- Observations and measurement campaigns including the observation of optical properties of clouds and aerosols
- Radiative transfer in cloud-free and cloudy atmosphere including three-dimensional aspects and complex topography as well as radiative properties of the surface
- Parametrizations of radiation and clouds
- Modelling of radiation and clouds on all time-scales from nowcasting over short- and medium range numerical weather predication to decadal predictions and climate projections
- Verification of NWP and climate model outputs using satellite and ground-based observations
- Validation of satellite products using ground-based observations
- Use of modelled and observed radiation and cloud data in various applications such as renewable energy and agriculture.
Cloud cover has the strongest influence on the variability of shortwave radiation flux (SR) among atmospheric factors. Depending on cloud genera, its properties, and its position relative to the Sun, SR intensity generally decreases due to absorption within cloud cover. However, certain cloud genera, mainly cirrus clouds, can enhance reflection, increasing the diffuse component of SR and, in some cases, leading to higher SR compared to clear-sky conditions. The effect of cloud cover on SR intensity was investigated at the Czech J. G. Mendel station, located in the northeastern part of the Antarctic Peninsula. This region is characterized by high cloud cover variability due to its position within the circumpolar trough of mean sea level pressure. Despite this, cloud and SR observations in Antarctica remain relatively limited. To fill this gap, summer experiment based on ground-based instrumentation and observation techniques was conducted between February and March 2026. All-sky images were obtained by Wx Labs camera every 5 minutes in HDR and automatic, long and short exposure to estimate cloud cover properties. The total cloud cover, layer cloud amount and cloud genera were also observed manually every hour from 7:00 to 21:00 local time. SR intensity was measured by Kipp&Zonen CMP-11 pyranometer at 10-min interval while clear-sky SR was estimated using Bird and Hulstrom radiative transfer model. To analyse effects of cloud cover and different cloud genera on SR variability, cloud modification factor (CMF) and cloud radiative effect (CRE) were calculated. Mean observed cloud cover was 60 %. Cloud genera with the most typical occurrence included cirrus, altocumulus, stratus and stratocumulus. The strongest attenuation of SR was associated with stratus clouds with typical CMF values between 0.1 and 0.5 and CRE values between -500 and -100 W.m-2. In contrast, cirrus and cirrostratus clouds had the weakest impact on SR intensity and occasionally led to its enhancement. CMF values typically ranged from 0.8 to 1.1, while CRE values predominantly fell between -200 and 100 W.m-2.
How to cite: Szymszová, S., Láska, K., Pickering, B., and Matějka, M.: Variability of cloud cover and cloud genera and its effects on shortwave solar radiation at J.G. Mendel station, northern Antarctic Peninsula, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-542, https://doi.org/10.5194/ems2026-542, 2026.
Surface solar radiation is a key driver of land–atmosphere interactions and ecosystem processes, yet photosynthetically active radiation (PAR) is often approximated from broadband shortwave radiation using simplified empirical relationships or fixed ratios. These approaches do not explicitly account for spectral differences in atmospheric attenuation or for the partitioning between direct and diffuse radiation, limiting their applicability across varying atmospheric conditions.
Here we introduce a physically constrained, top-of-atmosphere (TOA)-consistent mapping that links broadband shortwave radiation and PAR through a transmissivity-based framework. The formulation is grounded in the Beer–Lambert representation of atmospheric attenuation and expresses PAR as a spectrally integrated transformation of broadband shortwave radiation. This allows the reconstruction of both global and diffuse PAR using only broadband radiation inputs, without site-specific calibration or auxiliary predictors.
The approach is evaluated using multi-site radiometric observations spanning contrasting climatic regimes. Results show that global PAR can be reproduced with coefficients of determination up to r² ≈ 0.99, while diffuse PAR achieves r² values between ≈0.92 and 0.98 across sites. The formulation captures both the magnitude and variability of PAR and its diffuse component with a consistent parameterisation across environments, demonstrating robustness under different atmospheric conditions.
These findings indicate that PAR can be interpreted as a spectrally constrained extension of broadband shortwave radiation, rather than as an independent variable requiring empirical parameterisation. The proposed framework provides a physically consistent alternative to empirical and machine-learning approaches and is directly applicable to surface radiation datasets, reanalysis products, and observational networks. It offers potential for improving radiation inputs in ecosystem, land-surface, and climate modelling, particularly in contexts where only broadband measurements are available.
How to cite: López Lozano, I., Ezhova, E., Foyo Moreno, I., Alados Arboledas, I., and Kulmala, M.: A TOA-consistent transmissivity-based mapping of global and diffuse PAR from broadband shortwave radiation, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-320, https://doi.org/10.5194/ems2026-320, 2026.
Observed near-surface air temperature in the Netherlands has increased markedly over recent decades, yet the seasonal drivers of this trend remain insufficiently quantified. This study analyses the relative contributions of individual seasons to the observed temperature trend at 2 m height in De Bilt, the Netherlands, over the period 2001–2020.
Using annual and seasonal temperature observations from the Royal Netherlands Meteorological Institute (KNMI), an energy and radiation balance was constructed. Surface temperature data were combined with satellite-derived measurements of radiation fluxes and cloud properties at the top of the atmosphere from the CERES mission. This approach enables attribution of temperature changes to variations in shortwave and longwave radiation, greenhouse effect strength, and non-radiative energy exchanges.
The analysis reveals a statistically robust warming trend of approximately 0.4 °C per decade over the study period. Seasonal attribution indicates that winter dominates the long-term temperature increase, accounting for approximately 72% (range 41–100%) of the total trend. Winter warming amounts to approximately 1.2 °C and is primarily driven by the greenhouse effect, contributing about 77% (46–100%) of the seasonal warming, supplemented by reduced reflected shortwave radiation and increased latent heat and convective fluxes.
The greenhouse effect, defined as the difference between upward longwave radiation from the surface and outgoing longwave radiation at the top of the atmosphere, shows strong seasonal variability. It exhibits a pronounced warming tendency in winter (6.5 W m⁻² decade⁻¹), while displaying a cooling tendency in spring (−2.9 W m⁻² decade⁻¹) and autumn (−3.4 W m⁻² decade⁻¹). This variability is mainly governed by changes in surface-emitted longwave radiation rather than changes in outgoing longwave radiation. The direct radiative contribution of CO₂ shows little seasonal variation, averaging 0.19 W m⁻² decade⁻¹ and accounting for less than 10% of the total greenhouse effect.
Overall, the results demonstrate that recent temperature change in De Bilt is strongly seasonally asymmetric, with a wintertime greenhouse effect fueled by changes in upward longwave radiation, playing a dominant role.
How to cite: Overmars, B.: Seasonal Contributions to Recent Temperature Change (2001–2020) in De Bilt, the Netherlands, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-296, https://doi.org/10.5194/ems2026-296, 2026.
Low-cost optical particle counters (OPCs) are calibrated by manufacturers using a fixed refractive index (R.I.) to measure particle size based on light scattering onto a photodetector. While this R.I. is suitable for urban environments, nominal settings can introduce bias when measuring high concentrations of non-urban aerosol particles, such as Saharan dust. Two uncrewed aircraft systems (UAS) were deployed simultaneously during dust event in Orounda, Cyprus on 6 April 2022 by the University of Tübingen and Cyprus Institute. In-situ profiling of aerosol load was performed using an OPC-N3 and a Universal Cloud and Aerosol Sonde System (UCASS) sensor, respectively. Whereas the UCASS utilized an R.I. tailored to mineral dust (1.52+0.0020i), the OPC-N3 used a R.I. for urban air (1.50+0.00i).
An analysis of the sensitivity of the particle size distribution to the R.I. was conducted using real components between 1.48-1.56 and imaginary components between 0.0000i and 0.0028i. Application of a standard R.I. for saharan-derived mineral dust (1.53+0.0015i) improved agreement between the two systems, suggesting that observational discrepancies could be attributed in part to the differences in nominal R.I.. Furthermore, the backscatter coefficients derived from the corrected OPC systems are in good agreement with those obtained from the CIMEL CE376 lidar; however, optimal agreement was achieved between UAS and lidar when a greater imaginary component of 0.0024i was utilized.
To better understand the generalizability of this R.I. and sampling ability of the OPC-N3, a subsequent ground-based campaign was conducted at the Cyprus Institute, in which OPC-N3 observations were compared with a TEOM and a Palas Fidas 200. Correction with a R.I. of 1.53+0.0024i achieved the best cross-instrument agreement.
Under this correction, the bin denotation of the OPC-N3 was extended from 40 µm to 124 µm, with significant detection of particles with diameters reaching 86 µm during the 6 April 2022 dust event. These findings illustrate that a refractive index correction substantially enhances the ability of the OPC-N3 to measure and provide evidence of coarse, super-coarse, and giant mode particles.
How to cite: Gallatin, S., Savvakis, V., Schön, M., Kezoudi, M., Papetta, A., Marenco, F., Bange, J., and Platis, A.: Extension of Low-Cost Optical Particle Counter Measurement Range by Refractive Index Correction, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-358, https://doi.org/10.5194/ems2026-358, 2026.
Clouds exert a dominant and highly variable influence on the Earth's surface radiation budget, yet their accurate representation remains one of the largest sources of uncertainty in climate models (IPCC, 2021). Radiation closure studies offer a valuable means for quantifying these uncertainties across a range of cloud conditions, for retrieving cloud properties and evaluating model performances. In this work, results for different cloudy conditions will be presented for the Meteorological Observatory Lindenberg (MOL), Germany (52.21°N, 14.12°E; 127 m a.s.l.). MOL is a supersite, hosts the GRUAN lead centre and contributes to numerous international networks and facilities, such as Cloudnet, ACTRIS, GRUAN, BSRN, and AERONET. Thus, MOL provides high temporal and vertical resolution observations from both in-situ and ground-based remote sensing instrumentation. These synergetic data sets enable accurate characterisation of the atmospheric state — including thermodynamic profiles, aerosol and gas information — and are used as input for the radiative transfer calculations, conducted using libRadtran software package (Mayer and Kylling, 2005). The Cloudnet retrievals (Illingworth et al. 2007) provide the information on cloud macro- and microphysical properties for the simulations, and are also used to identify different cloud types based on the number of layers and their phase. The broadband shortwave and longwave fluxes collected within the BSRN framework (Driemel et al. 2018) provide the reference observations for the radiation closure evaluation. The present work focuses primarily on single-layer liquid water and ice cloud cases, selected from different years, and the sensitivity of calculated irradiances to cloud microphysical properties. Preliminary results show that good radiation closure can be achieved for liquid water clouds, especially for longwave irradiance, whereas larger discrepancies between simulated and observed fluxes occur for ice clouds, in particular for shortwave irradiance, highlighting the sensitivity of radiative transfer calculations to ice crystal habit and effective radius assumptions.
Bibliography
Driemel et al. (2018): Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017); doi: 10.5194/essd-10-1491-2018
Illingworth et al. (2007): Cloudnet: Continuous evaluations of cloud profiles in seven operational models using ground-based observations; doi:10.1175/BAMS-88-6-883
IPCC (2021): Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; doi: 10.1017/9781009157896
Mayer and Kylling (2005): Technical note: The libRadtran software package for radiative transfer calculations – description and examples of use; doi: 10.5194/acp-5-1855-2005
How to cite: Frangipani, C., Sedhu Madhavan, A., Knist, C., and Wacker, S.: Radiation closure study under different cloudy conditions for a mid-latitude site, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-404, https://doi.org/10.5194/ems2026-404, 2026.
The Meteorological Observatory Lindenberg, a key site in the Baseline Surface Radiation Network (BSRN), has provided continuous measurements of surface shortwave (SW) and longwave (LW) radiation for 30 years. As an ACTRIS national remote sensing facility, it also offers over 20 years of Cloudnet products. Together, these long-term datasets form a critical resource for cloud and radiation studies that enable detailed investigations into how different cloud macrophysical and microphysical properties affect surface SW and LW radiation.
This study focuses on parameterizing downward longwave (LW↓) radiation at the surface using the combined BSRN and Cloudnet datasets under single-layer ice and water cloud conditions. Previous studies have developed LW↓ parameterizations for all-sky conditions, using variables such as cloud fraction, cloud base temperature, and liquid water path (Trigo et al., 2010; Schmetz et al., 1986; Gupta et al., 2010; Zhou et al., 2007). However, many of these parameterizations were based on satellite data, where retrieval of cloud base height involves indirect methods and introduces significant uncertainties, potentially affecting the accuracy of surface LW↓ estimates (Yun Jiang et al., 2023; Yu et al., 2025). While ground-based datasets have primarily been used for validation, their continued importance for methodological development is emphasized in recent work (Feng Yang et al., 2020).
We begin by calculating the LW cloud radiative effect (CRE) at the surface as the difference in radiative flux between all-sky and cloud-free conditions (e.g., Cronin et al., 2006). In this study, cloud-free LW↓ radiation is estimated using the Dupont parameterization, which relies on atmospheric temperature and integrated water vapor profiles (Dupont et al., 2008).
The resulting LW CRE values are then correlated with cloud macrophysical and microphysical properties to examine their influence on surface LW↓ radiation. Statistical analyses are performed to identify the cloud parameters most significantly affecting LW↓, guiding the selection of variables for parameterization. Finally, we present two distinct parameterizations for surface LW↓ radiation under single-layer water and ice cloud conditions, based on the cloud properties determined to have the greatest impact.
How to cite: Sedhu Madhavan, A., Frangipani, C., Wacker, S., and Knist, C.: Parameterizing Surface Downwelling Longwave Radiation under Single Layer Water and ice clouds based on Cloud properties from Ground based remote sensing, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-134, https://doi.org/10.5194/ems2026-134, 2026.
Evaluations of medium-term radiation forecasts from Numerical Weather Prediction (NWP) models, despite being sparse, have gained increasing attention for model verification (Ahlgrimm & Forbes, 2012 ; Ahlgrimm et al. 2016 ; Tuononel et al. 2018). Indeed, surface radiative fluxes are a direct proxy of cloud representation which remains a large source of uncertainty in NWP forecasts. Recent studies have shown that the French operational model AROME (Seity et al. 2011) exhibits a positive SSI bias in cloudy conditions across all France, suggesting optically too thin clouds and/or underestimated cloud clover on average (Magnaldo et al. 2024). The objective of our study is to better constrain the origin of this positive bias and to attribute it to specific cloud conditions.
To this end, we use one year of observed geometrical and macrophysical properties derived from the L3 products of the network of cloud observations ACTRIS-Cloudnet (Illinworth et al., 2007). These properties are retrieved at Palaiseau (France), Cabauw (Germany), and Julich (Germany) observatories and consist in Total Cloud Cover (TCC), Cloud Base Height (CBH), Cloud Top Height (CTH), Liquid Water Path (LWP), Ice Water Path (IWP), Integrated Cloud Thickness (ICT) and the number of cloudy layers. These observed features are use to train and label each cloud profile using a Kmeans clustering approach. Once the clusters are constructed, each hour is labelled both from the observations and from the model outputs in order to create a contingency table and identify the dominant pair in terms of bias.
Our results show that low level stratiform clouds in the observations, dominate the overall positive bias. While these clouds typically correspond to overcast conditions (TCC > 90%), AROME seems to almost systematically underestimate their TCC. This underestimation is strongly correlated to a lack of total condensed mass (underestimated LWP), itself correlated to an insufficient vertical extension of the cloud. While the mechanisms at play are numerous, the presentation will investigate some deficiencies using different diagnostic variables. The proposed methodology could be used to systematically evaluate NWP models and directly point to the dominant situations in terms of errors. It could also be extended spatially thanks to the EarthCARE satellite mission to increase its robustness and impact.
How to cite: Ewart, M., Libois, Q., Riette, S., Magnaldo, M.-A., and Lac, C.: Unravelling Systematic Surface Solar Irradiance Biases in NWP Models Using Detailed Cloud Observations: an Application to AROME, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-670, https://doi.org/10.5194/ems2026-670, 2026.
Low clouds are essential to the energy budget and the hydrological cycle, but simulation of low clouds in most AGCMs (atmospheric general circulation models) remains a challenge. The critical relative humidity (RHc) has great significance for cloud parameterization. Conventional AGCMs commonly employ a globally uniform RHc as an empirical constant, which fails to consider the physical relationship between cloud formation and temperature, resulting in considerable underestimation of low clouds over subtropical oceans, biased vertical cloud structure, and deviations in radiative forcing and precipitation. To address these problems, this study determines the optimal RHc thresholds under different temperature intervals using CloudSat/CALIPSO satellite observations and the threat score (TS) method. Based on diagnostic results of CloudSat/CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) satellite data, we propose a fourth-order curve-fitting formula for RHc with respect to temperature (coefficient of determination (R²) = 0.9659). The method was implemented in CAM6 (Community Atmosphere Model, version 6). Compared with the original scheme, the dynamic RHc significantly reduces the negative bias of low clouds over mid- and low-latitude oceans, increases the low cloud fraction by 20 %. Furthermore, the dynamic RHc has an impact on the vertical distribution of cloud amount, significantly increasing the cloud fraction below 700 hPa and reducing it above 400 hPa. The increase in low clouds is accompanied by an increase in liquid water path, which helps reduce the shortwave cloud forcing bias in the subtropics. Besides, the change in cloud fraction caused by the dynamic RHc has an impact on the simulation of precipitation, improving the positive precipitation bias over some land regions and strengthening shallow convective precipitation over tropical oceans. Finally, the simulation results at 1° and 2° indicate that the method is insensitive to the choice of model resolution. This dynamic RHc scheme offers a physically justified and computationally efficient way to improve low-cloud simulation in AGCMs, thereby reducing uncertainties in cloud radiative effects and precipitation simulations.
How to cite: Wang, M. and Wang, L.: A dynamic critical relative humidity based on temperature in cloud parameterization to improve low cloud in an AGCM, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-785, https://doi.org/10.5194/ems2026-785, 2026.
Neglecting three-dimensional (3D) cloud–radiation interactions in weather and climate models can significantly distort surface irradiance estimates. Understanding how these effects depend on factors such as cloud properties, surface albedo and solar angle is the central goal of the DFG research unit C3SAR. Accurate representation of surface irradiance is becoming increasingly important as numerical weather prediction (NWP) and cloud-resolving models advance toward ever higher spatial resolutions.
Traditionally, analyses have focused on mean bias, which is computationally efficient - especially when using Monte Carlo methods - since pixel-to-pixel noise cancels out. This allows large-scale studies of realistic, high-resolution cloud scenes. However, relying solely on mean bias can obscure important 3D effects, such as sharper and displaced cloud shadows (Gristey, 2019).
We present a method to go beyond bias by computing pixel-wise root-mean-square differences (RMSD) between 1D and 3D simulations, as well as metrics of surface flux variability. Using novel statistical techniques applicable to general Monte Carlo simulations, this approach retains the computational efficiency of traditional bias calculations. This enables large-scale analysis not only of mean bias but also of pixel-wise differences and variability across statistically representative ensembles of high-resolution surface irradiance fields derived from physically consistent radiative transfer simulations.
We apply this method to systematically investigate how 3D bias, local deviations, and variability depend on solar angle and cloud properties, using MYSTIC (Mayer, 2009) radiative transfer simulations on large-eddy simulation (LES) datasets at kilometer-scale domains with 10 m horizontal resolution. Furthermore, by coarsening the LES fields, we assess how cloud resolution influences 3D effects and determine the resolutions required to capture them accurately. Comparing fully 3D radiative transfer with independent-column approximations (ICA), we quantify the limitations of ICA in representing 3D cloud–radiation interactions, providing valuable guidance for next-generation cloud-resolving models.
How to cite: Schwärzel, D. and Mayer, B.: Beyond Bias: Systematic Analysis of 3D Cloud Radiative Effects on Surface Irradiance and Their Dependence on Cloud Properties, Albedo, and Solar Angle, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-352, https://doi.org/10.5194/ems2026-352, 2026.
How to cite: Wagner, J. K., Kloster, D., Kunkel, T., Schwärzel, D., Senf, F., and Mayer, B.: Tracking 3D Cloud-Induced Radiative Effects of individual cumulus cells within ICON-MYSTIC Case studies, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-195, https://doi.org/10.5194/ems2026-195, 2026.
The Earth Clouds, Aerosol and Radiation explorer is a joint ESA (European Space Agency)/JAXA (Japan Aerospace Exploration Agency)
satellite mission. EarthCARE was launched in May 2024 and is providing unique data well-suited for studying the role of clouds and aerosols in Earth's
atmosphere.
EarthCARE carries 4 different instruments. A Cloud profiling Doppler radar, a multi-spectral imager, a broad-band radiometer and a
cloud/aerosol lidar. The Atmospheric Lidar (ATLID) instrument is the High Spectral Resolution Lidar (HSRL) carried on EarthCARE. ATLID
provides high-resolution vertical profiles of cloud and aerosol optical properties.
In order to transform raw ATLID measurements into scientifically meaningful products, specialized HSRL specific processing is
required. This paper presents an overview of two key ATLID processing streams: the A-FM (ATLID Feature Mask) and A-PRO (ATLID Profile)
processors.
The A-FM processor focuses on the detection and classification of atmospheric features. Using calibrated backscatter signals, it applies
a sequence of algorithms for noise filtering, signal threshold, and layer detection to distinguish between clear sky, attenuated regions,
and significant targets.
The A-PRO processor builds upon A-FM results in order to generate quantitative atmospheric profiles. It retrieves parameters such as
backscatter coefficient, extinction coefficients, lidar-ratio, and linear depolarization ratio. The processor uses the optical properties
to classify the detected atmospheric targets (e.g. water cloud, ice clouds, and aerosol-type).
This paper outlines the core algorithms underlying both processors, highlighting their design principles. A number of illustrative
examples are presented to demonstrate their performance across different atmospheric scenarios, including cirrus clouds,
boundary-layer aerosols, and complex multi-layer structures. Applications of the derived products are also discussed
How to cite: Donovan, D., van Zadelhoff, G.-J., Apituley, A., Chantry, A., de Graaf, M., Alves Gouveia, D., Hemminga, D., de Kloe, J., Wang, P., and Wang, X.: ATLID aerosol and cloud products., EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-561, https://doi.org/10.5194/ems2026-561, 2026.
Extreme wildfire events are likely to increase in number in the near future and observations of regional radiative effects of smoke can be used to challenge and improve climate model simulations, which are currently relying almost exclusively on model-model intercomparisons, while models disagree on the magnitude and sign of the radiative forcing by aerosols.
A methodology is presented to measure the instantaneous aerosol direct radiative effect of wildfire smoke using satellite observations of aerosol optical thickness and a radiative transfer model, and determine the radiative heating and cooling of the smoke in both clear-sky and cloud scenes. Radiative effects of smoke are defined as the radiative effect with and without smoke in the atmosphere, which are necessarily computed using model simulations. Regionally, aerosol-radiation interactions can be an order of magnitude larger than their global mean values, especially during extreme events. Results at the top of the atmosphere and at the surface for a case of extreme wildfires in Chile in 2023 will be shown. The results are compared to retrievals of the aerosol direct radiative effects of smoke above clouds using hyperspectral measurements, to show the ability of direct retrievals of aerosol effects from satellite measurements. These results are important to validate and challenge climate models, and will help the development of radiative effect retrievals from more dedicated missions, like PACE and EarthCARE. The SpexONE instrument of PACE is capable of separating aerosols and clouds using the polarisation of light, while the ATmospheric LIDar (ATLID) on EarthCARE is an active instrument, which provides profiles of aerosol extinction and heating rates. In addition, the upcoming EUMETSAT mission Metop-Second Generation A (Metop-SG A) will carry ahyperspectral spectrometer Sentinel-5 and the Multi-viewing Multi-channel Multi-polarisation Imager (3MI). On this mission, the hyper- and multi-spectral and polarization capabilities are combined and improved with mutli-viewing infromation. The approach presented here will be useful for the quantification of aerosol direct and semi-direct effects from space, which is urgently needed to improve the radiative interaction schemes between aerosols and clouds in climate models. Better observations of both aerosols and cloud properties, and direct observations of aerosol-cloud- radiation interactions will improve our ability to attribute climate change, quantify climate sensitivity, and improve the accuracy of future climate change projections.
How to cite: de Graaf, M.: Instantaneous Direct Radiative Effects of Aerosols in Cloud and Clear-sky Scenes from Passive Space-borne Multi-spectral Observations, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-113, https://doi.org/10.5194/ems2026-113, 2026.
In recent decades, satellite-based observations of clouds and surface radiation (derived from estimated cloud albedo) are progressively improving in quality and have experienced a significant increase in spatial resolution with the new third generation of Meteosat satellites (MTG). Within the DUETT project, the German Meteorological Service (DWD) is pursuing the goal to supplement conventional solar radiation measurements at ground stations with satellite data and thus to increase the spatial coverage of solar radiation information over Germany. Data of solar radiation from 42 pyranometer stations are combined with corresponding data based on measurements from Meteosat-SEVIRI (with a spatial resolution of about 5 km). In a new version presented here, data from the new Meteosat-FCI instrument (1 km resolution) are used as input. In this version of DUETT hourly products of the global horizontal irradiance (GHI) and the sunshine duration (SDU) are provided on a 1 x 1 km grid for Germany in near real-time. In addition, new data of diffuse horizontal irradiance (DIF) will be provided in the course of 2026.
Merging is performed in four main steps: 1. Data aggregation 2. Adjustments regarding errors by snow cover and water vapour column 3. Global bias correction 4. Interpolation of residual local deviations. For SDU, the merging is based on satellite data of direct normal irradiance (DNI), which are ‘translated’ and compared with station data of SDU. Based on the combined gridded data, additional point data are determined at the coordinates of 576 measurement sites of DWD. These pseudo station data are optimized by a subsequent correction regarding the influence of surrounding topography. For both, grid and point data, uncertainties are estimated based on three known error sources. We present the latest data version for the three variables GHI, SDU and DIF. For the new DUETT variable DIF, the steps of the data combination are illustrated and validation results based on cross validation are presented for all three parameters. Furthermore, an outlook is given on planned improvements of the DUETT data in the near future.
How to cite: Brinckmann, S., Trentmann, J., and Pfeifroth, U.: New 1km-version of the DUETT radiation data (using satellite and station data) based on the new Meteosat-FCI instrument, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-576, https://doi.org/10.5194/ems2026-576, 2026.
The incoming surface solar radiation is an essential climate variable as defined by GCOS. Long term monitoring of this part of the earth’s energy budget is required to gain insights on the state and variability of the climate system.
The EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF) generates and distributes high quality long-term climate data records (CDR) of energy and water cycle parameters, which are freely available.
The CLARA and SARAH data records will be extended and updated in their next releases. Especially the latter will become a quasi-global data record in its upcoming edition - SARAH-4. Data from five geostationary orbits will be processed, allowing the provision of global data (excluding the high latitudes) in high spatial (0.05°) and temporal (30 minutes) resolution. In addition, SARAH-4 will employ daily varying aerosol information from MERRA-2 (in contrast to climatological aerosol information used in SARAH-3). Finally, the successor of the METEOSAT SEVIRI instrument, FCI, will be used for a consistent near-realtime data processing for the METEOSAT-Prime orbit.
The presentation will give an overview of the status quo of CM SAF’s surface solar radiation data records and will give insights into the ongoing developments. The current status and the recent results of the upcoming SARAH-4 data records will be presented. The focus will be on the impact of the daily aerosol information on the surface solar radiation record, which leads to improvements in case of aerosol-loaded atmospheric conditions. Some general validation results of the intermediate SARAH-4 data records will be shown as well.
How to cite: Pfeifroth, U., Eller, J., Kremmling, B., and Trentmann, J.: Satellite-based surface solar radiation data records from the CM SAF – developing towards the new SARAH-4 climate data record , EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-140, https://doi.org/10.5194/ems2026-140, 2026.
Aerosol particles mainly come from polluted particles, dust, sea salt and biomass combustion. Aerosols can affect the global climate and regional meteorology in two ways. Aerosol-cloud interactions (ACI) refer to aerosols as cloud condensation nuclei (CCN) and ice nuclei (IN) participating in cloud microphysical processes. Aerosol-Cloud-Interactions (ACIs) are possibly important factors affecting precipitation while are usually not considered in numerical weather prediction systems (NWP) due to the complexities and uncertainties involved by considering aerosol information. This research focused on the use of the Thompson aerosol-aware scheme in WRF (Weather Research and Forecasting model). This paper summarized the process analysis and response sensitivity to aerosol concentrations during a heavy rainfall event in Beijing. The July 16, 2018 rainfall event -a typical precipitation case involving both warm and cold-phase clouds was thoroughly analyzed, including cloud fraction, downward shortwave radiation, water vapor consumption, cloud microphysical processes, net latent heat etc., which veriffed that the aerosol-cloud interaction paths were accurately and adequately considered in Thompson aerosol-ware scheme. By tracking key parameter changes, it is conffrmed that the CCN/IN activation and relative roles in changing precipitation at different stages are reasonable in Thompson aerosol-aerosol scheme. According to the rainfall characteristics, the precipitation was divided into two phases. And the difference in the aerosol effect on precipitation in the two precipitation phases is mainly due to the complexity of cold rain process and hypothesis. Sensitivity simulations also revealed that the current concentrations of background aerosol in Beijing are actually high enough and further ten-times of aerosol concentration increase didn't bring signiffcant additional precipitation response.
How to cite: Guo, C.: Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson aerosol aware scheme in WRF: Process analysis and response sensitivity to aerosol concentrations during a Heavy Rainfall Event in Beijing, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-122, https://doi.org/10.5194/ems2026-122, 2026.
The Global Energy Balance Archive (GEBA) is an international data center for globally measured energy fluxes at the Earth’s surface, hosted at ETH Zurich (Wild et al., 2017, ESSD). Knowledge of the spatio-temporal distribution of surface energy fluxes is essential for understanding the origin and evolution of the Earth’s climate, and is also highly relevant for practical applications in sectors such as renewable energy, agriculture, water management, and tourism.
GEBA currently contains more than 700,000 monthly mean entries for various components of the surface energy balance. The most widely represented quantity is surface shortwave irradiance, also known as global radiation. Many of the historical records stored in GEBA extend over multiple decades, with the longest record (Stockholm) dating back to 1927.
GEBA data have been widely used in scientific studies addressing, for example, the quantification of the Earth’s energy balance, the estimation of long-term trends which enabled the detection of multi-decadal variations known as “global dimming” and “brightening”, and the evaluation of surface fluxes in climate models, reanalyses and satellite-derived products. Since becoming accessible online in 1997, GEBA has served the international climate research community for almost 30 years. Recently, GEBA underwent a major technical modernization, replacing its legacy infrastructure from the 1990s with a PostgreSQL database system. This redesign and its ongoing operational maintenance have been co-funded since 2019 by the Federal Office of Meteorology and Climatology (MeteoSwiss) within the framework of the Global Climate Observing System (GCOS) Switzerland.
Beyond regular data updates and the integration of new datasets, current developments in GEBA focus on implementing a versioning system to ensure traceable documentation of the GEBA data status, as well as on applying advanced quality-control procedures developed at DWD/CMSAF. In particular, homogeneity tests are being introduced to detect outliers, inhomogeneities, and breakpoints in GEBA station time series, based on comparisons with multiple independent satellite-derived and reanalysis datasets (e.g., SARAH-3, CLARA-A3, and ERA5).
How to cite: Wild, M., Smith, P., Sedlacek, J., Trentmann, J., and Pfeifroth, U.: The Global Energy Balance Archive (GEBA): a database for worldwide measured surface energy fluxes, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-164, https://doi.org/10.5194/ems2026-164, 2026.
The BSRN station at Gobabeb, located on the edge of the Namib Desert and close to the Atlantic Ocean, was established by the University of Basel (UniBas) in 2012. Since then, continuous and nearly gap-free, high-quality observations of the radiation balance and ancillary atmospheric parameters have been conducted in a climatically very sensitive region. This area is characterized by complex interactions between aerosols and clouds, strongly contrasting surface albedo, and alternating moist and dry conditions, yet it lacks accurate observations of radiative fluxes—a situation that is, in fact, typical for much of the Southern Hemisphere. Consequently, these observations are highly valuable for environmental sciences in general, and for Earth system modeling as well as satellite calibration and validation (CAL/VAL) activities in particular.
Unfortunately, UniBas has decided to withdraw from its BSRN activities. In response, the German Weather Service (DWD) and the Karlsruhe Institute of Technology (KIT)—which is already involved in in situ land surface temperature measurements and greenhouse gas observations at Gobabeb—have decided to step in and continue these important measurements. In 2025, DWD, in collaboration with UniBas, deployed a second tracking system for observing downwelling solar and terrestrial radiative fluxes. This was done to ensure continuity of the observations, as the existing systems have aged and are increasingly affected by the harsh environmental conditions.
We will present results and applications of the radiation products from the past 15 years, preliminary findings from the concurrent observations conducted by UniBas and DWD, and outline potential future activities at the Gobabeb site including a possible extension to a station of the Global Reference Upper Air Network (GRUAN).
How to cite: Vogt, R., Wacker, S., and Göttsche, F.: The BSRN-station Gobabeb: High quality observations of radiative fluxes in Namibia, EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-818, https://doi.org/10.5194/ems2026-818, 2026.
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