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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.

The Agile Dual Auroral Radar Network (AgileDARN) radar has been developed as an all-digital high-frequency (HF) coherent scatter radar. Based on phased array antenna and digital signal processing techniques, digital beam forming (DBF) is employed for both transmission and reception, which steers toward the desired direction based on the relative phases within the array elements. However, the amplitude and phase imbalances among channels will lead to substantial degradation of DBF performance, including the beam direction deviation, broadening of the main lobe, deteriorated side lobes, antenna gain degradation, and ultimately affects the radar detection accuracy. Here, we present a complete method for calibrating these imbalances in transceivers and linear array elements in AgileDARN radar: internal calibration and external calibration. During the internal calibration, the calibration signals with the same amplitude and phase as the multi-pulse sequence are sent to all transceivers internally. The variance of the outputs illustrates the transceivers' imbalances and can be processed to implement calibration. Further, the meteor echoes and linear least squares fitting method are employed for external calibration, which calibrates the amplitude and phase imbalances among the cables and phased array elements. The technique has been successfully implemented, and we present the results of measured data observed by the AgileDARN HF radar before and after calibration.

Mass loss around the Antarctic Ice Sheet is driven by basal melting and iceberg calving,which constitute the two dominant paths of freshwater flux into the Southern Ocean. Although of similarmagnitude, icebergs play an important and still not fully understood role in the balance of heat andfreshwater around Antarctica. This lack of understanding is partly due to operational difficulties inlarge-scale monitoring in polar regions, despite observational and remote sensing efforts. In this study, anovel machine learning approach, augmented by visual inspection, was applied to three SyntheticAperture Radar (SAR) mosaics of the whole Antarctic continent and its adjacent coastal zone. Althoughoriginally intended for a mapping of the Antarctic continent, the SAR mosaics allow us to document theevolution and distribution of the size (and mass) of icebergs in the pan-Antarctic near-coastal zone for theyears 1997, 2000, and 2008. Our novel algorithm identified 7,649 icebergs in 1997, 13,712 icebergs in 2000,and 7,246 icebergs in 2008 with surface areas between 0.1 and 4,567.82 km2and total masses of 4,641.53,6,862.81, and 5,263.69 Gt, respectively. Large regional variability was observed, although a zonal patterndistribution is present. This has implications for future climate modeling studies that try to estimate thefreshwater flux from melting icebergs, which demands a realistic representation of the interannuallyvarying near-coastal iceberg pattern to initialize the simulations.

The melting layer of precipitation has a major impact on remote sensing and telecommunications. However, there is a shortage of observational studies to validate and constrain the melting layer models especially for high-frequency radar bands. In this paper, we report how multifrequency radar Doppler spectra can be used to retrieve the melting layer attenuation at Ka- and W-bands. The presented analysis is based on identifying Rayleigh scattering regions in radar Doppler spectra measurements where dual-wavelength spectral ratios can be related to differential attenuation. We show that the estimated attenuation at Ka- and W-bands agrees reasonably well with previously reported studies, but there are indications of differences at higher rain rates. We advocate that this technique can be applied to long-term observations to advance our knowledge of the melting process. The parameterizations of melting layer attenuation as a function of rain rate and radar reflectivity are also presented.

As the most recent 3 km soil moisture product from the Soil Moisture Active Passive (SMAP) mission, the SMAP/Sentinel-1 L2_SM_SP product has a unique capability to provide global-scale 3 km soil moisture estimates through the fusion of radar and radiometer microwave observations. The spatial and temporal availability of this high-resolution soil moisture product depends on concurrent radar and radiometer observations which is significantly restricted by the narrow swath and low revisit schedule of the Sentinel-1 radars. To address this issue, this paper presents a novel two-layer machine learning-based framework which predicts the brightness temperature and subsequently the soil moisture at gap areas. The proposed method is able to gap-fill soil moisture satisfactorily at areas where the radiometer observations are available while the radar observations are missing. We find that incorporating historical radar backscatter measurements (30-day average) into the machine learning framework boosts its predictive performance. The effectiveness of the two-layer framework is validated against regional hold-out SMAP/Sentinel-1 3 km soil moisture estimates at four study areas with distinct climate regimes. Results indicate that our proposed method is able to reconstruct 3 km soil moisture at gap areas with higher Pearson correlation coefficient (47%/35%/20%/80% improvement of mean R, at Arizona/Oklahoma/Iowa/Arkansas) and lower unbiased Root Mean Square Error (20%/10%/7%/26% improvement of mean ubRMSE) when compared to the SMAP 33 km soil moisture product. Additional validation against airborne data and in-situ data from soil moisture networks is also satisfactory.

We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks (CNNs). We apply the CGAN to generating two-dimensional cloud vertical structures that would be observed by the CloudSat satellite-based radar, using only the collocated Moderate-Resolution Imaging Spectrometer (MODIS) measurements as input. The CGAN is usually able to generate reasonable guesses of the cloud structure, and can infer complex structures such as multilayer clouds from the MODIS data. This network, which is formulated probabilistically, also estimates the uncertainty of its own predictions. We examine the statistics of the generated data, and analyze the response of the network to each input parameter. The success of the CGAN in solving this problem suggests that generative adversarial networks are applicable to a wide range of problems in atmospheric science, a field characterized by complex spatial structures and observational uncertainties.

The performance of the high-frequency hybrid sky-surface wave radar is often degraded due to the distortion of radar echoes induced by time-varying ionosphere. Here a modified S-transform method based on the direct wave for ionosphere decontamination of high-frequency hybrid sky-surface wave radar is presented. First, we extract the time-varying-ionosphere-distorted phase of the echoes from the direct wave of high-frequency hybrid sky-surface wave radar by using the modified S-transform. Then this phase function is used to decontaminate the ionosphere distortion of radar echo at each range bins. Simulations and experimental data are conducted to validate the performance of the proposed method on decontaminating the distortion. The experimental data are acquired by Wuhan Universitys ocean state measuring and analyzing radar. A mass of radar data is processed and analyzed, demonstrating that the decontamination effect is statistically significant. The method is not only effective to remove the Doppler shift of direct wave and Bragg peaks caused by ionosphere but also able to narrow the broadened direct wave and Bragg peaks. Meanwhile, the SNR is improved in most range bins; the background noise is reduced detectably, sometimes significantly; and some isolated radar echoes near direct wave or Bragg peaks are recovered or enhanced. These results indicate that the proposed decontamination algorithm works well.

We use more than 10years of the Martian topside ionospheric data measured by the Mars Advanced Radar for Subsurface and Ionosphere Sounding radar sounder on board the Mars Express spacecraft to der ...

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2018-12-14, 資料番号: PA1910015000

The 2B-CLDCLASS-LIDAR R05 (2BCL5) and the raDAR/liDAR (DARDAR) satellite retrievals of cloud occurrence are compared as a function of altitude and latitude. The largest disparities are observed at ...

Refractivity-from-clutter (RFC) is a technique for remote sensing of the lowest portion of vertical atmospheric refractivity profile, particularly evaporation and surface-based ducts. It uses the sea-surface backscattered radar signal to estimate the ducting conditions. The Lower Atmospheric Propagation (LATPROP) radar is a software-defined, coherent-on-receive remote sensing system built on a low-cost, commercial off-the-shelf marine radar platform operating at 9.41 GHz. The system is designed to specifically operate as a RFC-capable platform sensitive to low clutter power returns from the sea surface. This paper examines the appropriate radar design, system modifications, and radar signal processing steps, and provides a comparison with existing RFC-capable systems. The LATPROP system was deployed during the Coupled Air-Sea Processes and electromagnetic Ducting Research West Campaign that took place off of the coast of Southern California during October 2017 and was shown to accurately capture the effects of the ducting conditions.

In this study, we propose a volume‐to‐point framework for quantitative precipitation estimation (QPE) based on the Quantitative Precipitation Estimation and Segregation Using Multiple Sensor (QPESUMS) Mosaic Radar data set. With a data volume consisting of the time series of gridded radar reflectivities over the Taiwan area, we used machine learning algorithms to establish a statistical model for QPE in weather stations. The model extracts spatial and temporal features from the input data volume and then associates these features with the location‐specific precipitations. In contrast to QPE methods based on the Z–R relation, we leverage the machine learning algorithms to automatically detect the evolution and movement of weather systems and associate these patterns to a location with specific topographic attributes. Specifically, we evaluated this framework with the hourly precipitation data of 45 weather stations in Taipei during 2013–2016. In comparison to the operational QPE scheme used by the Central Weather Bureau, the volume‐to‐point framework performed comparably well in general cases and excelled in detecting heavy‐rainfall events. By using the current results as the reference benchmark, the proposed method can integrate the heterogeneous data sources and potentially improve the forecast in extreme precipitation scenarios.

Spaceborne radars offer a unique three‐dimensional view of the atmospheric components of the Earth's hydrological cycle. Existing and planned spaceborne radar missions provide cloud and precipitation information over the oceans and land difficult to access in remote areas. A careful look into their measurement capabilities indicates considerable gaps that hinder our ability to detect and probe key cloud and precipitation processes. The international community is currently debating how the next generation of spaceborne radars shall enhance current capabilities and address remaining gaps. Part of the discussion is focused on how to best take advantage of recent advancements in radar and space platform technologies while addressing outstanding limitations. First, the observing capabilities and measurement highlights of existing and planned spaceborne radar missions including TRMM, CloudSat, GPM, RainCube, and EarthCARE are reviewed. Then, the limitations of current spaceborne observing systems, with respect to observations of low‐level clouds, midlatitude and high‐latitude precipitation, and convective motions, are thoroughly analyzed. Finally, the review proposes potential solutions and future research avenues to be explored. Promising paths forward include collecting observations across a gamut of frequency bands tailored to specific scientific objectives, collecting observations using mixtures of pulse lengths to overcome trade‐offs in sensitivity and resolution, and flying constellations of miniaturized radars to capture rapidly evolving weather phenomena. This work aims to increase the awareness about existing limitations and gaps in spaceborne radar measurements and to increase the level of engagement of the international community in the discussions for the next generation of spaceborne radar systems., Key Points Current spaceborne radars offer a unique three‐dimensional view of the atmospheric component of the Earth's hydrological cycleCurrent spaceborne radar have limitations with respect to observations of low‐level clouds, midlatitude/high‐latitude precipitation, and convectionRecent advances in radar technology will enable filling current science gaps

This study examines the impacts of humidity adjustment in a cloud analysis system on the analysis and forecast of a squall line that occurred in southeast China on 23–24 April 2007. Radial velocity data are assimilated using the ARPS three‐dimensional variational system while reflectivity data are assimilated by a cloud analysis system. Experiments with two different humidity adjustment schemes are performed, with the original and enhanced versions. Another experiment does not adjust moisture. Both schemes generally decrease the humidity in front of the convective line and increase the humidity within the convective and stratiform regions of squall line compared to no humidity adjustment, and the original scheme produces the higher humidity within precipitation regions, especially the stratiform region. Both schemes improve the forecast of squall line structure, including the leading convective line, a transition zone, and a trailing stratiform region. Among the three experiments, the enhanced scheme produces the highest precipitation forecast skill. The latent heating rates are also diagnosed to investigate the microphysical responses to the humidity adjustment. The cooling outside of the observed precipitation regions corresponding to the humidity reduction also acts to suppress spurious precipitation. Water vapor condensation into cloud water and cloud water evaporation generally dominate the latent heating/cooling below the freezing level. Compared to the enhanced scheme, the original scheme releases much more latent heat in the middle troposphere, causing more warming. This is linked to the higher cloud water condensation rate, due to the higher amount of moisture addition/adjustment by the original scheme.

Nowcasting and early warning of severe convective weather play crucial roles in heavy rainfall warning, flood mitigation, and water resource management. However, achieving effective temporal‐spatial resolution nowcasting is a very challenging task owing to the complex dynamics and chaos. Recently, an increasing amount of research has focused on utilizing deep learning approaches for this task because of their powerful abilities in learning spatiotemporal feature representation in an end‐to‐end manner. In this paper, we present convolutional long short‐term memory with a layer called star‐shape bridge to transfer features across time steps. We build an end‐to‐end trainable model for the nowcasting problem using the radar echo data set. Furthermore, we propose a raining‐oriented loss function inspired by the critical success index and utilize the group normalization technique to refine the convergence performance in optimizing our deep network. Experiments indicate that our model outperforms convolutional long short‐term memory with the cross entropy loss function and the conventional extrapolation method.

Artificial E-region field-aligned plasma density irregularities (AFAIs) have been generated using the HAARP ionospheric modification facility in Gakona and observed with a 30-MHz coherent scatter radar imager in Homer, Alaska. The AFAIs were generated using a distinctive, twisted-beam antenna pattern that illuminated a particularly broad volume overhead. The broad beam facilitates studies of natural sporadic E layer patches when they are present. The center of the pattern was pointed at different angles between zenith and magnetic zenith to examine the effects on the AFAI morphology. Radar images of AFAIs generally resemble the radiation pattern of the HF source, but the irregularities are strongest within a narrow range of zenith angles bounded approximately by the Spitze angle. A number of factors which might influence AFAI generation and detection are examined. The most important is most likely the requirement for the pump mode to have a standing-wave component for thermal parametric instability to operate.

A Raman depolarization lidar was deployed at Cape Grim, Australia (40.7°S, 144.7°E), at the northern edge of the Southern Ocean from July 2013 to February 2014 from which we determine cloud boundaries, cloud phase, ice virga, and cloud effective top heights. We compare surface-based lidar with results from the raDAR/liDAR (DARDAR) data set within 1,000 km of Cape Grim. DARDAR combines information from the CloudSat and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instruments. We extract single-layer clouds that are sufficiently thin for signal to be present on the farside of the cloud and which have a liquid cloud top phase. These conditions maximize the likelihood that both surface-based lidar and DARDAR are observing the full vertical extent of the same clouds. Differences in low-level cloud occurrence frequencies for these single-layer clouds reveal that DARDAR underestimates cloud at 0.2â€"1.0 km altitude by a factor of 3 compared with the surface-based lidar. When multiple cloud decks are present, the underestimate in this altitude region is around 2.5 times. Heterogeneous glaciation observed by the Cape Grim lidar in midlevel stratiform supercooled water clouds is similar to that reported by previous surface-based observations adjacent to the Southern Ocean, with half of these clouds precipitating ice at cloud top temperatures of −20°C. This transition occurs around -15°C in the DARDAR data set, and this difference is likely due to the reduced sensitivity of surface-based lidar in detecting precipitating ice compared with what a surface-based radar could observe.

In this paper we consider the radiolocation equation for the Incoherent Scatter Radar (ISR) method, the equation accounts for refraction effect and Faraday rotation in the VHF frequency domain. Our research shows that we cannot ignore refraction in processing the incoherent scatter data when lag-product is used to obtain the ionospheric characteristics. Effect of refraction can explain some uncertainties that arise during processing and interpretation of the ISR data. This effect shows that together with the "ion line" of incoherent scatter spectrum, a relatively small "electron line" also plays a significant role in forming the ionosphere response. The “electron line” behaves as an additional "radar induced" wideband radiolocation signal affected by refraction and Faraday rotation; it depends on the ionosphere temperature and ion composition. The presence of a wideband “electron line” produces specific distortions of the ISR spectrum during 3D-forming of the ionosphere response inside the ISR antenna diagram pattern. During testing, we found that the signal from the ISR “electron line” can be measured at much higher altitudes comparing to the “ion line”. Hence, to improve the incoherent scatter technique, we can take the “electron line” and refraction into account.

A method has been developed to determine the angle-of-arrival (AoA) of incident radiation using pre-computed lookup tables. The phase difference between two receiving antennas can be used to infer AoA as measured from the pair baseline, but there will be more than one possible solution for antenna spacings greater than or equal to half a wavelength. Larger spacings are preferable to minimize mutual coupling of elements in the receive array and to decrease the relative uncertainty in measured phase difference. We present a solution that uses all unique antenna pairs to determine probabilities for all possible azimuth and zenith values. Prior to analysis, the expected phase differences for all AOAs are calculated for each antenna pair. For a received signal, histograms of possible AoAs for each antenna pair phase difference are extracted and added to produce a two dimensional probability density array that will maximize at the true value of the AoA. A benefit of this method is that all possible antenna pairs are utilized, rather than the restriction to specific pairs along baselines used by some interferometer algorithms. Numerical simulations indicate that performance of the suggested algorithm exceeds that of existing methods, with the benefit of additional flexibility in antenna placement. Meteor radar data has been used to test this method against existing methods, with excellent agreement between the two approaches. This method of AoA determination will allow the construction of low-cost interferometric direction finding arrays with different layouts, including construction of difficult terrain and three-dimensional antenna arrangements.

We present analysis methods for studying the structuring and motion of ionospheric irregularities at the sub-kilometer scale sizes that produce L-band scintillations. Spaced receiver methods are used for Global Navigation Satellite System (GNSS) receivers' phase measurements over approximately sub-kilometer to kilometer-length baselines for the first time. The quantities estimated by these techniques are: plasma drift velocity, diffraction anisotropy magnitude and orientation, and characteristic velocity. Uncertainties are quantified by ensemble simulation of noise on the phase signals carried through to the observations of the spaced-receiver linear system. These covariances are then propagated through to uncertainties on drifts through linearization about the estimated values of the state. Five receivers of the Scintillation Auroral Global Positioning System (GPS) Array (SAGA) provide 100 Hz power and phase data for each channel at L1 frequency. The array is sited in the auroral zone at Poker Flat Research Range, Alaska. A case study of a single scintillating satellite observed by the array is used to demonstrate the spaced receiver and uncertainty estimation process. A second case study estimates drifts as measured by multiple scintillating channels. These scintillations are correlated with auroral activity, based on all-sky camera images. Measurements and uncertainty estimates made over a 30-minute period are made and compared to a collocated incoherent scatter radar, and show good agreement in horizontal drift speed and direction during periods of scintillation for which the characteristic velocity is less than the drift velocity.

To understand the microphysical processes that impact diabatic heating and cloud lifetimes in convection, we need to characterize the spatial distribution of supercooled liquid water. To address this observational challenge, vertically pointing active sensors at the Darwin Atmospheric Radiation Measurement (ARM) site are used to classify cloud phase within a deep convective cloud in a shallow to deep convection transitional case. The cloud cannot be fully observed by a lidar due to signal attenuation. Thus we develop an objective method for identifying hydrometeor classes, including mixed-phase conditions, using k-means clustering on parameters that describe the shape of the Doppler spectra from vertically pointing Ka band cloud radar. This approach shows that multiple, overlapping mixed-phase layers exist within the cloud, rather than a single region of supercooled liquid, indicating complexity to how ice growth and diabatic heating occurs in the vertical structure of the cloud.

A novel operating mode for PFISR has been developed to support the study of GPS L1 scintillations arising from electron density gradients over Alaska. Previous authors have combined GPS scintillation data with either PFISR or multi-spectral imagery in this region. This analysis combines all three techniques to determine both gradient parameters as well as the most likely ionospheric source, including a conjunction analysis between GPS signals and known PFISR beams. This approach was used to study scintillation events observed in December 2015. These events were recorded during the recovery phase of a geomagnetic storm that ensued on December 20th when the Earth passed through the path of a solar CME (DST minimum near -170nT, Kp peak of 7). Two scintillation events were detected through analysis of the phase scintillation index (σϕ), calculated from an ASTRA CASES GPS receiver at Poker Flat. Values of σϕ≃0.5 and σϕ≃1.6 cycles were recorded for these events. Concurrently, an experiment on PFISR was run to discern the electron density along the GPS signal line of site; and, the imager observed the 428.7nm, 557.7nm and 630.0nm emission lines during the experiment window. From the ratios of derived emission intensities, estimates of precipitating particle energies were made allowing confirmation of the altitudes associated with the electron density peaks determined using raw radar returns from PFISR. Results indicate that temporal and spatial electron density gradients existed along the signal's path during periods of highest scintillation, which resulted from impact ionization from auroral particle precipitation at E region altitudes.

Incoherent scatter radar observations of ionospheric plasmas rely on echoes from electron density fluctuations with properties governed by the dispersion relations for ion-acoustic and Langmuir waves. Radar observations of echoes associated with Langmuir waves (plasma lines) from thermal plasma are weak and only a few near-thermal level measurements have been reported. Plasma line echoes are typically only observed with existing radars only when the Langmuir waves are enhanced by suprathermal electrons. A new observation technique has been developed which is sensitive enough to allow observations of these echoes without the presence of suprathermal electrons up to at least 1000 km. This paper presents recent observations from the Arecibo Observatory 430-MHz incoherent scatter radar which show plasma-line echoes during the night when no suprathermal enhancement is expected to be present. The observations are compared with theory, and the results are found to be in agreement with classical incoherent scatter theory for thermal plasmas. The theoretical ratio of the ion line and plasma line power spectral density is within approximately 3 dB of the predicted value. The finding adds a new observational capability, allowing electron density to also be observed at night using the plasma line well into the top side of the ionosphere, increasing the accuracy of the electron density measurement.

Despite marked progress over the past several decades, convective storm nowcasting remains a challenge because most nowcasting systems are based on linear extrapolation of radar reflectivity without much consideration for other meteorological fields. The variational Doppler radar analysis system (VDRAS) is an advanced convective-scale analysis system capable of providing analysis of 3-D wind, temperature, and humidity by assimilating Doppler radar observations. Although potentially useful, it is still an open question as to how to use these fields to improve nowcasting. In this study, we present results from our first attempt at developing a Support Vector Machine (SVM) Box-based nOWcasting (SBOW) method under the machine learning framework using VDRAS analysis data. The key design points of SBOW are as follows: 1) The study domain is divided into many position-fixed small boxes and the nowcasting problem is transformed into one question, i.e., will a radar echo > 35 dBZ appear in a box in 30 minutes? 2) Box-based temporal and spatial features, which include time trends and surrounding environmental information, are constructed; and 3) The box-based constructed features are used to first train the SVM classifier, and then the trained classifier is used to make predictions. Compared with complicated and expensive expert systems, the above design of SBOW allows the system to be small, compact, straightforward, and easy to maintain and expand at low cost. The experimental results show that, although no complicated tracking algorithm is used, SBOW can predict the storm movement trend and storm growth with reasonable skill.

Author(s): Huang, MH; Fielding, EJ; Dickinson, H; Sun, J; Gonzalez-Ortega, JA; Freed, AM; Burgmann, R | Abstract: The 4 April 2010 Mw 7.2 El Mayor-Cucapah (EMC) earthquake in Baja, California, and Sonora, Mexico, had primarily right-lateral strike-slip motion and a minor normal-slip component. The surface rupture extended about 120 km in a NW-SE direction, west of the Cerro Prieto fault. Here we use geodetic measurements including near- to far-field GPS, interferometric synthetic aperture radar (InSAR), and subpixel offset measurements of radar and optical images to characterize the fault slip during the EMC event. We use dislocation inversion methods and determine an optimal nine-segment fault geometry, as well as a subfault slip distribution from the geodetic measurements. With systematic perturbation of the fault dip angles, randomly removing one geodetic data constraint, or different data combinations, we are able to explore the robustness of the inferred slip distribution along fault strike and depth. The model fitting residuals imply contributions of early postseismic deformation to the InSAR measurements as well as lateral heterogeneity in the crustal elastic structure between the Peninsular Ranges and the Salton Trough. We also find that with incorporation of near-field geodetic data and finer fault patch size, the shallow slip deficit is reduced in the EMC event by reductions in the level of smoothing. These results show that the outcomes of coseismic inversions can vary greatly depending on model parameterization and methodology.

Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) radar sounder on board the Mars Express spacecraft revealed oblique reflections coming systematically from apparently stable dens ...

The high-power HF (High Frequency) facility (commonly known as Heating) near Tromso, Norway, which is an essential part of the EISCAT (European Incoherent SCATter) Scientific Association, has been upgraded in certain key areas in recent years. It is one of only four similar facilities in the world operating at present. An updated description of the facility is given, together with scientific motivation and some results. The main high-power parts such as transmitters, feed-system and antennas remain essentially the same as built in the late 1970s. The improvements are in the areas of radio frequency waveform generation, computer control and monitoring. In particular, fast stepping in frequency is now possible, an important aspect in examining features close to harmonics of the electron gyrofrequency. One antenna array has been modified to allow reception to implement an HF radar mode for mesospheric and magnetospheric probing. More realistic modelling of the antenna gain gives improved estimates of the total effective radiated power for both wanted and unwanted circular polarizations. Results are presented using these new capabilities, but their full scientific potential has yet to be achieved.

Ultrahigh frequency space-based synthetic aperture radar (SAR) can suffer from the degrading effects of a scintillating ionosphere which modulates both the phase and the amplitude of the radar signal. In this paper, we use Global Navigation Satellite System (GNSS) signals to synthesize an L-band SAR point spread function (PSF). The process of transforming the GNSS signal to the equivalent SAR PSF is described. The synthesized PSF is used to explore the possibility of using a phase correction determined from a point target in a SAR image to correct the ionospheric degradation. GNSS data recorded on equatorial Ascension Island during scintillation events are used to test the feasibility of this approach by applying a phase correction to one GNSS receiver from another located along a magnetic east-west baseline. The peak-to-sidelobe ratios of the synthesized L-band SAR point spread functions before and after the correction are compared, and it is shown that this approach improves the L-band PSF over distances of ~3000 m in the ionosphere, corresponding to 6000 m on the ground.

In this article a potential role of snowflake growth by aggregation on formation of dual-polarization radar signatures in winter storms is discussed. We advocate that the observed bands of increased values of specific differential phase (Kdp) can be linked to the onset of aggregation. These bands are caused by high number concentrations of oblate relatively dense ice particles and take place in regions where an ice phase “seeder-feeder” is active. On the other hand, the differential reflectivity (Zdr) bands, in absence of detectable Kdp values, are observed in the areas where crystal growth is the dominating snow growth mechanism and ice particle number concentration is lower. This distinction in underlying processes explains why Kdp and Zdr bands are not always observed at the same time. Furthermore, based on surface observations of snowflakes, it is determined that early aggregates, consisting of a small number of ice crystals, are oblate. These oblate particles could contribute to the reported dual-polarization radar signatures in snow, especially to the Kdp. This could help to explain why, where observed at the same type, Kdp and Zdr bands do not match and the altitude of the peak value of Kdp is usually lower than the Zdr one. It also means that dual-polarization radar signatures of snowflakes may depend on a stage of aggregation.

We propose that by using unmatched decoding for alternating codes, estimates of the autocorrelation function of the backscatter can be estimated at ranges between those resolved by matched filtering. The number of additional correlation estimates obtained in this way is twice that obtained using matched filtering, although each estimate contributes with only a quarter of the power in comparison. This leads to improvements in code performance by a factor of 1.5, and an increase in the speed of incoherent scatter radar experiments by the same factor.

We present three case studies that examine optical and radar methods for specifying precipitating auroral flux parameters and conductances. Three events were chosen corresponding to moderate nonsubstorm auroral activity with 557.7 nm intensities greater than 1kR. A technique that directly fits the electron number density from a forward electron transport model to alternating code incoherent scatter radar data is presented. A method for determining characteristic energy using neutral temperature observations is compared against estimates from the incoherent scatter radar. These techniques are focused on line-of-sight observations that are aligned with the local geomagnetic field. Good agreement is found between the optical and incoherent scatter radar methods for estimates of the average energy, energy flux, and conductances. The Pedersen conductance predicted by Robinson et al. (1987) is in very good agreement with estimates calculated from the incoherent scatter radar observations. However, we present an updated form of the relation by Robinson et al. (1987), ΣH/ΣP=0.57〈E〉0.53, which was found to be more consistent with the incoherent scatter radar observations. These results are limited to similar auroral configurations as in these case studies. Case studies are presented that quantify auroral electron flux parameters and conductance estimates which can be used to specify the magnitude of energy dissipated within the ionosphere resulting from magnetospheric driving.

Simultaneous derivation of multiple ionospheric parameters from the incoherent scatter power spectra in the F1 region is difficult because the spectra have only subtle differences for different combinations of parameters. In this study, we apply a particle swarm optimizer (PSO) to incoherent scatter power spectrum fitting and compare it to the commonly used least squares fitting (LSF) technique. The PSO method is found to outperform the LSF method in practically all scenarios using simulated data. The PSO method offers the advantages of not being sensitive to initial assumptions and allowing physical constraints to be easily built into the model. When simultaneously fitting for molecular ion fraction (fm), ion temperature (Ti), and ratio of ion to electron temperature (γT), γT is largely stable. The uncertainty between fm and Ti can be described as a quadratic relationship. The significance of this result is that Ti can be retroactively corrected for data archived many years ago where the assumption of fm may not be accurate, and the original power spectra are unavailable. In our discussion, we emphasize the fitting for fm, which is a difficult parameter to obtain. PSO method is often successful in obtaining fm, whereas LSF fails. We apply both PSO and LSF to actual observations made by the Arecibo incoherent scatter radar. The results show that PSO method is a viable method to simultaneously determine ion and electron temperatures and molecular ion fraction when the last is greater than 0.3.

Using long-term radar-based ground measurements from the Atmospheric Radiation Measurement Program, we derive the inhomogeneity of cloud liquid water as represented by the shape parameter of a gamma distribution. The relationship between the inhomogeneity and the model grid size as well as atmospheric condition is presented. A larger grid scale and more unstable atmosphere are associated with larger inhomogeneity that is described by a smaller shape parameter. This relationship is implemented as a scale-aware parameterization of the liquid cloud inhomogeneity in the Community Earth System Model (CESM) in which the shape parameter impacts the cloud microphysical processes. When used in the default CESM1 with the finite-volume dynamic core where a constant liquid inhomogeneity parameter was assumed, it reduces the cloud inhomogeneity in high latitudes and increases it in low latitudes. This is due to both the smaller (larger) grid size in high (low) latitudes in the longitude-latitude grid setting of CESM and the more stable (unstable) atmosphere. The single-column model and general circulation model sensitivity experiments show that the new parameterization increases the cloud liquid water path in polar regions and decreases it in low latitudes. An advantage of the parameterization is that it can recognize the spatial resolutions of the CESM without special tuning of the cloud water inhomogeneity parameter.

A study is presented of artificial ionospheric turbulence (AIT) induced by HF heating at High Frequency Active Auroral Research Program (HAARP) using frequencies close to the fourth electron gyroharmonic, in a broad range of radiated powers and using a number of different diagnostics. The diagnostics include GPS scintillations, ground-based stimulated electromagnetic emission (SEE), the HAARP ionosonde, Kodiak radar, and signals received at the Ukrainian Antarctic Station (UAS). The latter allowed analysis of waves scattered by the AIT into the ionospheric waveguide along Earth's terminator, 15.6 mm from the HAARP facility. For the first time, the amplitudes of two prominent SEE features, the downshifted maximum and broad upshifted maximum, were observed to saturate at ~50% of the maximum HAARP effective radiated power. Nonlinear effects in slant total electron content, SEE, and signals received at UAS at different transmitted frequencies and intensities of the pump wave were observed. The correlations between the data from different detectors demonstrate that the scattered waves reach UAS by the waveguide along the Earth's terminator, and that they were injected into the waveguide by scattering off of artificial striations produced by AIT above HAARP, rather than via direct injection from sidelobe radiation.