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The mesosphere and lower thermosphere (MLT) is a dynamic layer of the earth's atmosphere. This region marks the interface at which neutral atmosphere dynamics begin to influence the upper atmosphere and ionosphere. However, our understanding of this region and our ability to accurately simulate it in global circulation models (GCMs) is limited by a lack of observations, especially in remote locations. To this end, a meteor radar was deployed from 2016 to 2020 on the remote mountainous island of South Georgia (54 ∘ S, 36 ∘ W) in the Southern Ocean. In this study we use these new measurements to characterise the fundamental dynamics of the MLT above South Georgia including large-scale winds, solar tides, planetary waves (PWs), and mesoscale gravity waves (GWs). We first present an improved method for time–height localisation of radar wind measurements and characterise the large-scale MLT winds. We then determine the amplitudes and phases of the diurnal (24 h), semidiurnal (12 h), terdiurnal (8 h), and quardiurnal (6 h) solar tides at this latitude. We find very large amplitudes up to 30 m s -1 for the quasi 2 d PW in summer and, combining our measurements with the meteor SAAMER radar in Argentina, show that the dominant modes of the quasi 5, 10, and 16 d PWs are westward 1 and 2. We investigate and compare wind variance due to both large-scale "resolved" GWs and small-scale "sub-volume" GWs in the MLT and characterise their seasonal cycles. Last, we use our radar observations and satellite temperature observations from the Microwave Limb Sounder to test a climatological simulation of the Whole Atmosphere Community Climate Model (WACCM). We find that WACCM exhibits a summertime mesopause near 80 km altitude that is around 10 K warmer and 10 km lower in altitude than observed. Above 95 km altitude, summertime meridional winds in WACCM reverse to poleward, but this not observed in radar observations in this altitude range. More significantly, we find that wintertime zonal winds between 85 to 105 km altitude are eastward up to 40 m s -1 in radar observations, but in WACCM they are westward up to 20 m s -1. We propose that this large discrepancy may be linked to the impacts of secondary GWs (2GWs) on the residual circulation, which are not included in most global models, including WACCM. These radar measurements can therefore provide vital constraints that can guide the development of GCMs as they extend upwards into this important region of the atmosphere. [ABSTRACT FROM AUTHOR]

Over the past 50 years, the British Antarctic Survey (BAS) has been one of the major acquirers of aerogeophysical data over Antarctica, providing scientists with gravity, magnetic, and radar datasets that have been central to many studies of the past, present, and future evolution of the Antarctic Ice Sheet. Until recently, many of these datasets were not openly available, restricting further usage of the data for different glaciological and geophysical applications. Starting in 2020, scientists and data managers at BAS have worked on standardizing and releasing large swaths of aerogeophysical data acquired during the period 1994–2020, including a total of 64 datasets from 24 different surveys, amounting to ∼ 450 000 line-km (or 5.3 million km 2) of data across West Antarctica, East Antarctica, and the Antarctic Peninsula. Amongst these are the extensive surveys over the fast-changing Pine Island (BBAS 2004–2005) and Thwaites (ITGC 2018–2019 & 2019–2020) glacier catchments, and the first ever surveys of the Wilkes Subglacial Basin (WISE-ISODYN 2005–2006) and Gamburtsev Subglacial Mountains (AGAP 2007–2009). Considerable effort has been made to standardize these datasets to comply with the FAIR (findable, accessible, interoperable and re-usable) data principles, as well as to create the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/ , last access: 18 July 2022), which serves as a user-friendly interface to interact with and download the newly published data. This paper reviews how these datasets were acquired and processed, presents the methods used to standardize them, and introduces the new data portal and interactive tutorials that were created to improve the accessibility of the data. Lastly, we exemplify future potential uses of the aerogeophysical datasets by extracting information on the continuity of englacial layering from the fully published airborne radar data. We believe these newly released data will be a valuable asset to future glaciological and geophysical studies over Antarctica and will significantly extend the life cycle of the data. All datasets included in this data release are now fully accessible at https://data.bas.ac.uk (British Antarctic Survey, 2022). [ABSTRACT FROM AUTHOR]

The retrieval of continuous ice core records of more than 1 Myr is an important challenge in palaeo-climatology. For identifying suitable sites for drilling such ice, knowledge of the subglacial topography and englacial layering is crucial. For this purpose, extensive ground-based ice radar surveys were carried out over Dome Fuji in the East Antarctic plateau during the 2017/18 and 2018/19 austral summers by the Japanese Antarctic Research Expedition, on the basis of ground-based radar surveys conducted over the previous ∼ 30 years. High-gain Yagi antennae were used to improve the antenna beam directivity, thereby significantly decreasing hyperbolic features of unfocused along-track diffraction hyperbolae in the echoes from mountainous ice–bedrock interfaces. We combined the new ice thickness data with the previous ground-based data, recorded since the 1980s, to generate an accurate high-spatial-resolution (up to 0.5 km between survey lines) ice thickness map. This map revealed a complex landscape composed of networks of subglacial valleys and highlands. Based on the new map, we examined the roughness of the ice–bed interface, the bed surface slope, the driving stress of ice and the subglacial hydrological condition. These new products and analyses set substantial constraints on identifying possible locations for new drilling. In addition, our map was compared with a few bed maps compiled by earlier independent efforts based on airborne radar data to examine the difference in features between datasets. Our analysis suggests that widely available bed topography products should be validated with in situ observations where possible. [ABSTRACT FROM AUTHOR]

The coherent backscatter opposition effect (CBOE) enhances the backscatter intensity of electromagnetic waves by up to a factor of 2 in a very narrow cone around the direct return direction when multiple scattering occurs in a weakly absorbing, disordered medium. So far, this effect has not been investigated in terrestrial snow in the microwave spectrum. It has also received little attention in scattering models. We present the first characterization of the CBOE in dry snow using ground-based and spaceborne bistatic radar systems. For a seasonal snowpack in the Ku-band (17.2 GHz), we found backscatter enhancement of 50 %–60 % (+1.8 –2.0 dB) at a zero bistatic angle and a peak half-width at half-maximum (HWHM) of 0.25 ∘. In the X-band (9.65 GHz), we found backscatter enhancement of at least 35 % (+1.3 dB) and an estimated HWHM of 0.12 ∘ in the accumulation areas of glaciers in the Jungfrau–Aletsch region, Switzerland. Sampling of the peak shape at different bistatic angles allows estimating the scattering and absorption mean free paths, ΛT and ΛA. In the VV polarization, we obtained ΛT=0.4±0.1 m and ΛA=19±12 m at the Ku-band and ΛT=2.1±0.4 m and ΛA=21.8±2.7 m at the X-band, assuming an optically thick medium. The HH polarization yielded similar results. The observed backscatter enhancement is thus significant enough to require consideration in backscatter models describing monostatic and bistatic radar experiments. Enhanced backscattering beyond the Earth, on the surface of solar system bodies, has been interpreted as being caused by the presence of water ice. In agreement with this interpretation, our results confirm the presence of the CBOE at X- and Ku-band frequencies in terrestrial snow. [ABSTRACT FROM AUTHOR]

Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectrabased data processing framework to improve the data quality of a multi-mode pulse-compressed Ka/Ku radar system. Firstly, non-meteorological clutter close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, the abnormal distribution of the probability density of the Doppler spectrum in presence of range sidelobe due to the implementation of the pulse compression technique was identified and used to separate sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shiftthen-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the results show good performance of clutter/sidelobe suppression and spectral merging. [ABSTRACT FROM AUTHOR]

Dual-wavelength (3.2 and 0.32 cm, i.e., X- and W-radar bands) radar ratio (DWR) measurements in ice clouds and precipitation using the Canada's National Research Council Institute for Aerospace Research airborne radar are compared to closely collocated particle microphysical in situ sampling data in order to develop relations between DWR and characteristic hydrometeor size. This study uses the radar and in situ data sets collected during the In-Cloud ICing and Large-drop Experiment (ICICLE) campaign. Since atmospheric particle scattering at X-band is predominately in the Rayleigh regime and the W-band frequency is the highest frequency usually used for hydrometeor remote sensing, the X-Wband combination provides relatively strong dual-wavelength reflectivity difference. This study considers radar and in situ measurements conducted in relatively homogeneous cloud and precipitation conditions. Measurements show that under these conditions, the difference between the X-band radar reflectivities observed with vertical and horizontal pointing of the radar beam are generally small and often negligible. However, W-band band reflectivities at vertical beam pointing are, on average, larger than those for horizontal beam pointing by about 4 dB, which is a non-Rayleigh scattering effect from oriented non-spherical particles. A horizontal beam DWR - mean volume particle size, Dv, relation provides robust estimates of this characteristic size for populations of particles with different habits. Uncertainties of Dv retrievals using DWR are around 0.6 mm when Dv is greater than approximately 1 mm. Size estimates using vertical radar beam DWRs have larger uncertainties due to smaller dual-wavelength signals and stronger influences of hydrometeor habits and orientations at this geometry of beam pointing. Mean relationships among different characteristic sizes describing the entire particle size distribution (PSD) such as Dv, and other characteristic sizes used in different applications (e.g., the mean, effective, and median sizes) are derived, so the results of this study can be used for estimating different PSD characteristic sizes. [ABSTRACT FROM AUTHOR]

The pre-Contact Maya peoples build roads (Sacbeob) to facilitate movement between and within cities and surroundings. Given the dense forest cover in the Lowland Maya region now known as Yucatan, ground surveys of sacbeob are time-consuming and challenging to perform. Remote sensing can be a good alternative as it offers the advantages of extensive regional coverage, zero disturbances to cultural resources, and an opportunity to acquire data in less accessible areas on a cost-effective basis. LiDAR technology is highly valuable to detect man-made structures, but this technology is costly and is time-consuming to acquire data over a large area. Satellite imagery presents an alternative for mapping large areas. Previous studies documented linear features that could represent sections of sacbeob between Calakmul and El Mirador using Landsat-5 TM green, red, and near-infrared images (Folan et al. 1995). This study used Landsat-7 and Landsat-8 optical images having more bands and radar imagery (Sentinel-1 C-VH&VV, and Alos-1 PalSAR L-HH&HV) to connect sacbeob mapped by Folan et al. (1995) between three ancient Maya cities (Calakmul, El Mirador, and Uxul). Our results suggest that radar images with the capacity to penetrate dense forest cover can contribute to the identification and documentation of ancient road systems. Such a study provides an ideal starting point for targeted ground verification. [ABSTRACT FROM AUTHOR]

One of the most popular research areas is low-cost navigation and positioning systems for autonomous vehicles. Determining a vehicle's position within a lane is critical for achieving high automation. Vehicle navigation and positioning relied heavily on the Global Navigation Satellite System (GNSS) service in open-sky scenarios. Nonetheless, GNSS signals were easily degraded due to various environmental situations such as urban canyons caused by multi-path effects and Non-Line-of-Sight (NLOS) issues. To perform robustly in complex scenarios, sensor fusion is the most common solution. The following paper presents a radar visual odometry framework to improve the lack of scale factors for monocular cameras and poor angular resolution for radar. The framework is based on the characteristics of camera and radar sensors which have complementary advantages in each other. The results show that the proposed framework can be used to estimate general 2D motion in an indoor environment and correct the unknown scale factor of Monocular Visual Odometry in a real-world setting. [ABSTRACT FROM AUTHOR]

Mesoscale, wave-like perturbations in horizontal air motions in the troposphere (velocity waves) are associated with vertical velocity, temperature, and pressure perturbations that can initiate or enhance precipitation within clouds. The ability to detect velocity waves from horizontal wind information is an important tool for atmospheric research and weather forecasting. This paper presents a method to routinely detect velocity waves using Doppler radial velocity data from a scanning weather radar. The method utilizes the difference field between consecutive position plan indicator (PPI) scans at a given elevation angle. Using the difference between fields a few minutes apart highlights small-scale perturbations associated with waves because the larger-scale wind field changes more slowly. Image filtering retains larger contiguous velocity bands and discards noise. Wave detection scales are limited by the size of the temporal difference relative to the wave motion and the radar resolution volume size. [ABSTRACT FROM AUTHOR]

Ensemble simulations with the Terrestrial Systems Modelling Platform (TSMP) covering northwestern Germany are evaluated for three summertime convective storms using polarimetric X-band radar measurements. Using a forward operator, the simulated microphysical processes have been evaluated in radar observation space. Observed differential reflectivity (ZDR) columns, which are proxies for updrafts, and multi-variate fingerprints for size sorting and aggregation processes are captured by the model, but co-located specific differential phase (KDP) columns in observations are not reproduced in the simulations. Also, the simulated ZDR columns, generated by only small-sized supercooled drops, show smaller absolute ZDR values and a reduced width compared to their observational counterparts, which points to deficiencies in the cloud microphysics scheme as well as the forward operator, which does not have explicit information of water content of ice hydrometeors. Above the melting layer, the simulated polarimetric variables also show weak variability, which can be at least partly explained by the reduced particle diversity in the model and the inability of the T -matrix method to reproduce the polarimetric signatures of snow and graupel; i.e. current forward operators need to be further developed to fully exploit radar data for model evaluation and improvement. Below the melting level, the model captures the observed increase in reflectivity, ZDR and specific differential phase (KDP) towards the ground. The contoured frequency altitude diagrams (CFADs) of the synthetic and observed polarimetric variables were also used to evaluate the model microphysical processes statistically. In general, CFADs of the cross-correlation coefficient (ρhv) were poorly simulated. CFADs of ZDR and KDP were similar but the model exhibits a relatively narrow distribution above the melting layer for both, and a bimodal distribution for ZDR below the melting layer, indicating either differences in the mechanism of precipitation formation or errors in forward operator which uses a functional form of drop size distribution. In general, the model was found to underestimate the convective area fraction, high reflectivities, and the width/magnitude of ZDR columns, all leading to an underestimation of the frequency distribution for high precipitation values. [ABSTRACT FROM AUTHOR]

In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81 ∘ N. Excellent agreement (difference less than 1 %) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45 % and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73 ∘ N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5. [ABSTRACT FROM AUTHOR]

Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments and aims to optimize the radar observation strategy for deep convection through the use of cloud-resolving model simulations coupled with a radar simulator and a cell tracking algorithm. Our analysis results include the following four outcomes. First, a 5-7 m s-1 median difference in maximum updrafts of tracked cells was shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute captures the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves the multi-Doppler radar updraft retrievals above 5 km AGL for regions with updraft velocities greater than 10 m s-1. Third, we propose an optimized strategy which is composed of cell tracking by quick (1-2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single range-height indicator updraft retrieval technique for cells close to the radars, where the multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s-1. [ABSTRACT FROM AUTHOR]

Latent heating (LH) is an important quantity in both weather forecasting and climate analysis, being the essential factor driving convective systems. Yet, inferring LH rates from our current observing systems is challenging at best. For climate studies, LH has been retrieved from the Precipitation Radar on the Tropical Rainfall Measuring Mission (TRMM) using model simulations in the look-up table (LUT) that relates instantaneous radar profiles to corresponding heating profiles. These radars, first on TRMM and then Global Precipitation Measurement Mission (GPM), provide a continuous record of LH. However, temporal resolution is too coarse to have a significant impacts on forecast models. In operational forecast models such as High-Resolution Rapid Refresh, convection is initiated from LH derived from ground based radar. Despite the high spatial and temporal resolution of ground-based radars, one disadvantage of using these sources is that its data are only available over well observed land areas. This study develops a method to derive LH from the Geostationary Operational Environmental Satellite-16 (GOES-16) in near-real time. Even though the visible and infrared channels on the Advanced Baseline Imager (ABI) provide mostly cloud top information, rapid changes in cloud top visible and infrared properties, when formulated as a LUT similar to those used by the TRMM and GPM radars, can equally be used to derive LH profiles for convective regions based on model simulations with a convective classification scheme and channel 14 (11.2 µm) brightness temperatures. Convective regions detected by GOES-16 are assigned LH from the LUT, and they are compared with LH from the Next Generation Weather Radar (NEXRAD) and one of the Dual-frequency Precipitation Radar (DPR) products, the Goddard Convective-Stratiform Heating (CSH). LH obtained from GOES-16 show similar magnitude with NEXRAD and CSH, and vertical distribution of LH is also very similar with CSH. One month analysis of total LH from convective clouds from GOES-16 and NEXRAD shows good correlation between the two products. Finally LH profiles from GOES-16 and NEXRAD are applied to WRF simulations for convective initiation and their results are compared to investigate their impacts in precipitation forecasts. Results show that LH from GOES-16 have similar impacts as NEXRAD, and improves the forecast significantly. [ABSTRACT FROM AUTHOR]

A method is developed to use both polarimetric and dual-frequency radar measurements to retrieve microphysical properties of falling snow. It is applied to the Ku- and Ka-band measurements of the NASA dual-polarization, dual-frequency Doppler radar (D3R) obtained during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018) field campaign and incorporates the Atmospheric Radiative Transfer Simulator (ARTS) microwave single-scattering property database for oriented particles. The retrieval uses optimal estimation to solve for several parameters that describe the particle size distribution (PSD), relative contribution of pristine, aggregate, and rimed ice species, and the orientation distribution along an entire radial simultaneously. Examination of Jacobian matrices and averaging kernels shows that the dual-wavelength ratio (DWR) measurements provide information regarding the characteristic particle size, and to a lesser extent, the rime fraction and shape parameter of the size distribution, whereas the polarimetric measurements provide information regarding the mass fraction of pristine particles and their characteristic size and orientation distribution. Thus, by combining the dual-frequency and polarimetric measurements, some ambiguities can be resolved that should allow a better determination of the PSD and bulk microphysical properties (e.g., snowfall rate) than can be retrieved from single-frequency polarimetric measurements or dual-frequency, single-polarization measurements. The D3R ICE-POP retrievals were validated using Precipitation Imaging Package (PIP) and Pluvio weighing gauge measurements taken nearby at the May Hills ground site. The PIP measures the snow PSD directly, and its measurements can be used to derived the snowfall rate (volumetric and water equivalent), mean volume-weighted particle size, and effective density, as well as particle aspect ratio and orientation. Four retrieval experiments were performed to evaluate the utility of different measurement combinations: Ku-only, DWR-only, Ku-pol, and All-obs. In terms of correlation, the volumetric snowfall rate (r=0.95) and snow water equivalent rate (r=0.92) were best retrieved by the Ku-pol method, while the DWR-only method had the lowest magnitude bias for these parameters (-31 % and -8 %, respectively). The methods that incorporated DWR also had the best correlation to particle size (r=0.74 and r=0.71 for DWR-only and All-obs, respectively), although none of the methods retrieved density particularly well (r=0.43 for All-obs). The ability of the measurements to retrieve mean aspect ratio was also inconclusive, although the polarimetric methods (Ku-pol and All-obs) had reduced biases and mean absolute error (MAE) relative to the Ku-only and DWR-only methods. The significant biases in particle size and snowfall rate appeared to be related to biases in the measured DWR, emphasizing the need for accurate DWR measurements and frequent calibration in future D3R deployments. [ABSTRACT FROM AUTHOR]

This study presents the first-ever complete characterization of random errors in dual-polarimetric spectral observations of meteorological targets by cloud radars. The characterization is given by means of mathematical equations for joint probability density functions (PDFs) and error covariance matrices. The derived equations are checked for consistency using real radar measurements. One of the main conclusions of the study is that the convenient representation of spectral polarimetric measurements including differential reflectivity ZDR , correlation coefficient ρHV , and differential phase ΦDP is not suited for the proper characterization of the error covariance matrix. This is because the aforementioned quantities are complex, non-linear functions of the radar raw data, and thus their error covariance matrix is commonly derived using simplified linear relations and by neglecting the correlation of errors. This study formulates the spectral polarimetric measurements in terms of a different set of quantities that allows for a proper analytic treatment of their error covariance matrix. The results given in this study allow for utilization of spectral polarimetric measurements for advanced meteorological applications, among which are variational retrieval techniques, data assimilation, and sensitivity analysis. [ABSTRACT FROM AUTHOR]

We present a method to combine CryoSat-2 (CS2) radar altimeter and Sentinel-1 synthetic aperture radar (SAR) data to obtain sea ice thickness (SIT) estimates for the Barents and Kara seas. From the viewpoint of tactical navigation, along-track altimeter SIT estimates are sparse, and the goal of our study is to develop a method to interpolate altimeter SIT measurements between CS2 ground tracks. The SIT estimation method developed here is based on the interpolation of CS2 SIT utilizing SAR segmentation and segmentwise SAR texture features. The SIT results are compared to SIT data derived from the AARI ice charts; to ORAS5, PIOMAS and TOPAZ4 ocean–sea ice data assimilation system reanalyses; to combined CS2 and Soil Moisture and Ocean Salinity (SMOS) radiometer weekly SIT (CS2SMOS SIT) charts; and to the daily MODIS (Moderate Resolution Imaging Spectroradiometer) SIT chart. We studied two approaches: CS2 directly interpolated to SAR segments and CS2 SIT interpolated to SAR segments with mapping of the CS2 SIT distributions to correspond to SIT distribution of the PIOMAS ice model. Our approaches yield larger spatial coverage and better accuracy compared to SIT estimates based on either CS2 or SAR data alone. The agreement with modelled SIT is better than with the CS2SMOS SIT. The average differences when compared to ice models and the AARI ice chart SIT were typically tens of centimetres, and there was a significant positive bias when compared to the AARI SIT (on average 27 cm) and a similar bias (24 cm) when compared to the CS2SMOS SIT. Our results are directly applicable to the future CRISTAL mission and Copernicus programme SAR missions. [ABSTRACT FROM AUTHOR]

Ice crystals are mechanically and dielectrically anisotropic. They progressively align under cumulative deformation, forming an ice-crystal-orientation fabric that, in turn, impacts ice deformation. However, almost all the observations of ice fabric are from ice core analysis, and its influence on the ice flow is unclear. Here, we present a non-linear inverse approach to process co- and cross-polarized phase-sensitive radar data. We estimate the continuous depth profile of georeferenced ice fabric orientation along with the reflection ratio and horizontal anisotropy of the ice column. Our method approximates the complete second-order orientation tensor and all the ice fabric eigenvalues. As a result, we infer the vertical ice fabric anisotropy, which is an essential factor to better understand ice deformation using anisotropic ice flow models. The approach is validated at two Antarctic ice core sites (EPICA (European Project for Ice Coring in Antarctica) Dome C and EPICA Dronning Maud Land) in contrasting flow regimes. Spatial variability in ice fabric characteristics in the dome-to-flank transition near Dome C is quantified with 20 more sites located along with a 36 km long cross-section. Local horizontal anisotropy increases under the dome summit and decreases away from the dome summit. We suggest that this is a consequence of the non-linear rheology of ice, also known as the Raymond effect. On larger spatial scales, horizontal anisotropy increases with increasing distance from the dome. At most of the sites, the main driver of ice fabric evolution is vertical compression, yet our data show that the horizontal distribution of the ice fabric is consistent with the present horizontal flow. This method uses polarimetric-radar data, which are suitable for profiling radar applications and are able to constrain ice fabric distribution on a spatial scale comparable to ice flow observations and models. [ABSTRACT FROM AUTHOR]

We present a procedure to detect landslide events by analyzing in-sequence data acquired from regional broadband seismic networks and spaceborne radar imagery. The combined used of these techniques is meant to exploit their complementary elements and mitigate their limitations when used singularly. To test the method, we consider a series of six slope failures associated to the Piz Cengalo rock avalanche that recently occurred in the Swiss Alps, a region where we can benefit from high spatial density and quality of seismic data, as well as from the high spatial and temporal resolution of the European Space Agency (ESA) Copernicus Sentinel-1 radar satellites. The operational implementation of the proposed approach, in combination with the future increase in availability of seismic and satellite data, can offer a new and efficient solution to build and/or expand landslide catalogues based on quantitative measurements and, thus, help in hazard assessments and the definition of early warning systems at regional scales. [ABSTRACT FROM AUTHOR]

Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with high-frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near-real time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavors between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to do the following: (i) harmonize deployment and maintenance practices; (ii) standardize data, metadata, and quality control procedures; (iii) centralize data management, visualization, and access platforms; and (iv) develop practical applications of societal benefit that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events, the investigation of transport processes, and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socioeconomic, technical, and scientific challenges to be faced during the implementation of this integrated HFR regional network. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea is a prominent climate-change hot spot, with many socioeconomically vital coastal areas being the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, high-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in coastal ocean observing systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-user and science-driven requirements, addressing regional challenges in three main topics: (i) maritime safety, (ii) extreme hazards and (iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean coastal areas required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to provide a set of recommendations for future improvement prospects to maximize the contribution to extending science-based HFR products into societally relevant downstream services to support blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals. [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 5 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 15 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]

Gangwon-do (GWD) has complex terrain and surface characteristics due to its location to the East Sea and the Taebaek Mountain range. This coastal location and rugged terrain can amplify snowfall mechanisms, making it challenging to accurately predict the amount and location. This study compares two methods for assimilating radar data and analyzed snowfall prediction results. The two methods compared are local ensemble transform Kalman filter (LETKF) and three-dimensional variational (3DVAR) data assimilation (DA). LETKF improved the water vapor amount and temperature using the covariance of the ensemble members, but 3DVAR improved the water vapor mixing ratio and temperature through an operator that assumed the atmosphere was saturated when reflectivity was above a certain threshold. In 2018, to understand the snowfall in GWD region and support the Pyeongchang Winter Olympic and Paralympic Games, a long-term heavy snow observation campaign was conducted. The International Collaborative Experiments for the 2018 Pyeongchang Olympic Games Projects (ICE-POP 2018) data are used to study and verify the numerical experiments. From the initial field verification using ICE-POP observation data (radiosonde), wind in LETKF was more accurately simulated than 3DVAR, but it underestimated the water vapor amount and temperature in the lower troposphere due to a lack of a water vapor and temperature observation operator. Snowfall in GWD was less simulated in LETKF, whereas snowfall of 10.0 mm or more was simulated in 3DVAR, resulting in an error of 2.62 mm lower than LETKF. The results signify that water vapor assimilation is important in radar DA and significantly impacts precipitation forecasts, regardless of the DA method used. Therefore, it is necessary to apply observation operators for water vapor and temperature in radar DA. [ABSTRACT FROM AUTHOR]

The subtidal surface water circulation at the northern margin of the Gulf of Cadiz, at the southern extremity of the Iberian upwelling system, is described based on validated hourly high frequency radar measurements from 2016 to 2020. Statistical analyses (mean, standard deviation, eccentricity and empirical orthogonal functions) are applied to the dataset, which is completed with ADCP time-series from multiple moorings at 5 inner-shelf stations and ERA5 wind. Off the shelf, the main circulation pattern consists of a slope current, best developed in summer when north-westerlies dominate, in particular at the most exposed western region. Other mechanisms than upwelling must contribute to this flow in order to explain its seasonal persistence. The slope circulation reverses for regional wind events with east component > 10 m.s-1, approximately. On the shelf, currents are mainly alongshore and balanced. The circulation is generally continuous along the coast, except for weak (< 0.1 m.s-1, broadly) poleward flows. In the latter case, the flow tends to remain equatorward near Cape Santa Maria. In winter, coastal poleward flows often extend over the entire margin and are mainly wind-driven. In summer, these flows generally consist of coastal counter currents (CCCs) with poleward direction opposed to the one of the slope current. The CCCs are associated with significant cyclonic recirculation, strongest at West, where a transient eddy is shortly observed for weak wind stress. This circulation develops after periods of strong north-westerlies, supporting that CCCs result from the unbalance of a regional along-shore pressure gradient. [ABSTRACT FROM AUTHOR]

The 2017 National Academy of Sciences Decadal Survey highlighted several high-priority objectives to be pursued in the decadal timeframe, and the next-generation Cloud, Convection and Precipitation (CCP) observing system is thereby contemplated. In this study, we develop a suite of hybrid Bayesian algorithms to evaluate two CCP remote sensor candidates including a W-band cloud radar and a (sub)millimeter-wave radiometer with channels in the 118–880 GHz frequency range for capability in constraining ice cloud microphysical quantities. The algorithms address active-only, passive-only, and synergistic active–passive retrievals. The hybrid Bayesian algorithms combine the Bayesian Monte Carlo integration and optimization process to retrieve quantities with uncertainty estimates. The radar-only retrievals employ the optimal estimation methodology, while the radiometer-involved retrievals employ ensemble approaches to maximize the posterior probability density function. A priori information is obtained from the Tropical Composition, Cloud and Climate Coupling (TC4) in situ data and CloudSat radar observations. End-to-end simulation experiments are conducted to evaluate the retrieval accuracies by comparing the retrieved parameters with known values. The experiment results suggest that the radiometer measurements possess high sensitivity to ice cloud particles with large water content. The radar-only retrievals demonstrate capability in reproducing ice water content profiles, but the performance in retrieving number concentration is poor. The synergistic observations enable improved pixel-level retrieval accuracies, and the improvements in ice water path retrievals are significant. The proposed retrieval algorithms could serve as alternative methods for exploring the synergistic active and passive concept, and the algorithm framework could be extended to the inclusion of other remote sensors to further assess the CCP observing system in future studies. [ABSTRACT FROM AUTHOR]

The representation of cloud microphysical processes contributes substantially to the uncertainty of numerical weather simulations. In part, this is owed to some fundamental knowledge gaps in the underlying processes due to the difficulty of observing them directly. On the path to closing these gaps, we present a setup for the systematic characterization of differences between numerical weather model and radar observations for convective weather situations. Radar observations are introduced which provide targeted dual-wavelength and polarimetric measurements of convective clouds with the potential to provide more detailed information about hydrometeor shapes and sizes. A convection-permitting regional weather model setup is established using five different microphysics schemes (double-moment, spectral bin ("Fast Spectral Bin Microphysics", FSBM), and particle property prediction (P3)). Observations are compared to hindcasts which are created with a polarimetric radar forward simulator for all measurement days. A cell-tracking algorithm applied to radar and model data facilitates comparison on a cell object basis. Statistical comparisons of radar observations and numerical weather model runs are presented on a data set of 30 convection days. In general, simulations show too few weak and small-scale convective cells. Contoured frequency by altitude diagrams of radar signatures reveal deviations between the schemes and observations in ice and liquid phase. Apart from the P3 scheme, high reflectivities in the ice phase are simulated too frequently. Dual-wavelength signatures demonstrate issues of most schemes to correctly represent ice particle size distributions, producing too large or too dense graupel particles. Comparison of polarimetric radar signatures reveals issues of all schemes except the FSBM to correctly represent rain particle size distributions. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting plays a vital role in preventing meteorological disasters, and Doppler radar data act as an important input for nowcasting models. When using the traditional extrapolation method it is difficult to model highly nonlinear echo movements. The key challenge of the nowcasting mission lies in achieving high-precision radar echo extrapolation. In recent years, machine learning has made great progress in the extrapolation of weather radar echoes. However, most of models neglect the multi-modal characteristics of radar echo data, resulting in blurred and unrealistic prediction images. This paper aims to solve this problem by utilizing the features of a generative adversarial network (GAN), which can enhance multi-modal distribution modeling, and design the radar echo extrapolation model GAN–argcPredNet v1.0. The model is composed of an argcPredNet generator and a convolutional neural network discriminator. In the generator, a gate controlling the memory and output is designed in the rgcLSTM component, thereby reducing the loss of spatiotemporal information. In the discriminator, the model uses a dual-channel input method, which enables it to strictly score according to the true echo distribution, and it thus has a more powerful discrimination ability. Through experiments on a radar dataset from Shenzhen, China, the results show that the radar echo hit rate (probability of detection; POD) and critical success index (CSI) have an average increase of 21.4 % and 19 %, respectively, compared with rgcPredNet under different intensity rainfall thresholds, and the false alarm rate (FAR) has decreased by an average of 17.9 %. We also found a problem during the comparison of the result graph and the evaluation index. The recursive prediction method will produce the phenomenon that the prediction result will gradually deviate from the true value over time. In addition, the accuracy of high-intensity echo extrapolation is relatively low. This is a question worthy of further investigation. In the future, we will continue to conduct research from these two directions. [ABSTRACT FROM AUTHOR]

The subglacial bed topography is critical for modelling the evolution of Thwaites Glacier in the Amundsen Sea Embayment (ASE), where rapid ice loss threatens the stability of the West Antarctic Ice Sheet. However, mapping of subglacial topography is subject to uncertainties of up to hundreds of metres, primarily due to large gaps of up to tens of kilometres in airborne ice-penetrating radar flight lines. Deterministic interpolation approaches do not reflect such spatial uncertainty. While traditional geostatistical simulations can model such uncertainty, they become difficult to apply because of the significant non-stationary spatial variation of topography over such large surface area. In this study, we develop a non-stationary multiple-point geostatistical (MPS) approach to interpolate large areas with irregular geophysical data and apply it to model the spatial uncertainty of entire ASE basal topography. We collect 166 high-quality topographic training images (TIs) of resolution 500 m to train the gap-filling of radar data gaps, thereby simulating realistic topography maps. The TIs are extensively sampled from deglaciated regions in the Arctic as well as Antarctica. To address the non-stationarity in topographic modelling, we introduce a Bayesian framework that models the posterior distribution of non-stationary TIs assigned to the local line data. Sampling from this distribution then provides candidate training images for local topographic modelling with uncertainty, constrained to radar flight line data. Compared to traditional MPS approaches that do not consider uncertain TI sampling, our approach results in a significant improvement in the topographic modelling quality and efficiency of the simulation algorithm. Finally, we simulate multiple realizations of high-resolution ASE topographic maps. We use the multiple realizations to investigate the impact of basal topography uncertainty on subglacial hydrological flow patterns. [ABSTRACT FROM AUTHOR]

Monitoring biomass changes in the Mediterranean ecosystems is important for better understanding their responses to climatic and anthropogenic changes. Multi-frequency SAR data (L-band PASLAR and C-band Sentinel-1) is investigated for developing a biomass estimation model along a climatic gradient in Israel. First, the relationships between biomass and transmissivity at various frequencies are discussed. Multi-frequency polarimetric radar vegetation index (MPRVI) is then proposed utilizing the ensemble average of degree of polarization and cross-polarized backscattering coefficients. After randomly partitioning the data for training set and testing set, the new MPRVI-based biomass model is evaluated. It shows a good agreement with reference biomass data with an r-square of 0.899 and a root-mean-squared error (RMSE) of 0.381 kg/m2 and with a relative RMSE (RRMSE) of 10.8%. [ABSTRACT FROM AUTHOR]

Raindrop size distributions (DSD) play a crucial role in quantitative rainfall estimation using weather radar. Thanks to dual-polarization capabilities, crucial information about the DSD in a given volume of air can be retrieved. One popular retrieval method assumes that the DSD can be modeled by a constrained gamma distribution in which the shape (µ) and rate (Λ) parameters are linked together by a deterministic relationship. In the literature, µ-Λ relationships are often taken for granted and applied without much critical discussion. In this study, we take another look at this important issue by conducting a detailed analysis of µ-Λ relations in stratiform rain and quantifying the accuracy of the associated DSD retrievals. Crucial aspects of our research include the sensitivity of µ-Λ relations to the temporal aggregation scale, drop concentration, inter-event variability and adequacy of the gamma distribution model. Our results show that µ-Λ relationships in stratiform rain are surprisingly robust to the choice of the sampling resolution, sample size and adequacy of the gamma model. Overall, the retrieved DSDs are in a rather decent agreement with ground observations (correlation coefficient of 0.57 and 0.74 for µ and Dm). The main sources of errors and uncertainty during the retrievals are calibration offsets in reflectivity (Zhh) and differential reflectivity (Zdr). Measurement noise and differences in scale between radar and disdrometers also play a minor role. The most difficult to retrieve parameter remains the raindrop concentration (NT), which can be off by several orders of magnitude. By removing problematic Zhh/Zdr pairs, the correlation coefficient for the retrieved NT values increases from 0.12 to 0.24, however even after the careful data filtering the accuracy of the retrieved values remains low. [ABSTRACT FROM AUTHOR]

The ultra-wideband (UWB)-based positioning has a wide internet of things applications such as smart medical and smart logistics, due to its high positioning accuracy. However, non-line-of-sight radio propagation degrades UWB positioning accuracy. The inertial measurement unit (IMU) can achieve positioning with high accuracy in a short time. In addition, the millimeter wave (mmWave) radar can work well in scenes such as fog, smoke, dust, and other small particles due to the longer wavelength of mmWave Radar, but the drift error of IMU and mmWave radar are all increased rapidly over time. This paper achieves the tight coupling of UWB and IMU, UWB and mmWave radar based on the extended Kalman filter, respectively. The field experiments were conducted based on a handheld platform in an indoor scene to evaluate and compare the performance of the fusion position systems; the experiment results demonstrated that the positioning accuracy by fusing UWB/IMU and UWB/mmWave was significantly higher than that of the positioning using a single sensor. [ABSTRACT FROM AUTHOR]

Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards. [ABSTRACT FROM AUTHOR]

The low density of conventional rain gauge networks is often a limiting factor for radar rainfall bias correction. Citizen rain gauges offer a promising opportunity to collect rainfall data at a higher spatial density. In this paper, hourly radar rainfall bias adjustment was applied using two different rain gauge networks: tipping buckets, measured by Thai Meteorological Department (TMD), and daily citizen rain gauges. The radar rainfall bias correction factor was sequentially updated based on TMD and citizen rain gauge data using a two-step Kalman filter to incorporate the two gauge datasets of contrasting quality. Radar reflectivity data from the Sattahip radar station, gauge rainfall data from the TMD, and data from citizen rain gauges located in the Tubma Basin, Thailand, were used in the analysis. Daily data from the citizen rain gauge network were downscaled to an hourly resolution based on temporal distribution patterns obtained from radar rainfall time series and the TMD gauge network. Results show that an improvement in radar rainfall estimates was achieved by including the downscaled citizen observations compared with bias correction based on the conventional rain gauge network alone. These outcomes emphasize the value of citizen rainfall observations for radar bias correction, in particular in regions where conventional rain gauge networks are sparse. [ABSTRACT FROM AUTHOR]

We present a high-resolution airborne radar data set (EGRIP-NOR-2018) for the onset region of the Northeast Greenland Ice Stream (NEGIS). The radar data were acquired in May 2018 with the Alfred Wegener Institute's multichannel ultra-wideband (UWB) radar mounted on the Polar 6 aircraft. Radar profiles cover an area of ∼24 000 km 2 and extend over the well-defined shear margins of the NEGIS. The survey area is centered at the location of the drill site of the East Greenland Ice-Core Project (EastGRIP), and several radar lines intersect at this location. The survey layout was designed to (i) map the stratigraphic signature of the shear margins with radar profiles aligned perpendicular to ice flow, (ii) trace the radar stratigraphy along several flow lines, and (iii) provide spatial coverage of ice thickness and basal properties. While we are able to resolve radar reflections in the deep stratigraphy, we cannot fully resolve the steeply inclined reflections at the tightly folded shear margins in the lower part of the ice column. The NEGIS is causing the most significant discrepancies between numerically modeled and observed ice surface velocities. Given the high likelihood of future climate and ocean warming, this extensive data set of new high-resolution radar data in combination with the EastGRIP ice core will be a key contribution to understand the past and future dynamics of the NEGIS. The EGRIP-NOR-2018 radar data products can be obtained from the PANGAEA data publisher (https://doi.pangaea.de/10.1594/PANGAEA.928569 ;). [ABSTRACT FROM AUTHOR]

Accurate precipitation estimation with weather radars is essential for hydrological and meteorological applications. The differential reflectivity (ZDR) is a crucial weather radar measurement that helps to improve quantitative precipitation estimates using polarimetric weather radars. However, a system bias between the horizontal and vertical channels generated by the radar produces an offset in ZDR. Existing methods to calibrate ZDR measurements rely on the intrinsic values of the ZDR of natural targets (e.g. drizzle or dry snow) collected at high elevation angles (e.g. higher than 40 ∘ or even at 90 ∘), in which ZDR values close to 0 dB are expected. However, not all weather radar systems can scan at such high elevation angles or point the antenna vertically to collect precipitation measurements passing overhead. Therefore, there is a need to develop new methods to calibrate ZDR measurements using lower-elevation scans. In this work, we present and analyse a novel method for correcting and monitoring the ZDR offset using quasi-vertical profiles computed from scans collected at 9 ∘ elevations. The method is applied to radar data collected through 1 year of precipitation events by two operational C-band polarimetric weather radars in the UK. The proposed method shows a relative error of 0.1 dB when evaluated against the traditional approach based on ZDR measurements collected at 90 ∘ elevations. Additionally, the method is independently assessed using disdrometers located near the radar sites. The results showed a reasonable agreement between disdrometer-derived and radar-calibrated ZDR measurements. [ABSTRACT FROM AUTHOR]

Prior geophysical surveys provided evidence for a hypersaline subglacial lake complex beneath the center of Devon Ice Cap, Canadian Arctic; however, the full extent and characteristics of the hydrological system remained unknown due to limited data coverage. Here, we present results from a new, targeted aerogeophysical survey that provides evidence (i) supporting the existence of a subglacial lake complex and (ii) for a network of shallow brine/saturated sediments covering ∼170 km2. Newly resolved lake shorelines indicate three closely spaced lakes covering a total area of 24.6 km2. These results indicate the presence of a diverse hypersaline subglacial hydrological environment with the potential to support a range of microbial habitats, provide important constraints for future investigations of this compelling scientific target, and highlight its relevance as a terrestrial analog for aqueous systems on other icy worlds. [ABSTRACT FROM AUTHOR]

Heavy rainfall events in mountainous areas can trigger thousands of destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape. Landslide locations are typically mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights on the processes behind the triggered landsliding require information on both the spatial extent and timing of landslides. Here we demonstrate that Sentinel-1 SAR amplitude time series can be used to constrain landslide timing to within a few days and present three methods to accomplish this based on time series of: (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability of amplitude between pixels within the landslide, and (iii) geometric shadows cast within the landslide. We test these methods on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that, when used in combination, our methods allow 20 % of landslides to be timed with an accuracy of 80 %. This will allow multi-temporal landslide inventories to be generated for long rainfall events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis. [ABSTRACT FROM AUTHOR]

As part of the EUREC 4 A field campaign, the research vessel Maria S. Merian probed an oceanic region between 6 to 13.8 ∘ N and 51 to 60 ∘ W for approximately 32 d. Trade wind cumulus clouds were sampled in the trade wind alley region east of Barbados as well as in the transition region between the trades and the intertropical convergence zone, where the ship crossed some mesoscale oceanic eddies. We collected continuous observations of cloud and precipitation profiles at unprecedented vertical resolution (7–10 m in the first 3000 m) and high temporal resolution (1–3 s) using a W-band radar and micro rain radar (MRR), installed on an active stabilization platform to reduce the impact of ship motions on the observations. The paper describes the ship motion correction algorithm applied to the Doppler observations to extract corrected hydrometeor vertical velocities and the algorithm created to filter interference patterns in the MRR observations. Radar reflectivity, mean Doppler velocity, spectral width and skewness for W-band and reflectivity, mean Doppler velocity, and rain rate for MRR are shown for a case study to demonstrate the potential of the high resolution adopted. As non-standard analysis, we also retrieved and provided liquid water path (LWP) from the 89 GHz passive channel available on the W-band radar system. All datasets and hourly and daily quicklooks are publically available, and DOIs can be found in the data availability section of this publication. Data can be accessed and basic variables can be plotted online via the intake catalog of the online book "How to EUREC 4 A". [ABSTRACT FROM AUTHOR]

Sensitivity experiments with a numerical weather prediction (NWP) model and polarimetric radar forward operator (FO) are conducted for a long-duration stratiform event over northwestern Germany to evaluate uncertainties in the partitioning of the ice water content and assumptions of hydrometeor scattering properties in the NWP model and FO, respectively. Polarimetric observations from X-band radar and retrievals of hydrometeor classifications are used for comparison with the multiple experiments in radar and model space. Modifying the critical diameter of particles for ice-to-snow conversion by aggregation (Dice) and the threshold temperature responsible for graupel production by riming (Tgr), was found to improve the synthetic polarimetric moments and simulated hydrometeor population, while keeping the difference in surface precipitation statistically insignificant at model resolvable grid scales. However, the model still exhibited a low bias (lower magnitude than observation) in simulated polarimetric moments at lower levels above the melting layer (- 3 to - 13 ∘ C) where snow was found to dominate. This necessitates further research into the missing microphysical processes in these lower levels (e.g. fragmentation due to ice–ice collisions) and use of more reliable snow-scattering models to draw valid conclusions. [ABSTRACT FROM AUTHOR]

The global coverage of the observational network of the wind waves is still characterized by the significant gaps in in situ observations. At the same time wind waves play an important role into the Earth' climate system specifically in the air-sea interaction processes and energy exchange between the ocean and the atmosphere. In this paper we present the SeaVision system for measuring wind waves' parameters in the open ocean with navigational marine X-band radar and prime data collection from the three research cruises in the North Atlantic (2020 and 2021) and Arctic (2021). Simultaneously with SeaVision observations of the wind waves we were collecting data in the same locations and time with Spotter wave buoy and running WaveWatch III model over our domains. Measurements with SeaVision were quality controlled and validated by comparison with Spotter buoy data and WaveWatch III experiments. Observations of the wind waves with navigational Xband radar are in agreement among these three sources of data, with the best agreement for wave propagation directions. The dataset that supports this paper consists of significant wave height, wave period and wave energy frequency spectrum from both SeaVision and Spotter buoy. Currently the dataset is available through the temporary link (https://sail.ocean.ru/tilinina2021/) while supporting dataset (Tilinina et al., 2021) is in technical processing at PANGAEA repository. The dataset can be used for validation of satellite missions as well as model outputs. One of the major highlights in this study is potential of all ships navigating into the open ocean and equipped with X-band marine radar to participate into the development of another observational network for the wind waves in the open ocean once cheap and independently operating version of the SeaVision (or any other system) is available. [ABSTRACT FROM AUTHOR]

Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with High-Frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near real-time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavours between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to: i) harmonize deployment and maintenance practices; ii) standardize data, metadata and quality control procedures; iii) centralize data management, visualization and access platforms; iv) develop practical applications of societal benefit, that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events or the investigation of transport processes and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socio-economic, technical and scientific challenges to be faced during the implementation of this integrated HFR regional network. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea is a prominent climate change hot spot, being their socio-economically vital coastal areas the most vulnerable targets for maritime safety, diverse met-ocean hazards and marine pollution. Providing an unprecedented spatial and temporal resolution at wide coastal areas, High-frequency radars (HFRs) have been steadily gaining recognition as an effective land-based remote sensing technology for a continuous monitoring of the surface circulation, increasingly waves and occasionally winds. HFR measurements have boosted the thorough scientific knowledge of coastal processes, also fostering a broad range of applications, which has promoted their integration in the Coastal Ocean Observing Systems worldwide, with more than half of the European sites located in the Mediterranean coastal areas. In this work, we present a review of existing HFR data multidisciplinary science-based applications in the Mediterranean Sea, primarily focused on meeting end-users and science-driven requirements, addressing regional challenges in three main topics: i) maritime safety; ii) extreme hazards; iii) environmental transport process. Additionally, the HFR observing and monitoring regional capabilities in the Mediterranean region required to underpin the underlying science and the further development of applications are also analyzed. The outcome of this assessment has allowed us to finally provide a set of recommendations for the future improvement prospects to maximize the contribution in extending the science-based HFR products into societal relevant downstream services to support the blue growth in the Mediterranean coastal areas, helping to meet the UN's Decade of Ocean Science for Sustainable Development and the EU's Green Deal goals. [ABSTRACT FROM AUTHOR]

Using a two-dimensional least-square fit to the Gaussian shape of the main lobe [15] gives a half-power beamwidth of 1.2 HT ht in azimuth and 1.0 HT ht in elevation (black lines in Fig.). Note that due to internal settings in the signal processor, the lowest recorded reflectivity value is HT ht 31.5 HT ht . The pointing offset is 0.45 HT ht in azimuth and 0.17 HT ht in elevation, respectively (black star). Only at the first range interval, shallow cumulus clouds with reflectivity values below HT ht HT ht are observed on both days. [Extracted from the article]

The appearance of second-trip echoes generated by mirror images over the ocean and by multiple scattering tails in correspondence with deep convective cores has been investigated for space-borne nadir-looking W-band cloud radar observations. Examples extracted from the CloudSat radar are used to demonstrate the mechanisms of formation and to validate the modelling of such returns. A statistical analysis shows that, for CloudSat observations, second-trip echoes are rare and appear only above 20 km (thus easy to remove). CloudSat climatology is then used to estimate the occurrence of second-trip echoes in the different configurations envisaged for the operations of the EarthCARE radar, which will adopt pulse repetition frequencies much higher than the one used by the CloudSat radar in order to improve its Doppler capabilities. Our findings predict that the presence of such echoes in EarthCARE observations cannot be neglected: in particular, over the ocean, mirror images will tend to populate the EarthCARE sampling window with a maximum frequency at its upper boundary. This will create an additional fake cloud cover in the upper troposphere (of the order of 3 % at the top of the sampling window and steadily decreasing moving downwards), and, in much less frequent instances, it will cause an amplification of signals in areas where clouds are already present. Multiple scattering tails will also produce second-trip echoes but with much lower frequencies: less than 1 profile out of 1000 in the tropics and practically no effects at high latitudes. At the moment, level-2 algorithms of the EarthCARE radar do not account for such occurrences. We recommend to properly remove these second-trip echoes and to correct for reflectivity enhancements, where needed. More generally this work is relevant for the design of future space-borne Doppler W-band radar missions. [ABSTRACT FROM AUTHOR]

Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics. [ABSTRACT FROM AUTHOR]

Glacier calving is a key dynamical process of the Greenland Ice Sheet and a major driver of its increasing mass loss. Calving waves, generated by the sudden detachment of ice from the glacier terminus, can reach tens of meters in height and provide very valuable insights into quantifying calving activity. In this study, we present a new method for the detection of source location, timing, and magnitude of calving waves using a terrestrial radar interferometer. This method was applied to 11 500 1 min interval acquisitions from Eqip Sermia, West Greenland, in July 2018. Over 7 d, more than 2000 calving waves were detected, including waves generated by submarine calving, which are difficult to observe with other methods. Quantitative assessment with a wave power index (WPI) yields a higher wave activity (+49 %) and higher temporally cumulated WPI (+34 %) in deep water than under shallow conditions. Subglacial meltwater plumes, occurring 2.3 times more often in the deep sector, increase WPI and the number of waves by a factor of 1.8 and 1.3, respectively, in the deep and shallow sector. We therefore explain the higher calving activity in the deep sector by a combination of more frequent meltwater plumes and more efficient calving enhancement linked with better connections to warm deep ocean water. [ABSTRACT FROM AUTHOR]

Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme "Polarimetric Radar Observations meet Atmospheric Modelling (PROM)", funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue "Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes", this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years. [ABSTRACT FROM AUTHOR]

Southwest Monsoon (Habagat) and Typhoon Luis caused a deep-seated landslide that struck Sitio Kayang, Brgy. Immuli, Pidigan, Abra on August 15, 2018. Rainfall-induced deep-seated landslides displace partially at a time which necessitates the determination of remaining landslide volume along the slope. In this study, the potential landslide volume and mass transport were estimated using several remote sensing products, including SAR (Synthetic Aperture Radar) data and LiDAR-DTM (Light Detection and Ranging-Digital Terrain Model). The post-landslide DTM was generated using Sentinel-1 SAR data. The potential landslide volume and landslide failure surfaces were ascertained through the stability analysis using Scoops3D, while the mass transport volume was obtained from the pre- and post-landslide DTM. Results showed that the estimated total volume in all the landslide areas was 135,962 m3. Meanwhile, the remaining landslide volume (i.e., difference between potential volume from pre-landslide event and volume of transported mass) yielded illogical values due to the derived large mass transport values. This blunder may be attributed to the generalization of the transported volume (due to Sentinel-1 DTM coarse resolution), and decorrelation due to vegetation cover. Overall, the LiDAR-DTM data delivered a high-resolution estimation of the potential landslide volume and proved to be useful for landslide application studies. Future studies may incorporate field data (e.g., geotechnical parameters, groundwater, landslide actual measurements) for more accurate performance of stability analysis and may best to utilize LiDAR-DTM in post-landslide volume computation for a more reliable estimation of mass transport and potentially remaining landslide volume. [ABSTRACT FROM AUTHOR]

The WIVERN (WInd VElocity Radar Nephoscope) mission, soon entering in Phase-0 of the ESA Earth Explorer program, promises to complement Doppler wind lidar by globally observing, for the first time, vertical profiles of winds in cloudy areas. This work describes an end to end simulator of the WIVERN conically scanning 94 GHz Doppler radar, the only payload of the mission. Specific features of the simulator are: the conically scanning geometry; the inclusion of cross-polarization effects and of the simulation of a radiometric mode; the applicability to global cloud model outputs via an orbital model; the incorporation of a mispointing model accounting for thermo-elastic distortions, microvibrations, star-trackers uncertainties, etc.; the inclusion of the surface clutter. Some of the simulator capabilities are showcased for a case study involving a full rotational scan of the instrument. The simulator represents a very useful tool for studying the performances of the WIVERN concept and possible trade-offs for the different configurations (e.g. different antenna sizes, pulse lengths, antenna patterns, .....). Thanks to its modular structure the simulator can be easily adapted to different orbits, different scanning geometries and different frequencies. [ABSTRACT FROM AUTHOR]

This study elaborates how aircraft-based horizontal geometries of trade-wind cumulus clouds differ whether a one-dimensional (1D) profiler or a two-dimensional (2D) imager is used. While nadir profiling devices are limited to 1D realisation of the cloud transect size with limited representativeness of horizontal cloud extension, 2D imagers enhance our perspectives by mapping the horizontal cloud field. Both require high-resolution to detect the lower end of the cloud size spectrum. In this regard, the payload aboard the High Altitude and Long Range Research Aircraft (HALO) achieves a comparison and also a synergy of both measurement systems. Using the NARVAL-II campaign, we combine HALO observations from a 35.2 GHz cloud and precipitation radar (1D) and from the hyperspectral 2D imager specMACS, having a 30 times higher along-track resolution and compare their cloud masks. We examine cloud size distributions in terms of sensitivity to sample size, resolution and the considered field of view (2D or 1D). This specifies impacts on horizontal cloud sizes derived from the across-track perspective of the high-resolution imager in comparison to the radar curtain. We assess whether and how the trade-wind field amplifies uncertainties in cloud geometry observations along 1D transects through directional cloud elongation. Our findings reveal that each additional dimension, no matter of the device, causes a significant increase of observed clouds. The across-track field yields the highest increase in the cloud sample. The radar encounters difficulties to characterize the trade-wind cumuli size distribution. More than 60 % of clouds are subgrid scale for the radar. While the radar cannot resolve clouds shorter than 200 m and has a lower sensitivity, the amount of small invisible clouds leads to deviations in the size distribution. Double power law characteristics in the imager based cloud size distribution do not occur in radar observations. Along-track measurements do not necessarily cover the predominant cloud extent and inferred geometries lack of representativeness. Trade-wind cumuli show horizontal patterns similar to ellipses with a mean aspect ratio of 3: 2. Instead of circular estimations based on the 1D transect, elliptic fits maintain the cloud area size distribution. Increasing wind speed tends to stretch clouds more and tilts them into the wind field, which makes transect measurements more representative along this axis. [ABSTRACT FROM AUTHOR]

Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground- or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens and hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesoscale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four-dimensional wind field inversion method that makes use of Gaussian process regression (GPR), which is a nonparametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as 1 d of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model hyperparameters. The latter GPR approach has been applied to a 24 h dataset and shown to compare well to previously used homogeneous and gradient methods. Small-scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20–50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies. [ABSTRACT FROM AUTHOR]