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Cloud radar Doppler spectra are of particular interest for investigating cloud microphysical processes, such as ice formation, riming and ice multiplication. When hydrometeor types within a cloud radar observation volume have sufficiently different terminal fall velocities, they produce individual Doppler spectrum peaks, convoluted by dynamical effects. If these (sub-)peaks can be separated, properties of the underlying hydrometeor populations can potentially be estimated, such as their fall velocity, number, size and to some extent their shape. However, this task is complex and dependent on the cloud radar operation settings, atmospheric dynamics and hydrometeor characteristics. As a consequence, there is a need for adjustable tools that are able to detect peaks in cloud radar Doppler spectra to extract the valuable information contained in them. This paper presents the synergistic use of two cloud radar Doppler spectra peak analysis algorithms, PEAKO and peakTree. PEAKO is a supervised machine learning tool that can be trained to obtain the optimal parameters for peak detection in cloud radar Doppler spectra for specific cloud radar instrument settings. The learned Doppler spectrum peak detection parameters can then be applied by peakTree, which is used to detect, structure and interpret Doppler spectrum peaks. The application of the improved PEAKO-peakTree toolkit is demonstrated in two case studies. The interpretation is supported by forward simulated cloud radar Doppler spectra by the Passive and Active Microwave TRAnsfer tool (PAMTRA), which are also used to explore the limitations of the algorithm toolkit posed by turbulence and the number of spectral averages chosen in the radar settings. From the PAMTRA simulations, we can conclude that a minimum number of 20–40 spectral averages is desirable for Doppler spectrum peak discrimination. Furthermore, liquid peaks can only be reliably separated for turbulence eddy dissipation rate values up to approximately 0.0002 m2 s−3. The first case study demonstrates that the methods work for different radar systems and settings by comparing the results for two cloud radar systems which were operated simultaneously at a site in Punta Arenas, Chile. Detected peaks which can be attributed to liquid droplets agree well between the two systems, as well as with an independent liquid-predicting neural network. The second case study compares PEAKO-peakTree-detected cloud radar Doppler spectra peaks to in situ observations collected by a balloon-based holographic imager during a campaign in Ny-Ålesund, Svalbard. This case showcases the Doppler spectrum peak detection algorithms' ability to identify different hydrometeor types, but also reveals the limitations of the algorithm toolkit posed by strong turbulence and a low number of spectral averages. Despite these challenges, the algorithm toolkit offers a powerful means of extracting comprehensive information from cloud radar observations. In the future, we envision PEAKO-peakTree application on the one hand for interpreting cloud microphysics in case studies. The identification of liquid cloud peaks emerges as a valuable asset e.g. in studies on cloud radiative effects, seeder-feeder processes, or for tracing vertical air motions. Furthermore, the computation of the moments for each sub-peak enables the tracking of hydrometeor populations and the observation of growth processes along fall streaks. On the other hand, PEAKO-peakTree application could be extended to statistical evaluations of longer data sets. Both algorithms are openly available on GitHub, offering accessibility for the scientific community. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected 'best guess' rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of 'rainfall shadows'. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types. [ABSTRACT FROM AUTHOR]

Radar based microphysical retrievals of cloud and droplet properties are vital for informing model parameterisations of clouds and precipitation but these retrievals often do not capture the details of small droplets in light rain or drizzle. A state-of-the-art G-band radar is used here to demonstrate improvements to microphysical retrievals in a case study featuring light rain. Improvements are seen, as compared to W-band radar, in the retrieval of vertical wind speed, due to the location of Mie minima at smaller droplet sizes with the G-band radar. This, in turn, has an impact on the retrieval of the drop size distribution, allowing for better accuracy in the retrieval of the characteristic drop diameter and improvements in the retrieval of the number of concentration of small droplet sizes. The differential Doppler velocity between Ka- and G-bands shows increased dynamic range compared to the Ka-W pairing, particularly for instances presenting small characteristic drop diameters. The increased attenuation experienced at G-band enables improved retrievals of liquid water content and precipitation rate when paired with W-band or Ka-band as compared to the W-band and Ka- band pairing. This is particularly noticeable in periods of light rain where the W-band and Ka-band radars receive negligible attenuation while the attenuation at G-band is much greater. [ABSTRACT FROM AUTHOR]

The national German weather radar network operates in C-band between 5.6 and 5.65 GHZ. In a radar network, individual transmit frequencies have to be chosen such that radar-radar induced interferences are avoided. In an unique experiment the Hohenpeissenberg research radar and five operational systems from the radar network were used to characterize radar-radar induced interferences as a function of the radar frequency. The results allow to assess the possibility to add additional C-band radars with magnetron transmitters into the existing network. Based on the experiment, at least a 15 MHz separation of the nominal radar frequency is needed to avoid a radar-radar interference. The most efficient mitigation of radar-radar interference is achieved by the 'Radar Tango'. Latter refers to the synchronized scanning of all radar systems in the network. Based on those results, additional C-band radar systems can be added to the German weather radar network if a further improvement of the radar coverage is needed. [ABSTRACT FROM AUTHOR]

Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data. [ABSTRACT FROM AUTHOR]

Remote hail detection and hail size estimation using weather radar observations has the advantage of wide spatial coverage and high spatial and temporal resolution. Switzerland National Weather Service (MeteoSwiss) uses two radar-based hail metrics: the probability of hail at the ground (POH) to assess the presence of hail, and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. However, radar-based metrics are not direct measurements of hail and have to be calibrated with and verified against ground-based observations of hail, such as crowdsourced hail reports. Switzerland benefits from a particularly rich and dense dataset of crowdsourced hail reports from the MeteoSwiss app. We combine a new spatiotemporal clustering method (ST-DBSCAN) with radar reflectivity to filter the reports and use the filtered reports to verify POH and MESHS in terms of the Hit Rate, False Alarms Ratio (FAR), Critical Success Index (CSI), and Heidke Skill Score (HSS). Using a 4 km × 4 km maximum upscaling approach, we find FAR values between 0.3 and 0.7 for POH and FAR > 0.6 for MESHS. For POH, the highest CSI (0.37) and HSS (0.52) are obtained for a 60 % threshold, while for MESHS the highest CSI (0.25) and HSS (0.4) are obtained for a 2 cm threshold. We find that the current calibration of POH does not correspond to a probability and suggest a recalibration based on the filtered reports. [ABSTRACT FROM AUTHOR]

To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snow melt periods, wet snow absorbs the signal and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have a similar temporal behavior, and both feature an increase in backscatter during the dry snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation, but that changes in microstructure, stratigraphy, melt-freeze cycles, and snow wetness may complicate satellite-based snow depth retrievals. [ABSTRACT FROM AUTHOR]

In this paper, the implementation and validation of the torus mapping, a method to dealias radial wind observations by meteorological radars is presented. We apply the procedure to data from the Operational program for exchange of weather radar information (OPERA) over central part of Europe for the whole year 2021. The quality of the resulting radar winds is assessed by a comparison to colocated radiosonde and aircraft observations and also through analysis of first guess departures using a regional numerical weather prediction model. Performance of quality control on dealiased radial wind data is also evaluated. We show that the torus mapping method is a robust procedure on data with a sufficiently low amount of measurement noise. It produces datasets of comparable quality for a wide range of Nyquist velocity values and has potential for operational applications. [ABSTRACT FROM AUTHOR]

Radio-echo sounding is a standard technique for imaging the englacial stratigraphy of glaciers and ice sheets. In most cases, internal reflection horizons (IRHs) represent former glacier surfaces and comprise information about past accumulation, ice deformation and allow to link ice core chronologies. IRHs in the lower third of the ice column are often difficult to detect or coherently trace. In the polar ice sheets, progress in IRH detection has been made by using multistatic, phase-coherent radars, enabling synthetic-aperture radar (SAR) processing. However, these radar systems are often not suitable for deployment on mountain glaciers. We present a proof-of-concept study for a lightweight, phase-coherent, and ground-based radar system, based on the phase-sensitive radio echo-sounder (pRES). To improve the detectability of IRHs we additionally adapted a layer-optimized SAR (LO-SAR) processing scheme to this setup. We showcase the system capability at Colle Gnifetti, Switzerland/Italy, and detect significantly deeper and older IRHs compared to previously deployed pulsed radar systems. Continuous IRHs are now apparent down to the base of the glacier. Corresponding reflection mechanisms for this glacier are linked to a stratified acidic impurity which was deposited at a higher rate due to increased industrial activity in the area. Possible improvements of the system are discussed. If successfully implemented, these may provide a new way to map the deep internal structure of Colle Gnifetti and other mountain glaciers more extensively in future deployments. [ABSTRACT FROM AUTHOR]

Radar observations of winter storms often exhibit locally-enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally-enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field that is rescaled from reflectivity and incorporates an under and over estimate to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of datasets and applications. [ABSTRACT FROM AUTHOR]

Severe convective weather events, such as hail, lightning and heavy rainfall pose a great threat to humans and cause a considerable amount of economic damage. Nowcasting convective storms can provide warning signals and mitigate the impact of these storms. Dual-polarization weather radars are a crucial source of information for nowcasting severe convective events; nevertheless, they are most often not considered in nowcasting. These radars provide signatures of different hydrometeors. This work presents the importance of polarimetric variables as an additional data source for nowcasting thunderstorm hazards using an existing neural network architecture with convolutional and recurrent layers. This network has a common framework, which enables nowcasting of hail, lightning and heavy rainfall for lead times up to 60 min with a 5 min resolution. The study area is covered by the Swiss operational radar network, which consists of five operational polarimetric C-band radars. Results indicate that including polarimetric variables and quality indices improve the accuracy of nowcasting heavy precipitation and lightning, with the largest improvement found for heavy precipitation. [ABSTRACT FROM AUTHOR]

Stratiform rain and the overlying ice play crucial roles in the Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a space-borne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a significant increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling. [ABSTRACT FROM AUTHOR]

Hail remains a major threat to agriculture in Switzerland and beyond and assessments of current and future hail risk are of paramount importance for decision-making in the insurance industry and the agricultural sector. However, relating observational information on hail with crop-specific damages is challenging. Here, we build and systematically assess a model to predict hail damage footprints for field crops (wheat, maize, barley, rapeseed) and grapevine from the operational radar product Maximum Expected Severe Hail Size (MESHS) at different spatial resolutions. To this end, we combine the radar information with detailed geospatial information on agricultural land use and geo-referenced damage data from a crop insurer for 12 recent hail events in Switzerland. We find that for field crops, model skill gradually increases when the spatial resolution is reduced from 1 km down to 8 km. For even lower resolutions, the skill is diminished again. On the contrary, for grapevine, a lower model resolution tends to reduce skill, which is attributed to the different spatial distribution of field crops and grapevine in the landscape. It is shown that identifying a suitable MESHS thresholds to model damage footprints always involves trade-offs. For the lowest possible MESHS threshold (20 mm) the model predicts damage about two times too often (high frequency bias and number of false alarms) but also has a high probability of detection (80 %). The frequency bias decreases for larger thresholds and reaches an optimal value close to 1 for MESHS thresholds of 30–40 mm. However, this comes at the cost of a substantially lower probability of detection (around 50 %) while overall model skill remains largely unchanged. We argue that, ultimately, the best threshold selection therefore depends on the user need and the costs of a false alarm or a missed event. Finally, the frequency of false alarms can be substantially reduced when only areas with high cropland density are considered. Results from this simple, open-source model show that modelling of hail damage footprints to crops from single-polarization radar in Switzerland is skillful and is best done at 8 km resolution for field crops and 1 km for grapevine. They further allow different users of such models to identify the suitable threshold for their application, taking into account associated trade-offs. [ABSTRACT FROM AUTHOR]

Observations of glacier mass changes are key to understanding the response of glaciers to climate change and related impacts, such as regional runoff, ecosystem changes, and global sea-level rise. Spaceborne optical and radar sensors make it possible to quantify glacier elevation changes, and thus multi-annual mass changes, on a regional and global scale. However, estimates from a growing number of studies show a wide range of results with differences often beyond uncertainty bounds. Here, we present the outcome of a community-based inter-comparison experiment using spaceborne optical stereo (ASTER) and synthetic aperture radar interferometry (TanDEM-X) data to estimate elevation changes for defined glaciers and target periods that pose different assessment challenges. Using provided or self-processed digital elevation models (DEMs) for five test sites, 12 research groups provided a total of 97 spaceborne elevation-change datasets using various processing strategies. Validation with airborne data showed that using an ensemble estimate is promising to reduce random errors from different instruments and processing methods, but still requires a more comprehensive investigation and correction of systematic errors. We found that scene selection, DEM processing, and co-registration have the biggest impact on the results. Other processing steps, such as treating spatial data voids, differences in survey periods, or radar penetration, can still be important for individual cases. Future research should focus on testing different implementations of individual processing steps (e.g. co-registration) and addressing issues related to temporal corrections, radar penetration, glacier area changes, and density conversion. Finally, there is a clear need for our community to develop best practices, use open, reproducible software, and assess overall uncertainty in order to enhance inter-comparison and empower physical process insights across glacier elevation-change studies. [ABSTRACT FROM AUTHOR]

Downhill thunderstorms frequently occur in Beijing during the rainy seasons, leading to substantial precipitation. The accurate intensity prediction of these events remains a challenge, partly attributed to insufficient observational studies that unveil the thermodynamic and dynamic structures along the vertical direction. This study provides a comprehensive methodology for identifying both enhanced and dissipated downhill thunderstorms. In addition, a radar wind profiler (RWP) mesonet has been built in Beijing to characterize the pre-storm environment downstream to the thunderstorms at the mountain foot. This involves deriving vertical distributions of high-resolution horizontal divergence and vertical motion from the horizontal wind profiles measured by the RWP mesonet. A case of enhanced downhill thunderstorm on 28 September 2018 is carried out, which support the idea that the enhanced southerly flow and corresponding convergence detected by the RWP mesonet could favorably support the development of thunderstorms in the afternoon. The results also indicate that low-level convergence is an effective signal in accounting for convective maintenance. Statistical analysis based on radar reflectivity from April to September 2018–2021 have shown that a total of 63 thunderstorm events tend to be enhanced after moving into downhill and urban areas, accounting for about 66 % of the total number of downhill thunderstorm events. A critical region for intensified thunderstorms lies on the downslope side of the mountains west to Beijing. The evolution of the downhill storm is associated with the dynamic conditions over the plain compared to its initial morphology. The lifting induced by stronger westerly winds and vertical shear in the low and midlevel troposphere exerts a critical influence on the enhancement of convection. The findings underscore the significant role of RWP network in elucidating the evolution of downhill storm. [ABSTRACT FROM AUTHOR]

Snow provides critical water resources for billions of people, making the remote sensing of snow water equivalent (SWE) a highly prioritized endeavor, particularly given current and projected climate change impacts. Synthetic Aperture Radar (SAR) is a promising method for remote sensing of SWE because radar penetrates snow and SAR interferometry (InSAR) can be used to estimate changes in SWE (ΔSWE) between SAR acquisitions. We calculated ΔSWE retrievals from 10 NASA L-band Uninhabited Aerial Vehicle SAR (UAVSAR) acquisitions in northern Colorado during the winters of 2020 and 2021 and evaluated the retrievals against measurements of SWE from ground-penetrating radar (GPR) and terrestrial lidar scans (TLS) collected as part of the NASA SnowEx 2020 and 2021 Time Series Campaigns. Next, we evaluated the full UAVSAR time series at the northern Colorado sites using SWE measured at seven automated stations and ascertained whether coherence can be used as an accuracy metric for ΔSWE retrievals. For single InSAR pairs, UAVSAR ΔSWE retrievals displayed high correlation with TLS and GPR ΔSWE retrievals (overall r = 0.72–0.79) and yielded an RMSE of 19–22 mm. When compared to SWE at seven automated stations, cumulative SWE from UAVSAR retrievals exhibited poor agreement in 2020, but high agreement in 2021. We found that SWE can be reliably retrieved, even for lower coherences, as RMSE values ranged by <10 mm from coherences of 0.10 to 0.90. The upcoming NASA-ISRO SAR satellite mission, with a 12-day revisit period, offers an exciting opportunity to apply this methodology globally, but further quantification of limitations is necessary, particularly in forested environments and as the snowpack begins to melt. [ABSTRACT FROM AUTHOR]

Winds from two co-located two wind measuring instruments, a meteor radar and field widened Michelson interferometer at the Polar Environment Atmospheric Research Laboratory in Eureka, Nu, Canada (80° N, 86° W) are compared. The two instruments have very different temporal and spatial observational footprints. ERWIN provides airglow weighted winds from three nightglow emissions (O(1S) (oxygen green line, 557.7 nm), an O2 line (866 nm), and an OH line (843 nm)) on a ∼5 minute cadence for measurements at all three heights. As with Fabry-Perot airglow wind observations, these winds are airglow weighted winds from volumes of ∼8 km in height by ∼5 km radius. ERWIN's higher accuracy (1–2 m/s for the O(1S) and OH emissions and ∼4 m/s for the O2 emissions) and higher cadence allows more detailed wind comparisons of airglow and radar winds than previously possible. The best correlation is achieved using Gaussian weighting of meteor radar winds with peak height and vertical width being optimally determined. Peak heights agree well with co-located SABER airglow observations. Offsets between the two instruments are ∼ 1–2 m/s for the O2 and O(1S) emissions and less than 0.3/s for the OH emission. Wind direction are highly correlated with a ∼ 1:1 correspondence. On average meteor radar wind magnitudes are ∼ 40 % larger than those from ERWIN. Gravity wave airglow brightness weighting of observations is discussed. Non-quadrature phase offsets between the airglow weighting and gravity wave associated wind and temperature perturbations will result in enhanced or reduced layer weighted wind amplitudes. [ABSTRACT FROM AUTHOR]

NASA's Vapor In-cloud Profiling Radar (VIPR) is a tunable G-band radar designed for in-cloud and precipitation humidity remote sensing. VIPR estimates humidity using the differential absorption radar (DAR) technique. This technique exploits the difference between atmospheric attenuation at different frequencies ("on" and "off" an absorption line) and combines it with the ranging capabilities of the radar to estimate the absorbing gas concentration along the radar path. We analyze the VIPR humidity measurements during two NASA field campaigns: (1) the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) campaign, with the objective of studying wintertime snowstorms focusing on East Coast cyclones; and (2) the Synergies Of Active optical and Active microwave Remote Sensing Experiment (SOA2RSE) campaign which studied the synergy between DAR (VIPR) and differential absorption lidar (DIAL, the High altitude Lidar Observatory – HALO) measurements. We discuss a comparison with dropsondes launched during these campaigns as well as an intercomparison against the ERA5 reanalysis fields. Thus, this study serves as an additional evaluation of ERA5 lower tropospheric humidity fields. In addition, we show a smooth transition in water vapor profiles between the in-cloud and clear-sky measurements obtained from VIPR and HALO respectively, which highlights the complementary nature of these two measurement techniques for future airborne and space-based missions. [ABSTRACT FROM AUTHOR]

The planetary boundary layer (PBL) over the Tibetan Plateau (TP) exerts a significant influence on regional and global climate, while its vertical structures of turbulence and evolution features remain poorly understood, largely due to the scarcity of observation. This study examines the vertical profile and daytime variation of turbulence dissipation rate (ε) in the PBL over the TP using the high-resolution (6 min and 120 m) measurements from the radar wind profiler (RWP) network, combined with the hourly data from the ERA5 reanalysis. Observational analyses show that the magnitude of ε below 3 km under all-sky conditions exhibits large spatial discrepancy over the six RWP sites over the TP. Particularly, the values of ε at Minfeng and Jiuquan over the northern TP and Dingri over the southern TP are roughly an order of magnitude greater than those at Lijiang, Ganzi and Hongyuan over the eastern TP. This could be partially attributed to the difference of land cover across the six RWP sites. In terms of the diurnal variation, ε rapidly intensifies from 0900 local standard time (LST) to 1400 LST, and then gradually levels off in the late afternoon. Under clear-sky conditions, both ε and planetary boundary layer height (zi) are greater, compared with cloudy-sky conditions. This reveals that clouds would suppress the turbulence development and deduce zi. In the lower PBL (0.2< z / zi <0.5, where z is the height above ground level), the dominant influential factor for the development of turbulence is the surface-air temperature difference (Ts – Ta). By comparison, in the upper PBL (0.6< z / zi <1.0), both the and vertical wind shear (VWS) affect the development of turbulence. Above the PBL (1.0< z / zi <2.0), the shear production resulting from VWS dominates the variation of turbulence. Under cloudy-sky conditions, clouds are found to decrease the surface total solar radiation, thereby reducing Ts – Ta and surface sensible heat flux. This weakened sensible heat flux tends to inhibit the turbulent motion within PBL especially in the lower PBL and decrease the growth rate of zi. On the other hand, the strong VWS induced by clouds enhances the turbulence above the PBL. The findings obtained here underscore the importance of RWP network in revealing the fine-scale structures of the PBL over the TP and gaining new insight into the PBL evolution. [ABSTRACT FROM AUTHOR]

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 GPROF V7 and multiple configurations of the GPROF-NN retrieval 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 precipitation estimates over 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 reproduces the principal precipitation characteristics of each region. The effective resolution of GPROF V7 is found to be 51 km over CONUS and 18 km over the tropical Pacific. GPROF V7 produces robust precipitation estimates also for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPROF precipi- tation 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 by about twenty percent 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 comparged to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used in the training of the retrievals for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, microstructure and to surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem, and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of the prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than than 20 %, and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally. [ABSTRACT FROM AUTHOR]

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside four years of observations (2017–2021) from Parsivel2 disdrometer and a cloud profiler radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (-0.5 events/decade). However, the p-value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the necessity for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru. [ABSTRACT FROM AUTHOR]

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation Ice Bridge (OIB) over the period 2011–2019 to investigate vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile, and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ~338,450 km2, followed by firn replenishment over the years 2014–2019 spanning an area of ~664,734 km2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns. [ABSTRACT FROM AUTHOR]

C-Band weather radar data are commonly compromised by interference from external sources even though weather radars are the primary and therefore privileged user of this frequency band. This is also the case for the radar network of the German Weather Service (Deutscher Wetterdienst, DWD). Theoretically, dynamic frequency stepping (DFS) by devices operating in the C-Band should prevent any disturbance of the primary user. In practice, this does not always work as intended by the current regulations. As it is not possible to separate a superimposed interference signal from measured weather radar data, the protection of the frequency band is of utmost importance. Currently the only available option is to discard the compromised portions of the radar data. Therefore, the current best course of action is to shut down interference (RFI) sources as fast as possible. The automated RFI detection algorithm for the German C-Band weather radar network is operational since July 2017, which makes use of routinely measured radar moments. Built upon the data gathered since 2017, an RFI classification with respect to the severity and duration of RFIs was first implemented in 2019. An independent verification of the RFI detection algorithm was performed by using a commercially available WIFI adapter, which is directly integrated into the radar receiver. Subsequently, a mitigation workflow was implemented to efficiently identify and shut down detected RFI sources by the German Federal Network Agency (BNetzA). By following this workflow with great effort, the number of persistent RFIs is decreasing since October 2019 while a steady increase in short lived RFIs over the last 5 years exists. In total, 11889 RFIs have been identified since July 2017 until May 2022. The majority of these (94.8 %) are so short lived that an unambiguous identification by the BNetzA is, in general, not feasible. However, as stated by the C-Band regulations, any non-compliant transmitter compromising the operation of a weather radar has to be shut down. This is important, as even these short lived RFIs negatively affect the meteorological product generation. [ABSTRACT FROM AUTHOR]

Atmospheric Gravity Waves and Traveling Ionospheric Disturbances can be observed in the neutral atmosphere and the ionosphere at a wide range of spatial and temporal scales. Especially at medium scales, these oscillations are often not resolved in general circulation models and are parameterized. We show that ionospheric disturbances forced by upward propagating atmospheric gravity waves can be simultaneously observed with the EISCAT Very High Frequency incoherent scatter radar and the Nordic Meteor Radar Cluster. From combined multi-static measurements, both vertical and horizontal wave parameters can be determined by applying a specially developed Fourier filter analysis method. This method is demonstrated using the example of a strongly pronounced wave mode that occurred during the EISCAT experiment on 7 July 2020. Leveraging the developed technique, we show that the wave characteristics of Traveling Ionospheric Disturbances are notably impacted by the fall transition of the mesosphere/lower thermosphere. We also demonstrate the application of using the determined wave parameters to infer the thermospheric neutral wind velocities. Applying the dissipative anelastic gravity wave dispersion relation, we obtain vertical wind profiles in the lower thermosphere. [ABSTRACT FROM AUTHOR]

The Himalayan belt, formed due to Cenozoic convergence between the Eurasia and Indian craton, acts as a storehouse of large amounts of strain that results in large earthquakes that occur from the western to the eastern Himalayas. Glaciers also occur over a major portion of the high-altitude Himalayan region. In this study, we attempt to understand the impact of earthquakes on and around Himalayan glaciers in terms of vertical deformation and coherence change. Eight earthquake events of various magnitudes and hypocenter depths occurring in the vicinity of Himalayan glacial bodies have been analyzed using C-band Sentinel1-A/B synthetic aperture radar (SAR). Differential interferometric SAR (DInSAR) method is applied to the pre-and co-seismic single look complex (SLC) SAR imagery to capture glacial surface deformation potentially related to earthquake occurrence. The influence radius of each earthquake (Mw>4.5) is defined using shake maps. For the lower magnitude earthquakes (Mw<4.5), influence radii are computed using the linear relationship between influence radii and magnitudes of past earthquakes. The mean glacial displacement varies from -38.9 mm to -5.4 mm for the 2020 Tibet earthquake (Mw 5.7) and the 2021 Nepal earthquake (Mw 4.1). However, small glacial and ground patches processed separately for vertical displacements reveal that the glacial mass shows much greater seismic displacement than the ground surface. This indicates potential site-specific seismicity amplification properties of glacial bodies that need additional studies. Reduction in co-seismic coherence around the glaciers is observed in some cases, indicative of possible changes in the glacial moraine deposits and/or vegetation cover. The effect of two different seismic events (the 2020 and 2021 Nepal earthquakes) with different hypocenter depths but the same magnitude at almost equal distances from the glaciers is assessed; a shallow earthquake is observed to result in a larger impact on glacial bodies in terms of vertical displacement. Earthquakes may exacerbate or induce glacial hazards such as glacial surging, ice avalanches, landslides and failure of moraine-/ice-dammed glacial lakes. This research can assist in identifying areas at risk and provide valuable insights for planning and implementing measures for disaster risk reduction in the near future. [ABSTRACT FROM AUTHOR]

Two simple feedforward neural networks (MLPs) are trained to classify wet and dry periods using signal attenuation from commercial microwave links (CMLs) as predictors and high temporal resolution reference data as target. MLP GA is trained against nearby rain gauges and MLP RA is trained against gauge-adjusted weather radar. Both MLPs perform better than existing methods, showcasing their effectiveness in capturing the intermittent behaviour of rainfall. This study is the first using both radar and rain gauges for training and testing for CML wet-dry classification. Where previous studies has mainly focused on hourly reference data, our findings show that it is possible to predict wet and dry periods with a higher temporal precision. [ABSTRACT FROM AUTHOR]

Secondary ice production (SIP) has an essential role in cloud and precipitation microphysics. In recent years, substantial insights were gained into SIP by combining experimental, modeling, and observational approaches. Remote sensing instruments, and among them meteorological radars, offer the possibility to study clouds and precipitation in extended areas over long time periods, and are highly valuable to understand the spatio-temporal structure of microphysical processes. Multi-modal Doppler spectra measured by vertically-pointing radars reveal the coexistence, within a radar resolution volume, of hydrometeor populations with distinct properties; as such, they can provide decisive insight into precipitation microphysics. This paper leverages polarimetric radar Doppler spectra as a tool to study the microphysical processes that took place during a snowfall event on 27 January 2021, in the Swiss Jura Mountains, during the ICE GENESIS campaign. A multi-layered cloud system was present, with ice particles sedimenting through a supercooled liquid water (SLW) layer in a seeder-feeder configuration. Building on a Doppler peak detection algorithm, we implement a peak labeling procedure to identify the particle type(s) that may be present within a radar resolution volume. With this approach, we can visualize spatio-temporal features in the radar time series that point to the occurrence of distinct mechanisms at different stages of the event. By focusing on three 30-minute phases of the case study, and by using the detailed information contained in the Doppler spectra, together with dual-frequency radar measurements, aircraft in-situ images, and simulated profiles of atmospheric variables, we narrow down the possible processes which can be responsible for the observed signatures. Depending on the availability of SLW and the droplet sizes, on the temperature range, and on the interaction between the liquid and ice particles, various SIP processes are identified as plausible, with distinct fingerprints in the radar Doppler spectra. A simple modeling approach suggests that the ice crystal number concentrations likely exceed typical concentrations of ice nucleating particles by one to four orders of magnitude. While a robust proof of occurrence of a given SIP mechanism cannot be easily established, the multi-sensor data provides various independent elements each supporting the proposed interpretations. [ABSTRACT FROM AUTHOR]

Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time, therefore, to better understand and manage the related risks, knowledge of their probability at different spatial-temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial-temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid and arid areas across a few tens of kilometres, and its susceptibility to flash-flood. At-site intensity-duration-area-frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25 km2–500 km2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations, but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial-temporal scales. [ABSTRACT FROM AUTHOR]

We present and analyze a rockfall catalog from an active landslide complex in Brienz/Brinzauls of the Swiss Alps, collected with a new Doppler radar system. This radar system provides a complete and continuous time-series of rockfall events with volumes of 1m3 and bigger since 2018 and serves as automatic traffic control for an important cantonal road. In the period between January 2018 and September 2022, 6743 events were detected, which is two orders of magnitude higher activity than in stable continental cliffs. A few percent of all rockfall events reached the shadow zone, which hosts an important road and agricultural area. The Doppler radar data set allows us to investigate the triggering factors quantitatively. We found that the background rockfall activity is controlled by seasonal climatic triggers. In winter, more rockfalls are observed during thawing periods, whereas in summer the rockfall activity increases with hourly rainfall intensity. We also found that due to the geological setting in an active landslide complex, increased rockfall activity occurs clustered in space and time, triggered by local displacement hotspots. Thus, monitoring spatial and temporal variations of slope displacement velocity is crucial for detailed rockfall hazard assessment in similar geological settings. [ABSTRACT FROM AUTHOR]

Most Arctic clouds occur below 2 km altitude as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low- level cloud fraction, which is the amount of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from downlooking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC has been operated during four aircraft campaigns taking place in the vicinity of Svalbard during different times of the year and covering more than 25,000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions. A forward simulator converts MiRAC measurements to synthetic CloudSat radar reflectivities. These forward simulations are compared with the original CloudSat observations for four satellite underflights to prove the suitability of our forward- simulation approach. Above CloudSat's blind zone of 1 km and below 2.5 km, the forward simulations reveal that CloudSat would overestimate the MiRAC cloud fraction over all campaigns by about 6 percent points (pp) due to its horizontal resolution, by 12 pp due to its range resolution, and underestimate it by 10 pp due to its sensitivity. Especially during cold air outbreaks over open water, high reflectivity clouds appear below 1.5 km, which are stretched by CloudSat's pulse length causing the forward-simulated cloud fraction to be 16 pp higher than that observed by MiRAC. The pulse length merges multilayer clouds, whereas thin low-reflectivity clouds remain undetected. Consequently, 48 % of clouds observed by MiRAC belong to multilayer clouds, which reduces by a factor of 4 for the forward-simulated CloudSat counterpart. Despite the overestimation between 1 and 2.5 km, the overall low-level cloud fraction is strongly reduced due to CloudSat's blind zone that misses a cloud fraction of 32 % and half of the total (mainly light) precipitation amount. [ABSTRACT FROM AUTHOR]

The Cloud Profiling Radar (CPR) of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) has a new capability to observe the Doppler velocity related to the vertical air motion of the terminal velocity of hydrometeors. The new observation from space will be used to evaluate and improve the model. Before the launch of EarthCARE, we need to develop a methodology for using the CPR data for model evaluations. In this study, we evaluated simulated data by a stretched version of the global non-hydrostatic model over Japan with a ground-based CPR using an instrument design similar to the EarthCARE CPR. We chose two cases with different precipitation events in September 2019 using two cloud microphysics schemes. We introduced the categorization method for evaluating microphysics using Doppler velocity. The results show that the liquid and solid phases of hydrometeors are divided in Doppler velocity, and the model's terminal velocities of rain, snow, and graupel categories can be evaluated with the observation. The results also show that the choice of microphysics scheme has a more significant impact than the dependence on precipitation cases. We discussed the application of the EarthCARE-like simulation results using a satellite simulator. [ABSTRACT FROM AUTHOR]

This study analyzes the correlation between hydrometeor type and radar spectrum width (σv) according to wind speed that can occur the atmospheric disturbances such as turbulence and wind shear. The σv zones shown as peak values were identified only in stratiform precipitation and they are highly related to the hydrometeor growth zones. Statistical analysis was performed for eight precipitation cases under various conditions (precipitation type, season), focusing on the Dendrite Growth Zone (DGZ) and the Needle Growth Zone (NGZ), where Dendrite (DN) and Needle (NE) type snowflakes are dominant, respectively. They were determined by the Growth Zone Determination Algorithm (GZDA) that was proposed in this study. The intensity of the σv depends on atmospheric conditions (i.e., wind speed) and season (i.e., temperature). The high σv and negative relationship with the differential radar reflectivity (ZDR) in the DGZ for all cases is consistent with the aerodynamic properties of DN. As the range of σv was larger than that of ZDR, it was confirmed that the dependence of σv according to atmospheric conditions is significant. Contrastingly, the NGZ had a low σv and weak σv-ZDR negative relationship with a narrow range of σv, which is consistent with the aerodynamic properties of NE. The lower cross-correlation coefficient (ρhv) in the DGZ than that in the NGZ implies that the irregularities (particle shape and aerodynamics features) of DN were more pronounced than those of NE. Finally, as the altitudes of two growth zones were determined by temperature, the possibility of estimating sub-zero temperature by GZDA was confirmed. [ABSTRACT FROM AUTHOR]

The structure and distribution of sub-glacial water directly influences Antarctic ice mass loss by reducing basal shear stress and enhancing grounding line retreat. A common technique for detecting sub-glacial water involves analyzing the spatial variation in reflectivity from an airborne ice penetrating radar (IPR) survey. Basic IPR analysis exploits the high dielectric contrast between water and most other substrate materials, where a reflectivity increase ≥ 15 dB is frequently correlated with the presence of sub-glacial water. There are surprisingly few additional tools to further characterize the size, shape, or extent of hydrological systems beneath large ice masses. We adapted an existing radar backscattering simulator to model IPR reflections from sub-glacial water structures using the University of Texas Institute for Geophysics (UTIG) Multifrequency Airborne Radar Sounder with Full-phase Assessment (MARFA) instrument. Our series of hypothetical simulations modeled water structures from 5 m to 50 m wide, surrounded by bed materials of varying roughness. We compared the relative reflectivity from rounded Röthlisberger channels and specular flat canals, showing both types of channels exhibit a positive correlation between size and reflectivity. Large (> 20 m), flat canals can increase reflectivity by more than 20 dB , while equivalent Röthlisberger channels show only modest reflectivity gains of 8−13 dB. Changes in substrate roughness may also alter observed reflectivity by 3−6 dB. All of these results indicate that a sophisticated approach to IPR interpretation can be useful in constraining the size and shape of sub-glacial water, however a highly nuanced treatment of the geometric context is necessary. Finally, we compared simulated outputs to actual reflectivity from a single IPR flight line collected over Thwaites Glacier in 2022. The flight line crosses a previously proposed Röthlisberger channel route, with an obvious bright bed reflection in the radargram. Through multiple simulations, we demonstrated the important role that topography and water geometry can play in observed IPR reflectivity. We ultimately conclude the bright reflector from our IPR flight line is more likely a broad area of wide distributed water, such as a series of flat canals or sub-glacial lake, instead of a Röthlisberger channel. The approach outlined here has broad applicability for studying the basal environment of large glaciers, and can aid in constraining the geometry and extent of sub-glacial hydrologic structures. [ABSTRACT FROM AUTHOR]

Precipitation is arguably one of the most relevant surface variables impacting human lives on the planet, but global-coverage, high-resolution and good-quality observations are not readily available. In particular, gridded observational datasets are much needed for model development and forecast quality assessment. Here we compare the quality of four types of gridded precipitation products over Europe, namely: a rain-gauge interpolation; a satellite-derived product; a radar composite; and a reanalysis. Each product has its own strengths and weaknesses, and since each precipitation estimate uses different measuring techniques, we can employ a triangulation method to estimate the error variance of each product with respect to the unknown true values. Results show that: a) the satellite product has limited quality over Europe and may be problematic to use in quantitative forecast evaluation and diagnostics; b) the radar composite has spurious features that need to be considered when used in verification; c) all products struggle in topographically complex areas; d) the rain-gauge interpolation is not free of errors, despite rain gauges being often treated as ground truth in the literature; and e) the reanalysis dataset produces in some cases the best available estimates, in particular over the European near-coastal waters. [ABSTRACT FROM AUTHOR]

Having knowledge of the drop size distribution (DSD) is of particular interest to researchers as it is widely applied to quantitative precipitation estimation (QPE) methods. Polarimetric radar measurements have previously been utilized to derive DSD curve characteristics frequently modeled as a gamma distribution. Likewise, approaches using dual frequency measurements have shown positive results. Both cases have relied on the need to constrain the relationship between the DSD parameters based on prior knowledge or assumptions of the collected data. This paper presents a methodology for retrieving the DSD parameters using the dual frequency and polarimetric nature of measurements from a unique data set taken at co-located S-band and C-band dual polarization radars. Using the reflectivity and differential phase measurements from each radar, an optimization routine employing particle swarm optimization (PSO) and T-Matrix computation of radar parameters is able to accurately retrieve the gamma distribution parameters without the constraints required in previous methods. Retrieved results are compared to known truth data collected using a network of OTT PARSIVEL disdrometers in Taiwan in order to assess the success of this procedure. [ABSTRACT FROM AUTHOR]

Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in-situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: 1) the introduction of secondary, texture-based fields, calculated using statistical properties of Gray Level Co-occurrence Matrices (GLCM), and 2) a Gaussian Mixture Model (GMM), used to classify echo sources by combining radar variables with texture-based fields from 1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient, is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes, or by scientists for case study analyses or historical event identification. [ABSTRACT FROM AUTHOR]

Meteor radars have become a widely used instrument to study atmospheric dynamics, in particular in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular/transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and introduce a mathematical debiasing process to mitigate them. This process makes use of a spatio-temporal Laplace filter which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals a good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostic such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ±1-2 m/s for most of the analyzed data during two years of collected data, which is consistent to the values reported from GCMs for this time scale and spatial resolution. [ABSTRACT FROM AUTHOR]

In this work, we study a Mediterranean cyclone, that was called Helios and took place during the period 9–11 February 2023 in the southeastern part of Sicily and Malta Island, by a multiparametric approach combining microseism results with sea state and meteorological data provided by wavemeter buoy, HF Radar, hindcast maps and satellite SEVIRI images. The sub-tropical system Helios caused heavy rainfall, strong wind gusts and violent storm surges with significant wave heights greater than 5 meters. We deal with the relationships between such a system and the features of microseism (the most continuous and ubiquitous seismic signal on the Earth) in terms of spectral content, space-time variation of the amplitude and source locations tracked by means of two different methods (amplitude-based grid search and array techniques). By comparing the location of the microseism sources and the area affected by significant storm surges, derived from sea state data, we note that the microseism location results are in agreement with the real position of the storm surges. In addition, we are able to obtain the seismic signature of Helios using a method that exploits the coherence of continuous seismic noise. Hence, we show how an innovative monitoring system of the Mediterranean cyclones can be designed by integrating microseism information with other techniques routinely used to study meteorological phenomena. [ABSTRACT FROM AUTHOR]

Optical correlation, interferometry, and field investigation of laterally offset features were undertaken to analyze the kinematics of the 2020 Mw 6.6 Masbate earthquake. Coseismic displacement fields from optical correlation show a maximum displacement of 0.61 m corresponding to Mw 6.64 geodetic moment magnitude and a lone asperity in Cataingan. Post-seismic deformation from interferometry highlights a maximum 0.14 m sinistral displacement equivalent to a Mw 6.15 post-seismic moment magnitude, with coincident afterslip and coseismic slip distributions. The measured slip decreased towards the north, suggesting the presence of a slip barrier where stress can accumulate. Slip measurements and rupture length estimates characterize the Masbate segment as capable of producing unusually long ruptures with significant offsets despite the presence of creep. Post-seismic interferograms resolved the rupture far better than optical correlation, which was degraded due to high amplitude noise from sensor and environmental sources. Nevertheless, the resultant surface rupture morphology, as observed in optical correlation outputs and interferograms, demonstrated the presence of two transtensional basins in the north and south of the province, interlinked by a stepover of the respective Riedel shear zones. This review of the 2020 Mw 6.6 Masbate earthquake reveals new insights into the seismic hazard and seismotectonic setting of Masbate province in Central Philippines. [ABSTRACT FROM AUTHOR]

Since distributed satellite observations of elevation change and velocity became available in the 1990s, Thwaites, Pine Island, Haynes, Pope, and Kohler Glaciers, located in Antarctica's Amundsen Sea Embayment, have thinned and accelerated in response to ocean-induced melting and grounding-line retreat. We develop a crevasse image segmentation algorithm to identify and map surface crevasses on the grounded portions of Thwaites, Pine Island, Haynes, Pope, and Kohler Glaciers between 2015 and 2022 using Sentinel-1A satellite synthetic aperture radar (SAR) imagery. We also develop a geometric model for firn tensile strength dependent on porosity and the tensile strength of ice. On Pine Island and Thwaites Glaciers, which have both accelerated since 2015, crevassing has expanded tens of kilometers upstream of the 2015 extent. From the crevasse time series, we find that crevassing is strongly linked to principal surface stresses and consistent with von Mises fracture theory predictions. Our geometric model, analysis of SAR, and optical imagery, together with ice-penetrating radar data, suggest that these crevasses are near-surface features restricted to the firn. The porosity dependence of the near-surface tensile strength of the ice sheet may explain discrepancies between the tensile strength inferred from remotely-sensed surface crevasse observations and tensile strength measured in laboratory experiments, which often focus on ice (rather than firn) fracture. The near-surface nature of these features suggests that the expansion of crevasses inland has a limited direct impact on glacier mechanics. [ABSTRACT FROM AUTHOR]

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) addresses this challenge by applying novel machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC aims to offer 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. Assessment of the retrieval accuracy on a held-out test set demonstrates considerable skill in reproducing the reference IWP and IWC estimates. In addition, CCIC is extensively validated against both in-situ and remote sensing measurements from two flight campaigns and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and, to first order, even 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. [ABSTRACT FROM AUTHOR]

Long term observations of deep convective cloud (DCC) vertical velocity and mass flux were collected during the GoAmazon2014/5 experiment. Precipitation echoes from a surveillance weather radar near Manaus, Brazil are tracked to identify and evaluate the isolated DCC lifecycle evolution during the dry and wet seasons. A Radar Wind Profiler (RWP) provides precipitation and air motion profiles to estimate the vertical velocity, mass flux, and mass transport rates within overpassing DCC cores as a function of the tracked cell lifecycle stage. The average radar reflectivity factor (Z), DCC area (A), and surface rainfall rate (R) increased with DCC lifetime as convective cells were developing, reached a peak as the cells matured, and decreased thereafter as cells dissipated. As the convective cells mature, cumulative DCC properties exhibit stronger updraft behaviors with higher upward mass flux and transport rates above the melting layer (compared to initial and later lifecycle stages). In comparison, developing DCCs have the lowest Z associated with weak updrafts, and negative mass flux and transport rates above the melting layer. Over the DCC lifetime, the height of the maximum downward mass flux decreased whereas the height of maximum net mass flux increased. During the dry season, the tracked DCCs had higher Z, propagation speed, and DCC area, and were more isolated spatially compared to the wet season. Dry season DCCs exhibit higher Z, mass flux, and mass transport rate while developing whereas wet season DCCs exhibit higher Z, mass flux, and mass transport rates at later stages. [ABSTRACT FROM AUTHOR]

The Cloud Profiling Radar (CPR) on the Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) satellite is the first satellite-borne Doppler radar (EC-CPR). In our previous study, we examined the effects of horizontal (along-track) integration and simple unfolding methods on the reduction of Doppler errors in the EC-CPR observations, and those effects were evaluated using two limited scenes in limited latitude and low pulse repetition frequency (PRF) settings. In this study, the amount of data used was significantly increased, and the area of the data used was extended globally. Not only low PRF but also high PRF settings were examined. We calculated the EC-CPR-observed Doppler velocity from pulse-pair covariances using the radar reflectivity factor and Doppler velocity obtained from a satellite data simulator and a global storm-resolving simulation. The global data were divided into five latitudinal zones, and mean Doppler errors for 5 dBZe after 10 km integration were calculated. In the case of low PRF setting, the error without unfolding correction for the tropics reached a maximum of 2.2 m s-1 and then decreased toward the poles (0.43 m s-1). The error with unfolding correction for the tropics became much smaller at 0.63 m s-1. In the case of high PRF setting, the error without unfolding correction for the tropics reached a maximum of 0.78 m s-1 and then decreased toward the poles (0.19 m s-1). The error with unfolding correction for the tropics was 0.29 m s-1, less than half the value without the correction. The results of the analyses of the simulated data indicated that the zonal mean frequency of precipitation echoes was highest in the tropics and decreased toward the poles. Considering a limitation of the unfolding correction for discrimination between large upward velocity and large precipitation falling velocity, the latitudinal variation of the Doppler error can be explained by the precipitation echo distribution. [ABSTRACT FROM AUTHOR]

In this study, the interest of ground penetrating radar (GPR) time-lapse measurements for the estimation of hydrodynamic unsaturated soil parameters is investigated using synthetic infiltration experiments. Water movement and electromagnetic wave propagation in the unsaturated zone are modeled using a one-dimensional hydrogeophysical model. The GPR travel time data are analyzed for different reflectors: a moving reflector (the infiltration wetting front) and three fixed reflectors located at different depths in the soil. Global sensitivity analysis (GSA) is employed to assess the influence of the saturated hydraulic conductivity, the saturated and residual water contents, and the Mualem–van Genuchten shape parameters α and n of the soil on the GPR travel time data of the reflectors. Statistical calibration of the soil parameters is then performed using the Markov chain Monte Carlo (MCMC) method. The impact of the type of reflector (moving or fixed) is then evaluated by analyzing the calibrated model parameters and their confidence intervals for different scenarios. GSA results show that the sensitivities of the moving and fixed reflectors data to the hydrodynamic soil parameters are different whereas the fixed reflectors have similar sensitivities. Results of parameter estimation show that the use of only data from the moving or fixed reflectors does not allow a good identification of all soil parameters. When both data are combined, all soil parameters can be well estimated with narrow confidence intervals. [ABSTRACT FROM AUTHOR]

This study presents a simulation framework for cloud and precipitation measurements via spaceborne millimeter wave radar composed of nine sub modules. To demonstrate the influence of the assumed physical parameters and optimizing the microphysical modeling of the hydrometeors, we first conducted a sensitivity analysis. The results indicated that the radar reflectivity was highly sensitive to the particle size distribution (PSD) parameter of the median volume diameter and particle density parameter, which can cause reflectivity variations of several to more than 10 dB. The variation in the prefactor of the mass-power relations that related to riming degree may result in an uncertainty of approximately 30–45 %. The particle shape and orientation also had a significant impact on the radar reflectivity. The spherical assumption may result in an average overestimation of the reflectivity by approximately 4–8 %, dependent on the particle shape and orientation modeling. Typical weather cases were simulated using optimal physical modeling accounting for the particle shapes, typical PSD parameters corresponding to the cloud precipitation types, mass-power relations for snow and graupel, and melting modeling. We present and validate the simulation results for a cold front stratiform cloud and a deep convective process with observations from W-band cloud profiling radar (CPR) on the CloudSat satellite. The simulated brightness band features, echo structure, and intensity showed good agreement with the CloudSat observations; the average relative error in the vertical profile was within 20 %. Our results quantify the uncertainty in the millimeter wave radar echo simulation that may be caused by the physical model parameters and provide a scientific basis for optimal forward modeling. They also provide suggestions for prior physical parameter constraints for the retrieval of the microphysical properties of clouds and precipitation. [ABSTRACT FROM AUTHOR]

Satellite Synthetic Aperture Radar (SAR) data are commonly utilized for calculating sea ice displacements and, consequently, sea ice deformation strain rates. However, strain rate calculations often suffer from a poor signal-to-noise ratio, especially for products with a spatial resolution higher than 1 km. In this study, we applied a new filtering method to strain rate calculations derived from Sentinel-1 SAR image pairs with a spatial resolution of 800 m. Subsequently, we employed a power law to evaluate the deformation rates at decreasing spatial resolutions, assessing the quality of the filtered data. Upon positive evaluation of the filtered data, we introduced two innovative methods for sea ice deformation assessment. The first method, named 'damage parcels' (DP) tracking, involved the combined analysis of displacements and deformation strain rates to monitor divergence and convergence within the sea ice cover. Additionally, we proposed a new term to describe the behavior of the winter pack: 'Coherent Dynamic Elements' (CDE). CDEs are cohesive clusters of ice plates within the pack ice that move coherently along Linear Kinematic Features (LKFs). The second novel method developed in this study focused on exploring the geometrical properties of these CDEs. Both methods were applied to the winter collection of Sentinel-1 SAR imagery available during the N-ICE2015 campaign. Our results revealed a cyclically changing winter sea ice cover, marked by synoptic events and transitions from pack ice to the marginal ice zone. The DP were continuously tracked over a period of three weeks, including a major storm, revealing a remarkably slow healing process of existing LKFs. Furthermore, the CDE analysis demonstrated the presence of elongated CDEs with a density ranging from 5 to 20 per 100 km by 100 km, and the shortest distance between LKFs was found to be 5–10 km. [ABSTRACT FROM AUTHOR]

The regular and robust mapping of grounding lines is essential for various applications related to the mass balance of marine ice sheets and glaciers, especially in Antarctica and Greenland. With Differential Interferometric Synthetic Aperture Radar (DInSAR) interferograms, it is possible to accurately capture the tide-induced bending of the ice shelf at a continent-wide scale and a temporal resolution of a few days. While current processing chains typically automatically generate differential interferograms, grounding lines are still primarily identified and delineated on the interferograms by a human operator. This method is time-consuming and inefficient, considering the volume of data from current and future SAR missions. We developed a pipeline that utilizes the Holistically-Nested Edge Detection (HED) neural network to delineate DInSAR interferograms automatically. We trained HED in a supervised manner using 421 manually annotated grounding lines for outlet glaciers and ice shelves on the Antarctic Ice Sheet. We also assessed the contribution of non-interferometric features like elevation, ice velocity and differential tide levels towards the delineation task. Our best-performing network generated grounding lines with a median distance of 186 m from the manual delineations. Additionally, we applied the network to generate grounding lines for undelineated interferograms, demonstrating the network's generalization capabilities and potential to generate high-resolution temporal and spatial mappings. [ABSTRACT FROM AUTHOR]

We present a 1D numerical model which calculates the age of ice around Dome C. It accounts either for melting or for a layer of stagnant ice above the bedrock, depending on the value of an inverted mechanical ice thickness. It is constrained by horizons picked from radar observations and dated using the EPICA Dome C (EDC) ice core age profile. We used 3 different radar datasets with the widest reaching airbourne radar system covering an area of 10,000 km2 and zooming in to 5 km transects over Little Dome C (LDC) with a ground based system. We find that stagnant ice exists in many places including above the LDC relief where the new Beyond EPICA drill site (BELDC) is located. The modelled thickness of this layer of stagnant ice roughly corresponds to the thickness of the basal unit observed in one of the radar surveys and observations made with Autonomous phase-sensitive Radio-Echo Sounder (ApRES). At BELDC, the modelled stagnant ice thickness is 182 ± 63 m and the modelled maximum age (that we define as the age at a maximum age density of 20 kyr m−1) is 1.49 ± 0.18 Ma at a depth of 2505 ± 34 m. This is very similar to all sites situated on the LDC relief such as that of the Million Year Ice Core project being conducted by the Australian Antarctic Division (AAD). The model was also applied to radar data in the area 10–20 km north of EDC (North Patch, NP), where we find either a thin layer of stagnant ice (generally < 60 m) or a very low melt rate (< 0.1 mm yr−1). The modelled maximum age at NP is over 2 Ma in most places, with ice at 1.5 Ma having a resolution of 9–12 kyr m−1 , making it an exciting prospect for a future oldest ice drill site. [ABSTRACT FROM AUTHOR]

The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission is a joint effort by the European Space Agency (ESA) and the Japanese Aerospace Exploration Agency (JAXA). The EarthCARE mission features the first spaceborne 94-GHz Cloud Profiling Radar (CPR) with Doppler capability. The raw CPR observations and auxiliary information are used as input to three L2 algorithms 1) C-APC: Antenna Pointing Characterization, 2) C-FMR: CPR feature mask and reflectivity and 3) C-CD: Corrected CPR Doppler Measurements. These algorithms apply quality control and corrections to the CPR primary measurements and derive important geophysical variables such as hydrometeor locations, and best estimates of particle sedimentation fall velocities. The C-APC algorithm uses natural targets to introduce any corrections needed to the CPR raw Doppler velocities due to the CPR antenna pointing. The C-FMR product provides the feature mask based on only-reflectivity CPR measurements and quality controlled radar reflectivity profiles corrected for gaseous attenuation at 94 GHz. In addition, C-FMR provides best estimates of the Path Integrated Attenuation (PIA) and flags identifying the presence of multiple scattering in the CPR observations. Finally, the C-CD product provides the quality-controlled, bias-corrected mean Doppler velocity estimates (Doppler measurements corrected for antenna mis-pointing, non-uniform beam filling, and velocity folding). In addition, the best estimate of the particle sedimentation velocity is estimated using a novel technique. [ABSTRACT FROM AUTHOR]