We assess the current status of atmospheric ice mass estimates by first critically comparing satellite-based datasets, then examining global circulation and global storm-resolving models. The analysis focuses on the frozen water path, which offers a more consistent measure across modelling and observational datasets than cloud ice or other partial quantities. As a reference, we use three retrievals derived from the CloudSat mission. Despite using the same input data, these retrievals exhibit a significant spread. Still, common biases cannot be ruled out, and we argue that the uncertainty in overall means can be as high as 30 %. A recently developed machine learning product based on passive thermal infrared observations greatly extends the spatial and temporal coverage available for comparisons, but its local precision is limited compared to radar-based retrievals. Global circulation models continue to underestimate frozen water paths compared to the observational benchmark and fail to provide consistent representations of regional temporal changes or the annual cycle. Storm-resolving models, which operate at finer grid spacing and explicitly resolve convective dynamics, show better representation of total ice mass, with variations among them similar to the observational uncertainty. However, several issues were noted, such as apparent deviations in the spatial structures of tropical deep convection, and they differ significantly in their relative amounts of cloud ice, snow, and graupel. Together, these findings reveal progress but highlight continuing uncertainties that limit confidence in projections of cloud-related climate feedbacks.

Atmospheric Radiative Transfer Simulator (ARTS) is an open source general purpose radiative transfer model used for a wide range of applications from remote sensing to the interaction of climate and radiation. In the past, it was confined to the microwave and infrared spectral range, as ARTS could not simulate the interaction of solar radiation in the atmosphere. Here we close this gap and extend ARTS to the shortwave range. We introduce a solar source that can be used as a collimated beam source and as background source allowing to resolve the finite solar disc. Additionally, we implemented a new clear sky solver that supports collimated beam sources and solar background sources, and updated the ARTS DISORT interface to utilize DISORT's capability for collimated beam sources. This allows consistent line-by-line radiative transfer simulations from the microwave to the ultraviolet range with ARTS. We evaluated the shortwave capabilities of ARTS by comparing it with LBLRTM for clear sky simulations. The root mean square deviation between them is 0.70 W for the upward flux at top of the atmosphere and 0.66 W for the downward flux at the surface. Simulations of a sunrise as seen from space, of satellite observations and of full spectrum all sky radiative flux illustrate the new capabilities. With the new features, ARTS can be used for a wide range of new applications.

The balance between liquid and ice in clouds remains a major challenge in climate modeling, largely due to uncertainties in ice-related processes. We investigate the relative importance of four microphysical processes—primary ice nucleation (PIN), secondary ice production (SIP), sedimentation, and transport of ice crystals—for the supercooled liquid fraction (SLF) in mixed-phase clouds using three global climate models: EC-Earth3-AerChem, NorESM2-MM, and ECHAM6.3-HAM2.3. All models identify PIN as the dominant influence on SLF at cold temperatures in high northern latitudes, but diverge elsewhere and for higher temperatures. Implementing a unified SIP parameterization produced varied model responses, revealing fundamental differences in how microphysical processes interact within each model framework. These discrepancies suggest that each model prioritizes different processes in shaping the cloud phase. Such divergence may limit the reliability of conclusions regarding microphysical processes drawn from any single model.

Abstract. The first cloud property retrievals based on operational sub-millimetre measurements are presented, making use of the channels between 89 and 325 GHz of the Arctic Weather Satellite (AWS). The main quantities of the dataset are frozen water path (FWP) and the associated mass-weighted mean altitudes and particle sizes. In this first version, results are restricted to latitudes between 60° S and 60° N. The retrievals are based on detailed simulations of instrument observations. The actual inversion is made by a quantile regression neural network, and case-specific uncertainty estimates are provided. Retrievals performed on simulations suggest that retrieved FWP values are essentially unbiased across a wide dynamic range, from 10 kg m-2 down to 40 g m-2. The associated mass-weighted mean altitude is also essentially unbiased for the entire relevant range of 2 km to 12 km. The particle size estimates, however, show a slight bias for sizes other than 400 μm. Comparisons with other datasets provide strong indications that these results also extend to retrievals from real observations; for example, local and zonal means match those of existing radar/lidar-based retrieval products. The accuracy in FWP should be unprecedented among estimates based on passive satellite data, thanks to the new sensitivity afforded by sub-millimetre channels. The dataset complements cloud radar observations by providing a significantly broader spatial coverage. There is also potential to create a climate-relevant dataset, as the retrievals are directly applicable to the EPS-Sterna constellation, continuing the AWS observations up to 2045.

The Arctic Weather Satellite (AWS) from the European Space Agency is a small satellite launched in August 2024 carrying a microwave radiometer for atmospheric sounding. AWS features the first satellite-borne sub-millimeter wavelength ( GHz) channels for operational meteorology. This "sub-mm" band is more sensitive to scattering from ice clouds than traditional microwave frequencies and opens up new possibilities for measuring ice clouds and constraining their representations in atmospheric models. For example, 325 GHz radiances are roughly three times more sensitive to frozen water path than 183 GHz radiances. This letter explores how well sub-mm radiances are currently modeled by state-of-the-art radiative transfer simulators. It also highlights the novel information about ice clouds that can be exploited using these radiative transfer simulators, in the context of weather forecasting and physics-based retrievals.

We introduce Rain over Africa (RoA), a public retrieval algorithm providing near-real-time precipitation estimates over the African continent. The retrievals are based on Meteosat thermal infrared observations. Therefore, rain can be monitored constantly, minutes after input data dissemination. Despite this low latency, RoA accuracy is comparable to estimates requiring hours or more to obtain. Consequently, RoA is of particular interest where a rapid response is critical, such as for disaster preparedness. RoA retrievals employ a convolutional and quantile regression neural network: the latter enables detailed case-specific descriptions of the retrieval uncertainty. Four years of data from the calibration satellite in the GPM mission were used as training and evaluation labels. With this setup, limitations in earlier near-real-time retrievals for Africa were overcome. Moreover, the RoA network runs on regular workstations. With a 30-km effective resolution, RoA retrievals over land are more timely and detailed than the established IMERG precipitation estimates. RoA is also applicable over the surrounding ocean regions, maintaining a similar performance. However, there IMERG exhibits a better effective resolution, at least for its more favorable conditions. Additionally, RoA's probabilistic nature enables addressing the inherent uncertainties of satellite precipitation retrievals by using probabilities of exceeding precipitation thresholds. Further assessment reveals similar diurnal cycles between RoA and IMERG, although IMERG shows some instability. Visual inspection of rain evolution patterns also indicates that RoA is more consistent. Finally, an annual mean analysis including CHIRPS estimates shows regional differences among the three, with no clear outlier behavior for RoA.

The Arctic Weather Satellite (AWS) is a project led by the European Space Agency (ESA) that has several novel aspects. From a technical perspective, it serves as a demonstrator of how to expand the network of operational satellite-based microwave sensors cost-effectively and acts as the proto-flight model for a suggested constellation of satellites, denoted as EUMETSAT Polar System (EPS) Sterna. The design philosophy has been to reduce complexity and instead focus the efforts on critical parts and characterise the instrument well before the launch. The single instrument onboard is a 19-channel microwave cross-track radiometer. There are 15 channels covering ranges around 54, 89 and 174 GHz. These are channels similar to ones found on existing sensors, however, thanks to the short development process, allowing use of more modern and recent technology, the performance and resolution of these channels on AWS exceed or match similar sensors, despite being a small satellite. Additionally, four channels around 325.15 GHz form a completely new frequency band for observations from space. The addition of these new channels aims to improve sensitivity to ice hydrometeors. In this article, we outline the mission and describe the instrument in detail, to support the usage of radiances measured by AWS. The satellite was launched in August 2024, and the status towards the end of the commissioning phase is reflected here. For example, a characterisation of the noise performance is provided, showing that the target specifications have been met, for most channels with a margin. This is except for two channels identified to have technical issues already before the launch. If EPS-Sterna is selected by EUMETSAT, these and other identified problems will be corrected, but otherwise the constellation is expected to consist of recurrent models of AWS with minor modifications.

The atmospheric radiative transfer simulator ARTS is a software for computing atmospheric absorption, scattering, the transfer of radiation through an atmosphere, and sensor characteristics. It is written in C++ and can simulate remote sensing observations and radiative energy fluxes. The article describes version 2.6 of the software. There are numerous changes compared to the last ARTS publication, the most striking being that the program is now controlled by Python scripts, which is convenient and allows for great flexibility. The article discusses the ARTS history, the theory behind the computations of absorption and radiative transfer, available solvers for atmospheres with scattering, the computation of energy fluxes and heating rates, and the built-in system for inverting remote observations to atmospheric state variables by optimal estimation. ARTS is publicly available, open source, and free of charge.

The Ice Cloud Imager (ICI) will be hosted on the second generation of the EUMETSAT Polar System (EPS-SG). By measuring at microwave and sub-millimetre wavelengths, ICI will provide unparalleled global observations of ice clouds. EUMETSAT's official ICI level-2 product will offer retrievals of ice mass column properties. This study explores whether the capabilities of ICI can be extended to retrieve vertical profiles of ice mass.Using a retrieval database of ICI simulations, we trained a quantile regression neural network (QRNN) to retrieve ice water content (IWC) and profiles of the mean mass diameter of ice hydrometeors. Our retrieval setup is fast and simpler to implement than previous ICI profile retrieval approaches, and the study is more comprehensive in scope than earlier efforts. Comparisons between our retrieved and database profiles demonstrate that ICI observations are sensitive to IWC within the range of 10-2 and 1 g m-3, and performance is strongest between altitudes of 3 and 14 km. Our results also show that ICI observations are sensitive to mean mass diameter values up to 600 mu m, although successful retrievals of up to 800 mu m are observed. To assess the vertical resolution of the retrievals, we computed approximations of averaging kernels on the model predictions. We estimate the resolution of IWC profiles to be similar to 2.5 km. Retrievals of mean mass diameter achieve an estimated resolution of 2.5 km at an altitude of 5 km, with reduced resolution at higher altitudes.No operational product currently provides ice mass vertical information derived from passive microwave observations. However, this study demonstrates that ICI can fill this gap thanks to the presence of both microwave and sub-millimetre channels, with the sub-millimetre wavelengths providing particularly high sensitivity to cloud ice. Furthermore, the relatively broad swath of ICI observations lead to a higher spatial and temporal coverage than radar and lidar products can achieve. The global and long-term dataset that ICI will offer could therefore act as a valuable complement to CloudSat or EarthCARE-based retrievals. Future efforts could explore the inclusion of the Microwave Imager (MWI) observations to improve retrievals at low altitudes - a natural next step given that MWI is to be launched on the same platform as ICI.

Frozen cloud particles are an important component of the hydrological cycle and significantly influence the Earth's energy budget. Despite their important role, observational records constraining concentrations of atmospheric ice remain severely limited. Although combined radar and lidar estimates from the CloudSat and CALIPSO missions offer over a decade of high‐quality data on ice hydrometeor concentrations, these estimates remain sparse. In contrast, products derived from passive satellite sensors typically provide better spatiotemporal coverage but disagree with CloudSat‐based measurements. To address these limitations, we present a novel climate data record of total ice water path (TIWP), the Chalmers Cloud Ice Climatology (CCIC). It spans 40 years, from 1983 to the present, covering latitudes from S to N. CCIC offers TIWP estimates at three‐hourly resolution from 1983 and half‐hourly resolution from 2000 onwards. We demonstrate the long‐term stability of CCIC by directly comparing it with CloudSat/CALIPSO‐based estimates over the entire mission lifetime. Additionally, we assess CCIC against other long‐term TIWP records, revealing that CCIC yields the most accurate TIWP estimates compared to CloudSat/CALIPSO‐based reference estimates. Analysis of regional 40 year trends across four long‐term TIWP data sets indicates an increase of TIWP over the Southern Ocean and the east Bering Sea in two observational data sets and ERA5. The CCIC climate record closes the gap between existing long‐term TIWP records and CloudSat/CALIPSO‐based reference measurements. The estimates' continuous coverage and demonstrated accuracy make it a valuable resource for lifecycle studies of storms and the analysis of fine‐scale cloud features in a changing climate.

Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds. The Chalmers Cloud Ice Climatology (CCIC) is a novel cloud-property dataset that aims to provide an improved climate record of ice hydrometeor concentrations by applying state-of-the-art machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC offers a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically integrated and vertically resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification. A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessed on a held-out test dataset, the CCIC-provided IWP and IWC estimates achieve correlations exceeding 0.7 and 0.6, respectively, and biases better than −5 % and −2 % demonstrating considerable skill in estimating both IWP and IWC. In addition, CCIC is extensively validated against both in situ and remote sensing measurements from two flight campaign series and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community.

Ice clouds are poorly constrained in current global climate and weather models and have been used as a tuning parameter in the models to balance radiation budget at the top of atmosphere and precipitation at the surface. Sub-millimeter-wave (Submm) remote sensing can fill the sensitivity gap of cloud ice observations between visible/infrared (VIS/IR) and microwave (MW) frequencies. The added value from submm-wave bands has been recognized for achieving a better understanding of cloud, convection and precipitation (CCP) processes. Recent satellite observations at microwave frequencies showed promising results that additional information on cloud microphysical properties (e.g., ice particle shape and orientation) can be inferred from V-pol and H-pol radiances. Motivated by the added value from cloud polarimeters, a compact SWIRP (Submm-Wave and Long-Wave InfraRed Polarimeter) was developed under NASA's Instrument Incubator Program (IIP) to reduce instrument size, weight, power (SWaP) for future Earth science missions. Low-SWaP sensors like SWIRP will allow the cost-effective implementation of a distributed observing system to study fast cloud processes with the needed spatiotemporal sampling.

The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals.Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional (3D) variability of clouds. The radiative transfer calculations use empirically based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for the consideration of polarisation. The degrees of freedom (DoFs) of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations.Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm), and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10-2 and 101 kgm-2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals from real observations will be possible from day one of the ICI operational phase.

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals.

The first closure study involving passive microwave and submillimetre measurements of ice clouds with the consideration of oriented particles is presented, using a unique combination of polarised observations from the ISMAR spectral-like radiometer, two radars with frequencies of 35 and 95 GHz, and a variety of in situ instruments. Of particular interest to this study are the large V–H polarised brightness temperature differences measured from ISMAR above a thick frontal ice cloud. Previous studies combining radar and passive submillimetre measurements have not considered polarisation differences. Moreover, they have assumed particle habits a priori. We aim to test whether the large V–H measurements can be simulated successfully by using an atmospheric model consistent with in situ microphysics. An atmospheric model is constructed using information from the in situ measurements, such as the ice water content, the particle size distribution, and the mass and shape of particles, as well as background information obtained from dropsonde profiles. Columnar and dendritic aggregate particle models are generated specifically for this case, and their scattering properties are calculated using the independent monomer approximation under the assumption of horizontal orientation. The scattering properties are used to perform polarised radiative transfer simulations using ARTS to test whether we can successfully simulate the measured large V–H differences. Radar measurements are used to extrapolate the 1-D microphysical profile to derive a time series of particle size distributions which are used to simulate ISMAR brightness temperatures. These simulations are compared to the observations. It is found that particle models that are consistent with in situ microphysics observations are capable of reproducing the brightness temperature depression and polarisation signature measured from ISMAR at the dual-polarised channel of 243 GHz. However, it was required that a proportion of the particles were changed in order to increase the V–H polarised brightness temperature differences. Thus, we incorporated millimetre-sized dendritic crystals, as these particles were observed in the probe imagery. At the second dual-polarised channel of 664 GHz, the brightness temperature depressions were generally simulated at the correct locations; however, the simulated V–H was too large. This work shows that multi-frequency polarisation information could be used to infer realistic particle shapes, orientations, and representations of the split between single crystals and aggregates within the cloud.

Satellite data records of stratospheric water vapour have been compared to balloon-borne frost point hygrometer (FP) profiles that are coincident in space and time. The satellite data records of 15 different instruments cover water vapour data available from January 2000 through December 2016. The hygrometer data are from 27 stations all over the world in the same period. For the comparison, real or constructed averaging kernels have been applied to the hygrometer profiles to adjust them to the measurement characteristics of the satellite instruments. For bias evaluation, we have compared satellite profiles averaged over the available temporal coverage to the means of coincident FP profiles for individual stations. For drift determinations, we analysed time series of relative differences between spatiotemporally coincident satellite and hygrometer profiles at individual stations. In a synopsis we have also calculated the mean biases and drifts (and their respective uncertainties) for each satellite record over all applicable hygrometer stations in three altitude ranges (10–30 hPa, 30–100 hPa, and 100 hPa to tropopause). Most of the satellite data have biases <10 % and average drifts <1 % yr−1 in at least one of the respective altitude ranges. Virtually all biases are significant in the sense that their uncertainty range in terms of twice the standard error of the mean does not include zero. Statistically significant drifts (95 % confidence) are detected for 35 % of the ≈ 1200 time series of relative differences between satellites and hygrometers.

&lt;div&gt; &lt;div&gt;How much has rained in the last minutes anywhere in Africa? Contrary to Europe or North America, a dense network of weather radars providing this information is not available on the African continent. A new product, Rain over Africa, aims to find an answer to the question by retrieving rain rates from Meteosat geostationary infrared images and making them available to the public within minutes from satellite downlink. By using geostationary observations, rain retrievals with a resolution of 3-5 km and 15 min update time can be offered.&lt;/div&gt; &lt;br /&gt; &lt;div&gt;Machine learning is at the core of Rain over Africa. The GPM DPR and GMI combined precipitation L2B product was exploited to train a convolutional neural network. The trained model outputs a pixel-wise rain rate distribution free from traditional assumptions, enabling not only point estimates such as an expected value, but also non-Gaussian error estimates or likelihoods of extreme events by computing tail probabilities. Moreover, the Rain over Africa retrievals compare similar to the IMERG Late Run product, but can offer additional statistics at a finer spatiotemporal resolution, with a product latency of few minutes instead of hours.&lt;/div&gt; &lt;br /&gt; &lt;div&gt;Further details on the model and its performance, characteristics of the Rain over Africa product, how to access the data, and data availability, combined with a product outlook, will be given in this presentation.&lt;/div&gt; &lt;/div&gt;