Nonhydrostatic Icosahedral Model Research Articles

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

Efficient and stable coupling of the SuperdropNet deep-learning-based cloud microphysics (v0.1.0) with the ICON climate and weather model (v2.6.5)

Abstract. Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm-rain processes in cloud microphysics, with ICON (Icosahedral Nonhydrostatic) model v2.6.5. SuperdropNet is trained on computationally expensive droplet-based simulations and can serve as an inexpensive proxy within weather prediction models. SuperdropNet emulates the collision–coalescence of rain and cloud droplets in a warm-rain scenario and replaces the collision–coalescence process in the two-moment cloud microphysics scheme. We address the technical challenge of integrating SuperdropNet, developed in Python and PyTorch, into ICON, written in Fortran, by implementing three different coupling strategies: embedded Python via the C foreign function interface (CFFI), pipes, and coupling of program components via Yet Another Coupler (YAC). We validate the emulator in the warm-bubble scenario and find that SuperdropNet runs stably within the experiment. By comparing experiment outcomes of the two-moment bulk scheme with SuperdropNet, we find that the results are physically consistent and discuss differences that are observed in several diagnostic variables. In addition, we provide a quantitative and qualitative computational benchmark for three different coupling strategies – embedded Python, coupler YAC, and pipes – and find that embedded Python is a useful software tool for validating hybrid ML ESMs.

European CH4 inversions with ICON-ART coupled to the CarbonTracker Data Assimilation Shell

Abstract. We present the first application of the ICOsahedral Nonhydrostatic model with Aerosols and Reactive Trace gases (ICON-ART) in inverse modeling in inverse modeling of greenhouse gas fluxes with an ensemble Kalman smoother. For this purpose, we extended ICON-ART to efficiently handle gridded emissions, generate an ensemble of perturbed emissions during runtime and use nudging on selected variables to keep the simulations close to analyzed meteorology. We show that the system can optimize total and anthropogenic European CH4 fluxes on a national scale in an idealized setup using pseudo-observations from a realistic network of measurement stations. However, we were unable to constrain the sum of the natural emission sources of comparatively low magnitude. Also regions with low emissions and regions with low observational coverage could not be optimized individually for lack of observational constraints. Furthermore, we investigated the sensitivities towards different inversion parameters and design choices with 15 sensitivity runs using the same idealized setup, demonstrating the robustness of the approach when regarding some minimal requirements of the setup (e.g., number of ensemble members). Subsequently, we applied the system to real in situ observations from 28 European stations for three years, 2008, 2013 and 2018. We used a priori anthropogenic fluxes from the EDGARv6 inventory and a priori natural fluxes from peatlands and mineral soils, inland waters, the ocean, biofuels and biomass burning, and geology. Our results for the year 2018 indicate that anthropogenic emissions may be underestimated in EDGARv6 by ca. 25 % in the Benelux countries and, to a smaller degree, in northwestern France and southern England. In the rest of the domain, anthropogenic fluxes are corrected downwards by the inversion, suggesting an overestimation in the a priori. For most countries, this means that the a posteriori country-total anthropogenic emissions are closer to the values reported to the United Nations Framework Convention on Climate Change (UNFCCC) than the a priori emissions from EDGARv6. Aggregating the a posteriori emissions across the EU27 + UK results in a total of 17.4 Tg yr−1, while the a priori emissions were 19.9 Tg yr−1. Our a posteriori is close to the total reported to the UNFCCC of 17.8 Tg yr−1. Natural emissions are reduced from their a priori magnitude almost everywhere, especially over Italy and Romania–Moldova, where a priori geological emissions are high, and over the United Kingdom and Scandinavia, where emissions from peatlands and wetlands were possibly unusually low during the hot and dry summer of 2018. Our a posteriori anthropogenic emissions for the EU27 + UK fall within the range estimated by global top-down studies but are lower than most other regional inversions. However, many of these studies have used observations from different measurement stations or satellite observations. The spatial pattern of the emission increments in our results, especially the increase in the Benelux countries, also agrees well with other regional inversions.

Impact of Convection‐Permitting and Model Resolution on the Simulation of Mesoscale Convective System Properties Over East Asia

AbstractIn this study, two limited‐area convection‐permitting models (ICON‐CPMs) and convection‐parameterized models (ICON‐CParMs) covering the Asia monsoon region from ICOsahedral Nonhydrostatic model are conducted to investigate the sensitivities of Mesoscale Convective Systems (MCSs) to convection configurations and horizontal resolutions. We find that ICON‐CPMs outperform ICON‐CParMs and ERA5 in number, lifetime, size, shape, orientation, effective speed, and MCS‐mean precipitation. Compared to the satellite observation, MCSs in ICON‐CParMs and ERA5 are oversized (30%–66%) and have longer (21%–29%) lifetimes but occur less (37%–46%) frequently. When turning off the convection scheme, performances are improved to a large extent. The underestimation of MCS‐mean precipitation rates in ICON‐CParMs and ERA5 are improved in ICON‐CPMs. Moisture budgets show that low‐level winds rather than moisture determine the strength of MCS‐mean precipitation rate. The strong low‐level convergence produces strong updrafts in ICON‐CPMs and eventually results in strong MCS precipitation. The overly intense convective precipitation of MCSs in CPMs is an inherent problem since the scale of convection triggered at the gray zone grid spacing is larger than that in the real world. Although the MCS‐mean precipitation rate is closer to the satellite observation in ICON‐CPMs, it is actually a compensation for overly strong convective precipitation and overly weak stratiform precipitation. Upgrading resolution from 8 to 4 km in ICON‐CPMs has benefits in simulating MCS lifetime, size, shape, orientation, and precipitation but not in MCS number.

The Sensitivity of the Icosahedral Non-Hydrostatic Numerical Weather Prediction Model over Greece in Reference to Observations as a Basis towards Model Tuning

The ICON (icosahedral non-hydrostatic) numerical weather prediction model (NWP)’s sensitivity is evaluated for the geographical area of Greece. As ICON model has recently been endorsed operationally by the Members of the COSMO (Consortium for Small-scale Modeling); this attempt is in line with the further understanding of the model features, especially in the considered domain, characterized by a complex orography as well as an almost equally partitioned land–sea surface area. An extraordinary number of 24 model parameters have been tested for the whole 2020 year in reference to 88 Greek meteorological stations, with regard to the standard synoptic meteorological variables of 2 m temperature, 2 m minimum and maximum temperatures, dew-point temperature, 10 m wind intensity and 12 h accumulated precipitation. For these variables, the model sensitivity is given in terms of the annual average of all stations for the fifth lead day of the model runs when the sensitivity is expected to reach its peak. It was found that there is a considerable impact regarding the minimum and maximum values for many of the examined parameters in reference to their default values, and consideration is given to a heuristic recommendation on the selection of the most sensitive parameters.

How does cloud-radiative heating over the North Atlantic change with grid spacing, convective parameterization, and microphysics scheme in ICON version 2.1.00?

Abstract. Cloud-radiative heating (CRH) within the atmosphere and its changes with warming affect the large-scale atmospheric winds in a myriad of ways, such that reliable predictions and projections of circulation require reliable calculations of CRH. In order to assess the sensitivities of upper-tropospheric midlatitude CRH to model settings, we perform a series of simulations with the ICOsahedral Nonhydrostatic Model (ICON) over the North Atlantic using six different grid spacings, parameterized and explicit convection, and one- versus two-moment cloud microphysics. While sensitivity to grid spacing is limited, CRH profiles change dramatically with microphysics and convection schemes. These dependencies are interpreted via decomposition into cloud classes and examination of cloud properties and cloud-controlling factors within these different classes. We trace the model dependencies back to differences in the mass mixing ratios and number concentrations of cloud ice and snow, as well as vertical velocities. Which frozen species are radiatively active and the broadening of the vertical velocity distribution with explicit convection turn out to be crucial factors in altering the modeled CRH profiles.

Aerosol–cloud–radiation interaction during Saharan dust episodes: the dusty cirrus puzzle

Abstract. Dusty cirrus clouds are extended optically thick cirrocumulus decks that occur during strong mineral dust events. So far they have mostly been documented over Europe associated with dust-infused baroclinic storms. Since today's global numerical weather prediction models neither predict mineral dust distributions nor consider the interaction of dust with cloud microphysics, they cannot simulate this phenomenon. We postulate that the dusty cirrus forms through a mixing instability of moist clean air with drier dusty air. A corresponding sub-grid parameterization is suggested and tested in the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART). Only with the help of this parameterization is ICON-ART able to simulate the formation of the dusty cirrus, which leads to substantial improvements in cloud cover and radiative fluxes compared to simulations without this parameterization. A statistical evaluation over six Saharan dust events with and without observed dusty cirrus shows robust improvements in cloud and radiation scores. The ability to simulate dusty cirrus formation removes the linear dependency on mineral dust aerosol optical depth from the bias of the radiative fluxes. For the six Saharan dust episodes investigated in this study, the formation of dusty cirrus clouds is the dominant aerosol–cloud–radiation effect of mineral dust over Europe.

Dispersion and Aging of Volcanic Aerosols After the La Soufrière Eruption in April 2021

AbstractVolcanic aerosols change the atmospheric composition and thereby affect weather and climate. Aerosol dynamic processes such as nucleation, condensation, and coagulation modify the shape, size, and mass of aerosol particles, which influence their atmospheric lifetime and radiative properties. Nevertheless, most models omit these processes for ash particles. In this work, we explore the ash aerosol aging and sulfate production during the first 4 days following the 2021 La Soufrière (St. Vincent) eruption with the ICON‐ART model (ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases). Online coupling of ICON‐ART with a one‐dimensional volcanic plume model calculates volcanic emission, which makes it possible to resolve the different eruption phases of the noncontinuous La Soufrière eruption. We compared our simulated aerosol distribution and composition with observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, the Multiangle Imaging SpectroRadiometer (MISR) Research Aerosol (RA) Algorithm, and the Barbados Cloud Observatory (BCO). We show that online coupling is essential to adequately model the emissions and plume development close to the volcano. The modeled aerosol aging is in very good agreement with observations from MISR near the emission source and with CALIOP at larger distances. Furthermore, particle aging occurs faster in the troposphere than in the stratosphere due to the availability of water vapor and OH, but a layer of coated ash appears at the plume top due to faster oxidation of SO2 and lofting by aerosol‐radiation interaction. This paper gives the first direct comparison of aerosol aging in volcanic eruption plumes between simulations and observations.

Learning by Doing: Seasonal and Diurnal Features of Tropical Precipitation in a Global‐Coupled Storm‐Resolving Model

AbstractUsing the global and coupled ICOsahedral Nonhydrostatic model with the Sapphire configuration (ICON‐S) and a grid spacing of 5 km, we describe seasonal and diurnal features of the tropical rainbelt and assess the limits of ICON‐S in representing tropical precipitation. ICON‐S shows that, by resolving meso‐beta scale process, the rainbelt structure and its seasonality (zonal and meridional migration and enlargement) is reproduced, with better performance over land than over ocean and with a very high degree of agreement to observations. ICON‐S especially struggles in capturing the seasonal features of the tropical rainbelt over the oceans of the Eastern Hemisphere, an issue associated with a cold sea surface temperature (SST) bias at the equator. ICON‐S also shows that a perfect representation of the diurnal cycle of precipitation over land is not a requirement to capture the seasonal features of the rainbelt over land, while over the ocean, 5 km is sufficient to adequately represent the diurnal cycle of precipitation.

Process Studies of the Impact of Land-Surface Resolution on Convective Precipitation Based on High-Resolution ICON Simulations

This study investigated the relevant processes responsible for differences of convective precipitation caused by land-surface resolution. The simulations were performed with the ICOsahedral Nonhydrostatic model (ICON) with grid spacing of 156 m and Large Eddy Simulation physics. Regions of different orographic complexity, days with weak synoptic forcing and favourable convective conditions were selected. The resolution of land-surface properties (soil type, vegetation) and/or the orography was reduced from 156 to 5000 m. Analyses are based on backward trajectories (Lagrangian Analysis Tool (LAGRANTO)), heat budget and convective organisation potential (COP) calculations. On average, the relative difference of areal mean daily precipitation at 1250 and 5000 m land-surface resolutions compared to 156 m were 6% and 15%, respectively. No consistent dependency of precipitation on orography or land-surface properties was found. Both factors impact convective initiation over areas with embedded mesoscale-sized land-surface heterogeneities. The position of convective precipitation was often influenced by the resolution of orography. Coarsening from 156 to 5000 m considerably changed the location of wind convergence and associated convection initiation. It also affects the onset times of clouds (<20 min) and precipitation (≈1 h). Cloud aggregation and microphysical processes proved to be important for further development towards convective precipitation.

Impact of Holuhraun volcano aerosols on clouds in cloud-system-resolving simulations

Abstract. Increased anthropogenic aerosols result in an enhancement in cloud droplet number concentration (Nd), which consequently modifies the cloud and precipitation process. It is unclear how exactly the cloud liquid water path (LWP) and cloud fraction respond to aerosol perturbations. A volcanic eruption may help to better understand and quantify the cloud response to external perturbations, with a focus on the short-term cloud adjustments. The goal of the present study is to understand and quantify the response of clouds to a selected volcanic eruption and to thereby advance the fundamental understanding of the cloud response to external forcing. In this study we used the ICON (ICOsahedral Non-hydrostatic) model in its numerical weather prediction setup at a cloud-system-resolving resolution of 2.5 km horizontally, to simulate the region around the Holuhraun volcano for 1 week (1–7 September 2014). A pair of simulations, with and without the volcanic aerosol plume, allowed us to assess the simulated effective radiative forcing and its mechanisms, as well as its impact on adjustments of LWP and cloud fraction to the perturbations of Nd. In comparison to MODIS (Moderate Resolution Imaging Spectroradiometer) satellite retrievals, a clear enhancement of Nd due to the volcanic aerosol is detected and attributed. In contrast, no changes in either LWP or cloud fraction could be attributed. The on average almost unchanged LWP is a result of some LWP enhancement for thick clouds and a decrease for thin clouds.

Assimilation of crowd‐sourced surface observations over Germany in a regional weather prediction system

AbstractNear‐surface temperature and humidity observations over Germany, coming on the one hand from the citizen weather station's network Netatmo and on the other hand from synoptic weather stations, were assimilated into the limited are mode of the Icosahedral Nonhydrostatic Model with 2‐km resolution (ICON‐D2). For that we use the Kilometre‐Scale Ensemble Data Assimilation (KENDA) system and a bias‐correction approach that improves the assimilation of the observations by taking into account the diurnal cycle of temperature and humidity variables. Our results show that the assimilation of bias‐corrected observations from Netatmo stations reduces the forecast error considerably; meanwhile, the assimilation of Netatmo observations without bias correction leads to a strong warm bias with a negative impact on forecast performance. In contrast, for the assimilation of synoptic observations the usage of our bias‐correction approach does not lead to any significant decrease in the forecast error, yet reduces the bias for the diurnal cycle of synoptic stations. Overall, it can be concluded that the forecast quality can gain from assimilating Netatmo data, provided an effective bias‐correction approach is applied.

Case study of a moisture intrusion over the Arctic with the ICOsahedral Non-hydrostatic (ICON) model: resolution dependence of its representation

Abstract. The Arctic is warming faster than the global average and any other region of a similar size. One important factor in this is the poleward atmospheric transport of heat and moisture, which contributes directly to the surface and air warming. In this case study, the atmospheric circulation and spatio-temporal structure of a moisture intrusion event is assessed, which occurred from 5 to 7 June 2017 over the Nordic seas during an intensive measurement campaign over Svalbard. This analysis focuses on high-spatial-resolution simulations with the ICON (ICOsahedral Non-hydrostatic) model which is put in context with coarser-resolution runs as well the ERA5 reanalysis. A variety of observations including passive microwave satellite measurements is used for evaluation. The global operational ICON forecasts from the Deutscher Wetterdienst (DWD) at 13 km horizontal resolution are used to drive high-resolution Limited-Area Mode (LAM) ICON simulations over the Arctic with 6 and 3 km horizontal resolutions. The results show the skilful capacity of the ICON-LAM model to represent the observed spatio-temporal structure of the selected moisture intrusion event and its signature in the temperature, humidity and wind profiles, and surface radiation. In several aspects, the high-resolution simulations offer a higher accuracy than the global simulations and the ERA5 reanalysis when evaluated against observations. One feature where the high-resolution simulations demonstrated an advanced skill is the representation of the changing vertical structure of specific humidity and wind associated with the moisture intrusion passing Ny-Ålesund (western Svalbard); the humidity increase at 1–2 km height topped by a dry layer and the development of a low-level wind jet are best represented by the 3 km simulation. The study also demonstrates that such moisture intrusions can have a strong impact on the radiative and turbulent heat fluxes at the surface. A drastic decrease in downward shortwave radiation by ca. 500 W m−2 as well as an increase in downward longwave radiation by ca. 100 W m−2 within 3 h have been determined. These results highlight the importance of both moisture and clouds associated with this event for the surface energy budget.

The ICON Single-Column Mode

The single-column mode (SCM) of the ICON (ICOsahedral Nonhydrostatic) modeling framework is presented. The primary purpose of the ICON SCM is to use it as a tool for research, model evaluation and development. Thanks to the simplified geometry of the ICON SCM, various aspects of the ICON model, in particular the model physics, can be studied in a well-controlled environment. Additionally, the ICON SCM has a reduced computational cost and a low data storage demand. The ICON SCM can be utilized for idealized cases—several well-established cases are already included—or for semi-realistic cases based on analyses or model forecasts. As the case setup is defined by a single NetCDF file, new cases can be prepared easily by the modification of this file. We demonstrate the usage of the ICON SCM for different idealized cases such as shallow convection, stratocumulus clouds, and radiative transfer. Additionally, the ICON SCM is tested for a semi-realistic case together with an equivalent three-dimensional setup and the large eddy simulation mode of ICON. Such consistent comparisons across the hierarchy of ICON configurations are very helpful for model development. The ICON SCM will be implemented into the operational ICON model and will serve as an additional tool for advancing the development of the ICON model.

The impact of GPS and high-resolution radiosonde nudging on the simulation of heavy precipitation during HyMeX IOP6

Abstract. Heavy precipitation is one of the most devastating weather extremes in the western Mediterranean region. Our capacity to prevent negative impacts from such extreme events requires advancements in numerical weather prediction, data assimilation, and new observation techniques. In this paper we investigate the impact of two state-of-the-art data sets with very high resolution, Global Positioning System (GPS)-derived zenith total delays (GPS-ZTD) with a 10 min temporal resolution and radiosondes with ∼ 700 levels, on the representation of convective precipitation in nudging experiments. Specifically, we investigate whether the high temporal resolution, quality, and coverage of GPS-ZTDs can outweigh their lack of vertical information or if radiosonde profiles are more valuable despite their scarce coverage and low temporal resolution (24 to 6 h). The study focuses on the Intensive Observation Period 6 (IOP6) of the Hydrological cycle in the Mediterranean eXperiment (HyMeX; 24 September 2012). This event is selected due to its severity (100 mm/12 h), the availability of observations for nudging and validation, and the large observation impact found in preliminary sensitivity experiments. We systematically compare simulations performed with the Consortium for Small-scale Modeling (COSMO) model assimilating GPS, high- and low-vertical-resolution radiosoundings in model resolutions of 7 km, 2.8 km, and 500 m. The results show that the additional GPS and radiosonde observations cannot compensate for errors in the model dynamics and physics. In this regard the reference COSMO runs have an atmospheric moisture wet bias prior to precipitation onset but a negative bias in rainfall, indicative of deficiencies in the numerics and physics, unable to convert the moisture excess into sufficient precipitation. Nudging GPS and high-resolution soundings corrects atmospheric humidity but even further reduces total precipitation. This case study also demonstrates the potential impact of individual observations in highly unstable environments. We show that assimilating a low-resolution sounding from Nîmes (southern France) while precipitation is taking place induces a 40 % increase in precipitation during the subsequent 3 h. This precipitation increase is brought about by the moistening of the 700 hPa level (7.5 g kg−1) upstream of the main precipitating systems, reducing the entrainment of dry air above the boundary layer. The moist layer was missed by GPS observations and high-resolution soundings alike, pointing to the importance of profile information and timing. However, assimilating GPS was beneficial for simulating the temporal evolution of precipitation. Finally, regarding the scale dependency, no resolution is particularly sensitive to a specific observation type; however, the 2.8 km run has overall better scores, possibly as this is the optimally tuned operational version of COSMO. Future work will aim at a generalization of these conclusions, investigating further cases of the autumn 2012, and the Icosahedral Nonhydrostatic Model (ICON) will be investigated for this case study to assert whether its updates are able to improve the quality of the simulations.

Mountain-wave-induced polar stratospheric clouds and their representation in the global chemistry model ICON-ART

Abstract. Polar stratospheric clouds (PSCs) are a driver for ozone depletion in the lower polar stratosphere. They provide surface for heterogeneous reactions activating chlorine and bromine reservoir species during the polar night. The large-scale effects of PSCs are represented by means of parameterisations in current global chemistry–climate models, but one process is still a challenge: the representation of PSCs formed locally in conjunction with unresolved mountain waves. In this study, we investigate direct simulations of PSCs formed by mountain waves with the ICOsahedral Nonhydrostatic modelling framework (ICON) with its extension for Aerosols and Reactive Trace gases (ART) including local grid refinements (nesting) with two-way interaction. Here, the nesting is set up around the Antarctic Peninsula, which is a well-known hot spot for the generation of mountain waves in the Southern Hemisphere. We compare our model results with satellite measurements of PSCs from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and gravity wave observations of the Atmospheric Infrared Sounder (AIRS). For a mountain wave event from 19 to 29 July 2008 we find similar structures of PSCs as well as a fairly realistic development of the mountain wave between the satellite data and the ICON-ART simulations in the Antarctic Peninsula nest. We compare a global simulation without nesting with the nested configuration to show the benefits of adding the nesting. Although the mountain waves cannot be resolved explicitly at the global resolution used (about 160 km), their effect from the nested regions (about 80 and 40 km) on the global domain is represented. Thus, we show in this study that the ICON-ART model has the potential to bridge the gap between directly resolved mountain-wave-induced PSCs and their representation and effect on chemistry at coarse global resolutions.

Validation of Aeolus winds using radiosonde observations and numerical weather prediction model equivalents

Abstract. In August 2018, the first Doppler wind lidar, developed by the European Space Agency (ESA), was launched on board the Aeolus satellite into space. Providing atmospheric wind profiles on a global basis, the Earth Explorer mission is expected to demonstrate improvements in the quality of numerical weather prediction (NWP). For the use of Aeolus observations in NWP data assimilation, a detailed characterization of the quality and the minimization of systematic errors is crucial. This study performs a statistical validation of Aeolus observations, using collocated radiosonde measurements and NWP forecast equivalents from two different global models, the ICOsahedral Nonhydrostatic model (ICON) of Deutscher Wetterdienst (DWD) and the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecast System (IFS) model, as reference data. For the time period from the satellite's launch to the end of December 2019, comparisons for the Northern Hemisphere (23.5–65∘ N) show strong variations of the Aeolus wind bias and differences between the ascending and descending orbit phase. The mean absolute bias for the selected validation area is found to be in the range of 1.8–2.3 m s−1 (Rayleigh) and 1.3–1.9 m s−1 (Mie), showing good agreement between the three independent reference data sets. Due to the greater representativeness errors associated with the comparisons using radiosonde observations, the random differences are larger for the validation with radiosondes compared to the model equivalent statistics. To achieve an estimate for the Aeolus instrumental error, the representativeness errors for the comparisons are determined, as well as the estimation of the model and radiosonde observational error. The resulting Aeolus error estimates are in the range of 4.1–4.4 m s−1 (Rayleigh) and 1.9–3.0 m s−1 (Mie). Investigations of the Rayleigh wind bias on a global scale show that in addition to the satellite flight direction and seasonal differences, the systematic differences vary with latitude. A latitude-based bias correction approach is able to reduce the bias, but a residual bias of 0.4–0.6 m s−1 with a temporal trend remains. Taking additional longitudinal differences into account, the bias can be reduced further by almost 50 %. Longitudinal variations are suggested to be linked to land–sea distribution and tropical convection that influences the thermal emission of the earth. Since 20 April 2020 a telescope temperature-based bias correction scheme has been applied operationally in the L2B processor, developed by the Aeolus Data Innovation and Science Cluster (DISC).

High-Resolution Simulation of Polar Lows over Norwegian and Barents Seas Using the COSMO-CLM and ICON Models for the 2019–2020 Cold Season

The lack of meteorological observations at high latitudes and the small size and relatively short lifetime of polar lows (PLs) constitute a problem in the simulation and prediction of these phenomena by numerical models. On the other hand, PLs, which are rapidly developing, can lead to such extreme weather events as stormy waves, strong winds, the icing of ships, and snowfalls with low visibility, which can influence communication along the Arctic seas. This article is devoted to studying the possibility of the numerical simulation and prediction of polar lows by different model configurations and resolutions. The results of the numerical experiments for the Norwegian and Barents seas with grid spacings of 6.5 and 2 km using the ICON-Ru configurations of the ICON (ICOsahedral Nonhydrostatic) model and with a grid spacing of 6.5 km using the COSMO-CLM (Climate Limited-area Modeling) configuration of the COSMO (COnsortium for Small-scale MOdelling) model are presented for the cold season of 2019–2020. All the used model configurations demonstrated the possibility of the realistic simulation of polar lows. The ICON model showed slightly more accurate results for the analyzed cases. The best results showed runs with lead times of less than a day.

Multilayer cloud conditions in trade wind shallow cumulus – confronting two ICON model derivatives with airborne observations

Abstract. Airborne remote sensing observations over the tropical Atlantic Ocean upstream of Barbados are used to characterize trade wind shallow cumulus clouds and to benchmark two cloud-resolving ICON (ICOsahedral Nonhydrostatic) model simulations at kilometer and hectometer scales. The clouds were observed by an airborne nadir-pointing backscatter lidar, a cloud radar, and a microwave radiometer in the tropical dry winter season during daytime. For the model benchmark, forward operators convert the model output into the observational space for considering instrument-specific cloud detection thresholds. The forward simulations reveal the different detection limits of the lidar and radar observations, i.e., most clouds with cloud liquid water content greater than 10−7 kg kg−1 are detectable by the lidar, whereas the radar is primarily sensitive to the “rain” category hydrometeors in the models and can detect even low amounts of rain. The observations reveal two prominent modes of cumulus cloud top heights separating the clouds into two layers. The lower mode relates to boundary layer convection with tops closely above the lifting condensation level, which is at about 700 m above sea level. The upper mode is driven by shallow moist convection, also contains shallow stratiform outflow anvils, and is closely related to the trade inversion at about 2.3 km above sea level. The two cumulus modes are sensed differently by the lidar and the radar observations and under different liquid water path (LWP) conditions. The storm-resolving model (SRM) at a kilometer scale barely reproduces the cloud modes and shows most cloud tops being slightly above the observed lower mode. The large-eddy model (LEM) at hectometer scale reproduces better the observed cloudiness distribution with a clear bimodal separation. We hypothesize that slight differences in the autoconversion parameterizations could have caused the different cloud development in the models. Neither model seems to account for in-cloud drizzle particles that do not precipitate down to the surface but generate a stronger radar signal even in scenes with low LWP. Our findings suggest that even if the SRM is a step forward for better cloud representation in climate research, the LEM can better reproduce the observed shallow cumulus convection and should therefore in principle better represent cloud radiative effects and water cycle.

Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.