UP3.6

In recent years, the reanalysis efforts have been focused on increasing the spatio-temporal resolution to better capture the variability of the atmospheric state between the mesoβ up to synoptic scales. An approach to gain a substantial increase in horizontal resolution are regional reanalyses which utilize a limited area model instead of a global one and thus focus on a specific region of the world. For the European continent several regional reanalyses are now available, e.g., HARMONIE (11km) or COSMO-REA6 (6km).

This work presents ongoing work of a variety of applications and evaluations made possible through the hourly 6km data of the COSMO-REA6 reanalysis product. 
Snow and precipitation analysis and events are going to represent better the smaller scales. Here we will show the representation of the scaling properties of the precipitation extremes and an evaluation of the 2005 extreme early winter snow event which happened in Western Germany.  With this reanalysis we also investigated the potential of season identification, finding that gridpoint-wise vertical temperature and wind information provides efficient ways to classify probabilistically a single day into a specific season.  Moreover, the calculation of the bio-climatic variables from COSMO-REA6 allowed the comparison with the Worldclim 2.0 dataset. The main advantage of the newly derived bio-climatic variables is the internal consistency between the temperature and precipitation-related variables. Furthermore, from the increment analysis, we investigate the soil moisture biases and how they relate to the missing representation of irrigation processes in the COSMO model. Results show that there is a similarity in the specific humidity increments and both the irrigation annual cycle and spatial distribution of irrigated areas. With physically consistent spatio-temporal fields it is possible to investigate human discomfort measurements and their relation to temperature indexes. Lastly, extended use of the COSMO-REA6 data was made for renewable energy applications both for wind turbines and photovoltaic cells. The examples given in this presentation will highlight the general potential of high-resolution reanalyses to be part of transdisciplinary problems related to climate and weather variability.  

How to cite: Hense, A. and the The Bonn/Cologne group of Hans Ertel Center for Weather Research: The  regional reanalysis COSMO-REA6 and its transdisciplinary applications, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-464, https://doi.org/10.5194/ems2021-464, 2021.

The Copernicus European regional reanalysis (https://climate.copernicus.eu/regional-reanalysis-europe) is produced as part of the Copernicus Climate Change Service (C3S). The presentation will introduce the service and its main objectives as well as it will give and overview of available data. Data quality will be demonstrated by comparison with ERA5 and other gridded datasets.

In the first phase of the service, systems inherited from the FP7 project UERRA (Uncertainties in Ensembles of Regional ReAnalyses, http://www.uerra.eu) were applied extending the UERRA-HARMONIE as well as the MESCAN-SURFEX datasets. These datasets contain analyses of the atmosphere, the surface and the soil. UERRA-HARMONIE is a full model system including a 3D-Var data assimilation scheme for upper air observations and an OI-scheme for surface observations. MESCAN-SURFEX is a complementary 2D surface analysis system interfaced to a land surface model. Data is available for entire Europe at a horizontal resolution of 11 km for UERRA-HARMONIE and at 5.5 km for MESCAN-SURFEX. The systems provide four analyses per day – at 0 UTC, 6 UTC, 12 UTC, and 18 UTC. Between the analyses ranges, forecasts of the systems are available with hourly resolution. More than fifty parameters are available on various level types. Data are available for the period 1961 – July 2019 through Copernicus Climate Data Store (CDS).

In spring 2020, the service started the production of the next generation regional reanalysis. The successor comprises three components:
- CERRA (5.5 km horizontal resolution)
- CERRA-EDA (10-member ensemble at 11 km resolution)
- CERRA-Land (5.5 km horizontal resolution)

In addition to the higher resolution, CERRA is more sophisticated than UERRA. For instance, more observations are assimilated into CERRA, in particular remote sensing data. CERRA is produced with 3-hourly cycling and a flow depending part of the B-matrix is derived from CERRA-EDA. The production of CERRA, CERRA-EDA and CERRA-Land will complete in September/October 2021 and data will become available in the CDS shortly thereafter.

The quality of the regional reanalysis in comparison to ERA5 will be shown with results of the standard HARMONIE-verification package as well as based on certain case studies. For instance, the winter storm Gudrun (January 2005, southern Sweden) will be investigated.

How to cite: Schimanke, S., Isaksson, L., Edvinsson, L., Ridal, M., Berggren, L., Hopsch, S., El-Said, A., Glinton, M., Bazile, E., Le Moigne, P., Verrelle, A., Dahlgren, P., and Randriamampianina, R.: Copernicus European regional reanalysis, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-389, https://doi.org/10.5194/ems2021-389, 2021.

The Copernicus Climate Change Service of the European Commission aims to produce and deliver a land regional reanalysis for Europe covering the period from 1984 to the present at a resolution of 5.5 km.

Accurate spatial and temporal datasets of precipitation and surface variables are essential for water resource management and climate change studies. The need for higher spatial and temporal resolution of precipitation and surface variables is a demand that leads regional reanalysis.

CERRA-Land (Copernicus European Regional Re-Analysis) is a regional land surface reanalysis dataset which describes the evolution of soil moisture, soil temperature and snowpack. CERRA-Land is the result of a single stand alone integration of the SURFEX V8.1 land surface model driven by meteorological forcing from the CERRA atmospheric reanalysis and a daily accumulated surface precipitation analysis produced with the MESCAN system (Soci et al., 2016), which performs an optimal interpolation between CERRA forecast precipitation and in situ rain gauges.

No downscaling method was used to build up the forcing data because the system has the same grid as the CERRA atmospheric reanalysis. To better represent the permafrost active layer, the effect of soil organic carbon on hydraulic and thermal soil properties was taken into account in the northern part of the domain. To solve both heat and water transfer equations in the soil, a discretization of the soil into 14 layers was used. A 15 year spin-up of the model was required to reach an equilibrium.

The quality of CERRA-Land is assessed by comparisons to ground-based observations, such as of snow depth and turbulent, latent and sensible fluxes. A comparison with ERA5-Land will be done and the added value of this regional dataset will be discussed. The entire CERRA-Land dataset from 1984 to present is expected to be available for public release later in 2021.

How to cite: Verrelle, A., Glinton, M., Bazile, E., and Le Moigne, P.: CERRA-Land : A new land surface reanalysis at 5.5 km resolution over Europe, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-492, https://doi.org/10.5194/ems2021-492, 2021.

This presentation provides an overview of the different upper-air wind data records available at EUMETSAT for usage in global and regional reanalysis. The assimilation of Atmospheric Motion Vectors (AMV) is recognised to be important to reduce the forecast errors in NWP model runs. In support of the Copernicus Climate Change Service (C3S), EUMETSAT produced several AMV Climate Data Records (CDR) from geostationary and low-earth orbit satellites for assimilation into ECMWF’s next global reanalysis ERA6.

Since the launch of its first generation of geostationary satellites, EUMETSAT has developed its own unique algorithms to derive atmospheric motion vectors (AMVs). These algorithms are used to provide real time AMVs using images acquired from instruments on-board both polar and geostationary satellites. These AMVs are routinely assimilated into weather forecast models. EUMETSAT archived all image data from its instruments (MVIRI and SEVIRI) in geostationary orbit and the global record of Advanced Very High Resolution Radiometer (AVHRR) data back to the late 1970s providing a suitable data source for climate research allowing the production of consistent AMV CDRs over the entire period.

Two long AMV data records are available now from the geostationary sensors on Meteosat-2 to Meteosat-10 covering 1981-2017 over Africa and Europe and from AVHRR Global Area Coverage (GAC) data from 16 AVHRR instruments starting with the TIROS-N satellite and covering polar AMVs over the Northern and Southern hemisphere from 1978-2019. In addition, full resolution AVHRR images (Local Area Coverage (LAC)) from the AVHRR aboard the polar orbiting Metop-A and -B satellites were used to generate a CDR containing polar AMVs from single satellite retrievals and global AMVs from the combined Metop-A/B dual satellite retrieval starting in 2007 and 2013, respectively.

For all data records, the EUMETSAT AMV algorithm adapted for climate purposes was used and extensive validation of the data records were performed. It shows that the CDR are homogeneous and very stable over the period. They are suitable for usage in model reanalysis and climate analysis. The CDR are in agreement with ground based radiosonde and model data. For the polar AMVs, a remarkable agreement with MODIS AMVs has been found.

To better serve closer to real time needs for reanalysis, EUMETSAT is experimenting with the continuous production of an Interim Climate Data Record (ICDR) with a timeliness close to real-time. With a still not completely operational low-cost approach, a timeliness of 83% within 18 hours at similar quality was achieved.

In addition to the existing data records the presentation provides the plan for future improvements and new CDR releases for AMV data records in the coming years. In particular, the use of better information on multi-layer cloud objects in AMV retrievals is a central part for the improvements of the AMVs from geostationary orbit.  

How to cite: Doutriaux-Boucher, M., Huckle, R., Lattanzio, A., Sus, O., Onderwaater, J., Grant, M., and Schulz, J.: Climate data record of atmospheric motion vectors at EUMETSAT for usage in reanalysis, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-445, https://doi.org/10.5194/ems2021-445, 2021.

Regional winds are caused by small-scale pressure differences in a way that important air flows can arise in a very small and specific region. Sometimes an orographic feature, such as a channel like the Ebro Valley or the Strait of Gibraltar, lead the wind, due to mass conservation, to acquire a certain specific range of directions and considerable speed. In the regions where they are observed, the wind is of great importance not only for the climatology and meteorology of these areas but also for their culture and identity. However, it is difficult to analyze them using the most common reanalysis products, since their spatial resolutions are not high enough to properly describe the orographic characteristics that lead to the regional winds in specific locations. Here, we will explore the application of the COSMO-REA6 high resolution reanalysis system for the assessment of the main regional winds in the Iberian Peninsula: the cierzo wind in the Ebro Valley and the levante and poniente winds in the Strait of Gibraltar, for the 2000-2018 period. COSMO-REA6 uses a spatial resolution of 6 km (0.055º), which is much larger than previous reanalysis and regional modelling databases, so it can better capture the orography of the areas and therefore the regional winds we intend to study. The cierzo, levante and poniente winds are very relevant in the Iberian Peninsula due to their intensity and their frequency. Defined with a 5 m/s threshold for each hour, and their specific direction range, around 95, 150 and 110 wind days per year are obtained, respectively. Their study may also be important for other reasons, such as the production of renewable energy in these areas. First, we conduct a preliminary assessment of wind speed and direction with hourly data from weather stations, which have been obtained from the HadISD global sub-daily dataset. Then, we compare data from stations with COSMO-REA6 reanalysis in each location and produce a spatial description of the reanalysis in the Peninsula. We also study the atmospheric patterns associated with the regional winds characterized above. Due to the few studies that have been carried out on regional winds in the Iberian Peninsula, these results can be of great interest for various fields, such as meteorology, climatology and the generation of renewable energy.

How to cite: Ortega, M., Sánchez, E., Gutiérrez, C., and Molina, M. O.: Regional winds over the Iberian Peninsula with COSMO-REA6 high resolution regional reanalysis: cierzo, levante and poniente, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-140, https://doi.org/10.5194/ems2021-140, 2021.

The exploitation of offshore wind energy is an essential part of the German energy transition (Energiewende). The planning of new offshore wind farms demands detailed information on wind conditions at turbine hub heights in the North Sea and Baltic Sea. High-resolution reanalyses which are based on state-of-the-art numerical weather prediction (NWP) models combined with data assimilation systems offer the required meteorological data which are suitable for climatological assessment.

The regional reanalysis COSMO-REA6 operated by Germany’s national meteorological service (Deutscher Wetterdienst, DWD) provides hourly data of 6 km horizontal resolution for 1995-2019/08 (Kaspar et al., 2020). Moreover, hourly data of 31 km horizontal resolution for 1950 to present are available from the global reanalysis ERA5 produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). DWD delivers reanalysis data and statistical evaluation results to Bundesamt für Seeschifffahrt und Hydrographie (BSH) in order to facilitate offshore site tenders. Data and a report were recently published as part of the tenders for 2021 (https://pinta.bsh.de/).

Here we present an evaluation of the 100 m wind speed and direction from COSMO-REA6 and ERA5 based on a comprehensive statistical analysis. On the reference side the FINO measurements (Research platforms in the North Sea and Baltic Sea, https://www.fino-offshore.de/en/index.html) from FINO1 and FINO2 are used. The FINO measurements are not used by the data assimilation schemes of the two reanalyses and therefore constitute independent reference data. The focus is on episodes prior to the installation of wind farms in the direct vicinity of the FINO platforms to avoid wake effects. The quality of the two reanalyses is compared to other state-of-the-art reanalyses and wind atlas data.

Reference:

Kaspar et al. (2020): Regional atmospheric reanalysis activities at Deutscher Wetterdienst: review of evaluation results and application examples with a focus on renewable energy, Adv. Sci. Res., 17, 115–128, https://doi.org/10.5194/asr-17-115-2020.

How to cite: Spangehl, T., Borsche, M., Niermann, D., Kaspar, F., and Tinz, B.: Evaluation of high-resolution reanalyses for wind energy application in the North Sea and Baltic Sea, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-241, https://doi.org/10.5194/ems2021-241, 2021.

The number of severe weather events at the Arctic region increased significantly. Its formation related generally to the mesoscale processes including downslope windstorms over Novaya Zemlya, Svalbard, Tiksi bay accompanied by strong winds. Therefore, its investigation required detailed hydrometeorological and climatic information with a horizontal resolution of at least several kilometers. This work aims to investigate extreme wind speeds statistics associated with downslope windstorms and evaluate it according to the COSMO-CLM Russian Arctic hindcast, ASR reanalysis, stations and satellite data.

COSMO-CLM Russian Arctic hindcast created in 2020 covers the 1980–2016 period with grid size ~12 km and 1-hour output step, containing approximately a hundred hydrometeorological characteristics, as well at surface, as on the 50 model levels. The primary assessments of the surface wind speed and temperature fields showed good agreement with ERA-Interim reanalysis in large-scale patterns and many added values in the regional mesoscale features reproduction according to the coastlines, mountains, large lakes, and other surface properties.

Mean values, absolute and daily maxima of wind speed, high wind speed frequencies were estimated for the COSMO-CLM Russian Arctic hindcast and the well-known Arctic System Reanalysis (ASRv2) for a 2000-2016 period. COSMO-CLM showed higher mean and daily maximal wind speed areas concerned to coastal regions of Svalbard and Scandinavia, over the northern areas of Taymyr peninsula. At the same time, the absolute wind speed maxima are significantly higher according to ASRv2, specially over the Barents Sea, near the Novaya Zemlya coast (differences are up to 15-20 m/s). The same pattern observed by a number of days with wind speed above the 30 m/s threshold. Compared with station data, the ASRv2 reproduced mean wind speeds better at most coastal and inland station, MAE are within 3 m/s. For absolute wind speed maxima differences between two datasets get lower, the COSMO-CLM hindcast is quite better for inland stations.

Model capability to reproduce strong downslope windstorms evaluated according to the observations timeseries over Novaya Zemlya, Svalbard and Tiksi stations during bora conditions. Generally, the ASRv2 reproduced the wind direction closer to observations and the wind speed worser than COSMO-CLM. The extreme wind speed frequencies during bora cases have less errors according to COSMO-CLM hindcast (up to ~5%) compared to the ASRv2 data (up to 10%). At the same time, moderate wind speed frequencies are reproduced by ASRv2 better.

Five specific Novaya Zemlya bora cases were evaluated according to SAR satellite wind speed data. Both ASRv2 and COSMO-CLM overestimated mean wind speed (MAE 0.5-6 m/s), maximal wind speed bias has different signs, however, the COSMO-CLM is better in most cases. Extreme percentiles biases (99 and 99.9%), correlation, structure and amplitude (according to the SAL method) are closer to observations by the COSMO-CLM hindcast.

How to cite: Platonov, V. and Shestakova, A.: Downslope windstorms and associated extreme wind speed statistics evaluation according to the COSMO-CLM Russian Arctic hindcast, ASR reanalysis and observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-300, https://doi.org/10.5194/ems2021-300, 2021.

Snow cover is a key component of alpine environments and knowledge of its spatiotemporal variability, including long-term trends, is vital for a range of dependent systems like winter tourism, hydropower production, etc. Snow cover retreat during the past decades is considered as an important and illustrative indicator of ongoing climate change. As such, the monitoring of surface snow cover and the projection of its future changes play a key role for climate services in alpine regions.

In Switzerland, a spatially and temporally consistent snow cover climatology that can serve as a reference for both climate monitoring and for future snow cover projections is currently missing. To assess the value and the potential of currently available long term spatial snow data we compare a range of different gridded snow water equivalent (SWE) datasets for the area of Switzerland, including three reanalysis-based products (COSMO-REA6, ERA5, ERA5-Land). The gridded data sets have a horizontal resolution between 1 and 30 km. The performance of the data sets is assessed by comparing them against three reference data sets with different characteristics (station data, a high-resolution 1km snow model that assimilates snow observations, and an optical remote sensing data set). Four different snow indicators are considered (mean SWE, number of snow days, date of maximum SWE, and snow cover extent) in nine different regions of Switzerland and six elevation classes.

The results reveal high temporal correlations between the individual datasets and, in general, a good performance regarding both countrywide and regional estimates of mean SWE. In individual regions, however, larger biases appear. All data sets qualitatively agree on a decreasing trend of mean SWE during the previous decades particularly at low elevations, but substantial differences can exist. Furthermore, all data sets overestimate the snow cover fraction as provided by the remote sensing reference. In general, reanalysis products capture the general characteristics of the Swiss snow climatology but indicate some distinctive deviations – e.g. like a systematic under- respectively overestimation of the mean snow water equivalent.

How to cite: Goeldi, M., Gubler, S., Steger, C., Scherrer, S. C., and Kotlarski, S.: The Swiss snow climatology as seen by reanalyses and further gridded datasets, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-282, https://doi.org/10.5194/ems2021-282, 2021.

Regional reanalysis constitute of potentially attractive new data source for many applications. They can offer added value benefiting from their higher spatial and temporal resolution. On the other hand, similarly as other data sources in regular network, reanalysis comes with uncertainties especially in the case of extremes.

The monitoring capability of reanalysis is essential for their usage as the reference datasets for climate model validation as well as for hydrological models. In this contribution, we evaluate the agreement of precipitation between modern reanalysis products (Era5, Era5 Land, Harmonie and Mescan-Surfex; with resolution between 5.5 and 32 km) and observed data at different time scales, from annual to subdaily. Studied characteristics of precipitation are for instance annual cycle of precipitation amount and the number of wet days, diurnal cycle of precipitation, and extremes. The common period for all datasets is 2002 – 2018.

Observed data used in this study are represented by adjusted radar-derived precipitation totals in 1km raster over the Czech Republic. The adjusted radar-derived precipitation totals are gained as follows. First, the radar-derived rain field is spatially adjusted to measurements from the rain gauges as a whole. For each day, the ratio between the mean 1-day precipitation total calculated from all rain gauges and the mean 1-day precipitation total estimated from the corresponding radar pixels is determined and used for a multiplication of radar-derived precipitation in every pixel of the radar domain. Second, the spatially adjusted radar-derived rain rates are locally adjusted in individual pixels based on the distance from the closest rain gauge, whereas the weight of the observed precipitation is decreasing with increasing distance to the given pixel. Adjusted daily precipitation total is then divided according to 10 min radar-derived estimates, from which the precipitation accumulations of longer duration are calculated.

How to cite: Bližňák, V., Pokorná, L., and Rulfová, Z.: Uncertainties in ERA-5 and UERRA reanalyses detected by adjusted radar precipitation - a regional study for the Czech Republic, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-60, https://doi.org/10.5194/ems2021-60, 2021.

Climate change is set to affect extreme climate and meteorological events. The combination of interacting physical processes (climate drivers) across various spatial and temporal scales resulting to an extreme event is referred to as compound event. So far, climate change impacts on compound events in Greece such as daily cold-wet events have not been explored. The complex geography and topography of Greece forms a variety of regions with different local climate and a great range in daily minimum temperature and precipitation distributions. This leads to the assumption that there we will also observe a variety in the distribution of cold-wet events depending on the region. Aim of our study in this work is first to identify the cold-wet events based on observational data and then to examine the predictive capability of regional different climate models and ERA-Interim against observations from the Hellenic National Meteorological Service (HNMS) stations for the occurrence of cold-wet compound events in the present climate. The study will focus on the colder and wetter period of the year (November-April) to determine the extremes for this period. Specifically, the datasets employed are from two EURO-CORDEX Regional Climate Models (RCMs) with 0.11° horizontal resolution and validated ERA-Interim Reanalysis downscaled with the Weather Research and Forecasting (WRF) model at 5km horizontal resolution, for the historical period 1980-2004. In particular, the RCM datasets analyses have been produced from SMHI-RCA4 driven by MPI-M-MPI-ESM-LR Global Climate Model (GCM) and CLMcom-CLM-CCLM4-8-17 driven by MOHC-HadGEM2-ES GCM. After the comparison with the observations, the gridded data from the models will give us the ability to observe the spatial distribution of the compound events.

How to cite: Markantonis, I., Vlachogiannis, D., Sfetsos, T., Kioutsioukis, I., and Politi, N.: An Investigation of cold-wet Compound Events in Greece, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-188, https://doi.org/10.5194/ems2021-188, 2021.

Aerosols are known to affect atmospheric processes on a wide range of spatio-temporal scales, from dust storms reducing incoming solar radiation to aerosol-climate feedbacks. Although plenty of studies address aerosol radiative forcing, there are persistent differences in current aerosol estimates from both, observations and models. Global reanalyses are able to provide consistent estimates of aerosol distributions by combining these two data sources. However, continuous assimilation of single satellite products forces the analyses towards the satellites climatology including possible inaccuracies. This study investigates differences between current estimates of aerosol optical depth (AOD) by addressing two questions: (1.) How well do we know the large-scale spatio-temporal pattern of present-day AOD across state-of-the-art data? (2.) How does current global aerosol reanalyses perform in comparison to other model- and observation-based data sets? To answer these questions, AOD from the global CAMS and MERRA-2 reanalyses is compared to 8 satellite products, 1 established climatology and 4 multi-model ensembles. The comprehensive data set used in this study allows to evaluate the performance of individual products concerning different spatial and temporal aspects. The evaluation covers results from 1998 to 2019, including most recently available products like the climate model inter-comparison project CMIP6.

Spatially and temporally averaged AOD from MERRA-2 agrees well with the mean satellite climatology, while the CAMS climatology is higher than most other products. With relative standard deviations of about 11%, temporal variations of CAMS and MERRA-2 agree well with the mean satellite variation. However, averaged AOD from the individual satellites show large differences, ranging from 0.124 for MISR to 0.164 for MODIS. In addition to average differences, spatial patterns vary significantly between the individual data sets. Because the CAMS reanalysis only assimilates AOD from MODIS, it remains close to the MODIS climatology which overestimates AOD in most regions in comparison to other products. This overestimation is considerably increased over eastern China were CAMS simulates regional values of more than 1.2 during summer. By assimilating both, MODIS and MISR data, the MERRA-2 reanalysis is closer to the satellite mean under most conditions. Although annual deviations remain small compared to other models, MERRA-2 tends to underestimate AOD at the equator and overestimates AOD at higher latitudes especially during the winter-season. The spatio-temporal differences between individual aerosol data sets underline the need for further research on both satellite retrievals and model simulations for aerosols. For example, integrating multiple observations in a reanalysis system would allow to compensate for inaccuracies of the individual products. Further developing the multi-scale coupled ICON-ART system at the German Weather Service provides a promising environment to achieve accurate aerosol climatologies on high spatial resolution.

How to cite: Vogel, A., Alessa, G., Scheele, R., Weber, L., and Fiedler, S.: Assessment of present-day estimates of AOD from global reanalyses against different satellite products and multi-model ensembles, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-228, https://doi.org/10.5194/ems2021-228, 2021.