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Low‐level mixed‐phase clouds (MPCs) occur extensively in the Arctic, and are known to play a key role for the energy budget. While their characteristic structure is nowadays well understood, the significance of different precipitation‐formation processes, such as aggregation and riming, is still unclear. Using a 3‐year data set of vertically pointing W‐band cloud radar and K‐band Micro Rain Radar (MRR) observations from Ny‐Ålesund, Svalbard, we statistically assess the relevance of aggregation in Arctic low‐level MPCs. Combining radar observations with thermodynamic profiling, we find that larger snowflakes (mass median diameter larger than 1 mm) are predominantly produced in low‐level MPCs whose mixed‐phase layer is at temperatures between −15 and −10°C. This coincides with the temperature regime known for favoring aggregation due to growth and subsequent mechanical entanglement of dendritic crystals. Doppler velocity information confirms that these signatures are likely due to enhanced ice particle growth by aggregation. Signatures indicative of enhanced aggregation are however not distributed uniformly across the cloud deck, and only observed in limited regions, suggesting a link with dynamical effects. Low Doppler velocity values further indicate that significant riming of large particles is unlikely at temperatures colder than −5°C. Surprisingly, we find no evidence of enhanced aggregation at temperatures warmer than −5°C, as is typically observed in deeper cloud systems. Possible reasons are discussed, likely connected to the ice habits that form at temperatures warmer than −10°C, increased riming, and lack of particle populations characterized by broader size distributions precipitating from higher altitudes., Plain Language Summary: Low‐level mixed‐phase clouds (MPCs), that is, shallow clouds containing both liquid droplets and ice crystals, form frequently in the Arctic region. Their characteristic structure—consisting of one or multiple liquid layers at sub‐zero temperatures, from which ice crystals form and precipitate—is nowadays well understood. However, the processes that lead to the growth of ice crystals into snow have been overlooked. Using a 3‐year data set of radar observations from Ny‐Ålesund, in Svalbard, Norway, we are able to identify situations when the ice particle growth is dominated by aggregation of several individual crystals. Combining radar observations with temperature information, we find that larger snowflakes are only produced in MPCs if their liquid portion is at temperatures between −15 and −10°C. This coincides with the temperature regime known for favoring aggregation due to growth and subsequent entanglement of branched crystals. Surprisingly, we find no evidence of enhanced ice aggregation at temperatures warmer than −5°C, as is typically observed in deeper cloud systems. Possible reasons are discussed, likely connected to the ice crystal shapes that develop at temperatures warmer than −10°C, increased liquid droplet production, and lack of particles precipitating from higher altitudes., Key Points: Low‐level mixed‐phase clouds (MPCs) at Ny‐Ålesund produce large aggregates predominantly at dendritic‐growth temperatures. Enhanced aggregation due to dendritic growth occurs intermittently in limited regions of the MPC. The typically observed enhanced aggregation zone close to 0°C is absent in low‐level MPCs at the site., Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659

To assess the potential of radar rainfall nowcasting for early warning, nowcasts for 659 events were used to construct discharge forecasts for 12 Dutch catchments. Four open-source nowcasting algorithms were tested: Rainymotion Sparse (RM-S), Rainymotion DenseRotation (RM-DR), Pysteps deterministic (PS-D), and probabilistic (PS-P) with 20 ensemble members. As benchmark, Eulerian Persistence (EP) and zero precipitation input (ZP) were used. For every 5-min step in the available nowcasts, a discharge forecast with a 12-hr forecast horizon was constructed. Simulations using the observed radar rainfall were used as reference. Rainfall and discharge forecast errors were found to increase with both increasing rainfall intensity and spatial variability. For the discharge forecasts, this relationship depends on the initial conditions, as the forecast error increases more quickly with rainfall intensity when the groundwater table is shallow. Overall, discharge forecasts using RM-DR, PS-D, and PS-P outperform the other methods. Threshold exceedance forecasts were assessed by using the maximum event discharge as threshold. Compared to benchmark ZP, an exceedance is, on average, forecast 223 (EP), 196 (RM-S), 213 (RM-DR), 119 (PS-D), and 143 min (PS-P) in advance. The EP results are counterbalanced by both a high false alarm ratio (FAR) and inconsistent forecasts. Contrarily, PS-D and PS-P produce lower FAR and inconsistency index values than all other methods. All methods advance short-term discharge forecasting compared to no rainfall forecasts at all, though all have shortcomings. As forecast rainfall volumes are a crucial factor in discharge forecasts, a future focus on improving this aspect in nowcasting is recommended.

Random‐noise‐induced biases are inherent issues to the accurate derivation of second‐order statistical parameters (e.g., variances, fluxes, energy densities, and power spectra) from lidar and radar measurements. We demonstrate here for the first time an altitude‐interleaved method for eliminating such biases, following the original proposals by Gardner and Chu (2020, https://doi.org/10.1364/ao.400375) who demonstrated a time‐interleaved method. Interleaving in altitude bins provides two statistically independent samples over the same time period and nearly the same altitude range, thus enabling the replacement of variances that include the noise‐induced biases with covariances that are intrinsically free of such biases. Comparing the interleaved method with previous variance subtraction (VS) and spectral proportion (SP) methods using gravity wave potential energy density calculated from Antarctic lidar data and from a forward model, this study finds the accuracy and precision of each method differing in various conditions, each with its own strengths and weakness. VS performs well in high‐SNR, yet its accuracy fails at lower‐SNR as it often yields negative values. SP is accurate and precise under high‐SNR, remaining accurate in worse conditions than VS would, yet develops a positive bias under low‐SNR. The interleaved method is accurate in all SNRs but requires a large number of samples to drive random‐noise terms in covariances toward zero and to compensate for the reduced precision due to the splitting of return signals. Therefore, selecting the proper bias removal/elimination method for actual signal and sample conditions is crucial in utilizing lidar/radar data, as neglecting this can conceal trends or overstate atmospheric variability.

In this study, we develop statistical relationships between radar observables and drop size distribution properties in different latitude bands to inform radar rainfall retrieval techniques and understand underpinning microphysical reasons for differences reported in the literature between satellite mean zonal rainfall products at high latitudes (up to a factor 2 between products over ocean). A major assumption in satellite retrievals is the attenuation-reflectivity relationships for convective and stratiform precipitation. They are found to systematically produce higher attenuation than our relationships with all latitudes included or within individual latitude bands (except in the tropics). The scatter around fitted curves approximating the radar reflectivity-mass-weighted diameter Dm relationship and the dual-frequency ratio (ratio of Ka- to Ku-band reflectivities)-Dm relationships is found to be large and of the same magnitude. This result suggests that the added value of two radar frequencies to improve the Dm retrieval from space seems limited. In contrast, the relationship between Dm and the attenuation/reflectivity ratio is robust and not dependent on latitude. Direct relationships between rainfall and either reflectivity or attenuation are also found to be very robust. Attenuation-reflectivity, Dm-reflectivity, and rainfall rate-reflectivity relationships in the Southern Hemisphere high latitude and Northern Hemisphere polar latitude bands are fundamentally different from those at other latitude bands, producing smaller attenuation, much larger Dm, and lower rainfall rates. This implies that specific relationships need to be used for these latitude bands in radar rainfall retrieval techniques using such relationships. © 2019 Australian Bureau of Meteorology, Commonwealth of Australia.

This paper proposes an improved algorithm which combines neural networks with diverse 3‐D structural features to partition radar reflectivity into convective and stratiform precipitation types. Radar data used in this work were obtained from three networked X‐band Doppler radars located in Chengdu, China. The proposed algorithm consists of the two sections: six high‐resolution features, which could be extracted from radar volume scanning and expressed the characteristics of the target in many ways, are selected as input to the neural network; systematic self‐diagnosis is implemented; and the optimized model is determined according to analysis of bias and variance of classifier. Three state‐of‐art classification algorithms were implemented as references for algorithm evaluation. Both subjective comparison and statistical results convince that performance of the proposed algorithm is better than performance of traditional classification algorithms in various weather systems. The statistical results show that the proposed algorithm F‐score values are 3%, 24%, and 30% higher than the fuzzy logic, SHY95, and BL algorithms and the recognition speed of the proposed algorithm is 54 times, two times, and four times that of fuzzy logic, SHY95, and BL, respectively. Considering network training is an offline procedure, classification by proposed neural network algorithm has great potential in real‐time weather analysis for the precipitation classification.

Across the Antarctic Ice Sheet, accumulation heavily influences firn compaction and surface height changes. Therefore, accumulation varies over short distances ( 25 km) that are too coarse to resolve this variability. To address this limitation, we construct a fine-scale accumulation product from airborne snow radar observations by superimposing along-track fluctuations in accumulation onto an atmospheric reanalysis product. Our resulting airborne product reflects large-scale (>25 km) orographic precipitation patterns while providing robust and unprecedented insight into Antarctic accumulation variability on subgrid scales. On these smaller scales, we find significant, regionally dependent accumulation variability (𝜎(sub relative) > 40%). This variability in accumulation is correlated with variability in topographic surface slope in the wind direction (p < 0.01), confirming that subgrid-scale accumulation variability is driven by snow redistribution by wind.

The expansion of globally consistent satellite‐radar imagery presents new opportunities to measure Earth‐surface displacements on intercontinental scales. Yet global applications, including a complete assessment of the land contribution to relative sea‐level rise, first demand new solutions to unify relative satellite‐radar observations in a geocentric reference frame. The international network of Very Long Baseline Interferometry telescopes provides an existing, yet unexploited, link to unify satellite‐radar measurements on a global scale. Proof‐of‐concept experiments reveal the suitability of these instruments as high‐amplitude reflectors for satellite radar and thus provide direct connections to a globally consistent reference frame. Automated tracking of radar satellites is easily integrated into telescope operations alongside ongoing schedules for geodesy and astrometry. Utilizing existing telescopes in this way completely avoids the need for additional geodetic infrastructure or ground surveys and is ready to implement immediately across the telescope network as a first step toward using satellite radar on a global scale.

An edited version of this paper was published by AGU. Copyright 2019 American Geophysical Union.

Quasiperiodic radio frequency interference (RFI), such as those generated by telecommunication and active radar systems, is commonly encountered in radio astronomy observations. Such RFI-contaminated signals contain hidden periodicities due to cyclic features involved in their formation (e.g., carrier frequencies, periodic keying of the amplitude, and baud rates). RFI signal characterization and its subsequent excision based on the well-known cyclic spectrum analysis have been previously demonstrated; however, the high complexity of the algorithm and the computational cost of its implementation have limited its utility in radio astronomy, rendering less sophisticated solutions. To overcome this challenge, we present a novel method for RFI detection and mitigation based on efficient estimation of the cyclic spectrum by compressive statistical sensing (CSS) of sub-Nyquist data. CSS performs second-order statistical estimation such as cyclic spectrum using a reduced number of input samples, thereby enabling accelerated performance. To validate the feasibility of the proposed method, we conduct experiments with simulated data and assess the detection and mitigation results under different parameter settings, for example, interference-to-noise ratio, additional RFI sources, frequency resolution, and input data size. We demonstrate the real performance of the method by analyzing radio astronomy data (∼1.3 GHz) acquired with the L-wide band receiver at the Arecibo Observatory, which is typically corrupted by active air surveillance radars located nearby. Our CSS-based solution enables robust and efficient detection of the RFI frequency bands present in the L-band data, and subsequent excision by blanking is also demonstrated.

This study exploits the volumetric sampling capabilities of the Resolute Bay Incoherent Scatter Radar (RISR-N) in collaboration with all-sky imagery and in-situ measurements (DMSP) to examine the i...

Dynamics of polar outlet glaciers vary with ocean tides, providing a natural laboratory to understand basal processes beneath ice streams, ice rheology and ice-shelf buttressing. We apply Terrestrial Radar Interferometry to close the spatiotemporal gap between localized, temporally well-resolved GNSS and area-wide but sparse satellite observations. Three-hour flowfields collected over an eight day period at Priestley Glacier, Antarctica, validate and provide the spatial context for concurrent GNSS measurements. Ice flow is fastest during falling tides and slowest during rising tides. Principal components of the timeseries prove upstream propagation of tidal signatures $>$ 10 km away from the grounding line. Hourly, cm-scale horizontal and vertical flexure patterns occur $>$6 km upstream of the grounding line. Vertical uplift upstream of the grounding line is consistent with ephemeral re-grounding during low-tide impacting grounding-zone stability. On the freely floating ice shelves, we find velocity peaks both during high- and low-tide suggesting that ice-shelf buttressing varies temporally as a function of flexural bending from tidal displacement. Taken together, these observations identify tidal imprints on ice-stream dynamics on new temporal and spatial scales providing constraints for models designed to isolate dominating processes in ice-stream and ice-shelf mechanics.

In-situ phase-sensitive radar measurements from the Ronne Ice Shelf (RIS) reveal evidence of intermittent basal accretion periods at several sites that are melting in the long-term mean. Periods when ice is accreted at the ice-shelf base coincide with a decrease in the amplitude of the basal return of up to 4 dB. To quantify basal accretion we constrain simultaneously the dielectric constant, electrical conductivity, and thickness of the accreted ice. We do this by exploring the sensitivity of the received basal echo strength and phase to different transmit frequencies using the radar data in combination with a simple model. Along the western RIS we detect episodic basal accretion events leading to ice accumulation at a rate equivalent to 1-3 mm of meteoric ice per day. The inferred accumulation rates and electromagnetic properties of the accreted ice imply that these events are caused primarily by the deposition of frazil ice crystals. Our findings offer the possibility of monitoring and studying the evolution of boundaries between ice-shelf basal melting and accretion regimes using remote observations, collected from the ice-shelf surface.

Both high-power large aperture (HPLA) radars and smaller meteor radars readily observe the dense head plasma produced as a meteoroid ablates. However, determining the mass of such meteors based on ...

Satellite radar backscatter has the potential to provide useful information about the progression of volcanic eruptions when optical, ground-based, or radar phase-based measurements are limited. However, backscatter changes are complex and challenging to interpret: explosive deposits produce different signals depending on pre-existing ground cover, radar parameters and eruption characteristics. We use high temporal- and spatial-resolution backscatter imagery to examine the emplacement and alteration of pyroclastic density currents (PDCs), lahar and ash deposits from the June 2018 eruption of Volcan de Fuego, Guatemala, using observatory reports and rainfall gauge data to ground truth our observations. We use a temporally dense time series of backscatter data to reduce noise and extract deposit areas. We observe backscatter changes in six drainages, the largest deposit was 11.9-km-long that altered an area of 6.3 urn:x-wiley:21699313:media:jgrb55183:jgrb55183-math-0001 and had a thickness of 10.5 urn:x-wiley:21699313:media:jgrb55183:jgrb55183-math-00022 m in the lower sections as estimated from radar shadows. The 3 June eruption also produced backscatter signal over an area of 40 urn:x-wiley:21699313:media:jgrb55183:jgrb55183-math-0003, consistent with reported ashfall. We use transient patterns in backscatter time series to identify nine periods of high lahar activity in a single drainage system between June and October 2018. We find that the characterisation of subtle backscatter signals associated with explosive eruptions are best observed with (1) radiometric terrain calibration, (2) speckle correction, and (3) consideration of pre-existing scattering properties. Our observations demonstrate that SAR backscatter can capture the emplacement and subsequent alteration of a range of explosive deposits, allowing the progression of an explosive eruption to be monitored.

The polar summer mesosphere is the Earth’s coldest region, allowing the formation of mesospheric ice clouds. These ice clouds produce strong polar mesospheric summer echoes (PMSE) that are ...

Stochastic parameterizations are continuously providing promising simulations of unresolved atmospheric processes for global climate models (GCMs). One of the stochastic multi‐cloud model (SMCM) features is to mimic the life cycle of the three most common cloud types (congestus, deep, and stratiform) in tropical convective systems. To better represent organized convection in the Climate Forecast System version 2 (CFSv2), the SMCM parameterization is adopted in CFSv2 (SMCM‐CTRL) in lieu of the pre‐existing revised simplified Arakawa–Schubert (RSAS) cumulus scheme and has shown essential improvements in different large‐scale features of tropical convection. But the sensitivity of the SMCM parameterization from the observations is yet to be ascertained. Radar data during the Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign is used to tune the SMCM in the present manuscript. The DYNAMO radar observations have been used to calibrate the SMCM using a Bayesian inference procedure to generate key time scale parameters for the transition probabilities of the underlying Markov chains of the SMCM as implemented in CFS (hereafter SMCM‐DYNAMO). SMCM‐DYNAMO improves many aspects of the mean state climate compared to RSAS, and SMCM‐CTRL. Significant improvement is noted in the rainfall probability distribution function over the global tropics. The global distribution of different types of clouds, particularly low‐level clouds, is also improved. The convective and large‐scale rainfall simulations are investigated in detail.

Strong convection nowcasting has been gaining importance in operational weather forecasting. Recently, deep learning methods have been used to meet the increasing requirement for precise and timely nowcasting. One of the promising deep learning models is the convolutional gated recurrent unit (ConvGRU), which has been proven to perform better than traditional methods in strong convection nowcasting. Despite its encouraging performance, ConvGRU tends to produce blurry radar echo images and fails to model radar echo intensities that have multi‐modal and skewed distributions. To overcome these disadvantages, we tested the structural similarity (SSIM) and multiscale structural similarity (MS‐SSIM) indexes as loss functions. The SSIM and MS‐SSIM loss functions are composed of luminance, contrast, and structure and provide more information about the intensity, grade, and shape of the radar echo, which can reduce blurring. Due to multi‐layer downscaling, MS‐SSIM extracted more radar echo characteristics, and its extrapolation was the most realistic and accurate among all of the loss function schemes. Only the MS‐SSIM scheme successfully predicted strong radar echoes after 2 h, especially those at the rainstorm level.

Bright radar reflections observed beneath the south polar layered deposits (SPLD) by the Mars Advanced Radar for Subsurface and Ionosphere Sounding instrument were interpreted to represent liquid water, but the required amounts of salt and heat to form and maintain liquids in this location are implausible given what is known about Mars. Here, we present another hypothesis that accounts for the bright reflections: hydrated and cold clay-rich deposits at the base of the SPLD create the observed radar response. To support this hypothesis, we present experimental measurements and wave propagation modeling that show that smectites, cooled to 230 K, have real and imaginary parts of the dielectric permittivity large enough to cause the bright reflections, even when mixed with other materials. Further, we find that absorptions attributable to these minerals are present in south polar orbital visible-near infrared reflectance spectra. Because these minerals are present at the south pole and can cause the reflections, we believe this to be a more viable scenario than the liquid water interpretation.

NASA's Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) laser altimeter launched in Fall 2018, providing an invaluable addition to the polar altimetry record generated by ESA's CryoSat‐2 radar altimeter. The simultaneous operation of these two satellite altimeters enables unique comparison studies of sea ice altimetry, utilizing the different frequencies and profiling strategies of the two instruments. Here, we use freeboard data from ICESat‐2 to assess Antarctic snow freeboard retrievals from CryoSat‐2. We first discuss updates made to a previously published CryoSat‐2 retrieval process and show how this Version 2 algorithm improves upon the original method by comparing the new retrievals to ICESat‐2 in specific along‐track profiles as well as on the basin‐scale. In two near‐coincident along‐track profiles, we find mean snow freeboard differences (standard deviations of differences) of 0.3 (9.3) and 7.6 cm (9.6 cm) with 25 km binned correlation coefficients of 0.77 and 0.89. Monthly mean freeboard differences range between −2.9 (10.8) and 6.6 cm (16.8 cm) basin wide, with the largest differences typically occurring in Austral fall months that is hypothesized to be related to new ice growth and the use of static snow backscatter coefficients in the retrieval. Monthly mean correlation coefficients range between 0.57 and 0.80. While coincident data show good agreement between the two sensors, they highlight issues related to geometric and frequency sampling differences that can impact the freeboard distributions.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-05-05, 資料番号: PA2210052000

Venus’s heavy atmosphere tugs the planet’s surface enough to change the length of its day by up to 21 minutes.

In addition to spaceborne Interferometric Synthetic Aperture Radar (InSAR), airborne data such as those obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) have also been utilized to measure surface subsidence in permafrost areas in recent years. Motivated by the integration of multiplatform InSAR data, we generated two UAVSAR interferograms and one Advanced Land Observing Satellite (ALOS)‐2 L‐band interferogram over a permafrost area near Yellowknife, Canada, then compared the surface subsidence in the thaw seasons of 2017. The correlation coefficient and the root mean square error (RMSE) of subsidence difference are calculated to compare the airborne and spaceborne InSAR measurements. The results demonstrate that the two UAVSAR measurements are self‐consistent, with the correlation coefficient between independent airborne measurements ∼0.7. While the RMSE of the difference between surface subsidence measured by UAVSAR and ALOS2 is ∼2.0 cm, and the correlation coefficients are less than 0.41, that is, a noticeable deviation exists between the UAVSAR and ALOS2 results possibly due to different spatial resolution and the calibration processing of airborne and spaceborne InSAR data. In addition, both UAVSAR and ALOS2 interferograms show larger surface subsidence within taiga needleleaf forest regions than in regions of other biome types (including needleleaf forest, shrubland, and grassland). The results demonstrate that a scheme for the elimination of systematic differences needs to be developed before merging multisource InSAR results. This intercomparison will provide valuable insights for narrowing the gap between radar‐based measurements and planning the integration of airborne and satellite InSAR measurements in permafrost environments., Key Points We compare the seasonal subsidence derived from airborne and satellite InSAR measurementsTwo UAVSAR interferograms are self‐consistent while a significant deviation exists between the UAVSAR and ALOS2 resultsWe discuss the difference and the potential combination use of spaceborne and airborne InSAR to improve subsidence measurements over permafrost regions