Clear benefit on surface temperature | Inclusion in surface analysis |
Modification of glacier handling with annual snow depth re-initialisation and use of satellite derived snow albedo | Both domains | 2 | Clear benefit on surface temperature especially over glaciers | Inclusion in surface analysis |
Variational bias correction for aircraft data | West domain | 1 | Minor positive impact | Not ready for implementation |
Microwave radiance | Both domains | 10 | Slight positive impacts | Inclusion with amsu a/b, MHS, MSU |
Infrared radiance | Both domains | 4 | Slight positive impacts | Inclusion with data starting 2007 |
AMV wind, (polar and geostationary), reprocessed | Both domains | 4 | Neutral to positive | Inclusion |
Radio Occultation bending angle, reprocessed | Both domains | 3 | Neutral to positive | Inclusion |
Scatterometer | Both domains | 4 | Positive impacts | Inclusion |
OSISAF-ESA CCI | Both domains | 3 | Slightly positive | Inclusion |
PGD database | Both domains | 3 | Slightly positive | Inclusion |
Satellite snow on visible channel during summer half year | Both domains | 3 | Slightly positive | Inclusion |
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The presentation of the detailed verification results
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In the following, of all these experiments are not in the scope of this document, however hereafter we present some of the verifications in order to highlight the evaluations on individual components.
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Selection of quadratic grid
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One of the significant advantages with the CARRA reanalysis is the use of a large amount of additional surface observation data on top of the GTS data, the latter are the main data used by ERA5. These additional data amounts 3 to 5 times more than those from GTS, enhancing greatly the surface observation data coverage by CARRA for this Arctic region with generally sparse conventional observing network. The collected non-GTS data in CARRA consists mainly of those from national databases of the Nordic meteorological services and other external sources. For the domain of CARRA-West, e.g., additional station data are collected via DMI from the glacier observation networks of PROMICE (Programme for Monitoring of the Greenland Ice Sheet, Denmark), GC-NET (Greenland Climate Network), and the coastal network ASIAQ Greenland Survey.
As an illustration of quality enhancement with CARRA thanks to assimilation of the additional surface observations, in Figure 2.7, results of a sensitivity test with and without use of the non-GTS, additional surface observation data is shown. Here daily averaged time series for STD and BIAS T2m errors with CARRA-West is shown for the 40-day period between 1 Sept and 10 October 2009. In this test non-GTS data is turned off for 20 days between 10 September and 30 September. The remaining periods are all run with full data set with both GTS and additional data sets. The verification in Figure 2.7 is made with different combinations of station lists to illustrate the impact of assimilating additional surface data. Clearly, assimilation of additional observation data has been overall positive for all stations, especially for the glacier areas represented by glacier station list GCNET, PROMICE, which are both poorly modelled and seldom represented by observations in NWP models.
Figure 2.7: Daily averaged CARRA T2m time series between 1 Sept and 10 Oct 2009 for the CARRA-West domain. In contrast to ERA5, CARRA uses large amount of non-GTS surface observation data (in green). In the sensitivity test, these additional data are withdrawn during a 20-day period between Sept 10 and Sept 30 (in red). The plots are from upper left to lower right: a) is averaged for all stations, b) for Greenland stations, c) for Iceland, d) for Svalbard, e) for the glacier network GCNET in Greenland, f) for the glacier network PROMICE.
Impact of satellite snow information
For CARRA reanalysis, a dataset using the "CryoRisk Snow extent" product using the approach developed at MET Norway (Homleid et al, 2016), which is based on AVHRR data, has been produced. This is available daily at 5 km resolution for the CARRA period (Yang et al, 2020). The data set is activated for the period between March and October each year. In Figure 2.8, impact of the assimilation satellite snow data for CARRA is illustrated by comparing averaged T2m errors in STD and BIAS along forecast lead time for CARRA-West domain during the spring melting season, from 20 March to 30 April 2007. The results show some benefit in T2m temperature for the period, especially for an Iceland station list, indicating benefit of satellite snow information for simulation of melting seasons in Iceland.
Figure 2.8: Averaged CARRA T2m error in STD and BIAS along forecast lead time during 20 March and 30 April 2007 for (upper panel) CARRA-West domain and (lower panel) for an Iceland station list, comparing baseline (in red) with the run using satellite snow observation (in green).
Impact of Atmospheric Motion Vector (AMV) data
The impact of atmospheric motion vectors on forecast scores was tested by running assimilation experiments in January 2017. At first, the impact was neutral or negative for wind and temperature when forecasts were verified against radiosondes. After doing Desroziers diagnostics (Desroziers et al, 2005) on the observation minus first guess and observation minus analysis departures it seemed like there was a possibility that neighboring observation errors were correlated. Therefore the thinning distance was increased from 45 to 60 km. The observation errors for AMVs were also modified after comparing the CARRA settings with those of ERA5. After that, the forecast impact became neutral to positive. Experiments were done on both of the CARRA domains and the results were similar. Figure 2.9 shows the verifications for CARRA-East domain.
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Radiances from the Microwave Sounding Unit (MSU) on the NOAA-11 and NOAA-14 satellites are used from the start of the CARRA dataset in 1997. After spinning up the bias correction coefficients the impact on forecast scores was tested for 30 days in September 1997. The overall impact as shown in Figure 2.10 indicate indicates a neutral impact, with a very slight positive signal on temperature and wind speed compared to radiosondes.
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Figure 3.1: The daily averaged 2m temperature time series of STD and BIAS errors (Y-axis) by various versions of the CARRA reanalysis in comparison to synoptic stations for the month of (top to bottom) September 1997 (upper panel), January 2000 (mid panel) and April 2007 (lower panel).CARRA versions are shown with different colors in cyan for carra_alpha2, magenta for carra-beta1, blue for carra-beta2, green for carra-rc1, red for CARRA production.
Figure 3.1 shows the averaged 2m teperature daily time series for STD and BIAS in comparison to surface observations for the months of September 1997, January 2000 and April 2007 by various tagged CARRA versions. The results confirm that CARRA reanalysis system indeed progresses gradually in terms of verification scores with each intermediate release, in a rather solid manner. Especially for STD scores, there is in general a tendency of clear improvement from the earliest validated version, CARRA-alpha2, to the final CARRA version. Among these, the improvement in the final version of CARRA is believed to be associated with the activation of non-GTS observation data, which has the clearest impact on screen level temperature.
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