Displays

YOPPsiteMIP is a coordinated process-based model evaluation projects using based on high-frequency multi-variate observations at some selected Arctic and Antarctic supersites, during the Year of Polar Prediction (YOPP). The aim of YOPPsiteMIP is to deepen our understanding on the representation of environmental prediction systems of polar processes, both in the atmosphere, land, sea-ice or ocean components, and in the coupling at their interfaces. Both Arctic and Antarctic sites are selected at key location which host multiple multiple systems deployed for long-term monitoring and suites of instruments (such as lidars, radars, ceilometers, radiometers), that provide detailed measurements characterizing the vertical column of the atmosphere as well as the surface conditions and energy fluxes. These observations extend far beyond the traditional synoptic surface and upper-air observations, and offer the opportunity for deepening our understanding of the physical processes governing the polar environment weather and climate.

The unique open dataset of paired model -output and multi-variate observations enables detailed process-based diagnostics, where the target processes include: the vertical representation of cloud and hydrometeors microphysics, low level (mix-phase) clouds; the representation radiation, turbulence, energy and momentum fluxes; stable boundary layer; atmosphere-snow interaction and ocean-sea ice-atmosphere coupling; ocean mixing; etc.

Several numerical weather prediction and climate model centers participate in the activities and some multimodel evaluation results will be presented and common biases are identified. Activities ongoing and planned for the MOSAiC observational site will also be presented.

How to cite: Svensson, G.: YOPPsiteMIP: Year of Polar Prediction site Model Inter-comparison Project, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11272, https://doi.org/10.5194/egusphere-egu2020-11272, 2020.

Energy exchange at the snow-atmosphere interface in winter governs the evolution of temperature at the surface and within the snow, preconditioning the snowpack for melt during spring. This study illustrates a set of diagnostic tools that are useful for evaluating the energy exchange at the Earth surface in a numerical weather prediction model from a process-based perspective using in-situ observations. In particular, a new way to measure model improvement using relationships between different terms in the surface energy budget (SEB) is presented. These process-oriented diagnostics provide a holistic view the realism of the balance of terms in the SEB, ensuring that improvements in headline skill scores, such as 2m temperature, are happening for the right reasons. Correctly capturing such process relationships is a necessary step to achieve reliable weather forecasts.

These diagnostic techniques are applied to assess the impact of a new multi-layer snow scheme in the ECMWF-Integrated Forecast System at two high-Arctic sites (Summit, Greenland and Sodankylä, Finland). The multi-layer scheme is expected to replace a single layer snow scheme enhancing the 2m temperature forecast accuracy and reliability across the northern hemisphere in boreal winter. 

How to cite: Day, J., Arduini, G., Magnusson, L., Sandu, I., Beljaars, A., and Richardson, D.: Measuring model improvement using surface energy budget process relationships: the impact of a new snow model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5763, https://doi.org/10.5194/egusphere-egu2020-5763, 2020.

Geographically confined, equatorward excursions of cold air masses into ice-free regions account for the majority of oceanic heat loss in key regions for deepwater formation in the North Atlantic. These cold-air outbreaks (CAO) are frequently accompanied by the development of severe mesoscale weather features, such as intense low-level jets and polar lows. Exchange of heat, moisture and momentum between the ocean and atmosphere in response to mesoscale features, either directly, or indirectly via modulating the longevity and intensity of the cold air mass modulates the wind-driven oceanic gyres. Yet, it remains unclear how often mesoscale cyclones accompany cold-air outbreaks, and how mesoscale features modify the air-sea interactions. 

Focusing on two key regions, the Labrador Sea and the Greenland/Norwegian Sea, we outline the temporal evolution of CAO events and associated mesoscale cyclogenesis. We apply objective detection to both CAO events and mesoscale cyclones and introduce an alternative metric to characterize the cold air mass. Despite the nearly 20 degrees difference in latitude, CAOs over both regions exhibit rather similar evolution, surface fluxes, and thermodynamic structure. The large scale configuration during CAO onset comprises a very cold upper level through over the CAO region and a surface cyclone downstream. As the CAO matures the cold air mass extends towards the south-east, accompanied by enhanced surface fluxes and destabilization of the CAO airmass. About 2/3 of the CAO events are accompanied by mesoscale cyclogenesis, with the majority of mesoscale cyclones originating inside the cold air masses. Neither the duration nor the maturity of the CAO event is relevant for the initiation of mesoscale cyclogenesis. Genesis conditions for mesoscale cyclogenesis during CAOs over the Labrador Sea are moister and exhibit stronger surface fluxes compared to their Norwegian Sea counterparts.

How to cite: Terpstra, A., Renfrew, I., and Sergeev, D.: Characteristics of Cold Air Outbreaks and associated Polar Mesoscale Cyclones in the North-Atlantic region, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22220, https://doi.org/10.5194/egusphere-egu2020-22220, 2020.

Nowadays, satellite observations are providing primary information for initial conditions of state-of-the-art numerical weather prediction (NWP) systems and the amount of remote sensing data in the Global Observing System increases rapidly. However, the way such data are assimilated is usually conservative and sub-optimal especially in high resolution limited-area models. Our objective is to improve the use of scatterometer observations from polar-orbiting satellites by taking into account the observation footprint and reducing the observation representation error through the observation operator.

The variational assimilation system (including 3D- and 4D-Var) of HARMONIE-AROME is widely used for research and operational NWP purposes by many European countries. In most cases, the HARMONIE-AROME model and its data assimilation are run on higher resolution (corresponding to around 2.5km grid size or smaller) than the effective resolution of some satellite observations (e.g. the effective resolution of scatterometer instruments). The use of ASCAT scatterometer observations is studied in an Arctic data assimilation system (AROME-Arctic) and a new observation operator (called supermodding) is evaluated in terms of scatterometer representation error. The results are demonstrated through data assimilation diagnostics, observing system experiments and case studies focusing on the challenges of the Arctic weather forecasting as well.

How to cite: Mile, M., Randriamampianina, R., and Marseille, G.-J.: Impact study of scatterometer observations with improved representation error in an Arctic data assimilation system, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7012, https://doi.org/10.5194/egusphere-egu2020-7012, 2020.

Stably-stratified atmospheric conditions still challenge numerical weather forecast, especially in high latitudes where they are frequently observed all year around. In stably-stratified atmosphere, surface is colder than air above. Such conditions suppress vertical turbulent mixing and may lead to surface layer decoupling in numerical models. Enhanced mixing could prevent decoupling but being implemented without sufficient care results in damped response of the surface layer meteorological variables on fluctuations of the weather conditions. In this study, we investigate weather prediction errors related to such a damped response. We run a group of operational prediction models (HIRLAM-HARMONIE, SL-AV) with a set of different turbulence parametrizations that includes HARATU, TOUCANS, and pTKE schemes. The results are compared with real weather observations and idealized GABLS setups proposed for a high latitude domain. We found that the systematic warm temperature bias in the models is caused by too slow response of the modelled temperature on the implied cooling. The largest (and quickly growing) errors are found over the first few hours of cooling, whereas in longer perspective the errors diminish as the model equilibrates with more stationary weather conditions. We develop a theory that may explain the observed structure of weather prediction errors. The explanation is based on the well-known coupling between the turbulent mixing intensity and the thickness of the mixed layer embedded into the parametrization of the mixing length scale. The required enhanced mixing could be provided by the energy-flux balance scheme by Zilitinkevich et al., but it does not reduce the warm bias as it makes the mixed deeper and less responsive. We propose more accurate limitations on the mixed layer thickness to improve the temporal structure of the surface layer temperature response in the weather prediction models.

How to cite: Esau, I., Outten, S., and Tolstykh, M.: Structure of weather prediction errors in stably-stratified atmospheric conditions , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2010, https://doi.org/10.5194/egusphere-egu2020-2010, 2020.

The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) had a special observing period (SOP) from November 16, 2018 to February 15, 2019, during which observational activity during austral summer in the Antarctic was greatly enhanced. More than 2000 additional radiosondes were launched during this 3-month period, roughly doubling the amount from routine programs. Further, several YOPP-endorsed projects contributed to enhanced data collection on various atmospheric and oceanic properties, including the Characterization of the Antarctic Atmosphere and Low Clouds (CAALC) project at King George Island (Antarctic Peninsula) and the Dynamics, Aerosol, Cloud And Precipitation Observations in the Pristine Environment of the Southern Ocean (DACAPO-PESO) field experiment in Punta Arenas (Sub-Antarctic Chile). Here we use the YOPP-SH-SOP observations to investigate the vertical structure of atmospheric rivers (ARs), along with their impact on cloud properties, radiative budgets, and precipitation in the Atlantic sector of Antarctica, including coastal areas of sub-Antarctic Chile, the Antarctic Peninsula and Dronning Maud Land (DML).

ARs can transport anomalous heat and moisture from subtropical regions to the Antarctic, with important impacts on Antarctic surface mass balance. On the Antarctic Peninsula, the surface mass balance can be especially sensitive to AR events during summer, when surface temperatures vary around zero and frequent transitions occur between snow and rainfall. The importance of ARs for the coastal DML is also linked to precipitation events during summer, but is more strongly linked to extreme snowfall events (rather than rainfall), and such events have resulted in anomalously high snow accumulation in DML in recent years.

We will present case studies that demonstrate how combining extensive ground-based observations and radiosoundings from stations in the sub-Antarctic and Antarctic allow for detailed characterization of the temporal evolution of AR events. Analysis of the observations and model sensitivity studies (using Polar-WRF) with additional radiosonde assimilation show the influence of ARs on the Antarctic atmospheric, cloud properties and surface precipitation, as well as the challenges in correctly forecasting conditions during such events. Further, we use SOP enhanced radiosonde programs at Neumayer and Syowa stations to investigate the AR signatures in the atmospheric vertical profiles in the DML coastal areas. The AR events observed during YOPP-SH are put in the context of the longer-term radiosonde observations using 10 years (from 2009 to 2019) of the Integrated Global Radiosonde Archive (IGRA) Version 2 data. The increased frequency of radiosonde observations during YOPP was crucial for elucidating the important contribution these rare events make to the moisture transport towards Antarctica. They also showed an added value in improving the forecast of weather conditions during AR events, which have important consequences for air, ship and station operations in Antarctica.

How to cite: Gorodetskaya, I. V., Rowe, P. M., Kalesse, H., Silva, T., Hirasawa, N., Schmithüsen, H., Seifert, P., Park, S.-J., Choi, Y., and Cordero, R. R.: The vertical structure of atmospheric rivers and their impact in the Atlantic sector of Antarctica from the Year of Polar Prediction observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20313, https://doi.org/10.5194/egusphere-egu2020-20313, 2020.

Radiosonde measurements are potentially valuable indicators of upper air climate change because of their unique long-term availability and their high vertical extent and resolution. The radiosonde network, however, is not a long-term stable measurement system, since it was designed for operational use. Changes in the observation system are frequent and surf the purpose of competitive daily weather prediction, but result in more or less clear breakpoints in the observed long-term time series. These artificial biases need to be removed. We apply a bias adjustment scheme for radiosonde temperatures and humidity based on departures from a recent reanalysis, ERA5 potentially back to 1950. Newly digitized and recovered radiosonde data have been used within ERA5 for the first time. We present long-term bias adjustments and trends as preliminary results. In particular, we focus on the water vapour transport into the Arctic as a result of polar amplification and meridional heat exchange.

How to cite: Blaschek, M., Ambrogi, F., and Haimberger, L.: Long-term Arctic homogenized radiosondes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15418, https://doi.org/10.5194/egusphere-egu2020-15418, 2020.

The Polar Prediction Project (PPP, www.polarprediction.net) is a 10-year (2013–2022) endeavour initiated by the World Meteorological Organization’s (WMO) World Weather Research Programme (WWRP). Aim of this wide international endeavour is to promote cooperative weather and sea-ice research enabling development of improved environmental prediction services for the polar regions, on time scales from hours to seasonal.

The PPP flagship activity, the Year of Polar Prediction (YOPP), has been launched in mid-2017 as a coordinated two-year period of intensive observing, modelling, verification, user-engagement and education activities. Since then, scientists and operational forecasting centers worldwide have closely worked together to observe, model, and improve forecasts of the Arctic and Antarctic weather and climate systems. During three Special Observing Periods in the Arctic and Antarctic, routine observations such as radiosonde launches and buoy deployments were enhanced (in the Arctic: 1 February ­– 31 March 2018 and 1 July – 30 September 2018, in the Antarctic: 16 November 2018 – 15 February 2019), aiming to close gaps in atmospheric and sea-ice observations and to enable significant progress in environmental prediction capabilities for the polar regions and beyond.

in mid-2019, PPP has moved into its Consolidation Phase which will be key for the success of the initiative. Central activities and projects such as the YOPPSiteMIP initiative or the EU-project APPLICATE will significantly contribute to improving forecasts of weather and sea-ice conditions in polar regions and to make them available to its user community. Data collected during YOPP are available for everyone through the YOPP Data Portal (https://yopp.met.no/) to feed into improved environmental forecasting systems.

In this presentation, an overview of the main achievements accomplished during the three YOPP Special Observing Periods, current activities including two more Special Targeted Observing Periods (TOPs) as well as prospects for future evaluations of PPP are provided.

How to cite: Jung, T., Goessling, H., Werner, K., Pasqualetto, S., and Kirchhoff, K.: Recent Developments of the Year of Polar Prediction , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17902, https://doi.org/10.5194/egusphere-egu2020-17902, 2020.

The effects of a changing climate are manifesting rapidly, particularly in the Arctic region, and these changes are potentially influencing weather and climate in the mid-latitudes. To understand the scope of these changes and their impacts, it is fundamental to have a better understanding of these processes and work on enhancing weather and climate predictions. It is with this motivation that a European consortium of scientists set out to advance our capability to predict the weather and climate in the Arctic and beyond in the framework of the EU-funded H2020 project APPLICATE. The project started in 2016 with a budget of 8M€ with the objective of improving the representation of key processes in coupled atmosphere-sea ice-ocean models, delivering enhanced numerical weather forecasts, seasonal to interannual climate predictions and centennial climate projections. The project put particular emphasis on the linkages between the Arctic and mid-latitudes, which are explored through a coordinated multi-model approach using coupled atmosphere-ocean models. APPLICATE is contributing to the design of the future Arctic observing system to improve our capacity to reanalyse the climate system and enhance models’ predicting skills, establishing collaborations with other programmes (e.g., within the EU-Polar Cluster). The project has also strong stakeholder engagement and training components, which see the dissemination of the scientific results as a priority and aim to enhance the communication scope of the project and add to knowledge co-production. 

In this presentation, we will give an overview of APPLICATE activities as part of our effort to understand changes in the Arctic and their far-reaching impacts for both environment and communities. We will summarise the main achievements of the project since the start in November 2016 and outline the work of the various task teams until the end of the project. The results achieved so far demonstrate a vibrant engagement of young researchers in the field of climate science and the important role the project plays in developing these scientists.

How to cite: Bojovic, D. and Cristini, L. and the APPLICATE Consortium: Prospects for the APPLICATE Project on advanced prediction in the Arctic and beyond, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10333, https://doi.org/10.5194/egusphere-egu2020-10333, 2020.

We present results from a set of field campaigns conducted in an arctic valley and fjord environment in central Spitsbergen, Svalbard. These field campaigns, which are conducted as part of a graduate class at the University Centre in Svalbard (UNIS), address a range of phenomena typical for the arctic atmospheric boundary layer using both observational and numerical means. These phenomena include low-level jets, cold pools, drainage flows, and air-sea interactions, several of which typically are challenging to accurately model. On the observational side, we utilise a range of sensors and instrumentation platforms, such as portable weather stations, a tethersonde (anchored weather balloon), small temperature sensors (TinyTags), sonic anemometers, automatic weather stations, and drones. As of this year, the sensor suite will also constitute a wind lidar and a microwave temperature profiler. The resulting datasets represent a unique model-independent data set from a region where observations are otherwise sparse. On the numerical side, we utilise data from the high-resolution (2.5 km horizontal grid spacing) AROME-Arctic weather prediction model. AROME Arctic is run operationally by the Norwegian Meteorological Institute (MET Norway) for a domain covering Northern Fennoscandia, larger parts of the Barents Sea, and Svalbard. We use the model data both to plan our fieldwork and for interpreting our observations. In turn, we use the observations for improving our understanding of the mentioned phenomena and also for validating the model.

How to cite: Jonassen, M. O., Wickström, S., Cassano, J., Vihma, T., Spengler, T., Kral, S., Frank, L., Reuder, J., Valkonen, T., Kähnert, M., and Kristiansen, J.: Observations and simulations from an arctic fjord and valley environment in Svalbard, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13725, https://doi.org/10.5194/egusphere-egu2020-13725, 2020.

Satellite microwave radiances in atmospheric sounding bands, such as the 183GHz water vapour band, are an important source of data for Numerical Weather Prediction. However, these observations are frequently discarded in polar regions as they are also sensitive to the surface, and there is large uncertainty in the background surface emissivity which depends on the microphysical properties of the snowpack. We evaluate simulations of brightness temperature and emissivity from the Snow Microwave Radiative Transfer (SMRT) model for Arctic tundra snow at frequencies between 89 and 243GHz to assess the potential of being able to assimilate observations at key sounding frequencies, such as 183GHz. In-situ measurements of the surface snowpack were collected for 36 snow pits in Trail Valley Creek, near Inuvik, Canada during the March 2018 Measurements of Arctic Cloud, Snow, and Sea Ice nearby the Marginal Ice Zone (MACSSIMIZE) campaign, a collaboration between the Met Office, Northumbria University, Edinburgh University and the Universite de Sherbrooke. These snowpack measurements provide realistic microphysical snow properties as input to SMRT. We present the evaluation of SMRT simulations against surface-based radiometer observations and airborne observations taken with the Microwave Airborne Radiometer Scanning System (MARSS) and International Submillimetre Airborne Radiometer (ISMAR) on the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 research aircraft.

How to cite: Wivell, K., Sandells, M., Rutter, N., Fox, S., Harlow, C., and Essery, R.: Validating snow surface radiative transfer models between 89 and 243GHz using airborne observations over Arctic tundra, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3560, https://doi.org/10.5194/egusphere-egu2020-3560, 2020.

Polar expedition “Transarctica-2019” worked in the northern part of the Barents Sea in April 2019. One of the main goals was to study the interaction processes in the system “atmosphere – sea ice – ocean upper layer”. Complex synchronous observations in atmosphere, snow-ice cover and ocean were performed. Present study describes characteristics of atmospheric surface layer and heat balance of snow-ice cover during drift of RV “Akademik Treshnikov” to the north of the Archipelagos Franz Josef Land and Svalbard, in the area 80 – 82N, 30 – 45E, in comparison with observations at drifting station “North Pole-35”, worked in the same area in April 2008, and Research station “Ice Base Cape Baranova” in April 2019.

The characteristics of the near-ice atmospheric layer and energy exchange processes during the drift of the expedition Transarctica-2019 were significantly affected by the presence of clouds and the state of the ice cover. The influence of these factors led to decrease of radiative cooling of the surface, formation of warmer and wetter atmospheric boundary layer and to a weakening of the turbulent exchange between the atmosphere and the snow-ice cover.

Comparison of energy exchange characteristics calculated for the Bolshevik Island (79° N) and area, where expedition “Transarctica 2019” worked, showed good agreement between the monthly averaged values and trends in heat fluxes, despite the fact that in the first case the underlying surface was land surface, and in the second - sea ice cover.

Significantly different conditions were observed in the area of the drifting station “North Pole-35”, drifted in April 2008 about 300 km to the north of the “Transarctica 2019” area. The older and thicker sea ice cover and frequent occurrence of cloudless days, characterized by negative long-wave balance, caused here cooling of the surface, formation of a stable boundary layer, and large values of the sensible heat flux compared to observed during the expedition 2019. Position of “Transarctica-2019” to the south of the massifs of old and thick ice, in an area, characterized by medium-thick ice and, as consequence, more intense heat flux through sea ice cover, as well as the presence of leads, determined higher air and surface temperatures and relative humidity.

The work supported by the Ministry of Science and Higher Education of the Russian Federation (project no. RFMEFI61619X0108).

How to cite: Makhotina, I., Makshtas, A., and Kustov, V.: Arctic atmospheric surface layer in spring during expedition “Transarctica-2019”, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9047, https://doi.org/10.5194/egusphere-egu2020-9047, 2020.

Arctic sea ice extent has steadily declined in the past 30 years. Aside from the global impact on climate change, regional information on the sea ice presence and on its impact on oceanic and atmospheric patterns has witnessed a growing interest. There is a growing need for seasonal-to-decadal timescale climate forecasts to help inform local communities and industry stakeholders.

Here we examine the influence of sea-ice thickness observations on the predictability of the sea-ice and atmospheric circulation. We perform paired sets of ensembles with the HadGEM3 GCM starting from different initial conditions in a present-day control run. One set of ensembles start with complete information about the sea-ice conditions, and one set have degraded information. We investigate how the pairs of ensembles predict the subsequent evolution of the sea-ice, sea level pressure and circulation within the Arctic and beyond with the aim of quantifying the value of sea-ice observations for improving predictions.

How to cite: Flocco, D., Hawkins, E., Ponsoni, L., Massonnet, F., Feltham, D., and Fichefet, T.: Sea ice and atmosphere interactions and predictability: preliminary results using HadGEM3, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13771, https://doi.org/10.5194/egusphere-egu2020-13771, 2020.

Sea ice models have become essential components of weather, climate and ocean models. The reliability of process studies, environmental forecasts and climate projections alike depend on a realistic representation of sea ice. Developing and evaluating sea ice models requires methods for both large scales and fine-scale geomorphological structures such as linear kinematic features (LKF). We introduce a Multiscale Directional Analysis (MDA) method that diagnoses distributions of LKF orientation and intersection angles. The MDA method is different from previous methods in that it (a)  takes into account the width of LKFs instead of estimating the orientation of centerlines; (b) separates curve-like features from point-like features providing the opportunity to reach a unified definition of LKF in both numerical and observational fields; (c) estimates scale-dependent intersection angles.

How to cite: Mohammadi Aragh, M., Losch, M., and Goessling, H.: Comparing Arctic Sea Ice Model Simulations to Satellite observations by Multiscale Directional Analysis of Sea Ice Deformation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18293, https://doi.org/10.5194/egusphere-egu2020-18293, 2020.

Potentially high-impact warm and wet winter conditions have become increasingly common in recent decades in the arctic archipelago of Svalbard. In this study, we document present 2m temperature, precipitation and rain-on-snow (ROS) climate conditions in Svalbard and relate them to different atmospheric circulation (AC) types. For this purpose, we utilise a set of observations together with output from the high resolution numerical weather prediction model AROME-Arctic. We find that 2m median temperatures vary the most across AC types in winter and spring, and the least in summer. Southerly and southwesterly flow is associated with 10th percentile 2m temperatures above freezing in all seasons. In terms of precipitation, we find the highest amounts and intensities with onshore flow over open water. Sea ice appears to play a strong role in the local variability in both 2m temperature and precipitation. ROS is a frequent phenomenon in the study period, in particular below 250 m ASL. In winter, ROS only occurs with AC types from the southerly sector or during the passage of a low pressure centre or trough. Most of these events occur during southwesterly flow, with a low pressure center west of Svalbard.

How to cite: Wickström, S., Jonassen, M. O., Cassano, J., Vihma, T., and Kristiansen, J.: Present temperature, precipitation and rain-on-snow climate in Svalbard, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21647, https://doi.org/10.5194/egusphere-egu2020-21647, 2020.

Weather forecasting in the Arctic and Antarctic is a challenge above all due to rarity of observations to be assimilated in numerical weather prediction (NWP) models. As observations are expensive and logistically challenging, it is important to evaluate the benefit that additional observations could bring to NWP.

Considering the Arctic, in this study the effects of the spatial coverage of the network on numerical weather prediction were evaluated by comparing radiosonde observations from land station taken from Integrated Global Radiosonde Archive (IGRA) and radiosonde observations from expeditions in the Arctic Ocean with operational analyses and background fields (12‐hr forecasts) of the European Centre for Medium Range Weather Forecasts (ECMWF). The focus was on 850 hPa level temperature for the period January 2016 – September 2018. Comparison of the analyses and background fields showed that radiosoundings had a remarkable impact on improving operational analyses but the impact had a large geographical variation. In particular, radiosonde observations from islands (Jan Mayen and Bear Island) in the northern North Atlantic and from Arctic expeditions substantially improved analyses suggesting that those observations were critical for the quality of analyses and forecasts. Comparison of two cases with and without assimilation of radiosonde sounding data from expeditions of Icebreaker Oden in 2016 and 2018 in the central Artic Ocean showed that satellite observations were not able to compensate for the large spatial gap in the radiosounding network. In the areas where the network is reasonably dense, the density of the sounding network was not the most critical factor for the quality of background fields. Instead, the quality of background field was more related to how radiosonde observations were utilized in the assimilation and to the quality of those observations.

Considering the Antarctic, we applied radiosonde sounding and Unmanned Aerial Vehicles (UAV) observations from an RV Polarstern cruise in the ice-covered Weddell Sea in austral winter 2013 to evaluate the impact of their assimilation in the Polar version of the Weather Research and Forecasting (Polar WRF) model. Our experiments revealed small or moderate impacts of radiosonde and UAV data assimilation. In any case, the assimilation of sounding data from both radiosondes and UAVs improved the analyses of air temperature, wind speed, and humidity at the observation site for most of the time. Further, the impact on the results of 5-day long Polar WRF experiments was often felt over distances of at least 300 km from the observation site. All experiments succeeded in capturing the main features of the evolution of near-surface variables, but the effects of data assimilation varied between different cases. Due to the limited vertical extent of the UAV observations, the impact of their assimilation was limited to the lowermost 1-2 km layer, and assimilation of radiosonde data was more beneficial for modelled sea level pressure and near-surface wind speed. Considering the perspectives for technological advance, atmospheric soundings applying UAV have a large potential to supplement conventional radiosonde sounding observations.

The differences in the results obtained for the Arctic and Antarctic are discussed.

How to cite: Vihma, T., Naakka, T., Sun, Q., Nygård, T., Tjernström, M., Jonassen, M., Pirazzini, R., and Brooks, I.: Impact of assimilation of radiosonde and UAV observations on numerical weather prediction analyses and forecasts in the Arctic and Antarctic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21750, https://doi.org/10.5194/egusphere-egu2020-21750, 2020.

In the framework of the Applicate project (https://applicate.eu), ECMWF (European Centre for Medium-Range Weather Forecasts) performed global (Bormann et al. 2019) and Arctic (Lawrence et al. 2019) observing system experiments. Use of the results of these experiments as lateral boundary conditions (LBC) for our regional model opens opportunity to study the following: 1) the impact of observations through regional data assimilation (DA); 2) the impact of observations that are assimilated in a global model through LBC in a regional model; 3) the impact of global loss of observations in a regional model; and 4) the impact of non-Arctic observations in an Arctic regional model.

In the framework of the Alertness project, we performed experiments for the two special observation periods (SOP) 1 and 2 and found considerable impact (significant for some cases) of both conventional and satellite observations through both regional DA and LBC. So far, the impact of non-Arctic observations on our Arctic regional model AROME-Arctic analyses and forecasts was checked during SOP1 with microwave radiance only. The impact was found to be positive, especially on day-2 forecasts.

In this presentation, the impact of other non-Arctic observations (conventional and satellite) on our regional model AROME-Arctic will be discussed through different forecast skill scores verification.

How to cite: Randriamampianina, R.: Impact of non-Arctic observations on the AROME-Arctic regional model , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11160, https://doi.org/10.5194/egusphere-egu2020-11160, 2020.