Ground-based Radar Measurements Research Articles

The impact of the Hunga Tonga–Hunga Ha’apai volcanic eruption on the Peruvian atmosphere: from the sea surface to the ionosphere

The eruption of the Hunga Tonga Hunga Ha’apai volcano on 15 January 2022 significantly impacted the lower and upper atmosphere globally. Using multi-instrument observations, we described disturbances from the sea surface to the ionosphere associated with atmospheric waves generated by the volcanic eruption. Perturbations were detected in atmospheric pressure, horizontal magnetic field, equatorial electrojet (EEJ), ionospheric plasma drifts, total electron content (TEC), mesospheric and lower thermospheric (MLT) neutral winds, and ionospheric virtual height measured at low magnetic latitudes in the western South American sector (mainly in Peru). The eastward Lamb wave propagation was observed at the Jicamarca Radio Observatory on the day of the eruption at 13:50 UT and on its way back from the antipodal point (westward) on the next day at 07:05 UT. Perturbations in the horizontal component of the magnetic field (indicative of EEJ variations) were detected between 12:00 and 22:00 UT. During the same period, GNSS-TEC measurements of traveling ionospheric disturbances (TIDs) coincided approximately with the arrival time of Lamb and tsunami waves. On the other hand, a large westward variation of MLT winds occurred near 18:00 UT over Peru. However, MLT perturbations due to possible westward waves from the antipode have not been identified. In addition, daytime vertical plasma drifts showed an unusual downward behavior between 12:00 and 16:00 UT, followed by an upward enhancement between 16:00 and 19:00 UT. Untypical daytime eastward zonal plasma drifts were observed when westward drifts were expected. Variations in the EEJ are highly correlated with perturbations in the vertical plasma drift exhibiting a counter-equatorial electrojet (CEEJ) between 12:00 and 16:00 UT. These observations of plasma drifts and EEJ are, so far, the only ground-based radar measurements of these parameters in the western South American region after the eruption. We attributed the ion drift and EEJ perturbations to large-scale thermospheric wind variations produced by the eruption, which altered the dynamo electric field in the Hall and Pedersen regions. These types of multiple and simultaneous observations can contribute to advancing our understanding of the ionospheric processes associated with natural hazard events and the interaction with lower atmospheric layers.Graphical

Research on Three-Dimensional Cloud Structure Retrieval and Fusion Technology for the MODIS Instrument

Accurate three-dimensional (3D) cloud structure measurements are critical for assessing the influence of clouds on the Earth’s atmospheric system. This study extended the MODIS (Moderate-Resolution Imaging Spectroradiometer) cloud vertical profile (64 × 64 scene, about 70 km in width × 15 km in height) retrieval technique based on conditional generative adversarial networks (CGAN) to construct seamless 3D cloud fields for the MODIS granules. Firstly, the accuracy and spatial continuity of the retrievals (of 7180 samples from the validation set) were statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bidirectional ensemble binning probability fusion (CGAN-BEBPF) technique were developed, which improved the CGAN retrieval accuracy and support to construct seamless 3D clouds for the MODIS granules. The CGAN-BEBPF technique involved three steps: cloud masking, intensity scaling, and optimal value selection. It ensured adequate coverage of the low reflectivity areas while preserving the high-reflectivity cloud cores. The technique was applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system and the results were compared with ground-based radar measurements. The cloud structures of the CGAN-BEBPF results were highly consistent with the ground-based radar observations. The CGAN-EBEPF technique retrieved weak ice clouds at the top levels that were missed by ground-based radars and filled the gaps of the ground-based radars in the lower levels. The CGAN-BEBPF was automated to retrieve 3D cloud radar reflectivity along the MODIS track over the seas to the east and south of mainland China, providing valuable cloud information to support maritime and near-shore typhoons and convection prediction for the cloud-sensitive applications in the regions.

Dynamic Model Fidelity Effects on Covariance Based Track Association

This paper examines the performance of covariance-based track association with varying levels of dynamic model fidelity. To minimize the dynamic nonlinearities, the mean modified equinoctial (MME) elements are used. Ground-based radar measurements are generated by propagating the osculating orbits of 100 space objects in low Earth orbit using a 70×70 spherical harmonic gravity model (JGM-3). Atmospheric drag, solar radiation, and third-body gravity are not considered. To associate two tracks, the two sets of states and covariances are propagated to a common time. The Mahalanobis distance between the two tracks is then used to determine whether they associate. A higher-fidelity dynamic model for state propagation improves association performance, while the fidelity of the dynamic model for covariance propagation does not have a significant impact. The MME-based track association demonstrates excellent performance when incorporating J2, J22, J4, and J6 in the MME state propagation as well as the MME covariance propagation with J2. It achieves an accuracy of 99.92%, a true positive sensitivity of 92.43%, a false negative sensitivity of 7.57%, a false positive sensitivity of 0%, and a true negative sensitivity of 100% when associating tracks separated by 10 days.

Extreme melting at Greenland's largest floating ice tongue

Abstract. The 79° North Glacier (Nioghalvfjerdsbrae, 79NG) is one of three remaining glaciers with a floating tongue in Greenland. Although the glacier has been considered exceptionally stable in the past, earlier studies have shown that the ice tongue has thinned in recent decades. By conducting high-resolution ground-based and airborne radar measurements in conjunction with satellite remote-sensing observations, we find significant changes in the geometry of 79NG. In the vicinity of the grounding line, a 500 m high subglacial channel has grown since ∼ 2010 and has caused surface lowering of up to 7.6 m a−1. Our results show extreme basal melt rates exceeding 150 m a−1 over a period of 17 d within a distance of 5 km from the grounding line, where the ice has thinned by 32 % since 1998. We find a heterogeneous distribution of melt rates, likely due to variability in water column thickness and channelization of the ice base. Time series of melt rates show a decrease in basal melting since 2018, indicating an inflow of colder water into the cavity below 79NG. We discuss the processes that have led to the changes in geometry and conclude that the inflow of warm ocean currents has led to the extensive thinning of 79NG's floating ice tongue near the grounding line over the last 2 decades. In contrast, we hypothesize that the growth of the channel results from increased subglacial discharge due to a considerably enlarged area of summer surface melt due to the warming of the atmosphere.

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean

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

Impact of Adjusted and Nonadjusted Surface Observations on the Cold Season Performance of the Canadian Precipitation Analysis (CaPA) System

Abstract The Canadian Precipitation Analysis System (CaPA) is an operational system that uses a combination of weather gauge and ground-based radar measurements together with short-term forecasts from a numerical weather model to provide near-real-time estimates of 6- and 24-h precipitation amounts. During the winter season, many gauge measurements are rejected by the CaPA quality control process because of the wind-induced undercatch for solid precipitation. The goal of this study is to improve the precipitation estimates over central Canada during the winter seasons from 2019 to 2022. Two approaches were tested. First, the quality control procedure in CaPA has been relaxed to increase the number of surface observations assimilated. Second, the automatic solid precipitation measurements were adjusted using a universal transfer function to compensate for the undercatch problem. Although increasing the wind speed threshold resulted in lower amounts and worse biases in frequency, the overall precipitation estimates are improved as the equitable threat score is improved because of a substantial decrease in the false alarm ratio, which compensates the degradation of the probability of detection. The increase of solid precipitation amounts using a transfer function improves the biases in both frequency and amounts and the probability of detection for all precipitation thresholds. However, the false alarm ratio deteriorates for large thresholds. The statistics vary from year to year, but an overall improvement is demonstrated by increasing the number of stations and adjusting the solid precipitation amounts for wind speed undercatch.

Correction for the Attenuation Due to Atmospheric Gas and Stratiform Clouds in Triple-Frequency Radar Observations of the Microphysical Properties of Snowfall

For triple-frequency radar, the attenuation attributed to atmospheric gases and stratiform clouds is diverse due to different snowfall microphysical properties, particularly in regions far from the radar. When using triple-frequency ground-based radar measurements, evaluating the attenuation of the three radars at different heights is common to derive attenuation-corrected effective reflectivity. Therefore, this study proposes a novel quality-controlled approach to identify radar attenuation due to gases and stratiform clouds that can be neglected due to varying snowfall microphysical properties and assess attenuation along the radar observation path. The key issue lies in the lack of information about vertical hydrometeor and cloud distribution. Therefore, European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data are employed. The Self-Similar-Rayleigh-Gans Approximation (SSRGA) for the nonspherical scattering model in the Passive and Active Microwave TRAnsfer model 2 (PAMTRA2) is compared and analyzed against other scattering models to obtain the optimal triple-frequency radar attenuation correction strategies for stratiform cloud meteorological conditions with varying snowfall microphysical properties. This methodology paves the way for understanding differential attenuation attributed to gas and stratiform clouds with snowfall microphysical properties. Simultaneously, the bin-by-bin approximation method is used to perform the attenuation correction. The two-way attenuation correction increased up to 4.71 dB for heights above 6 km, remaining minimal for regions with heights below 6 km. These values, attributable to gases and stratiform clouds’ two-way attenuation, are nonnegligible, especially at distances far from the W-band radar at heights above 6 km. Both values are relatively small for the X- and Ka-band radars and can be neglected for the varying snowfall microphysical properties. The attenuation correction of triple-frequency radar reflectivity is validated using the cross-calibration and dual-frequency reflectivity ratios. The results show that the method is valid and feasible.

Analysis of in situ measurements of electron, ion and neutral temperatures in the lower thermosphere–ionosphere

Abstract. Simultaneous knowledge of the temperatures of electrons, ions and neutrals is key to the understanding and quantification of energy transfer processes in planetary atmospheres. However, whereas electron and ion temperature measurements are routinely obtained from ground-based incoherent scatter radars, simultaneous measurements of electron, ion and neutral temperature measurements can only be made in situ. For the Earth's lower thermosphere–ionosphere, the only available comprehensive in situ dataset of electron, ion and neutral temperatures to date is that of the Atmosphere Explorers C, D and E and the Dynamics Explorer 2 missions. In this study we first perform a cross-comparison of all co-temporal and co-spatial measurements between in situ electron and ion temperature measurements from the above in situ spacecraft missions with corresponding measurements from the Arecibo, Millstone Hill and Saint-Santin incoherent scatter radars, during times of overflights of these spacecraft over the radar fields of view. This expands upon a previous study that only considered data from the Atmosphere Explorer C. The results indicate good agreement between satellite and ground-based radar measurements. Subsequently, out of the above datasets, all instances where ion temperatures appear to be lower than neutral temperatures are identified and are studied statistically. Whereas current understanding indicates that ion temperatures are generally expected to be higher than neutral temperatures in the lower thermosphere–ionosphere, a non-negligible number of events is found where this does not hold true. The distribution of all such cases in altitude, latitude and longitude is presented and discussed. Potential causes leading to neutral temperatures being higher than ion temperatures are outlined, including both instrumental effects or measurement errors and physical causes. Whereas a conclusive case cannot be made based on the present analysis, it is speculated from the results presented herein that not all cases can be attributed to instrument effects or measurement errors. This can have significant implications for the current understanding that the energy of the ions is expected to be higher than that of the neutrals and points to the need for additional simultaneous in situ measurements in the lower thermosphere–ionosphere (LTI).

On the question of constructing the distribution of the flux density of meteoroids over the celestial sphere in ground-based single-position radar measurements of meteor activity and velocity: The experience of past years

The construction of the distribution of the flux density of meteor bodies over the celestial sphere is necessary for solving problems of meteor astronomy and others. To construct such distributions, experimental data on the coordinates of meteor radiants are needed. Meteor radars provide determination of the coordinates of the radiant in the presence of one transmitter and three spaced receivers. In this research, we are talking about ground-based meteor observations of the number of meteors using single-station meteor back-skatter radars as simpler than measurements of meteor radiants. In the 1950–80s in Kharkiv (Ukraine), active meteor radar research was carried out, both simple single-station and complex with remote points. The authors in the 1980s were participants in the meteor international program GLOBMET (Kashcheev, 1982; Roper, 1990). At that time, they modified an algorithm for calculating the flux density of meteor bodies over the heliocentric celestial sphere by number, range and velocity for radar observations with using single-station meteor backscatter. An algorithm was developed for dividing the celestial sphere into regions with a combination of “celestial” and “antenna” regions with a binding of a meteor to the antenna region in radar observations with a narrowly directed antenna without goniometr. This algorithm was applied to specific equipment for measuring the number of meteors by the Kharkiv Meteor Automated Radar System (MARS), 31.1 MHz. The correctness of the algorithm was confirmed by constructing the density distribution of the incident flux over the celestial sphere based on measurements in October-December 1989. The distributions are constructed using two methods for approximating the amplitude-time characteristics of signals. The results represent the experience of past years, which may be of interest as a scientific and technical solution.

Comparison between Compressive Sensing and Non-Uniform Array for a MIMO GBSAR with Elevation Resolution: Simulations and Experimental Tests

Ground-based synthetic aperture radars (GBSAR) are popular instruments widely used for the monitoring of infrastructures. One of the main problems of ground-based interferometric radars is the elevation ambiguity. Multiple-input multiple-output (MIMO) arrays could solve this problem. This work proposes a study on possible MIMO configurations to achieve elevation resolution in ground-based radar measurements. Specifically, two array configurations are compared: a random sparse array suitable for the compressive sensing technique, and a non-uniform array. The two solutions are compared by means of simulations and experimental tests. An ad hoc system has been developed to jointly test the two configurations, and results obtained in a controlled and real urban scenario are shown. It is found that both systems are able to solve elevation ambiguity. The non-uniform array seems to achieve good performance in a general scenario, while the CS processing can outperform the other only after optimization, depending on the specific scenario and application.

Empirical SNR-based model of the displacement accuracy for ground-based radar measurements

Ground-based radar interferometry offers significant capabilities for the observation of engineering structures and natural objects displacements. The quality of the recorded displacement is significantly influenced by the intensity of the reflected radar signal. In order to determine the actual accuracy of measurement performed in the field, two comparative experiments were carried out. In both cases, referential values were specified using precise instruments: a laser interferometer and a precise total station. This way, the error value equal to 0.1–0.2 mm declared by the manufacturer was confirmed in the field conditions. Furthermore, the relationship between SNR (signal-to-noise ratio) parameter and the actual measurement accuracy was analysed based on the acquired results. Due to the different ways of expressing the parameter, i.e. thermal and estimated SNR values, specific for the applied radar system, both of them were analysed. Finally, the model which allows determining the actual accuracy of displacement measurement in the field was proposed. It includes the SNR value and the distance to the object, so the accuracy can be reliably estimated upon these basic parameters without the need for additional measurement techniques.

Studies of noctilucent clouds from the stratosphere during the SONC balloon-borne experiment in 2021

On the night 16–17 August 2021, a balloon-borne experiment called Stratospheric Observations of Noctilucent Clouds (SONC) was successfully performed. A big scientific balloon, having onboard three automated cameras for studies of noctilucent clouds (NLC), was launched to 32.7 km altitude from Esrange (northern Sweden). All three NLC cameras and electronics were completely operational in the stratosphere for more than 10 h at low temperatures of about −30 °C. Two wide angle cameras registered an extended NLC field of about 1700 km long in the twilight sky sector from the north-west to the north-east of Esrange. NLC were of a moderate brightness and were located at high latitudes between 68° and 71°N. The NLC field was located in a cold area (138–142 K) below the frost point temperature (145–148 K) in the mesopause region that was confirmed by Aura/MLS satellite and Esrange lidar measurements. The balloon-borne NLC measurements were accompanied by ground-based lidar and radar measurements. The latter have registered Polar Mesosphere Summer Echoes (PMSE) in the same volume of the summer mesopause along with NLC observed from the stratosphere that has been performed for the first time above northern Scandinavia. We describe the technique and method of the NLC observation from the stratosphere as well as present the first scientific results of the SONC experiment.

GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm

Abstract. The Global Precipitation Measurement (GPM) mission measures global precipitation at a temporal resolution of a few hours to enable close monitoring of the global hydrological cycle. GPM achieves this by combining observations from a spaceborne precipitation radar, a constellation of passive microwave (PMW) sensors, and geostationary satellites. The Goddard Profiling Algorithm (GPROF) is used operationally to retrieve precipitation from all PMW sensors of the GPM constellation. Since the resulting precipitation rates serve as input for many of the level 3 retrieval products, GPROF constitutes an essential component of the GPM processing pipeline. This study investigates ways to improve GPROF using modern machine learning methods. We present two neural-network-based, probabilistic implementations of GPROF: GPROF-NN 1D, which (just like the current GPROF implementation) processes pixels individually, and GPROF-NN 3D, which employs a convolutional neural network to incorporate structural information into the retrieval. The accuracy of the retrievals is evaluated using a test dataset consistent with the data used in the development of the GPROF and GPROF-NN retrievals. This allows for assessing the accuracy of the retrieval method isolated from the representativeness of the training data, which remains a major source of uncertainty in the development of precipitation retrievals. Despite using the same input information as GPROF, the GPROF-NN 1D retrieval improves the accuracy of the retrieved surface precipitation for the GPM Microwave Imager (GMI) from 0.079 to 0.059 mm h−1 in terms of mean absolute error (MAE), from 76.1 % to 69.5 % in terms of symmetric mean absolute percentage error (SMAPE) and from 0.797 to 0.847 in terms of correlation. The improvements for the Microwave Humidity Sounder (MHS) are from 0.085 to 0.061 mm h−1 in terms of MAE, from 81 % to 70.1 % for SMAPE, and from 0.724 to 0.804 in terms of correlation. Comparable improvements are found for the retrieved hydrometeor profiles and their column integrals, as well as the detection of precipitation. Moreover, the ability of the retrievals to resolve small-scale variability is improved by more than 40 % for GMI and 29 % for MHS. The GPROF-NN 3D retrieval further improves the MAE to 0.043 mm h−1; the SMAPE to 48.67 %; and the correlation to 0.897 for GMI and 0.043 mm h−1, 63.42 %, and 0.83 for MHS. Application of the retrievals to GMI observations of Hurricane Harvey shows moderate improvements when compared to co-located GPM-combined and ground-based radar measurements indicating that the improvements at least partially carry over to assessment against independent measurements. Similar retrievals for MHS do not show equally clear improvements, leaving the validation against independent measurements for future investigation. Both GPROF-NN algorithms make use of the same input and output data as the original GPROF algorithm and thus may replace the current implementation in a future update of the GPM processing pipeline. Despite their superior accuracy, the single-core runtime required for the operational processing of an orbit of observations is lower than that of GPROF. The GPROF-NN algorithms promise to be a simple and cost-efficient way to increase the accuracy of the PMW precipitation retrievals of the GPM constellation and thus improve the monitoring of the global hydrological cycle.

Intercomparison of Cloud Vertical Structures over Four Different Sites of the Eastern Slope of the Tibetan Plateau in Summer Using Ka-Band Millimeter-Wave Radar Measurements

The eastern slope of the Tibetan Plateau is a crucial corridor of water-vapor transport from the Tibetan Plateau to Eastern China. This is also a region with active cloud initiation, and the locally hatched cloud systems have a profound impact on the radiation budget and hydrological cycle over the downstream Sichuan Basin and the middle reach of the Yangtze River. It is noteworthy that there is a strong diversification in the characteristics and evolution of the ESTP cloud systems due to the complex terrain. Therefore, in this study, ground-based Ka-band millimeter-wave cloud radar measurements collected at the Ganzi (GZ), Litang (LT), Daocheng (DC), and Jiulong (JL) sites of the ESTP in 2019 were analyzed to compare the vertical structures of summer nonprecipitating clouds, including cloud occurrence frequency, radar reflectivity factor, cloud base height, cloud top height, and cloud thickness. The occurrence frequency exhibits two peaks on the ESTP with maximum values of ~20% (2–4 km) and 15% (7–9 km), respectively. The greatest (smallest) occurrence frequency occurs in the JL (GZ). The cloud occurrence frequency of all sites increases rapidly in the afternoon, and the occurrence frequency of the DC presents larger values at 2–4 km. In contrast, the occurrence frequency in the JL shows another increase from 2000 LT to midnight at 7–11 km. Stronger radar echoes occur most frequently in the LT at 5–7 km, and hydrometeor sizes and phase states vary dramatically in mixed-phase clouds. A small number of radar echoes occur at midnight in the JL. A characteristic bimodality of the cloud base height and top height for single-layer, double-layer, and triple-layer clouds was observed. Clouds show a higher base height in the GZ and higher top height in the JL. The ESTP is dominated by thin clouds with thicknesses of 200–400 m. The cloud base height, top height, and thickness exhibit an increase in the afternoon, and higher top height occurs more frequently from midnight to the next early morning in the JL because of its mountain-valley terrain.

Validation of the Aeolus Level-2B wind product over Northern Canada and the Arctic

Abstract. In August 2018, the European Space Agency (ESA) launched the Aeolus satellite, whose Atmospheric LAser Doppler INstrument (ALADIN) is the first space-borne Doppler wind lidar to regularly measure vertical profiles of horizontal line-of-sight (HLOS) winds with global sampling. This mission is intended to assess improvement to numerical weather prediction provided by wind observations in regions poorly constrained by atmospheric mass, such as the tropics, but also, potentially, in polar regions such as the Arctic where direct wind observations are especially sparse. There remain gaps in the evaluation of the Aeolus products over the Arctic region, which is the focus of this contribution. Here, an assessment of the Aeolus Level-2B (L2B) wind product is carried out, progressing from specific locations in the Canadian North to the pan-Arctic. In particular, Aeolus data are compared to a limited sample of coincident ground-based Ka-band radar measurements at Iqaluit, Nunavut, to a larger set of coincident radiosonde measurements over the Canadian North, to Environment and Climate Change Canada (ECCC)'s short-range forecast, and to the reanalysis product, ERA5, from the European Centre for Medium-Range Weather Forecasts (ECMWF). Periods covered include the early phase of the first laser flight model (flight model A – FM-A; September to October 2018), the early phase of the second laser flight model (flight model B – FM-B; August to September 2019), and the middle phase of FM-B (December 2019 to January 2020). The adjusted r-squared between Aeolus and other local datasets is around 0.9 except for lower values for the comparison to the Ka-band radar, reflecting limited sampling opportunities with the radar data. This consistency is degraded by about 10 % for the Rayleigh winds in the summer due to solar background noise and other possible errors. Over the pan-Arctic, consistency, with correlation greater than 0.8, is found in the Mie channel from the planetary boundary layer to the lower stratosphere (near surface to 16 km a.g.l.) and in the Rayleigh channel from the troposphere to the stratosphere (2 to 25 km a.g.l.). In all three periods, Aeolus standard deviations are found to be 5 % to 40 % greater than those from ECCC-B and ERA5. We found that the L2B estimated error product for Aeolus is coherent with the differences between Aeolus and the other datasets and can be used as a guide for expected consistency. Our work shows that the high quality of the Aeolus dataset that has been demonstrated globally applies to the sparsely sampled Arctic region. It also demonstrates the lack of available independent wind measurements in the Canadian North, lending urgency to the need to augment the observing capacity in this region to ensure suitable calibration and validation of future space-borne Doppler wind lidar (DWL) missions.

Evaluation of Aeolus L2B wind product with wind profiling radar measurements and numerical weather prediction model equivalents over Australia

Abstract. Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s−1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s−1, respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s−1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction.

Chasing Snowstorms: The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) Campaign

Abstract The Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) is a NASA-sponsored field campaign to study wintertime snowstorms focusing on East Coast cyclones. This large cooperative effort takes place during the winters of 2020–23 to study precipitation variability in winter cyclones to improve remote sensing and numerical forecasts of snowfall. Snowfall within these storms is frequently organized in banded structures on multiple scales. The causes for the occurrence and evolution of a wide spectrum of snowbands remain poorly understood. The goals of IMPACTS are to characterize the spatial and temporal scales and structures of snowbands, understand their dynamical, thermodynamical, and microphysical processes, and apply this understanding to improve remote sensing and modeling of snowfall. The first deployment took place in January–February 2020 with two aircraft that flew coordinated flight patterns and sampled a range of storms from the Midwest to the East Coast. The satellite-simulating ER-2 aircraft flew above the clouds and carried a suite of remote sensing instruments including cloud and precipitation radars, lidar, and passive microwave radiometers. The in situ P-3 aircraft flew within the clouds and sampled environmental and microphysical quantities. Ground-based radar measurements from the National Weather Service network and a suite of radars located on Long Island, New York, along with supplemental soundings and the New York State Mesonet ground network provided environmental context for the airborne observations. Future deployments will occur during the 2022 and 2023 winters. The coordination between remote sensing and in situ platforms makes this a unique publicly available dataset applicable to a wide variety of interests.

Integration of robotic total station and digital image correlation to assess the three-dimensional surface kinematics of a landslide

In the field of landslide monitoring, the assessment of the spatially-distributed three-dimensional surface displacement is crucial to understand the underlying mechanisms. Nevertheless, available technologies and techniques that provide such a datum are few and often suffer spatio-temporal resolution, logistic and/or financial limitations. In this framework, we developed a methodology that merges the three-dimensional measurements at specific points, acquired by a robotic total station (RTS), and the spatially-distributed data obtained with digital image correlation (DIC) of time-lapse camera photographs, to achieve the spatially-distributed three-dimensional surface displacement. The integration method follows this procedure: i) the DIC results are orthorectified on an existing digital elevation model; ii) the RTS data are rototranslated into the camera coordinate system; iii) the ratio α between displacement vertical component and module measured by the RTS is calculated and interpolated across the region of interest; iv) the orthorectified DIC results are rescaled according to α, obtaining the three surface displacement components; v) the displacement vector is rototranslated into the geographical coordinate system. The sensitivity analysis respect to α revealed that the integration method can be successfully applied even with a limited number of RTS measurement points. The developed methodology has been applied to the Mont de La Saxe rockslide case study, during a phase of strong acceleration. In this period, the displacement magnitudes varied between 0.1 m and 10 m, thus providing a stress-test input for methodology development and validation. The results have been compared with independent ground-based interferometric radar measurements, obtaining 0.99 linear correlation coefficient and median absolute deviation of 0.086 m, which is comparable with the DIC measurement uncertainty. The proposed method is based on the use of low-cost portable and commonly used field equipment, thus it can be easily implemented in existing monitoring networks without additional financial costs.

Changes in the Eruptive Style of Stromboli Volcano before the 2019 Paroxysmal Phase Discovered through SOM Clustering of Seismo-Acoustic Features Compared with Camera Images and GBInSAR Data

Two paroxysmal explosions occurred at Stromboli on 3 July and 28 August 2019, the first of which caused the death of a young tourist. After the first paroxysm an effusive activity began from the summit vents and affected the NW flank of the island for the entire period between the two paroxysms. We carried out an unsupervised analysis of seismic and infrasonic data of Strombolian explosions over 10 months (15 November 2018–15 September 2019) using a Self-Organizing Map (SOM) neural network to recognize changes in the eruptive patterns of Stromboli that preceded the paroxysms. We used a dataset of 14,289 events. The SOM analysis identified three main clusters that showed different occurrences with time indicating a clear change in Stromboli’s eruptive style before the paroxysm of 3 July 2019. We compared the main clusters with the recordings of the fixed monitoring cameras and with the Ground-Based Interferometric Synthetic Aperture Radar measurements, and found that the clusters are associated with different types of Strombolian explosions and different deformation patterns of the summit area. Our findings provide new insights into Strombolian eruptive mechanisms and new perspectives to improve the monitoring of Stromboli and other open conduit volcanoes.

Snow Depth Retrieval With an Autonomous UAV-Mounted Software-Defined Radar

We present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). We coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600–2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. We image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. We present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rho = 0.9$ </tex-math></inline-formula> demonstrated. Finally, we compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.