Low-frequency Channel Research Articles

An integrated dual-frequency IVUS system with interleaved sampling and parallel signal processing based on FPGA

Abstract Due to the lack of high voltage and low frequency pulse transmission channels, commercial high frequency IVUS imaging system cannot meet the hardware requirements of ultrasound angiography. Therefore, it is of great research significance to develop a dual-frequency IVUS rotary imaging system with high integration, high sampling rate and sampling accuracy. In this work, an integrated dual-frequency IVUS system with interleaved sampling and parallel signal processing based on FPGA was developed. The central frequency of low-frequency channel is up to 10MHz, and the peak voltage range is 80-200V. The central frequency of the high-frequency channel is up to 60MHz, and the voltage peak-to-peak range is 40-80V. The acquisition channel is 500MHz and 12bit, which is realized by interleaved sampling of two ADC. The motor controlled ultrasonic transducer acquires 400-800 lines per turn, and eventually form a 360-degree IVUS hybrid imaging successfully.

Cross-correlation Techniques to Mitigate the Interloper Contamination for Line Intensity Mapping Experiments

Line intensity mapping (LIM) serves as a potent probe in astrophysics, relying on the statistical analysis of integrated spectral line emissions originating from distant star-forming galaxies. While LIM observations hold the promise of achieving a broad spectrum of scientific objectives, a significant hurdle for future experiments lies in distinguishing the targeted spectral line emitted at a specific redshift from undesired line emissions originating at different redshifts. The presence of these interloping lines poses a challenge to the accuracy of cosmological analyses. In this study, we introduce a novel approach to quantify line–line cross-correlations (LIM-LLX), enabling us to investigate the target signal amid instrumental noise and interloping emissions. For example, at a redshift of z ∼ 3.7, we observed that the measured auto-power spectrum of C ii 158 exhibited substantial bias, from interloping line emission. However, cross-correlating C ii 158 with CO(6–5) lines using an FYST-like experiment yielded a promising result, with a signal-to-noise ratio of ∼10. This measurement is notably unbiased. Additionally, we explore the extensive capabilities of cross-correlation by leveraging various CO transitions to probe the tomographic Universe at lower redshifts through LIM-LLX. We further demonstrate that incorporating low-frequency channels, such as 90 and 150 GHz, into FYST’s EoR-Spec-like experiment can maximize the potential for cross-correlation studies, effectively reducing the bias introduced by instrumental noise and interlopers.

Dual-path dehazing network with spatial-frequency feature fusion

With rapid improvement of deep learning, significant progress has been made in image dehazing, leading to favorable outcomes in many methods. However, a common challenge arises as most of these methods struggle to restore intricate details with vibrant colors in complex haze. In response to this challenge, we present a novel dual-path dehazing network with spatial-frequency feature fusion (DDN-SFF) to remove heterogeneous haze. The proposed dual-path network consists of a spatial-domain vanilla path and a frequency-domain frequency-guided path, effectively harnessing spatial-frequency knowledge. To maximize the versatility of the learned features, we introduce a relaxation dense feature fusion (RDFF) module in the vanilla path. This module can skillfully re-exploit features from non-adjacent levels and concurrently generate new features. In the frequency-guided path, we integrate the discrete wavelet transform (DWT) and introduce a frequency attention (FA) mechanism for the flexible handling of specific channels. More precisely, we deploy a channel attention (CA) and a dense feature fusion (DFF) module for low-frequency channels, whereas a pixel attention (PA) and a residual dense block (RDB) module are implemented for high-frequency channels. In summary, the deep dual-path network fuses sub-bands with specific spatial-frequency features, effectively eliminating the haze and restoring intricate details along with rich textures. Extensive experimental results demonstrate the superior performance of the proposed DDN-SFF over state-of-the-art dehazing algorithms.

A Physics-Based Method for Retrieving Land Surface Emissivities from FengYun-3D Microwave Radiation Imager Data

The passive microwave land surface emissivity (MLSE) plays a crucial role in retrieving various land surface and atmospheric parameters and in Numerical Weather Prediction models. The retrieval accuracy of MLSE depends on many factors, including the consistency of the input data acquisition time. The FengYun-3D (FY-3D) polar-orbiting meteorological satellite, equipped with passive microwave and infrared bands, offers time-consistent data crucial for MLSE retrieval. This study proposes a physics-based MLSE retrieval algorithm using all the input data from the FY-3D satellite. Based on the retrieved MLSE, the spatial distribution of the MLSE is closely correlated with the land cover types and topography. Lower emissivities prevailed over barren or sparsely vegetated regions, river basins, and coastal areas. Higher emissivities dominated densely vegetated regions and mountainous areas. Moderate emissivities dominated grasslands and croplands. Lower-frequency channels showed larger emissivity differences with different polarizations than those of higher-frequency channels in barren or sparsely vegetated regions. The MLSE across densely vegetated land areas, mountainous areas, and deserts showed small seasonal variations. However, woody savannas, grasslands, croplands, and seasonal snow-covered areas showed noticeable seasonal variations. For most land cover types, the differences between vertically and horizontally polarized emissivities remained relatively constant across seasons. However, certain grasslands in eastern Inner Mongolia and southern Mongolia showed clear seasonal variations. It is very difficult to verify the MLSE on a large scale. Consequently, the possible error sources in the retrieved MLSE were analyzed, including the brightness temperature errors (correlation coefficient ranging from 0.92 to 0.99) and the retrieved land surface temperature errors (Root Mean Square Error was 3.34 K and relation coefficient was 0.958).

Leveraging a Two-Level Attention Mechanism for Deep Face Recognition with Siamese One-Shot Learning

Discriminative feature embedding is used for largescale facial recognition. Many image-based facial recognition networks use CNNs like ResNets and VGG-nets. Humans prioritise different elements, but CNNs treat all facial pictures equally. NLP and computer vision use attention to learn the most important part of an input signal. The inter-channel and inter-spatial attention mechanism is used to assess face image component significance in this study. Channel scalars are calculated using Global Average Pooling in face recognition channel attention. A recent study found that GAP encodes low-frequency channel information first. We compressed channels using discrete cosine transform (DCT) instead of scalar representation to evaluate information at frequencies other than the lowest frequency for the channel attention mechanism. Later layers can acquire the feature map after spatial attention. Channel and spatial attention increase CNN facial recognition feature extraction. Channel-only, spatial-only, parallel, sequential, or channel-after-spatial attention blocks exist. Current face recognition attention approaches may be outperformed on public datasets (Labelled Faces in the Wild).

Structural, optical and magnetic properties of a new metal-organic CoII-based complex.

A mononuclear cobalt(ii) complex [C5H8N3]2[CoCl4(C5H7N3)2] (I) was synthesized and structurally characterized. Single crystal X-ray diffraction analysis indicates that monometallic Co(ii) ions acted as coordination nodes in a distorted octahedral geometry, giving rise to a supramolecular architecture. The latter is made up of a ½ unit form composed of an anionic element [Co0.5Cl2(C5H7N3)]- and one 2-amino-4-methylpyrimidinium cation [C5H8N3]+. The crystalline arrangement of this compound adopts the sandwich form where inorganic parts are sandwiched between the organic sheets following the [100] direction. More information regarding the structure hierarchy has been supplied based on Hirshfeld surface analysis; the X⋯H (X = N, Cl) interactions play a crucial role in stabilizing the self-assembly process of I, complemented by the intervention of π⋯π electrostatic interaction created between organic entities. Thermal analyses were carried out to study the thermal behavior process. Static magnetic measurements and ab initio calculations of compound I revealed the easy-axis anisotropy character of the central Co(ii) ion. Two-channel field-induced slow-magnetic relaxation was observed; the high-frequency channel is characterized by underbarrier relaxation with U eff = 16.5 cm-1, and the low-frequency channel involves a direct relaxation process affected by the phonon-bottleneck effect.

Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

Sea ice concentration satellite retrievals influenced by surface changes due to warm air intrusions: A case study from the MOSAiC expedition

Warm air intrusions over Arctic sea ice can change the snow and ice surface conditions rapidly and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC. Here we focus on two warm moist air intrusions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition that reached the research vessel Polarstern in mid-April 2020. After the events, SIC deviations between different satellite products, including climate data records, were observed to increase. Especially, an underestimation of SIC for algorithms based on polarization difference was found. To examine the causes of this underestimation, we used the extensive MOSAiC snow and ice measurements to model computationally the brightness temperatures of the surface on a local scale. We further investigated the brightness temperatures observed by ground-based radiometers at frequencies 6.9 GHz, 19 GHz, and 89 GHz. We show that the drop in the retrieved SIC of some satellite products can be attributed to large-scale surface glazing, that is, the formation of a thin ice crust at the top of the snowpack, caused by the warming events. Another mechanism affecting satellite products, which are mainly based on gradient ratios of brightness temperatures, is the interplay of the changed temperature gradient in the snow with snow metamorphism. From the two analyzed climate data record products, we found that one was less affected by the warming events. The low frequency channels at 6.9 GHz were less sensitive to these snow surface changes, which could be exploited in future to obtain more accurate retrievals of sea ice concentration. Strong warm air intrusions are expected to become more frequent in future and thus their influence on SIC algorithms will increase. In order to provide consistent SIC datasets, their sensitivity to warm air intrusions needs to be addressed.

MFF-Net: A multitask feature fusion network in dual-frequency domains for detecting smoke from one single picture

The industrial smoke detection is extreme significant for sustainable development and human health, but up to date, its related technologies have not met the real-time and accurate requirements, particularly in the memory-constrained conditions. Towards detecting smoke from one single picture more accurate and efficient, in this paper we present a multitask feature fusion network, dubbed MFF-Net. To specify, according to the characteristics of imbalance found in the frequency distribution of numerous smoke pictures, our MFF-Net is set up with a dual-channel framework, which is composed of the high-frequency and low-frequency channels that process different frequency components respectively. The high-frequency channel is built based on a stream of cross-level fusion inverted residual (CFIR) blocks, each of which encompasses the expansion layer, filtering layer, combination layer, and fusion layer, while the low-frequency channel is formed by embedding the attention mechanism into CFIR blocks. We concatenate the dual channels mentioned above to establish the MFF-Net, in order to systematically fuse high- and low-frequency features of a single smoke picture. As compared with the existing relevant detection models, the depthwise and pointwise convolutions are utilized to make networks more efficient with respect to size and speed, while the dense skip connection is used behind to fuse cross-level features (i.e., semantic and visual features) for keeping high accuracy. Experimental results show that our proposed MFF-Net can trade off multiple tasks in accuracy, memory and computational efficiency.

Thermophysical properties of surface deposits in Tsiolkovskiy crater and its geologic significance revealed by CE-2 MRM data

Tsiolkovskiy crater presents one of the few mare deposits on the lunar farside, which are significant to understanding the basaltic magmatism of the Moon. In this paper, for the first time, the thermophysical properties of surface deposits in the Tsiolkovskiy crater were assessed with the brightness temperature (TB), normalized TB (nTB), and TB difference (dTB) maps derived from the Chang'E-2 microwave radiometer data combined with the topography, composition, and rock abundance maps. The main results are presented as follows. (1) The nTB is highly influenced by surface topography, particularly at daytime, as observed in high-frequency maps. However, the influence is limited in the nTB maps at low-frequency channels and dTB maps. (2) We postulate that the substrate temperature in the SW portion of the crater floor might be considerably higher than previously understood. (3) Our observations reveal the presence of mare deposits occurring in two distinct episodes within the mare unit. Notably, the younger mare unit is situated in the NE portion of the crater. (4) The centimeter-scale rocks should be widely distributed in the NE portion beyond the Tsiolkovskiy crater. These findings hold significant implications for advancing our comprehension of the Moon's thermal and basaltic evolutionary processes.

Evaluating and Improving TROPICS Millimeter‐Wave Sounder's Precipitation Estimate Over Ocean

AbstractThis paper examines precipitation retrievals from Kidd et al. (2022, https://doi.org/10.3390/rs14132992) by applying the Precipitation Retrieval and Profiling Scheme to data observed by the NASA Time‐Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter‐wave Sounder (TMS) onboard the TROPICS Pathfinder satellite, a precursor to the TROPICS mission. We first compare the TMS precipitation retrieval performance over ocean with those from seven current operational passive microwave radiometers, including four conical scanning sensors [Special Sensor Microwave Imager/Sounders (SSMISs) onboard F16/F17/F18 satellites, and Advanced Microwave Scanning Radiometer 2 (AMSR2)] and three cross track scanning sensors [Microwave Humidity Sounders onboard NOAA19 and MetOpB satellites, and the Advanced Technology Microwave Sounder onboard the National Polar‐orbiting Partnership satellite], using precipitation estimates from Global Precipitation Measurement Mission Microwave Imager (GMI) as the reference. We show that TMS performs similarly to the current operational cross track scanning sensors, while worse than conical scanning sensors largely due to the lack of low frequency channels (e.g., 19 GHz). Second, we create an adjusted TMS precipitation retrievals by combining the original TMS precipitation and the precipitation propagated from AMSR2 and SSMISs to the time/location of the TMS overpass, using our previously developed microwave‐to‐microwave propagation technique. Results show that the adjusted TMS precipitation performs better than the original TMS estimates. For example, the correlation of the original TMS and GMI estimates is 0.35 but increases to 0.49 when the adjusted TMS result is compared to GMI. The propagation technique lays the foundation to improve TMS precipitation when incorporating them into level‐3 merged products.

Blind receiver design-based multi-dimensional stable distribution model using the EM algorithm

Multi-antenna receivers are a key technology for modern communication systems. Signal attenuation in the very low frequency (VLF) channel is a serious problem. In addition to high signal attenuation, the resulting noise on the VLF channel is non-Gaussian. Hence, to analyze and address the issue of the normal operation of the multi-antenna receiver in the VLF channel, we must study the signal detection problem in a multi-dimensional non-Gaussian fading channel. Motivated by the existing blind receiver in an underwater submarine single-antenna receiver, we study the signal detection and estimation algorithm under the fading channel for a multi-dimensional non-Gaussian noise model. In this study, we propose a blind receiver based on the expectation-maximization (EM) algorithm. The proposed blind receiver can reduce non-Gaussian noise. In addition, we propose a nonlinear receiver that can accurately receive the transmitted signal over the high-attenuation VLF communication systems. Numerical and simulation results over uncorrelated and correlated non-Gaussian channels confirm that the design of the proposed blind receiver is close to optimal, with low computation complexity.

Performance Investigation of DP-16QAM Ultra-wideband- Wavelength-Division Multiplexing Communication System: Optimum Power Consideration

Recently, there is increasing interest in using the 18 THz bandwidth offered by S+C+L band to increase the transmission capacity of fiber communication systems. This leads to the generation of ultra-wideband (UWB) wavelength-division multiplexing (WDM) optical communication systems. In these advanced systems, stimulated Raman scattering (SRS) causes a power transfer from high-frequency channels to low-frequency channels. This effect leads to an increase in the nonlinear interference (NLI) between the UWB-WDM channels. Power optimization techniques are required to balance transfer power between band channels, thus increasing the maximum transmission reach (MTR) along with increasing system capacity. In this paper, the transmission performance of S+C+L band system operating with dual-polarization 16-QAM signaling is investigated using enhanced Gaussian noise model. The transmitter and receiver for each DP channel use a -polarized laser and incorporate two identical configurations, one for x- and the other for y-state of polarization (SOP). The results are presented for two values of symbol rate, 40 and 80 GBaud, where the system carries 360 (=160+80+120) and 180 (=80+40+60) channels, respectively. The results revel that the MTR of both cases is equal to 12 100 km-spans when the channel lunch power equals to -4 and -2 dBm, respectively. This work also shows the effect of NLI components as a function of the number of spans, channel spacing, and channel launch power. The results show that the cross-phase modulation component of the NLI has high accumulated value with transmission distance, while the self-phase modulation component is almost constant.

Synchrotron emission from virial shocks around stacked OVRO-LWA galaxy clusters

ABSTRACT Galaxy clusters accrete mass through large-scale, strong, structure-formation shocks. Such a virial shock is thought to deposit fractions ξe and ξB of the thermal energy in cosmic-ray electrons (CREs) and magnetic fields, respectively, thus generating a leptonic virial ring. However, the expected synchrotron signal was not convincingly established until now. We stack low-frequency radio data from the OVRO-LWA around the 44 most massive, high latitude, extended MCXC clusters, enhancing the ring sensitivity by rescaling clusters to their characteristic, R500 radii. Both high (73 MHz) and co-added low (36–68 MHz) frequency channels separately indicate a significant (4–5σ) excess peaked at (2.4–2.6)R500, coincident with a previously stacked Fermi γ-ray signal interpreted as inverse-Compton emission from virial-shock CREs. The stacked radio signal is well fit (TS-test: 4–6σ at high frequency, 4–8σ at low frequencies, and 8–10σ joint) by virial-shock synchrotron emission from the more massive clusters, with $\dot{m}\xi _e\xi _B\simeq (1\!-\!4)\times 10^{-4}$, where $\dot{m}\equiv \dot{M}/(MH)$ is the dimensionless accretion rate for a cluster of mass M and a Hubble constant H. The inferred CRE spectral index is flat, p ≃ 2.0 ± 0.2, consistent with acceleration in a strong shock. Assuming equipartition or using $\dot{m}\xi _e\sim 0.6~{{\ \rm per\ cent}}$ inferred from the Fermi signal yields $\xi _B\simeq (2\!-\!9)~{{\ \rm per\ cent}}$, corresponding to B ≃ (0.1–0.3) $\mu$G magnetic fields downstream of typical virial shocks. Preliminary evidence suggests non-spherical shocks, with factor 2–3 elongations.

Low light color balancing and denoising by machine learning based approximation for underwater images

It is difficult for underwater archaeologists to recover the fine details of a captured image on the seabed when the image quality worsens due to the presence of more noisy artefacts, a mismatched device colour map, and a blurry image. To resolve this problem, we present a machine learning-based image restoration model (ML-IRM) for improving the visual quality of underwater images that have been deteriorated. Using this model, a home-made bowl set-up is created in which a different liquid concentration is used to replicate seabed water variation, and an object is dipped, or a video is played behind the bowl to recognise the object texture captured image in high-resolution for training the image restoration model is proposed. Gaussian and bidirectional pre-processing filters are used to both the high and low frequency components of the training image, respectively. To improve the clarity of the high-frequency channel background, soft-thresholding decreases the presence of distracting artefacts. On the other hand, the ML-IRM model can effectively keep the object textures on a low frequency channel. Experiment findings show that the proposed ML-IRM model improves the quality of seabed images, eliminates colour mismatches, and allows for more detailed information extraction. Blue shadow, green shadow, hazy, and low light test samples are randomly selected from all five datasets including U45 [1], EUVP [2], DUIE [3], UIEB [4], UM-ImageNet [5], and the proposed model. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are computed for each condition separately. We list the values of PSNR (at 16.99 dB, 15.96 dB, 18.09 dB, 15.67 dB, 9.39 dB, 17.98 dB, 19.32 dB, 14.27 dB, 12.07 dB, and 25.47 dB) and SSIM (at 0.52, 0.57, 0.33, 0.47, 0.44, and 0.23, respectively. Similarly, it demonstrates that the proposed ML-IRM achieves a satisfactory result in terms of colour correction and contrast adjustment when applied to the problem of improving underwater images captured in low light. To do so, high-resolution images were captured in two low-light conditions (after 6 p.m. and again at 6 a.m.) for the training image datasets, and the results of their observations were compared to those of other existing state-of-the-art-methods.

Evaluation of Snowfall Retrieval Performance of GPM Constellation Radiometers Relative to Spaceborne Radars

Abstract This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.

Empirical mode decomposition and power spectrum filtering for detection of frequency channels related to multi-scale geochemical anomalies: Metal exploration targeting in Moalleman district, NE Iran

Geochemical anomaly patterns for metal exploration targeting usually reveal non-stationarity due mainly to the non-linear synthesis of ore-forming processes. The spectrum–area (S–A) multifractal filtering and the bidimensional empirical mode decomposition (BEMD) are robust methodologies for detecting geochemical anomalies with different scaling properties. The former portrays the generalized scale invariance characteristics of a geochemical image in the two-dimensional Fourier frequency domain. It relies on estimating scaling exponents of power-law functions in the spectral energy density plane to design filter functions that amplify a geochemical signal in specific frequency channels. The BEMD decomposes a geochemical image into a number of bidimensional intrinsic mode functions (BIMFs). It represents the spatial–frequency–energy distribution of a given image that suits non-stationary geochemical signals arising from non-linear Earth systems. This paper gauges the relevance of BEMD and S–A in signalizing frequency channels that correspond to regional- and local-scale geochemical anomalies through a comparative experiment in the Moalleman district, NE Iran. The BEMD filtered the multi-element anomaly map adaptively into a set of BIMFs (BIMF1, BIMF2, and BIMF3) and a residual signal, in order of decreasing frequency, yielding geochemical patterns with scaling characteristics from local to regional. The S–A model filtered the multi-element anomaly map into very high, high, and low frequency channels that characterize noise signatures and geochemical anomalies associated with local and regional ore-forming processes, respectively. The results indicate that the high frequency channel derived from the BEMD filtering (i.e., BIMF1) can portray more effectively local-scale metal-vectoring anomalies distributed at/around ore-controlling faults/fractures. However, the low frequency channel yielded by S–A filtering has enhanced effectively geochemical anomaly architectures that were developed through regional metallogenesis. Therefore, we considered the local-scale geochemical anomalies (i.e., BIMF1) to delimit metal-vectoring targets, whereby four exploration priorities were defined as new areas for further metal prospecting within the study area.

Meta-Learning for Beam Prediction in a Dual-Band Communication System

Large antenna arrays and beamforming are necessary for the mmWave communication system, resulting in heavy time and energy consumption in the beam training stage. Therefore, dual-band operations are expected to be deployed in future communication systems, where low-frequency channels are used to meet basic communication needs, and millimeter wave (mmWave) channels are exploited when the high-rate transmission is required. Existing works utilize deep learning methods to extract low-frequency channel state information (CSI) to reduce the mmWave beam training overheads. However, an important limitation of deep learning approaches is that the model is usually trained in a given environment. When employed in an unseen environment, it usually requires a large amount of data to retrain. In this paper, a model-agnostic optimization algorithm based on meta-learning is proposed to provide a general mmWave beam prediction model. This model can be deployed to edge base stations and effectively adapted to the environment without the need for a heavy collection of data. Simulation results demonstrate that the proposed approach could reduce the model adaptation overheads. The meta-learning-based beam prediction model is robust and achieves high prediction accuracy and spectral efficiency in different signal-to-noise ratio (SNR) regimes.

Sea Ice Motion Vector Retrievals from AMSR2 89-GHz Data: Validation Algorithm with Simultaneous Multichannel Observations

Abstract In retrievals of sea ice motion vectors (SIMVs) based on passive microwave observations, the use of the high-resolution 89-GHz channel of the Advanced Microwave Scanning Radiometer 2 (AMSR2) has the advantage of enhancing the theoretical precision of correlation-based motion tracking. However, its higher sensitivity to atmospheric moisture than lower-frequency channels links maximum cross-correlation peaks to outlier vectors and obscures signals of valid vectors. This study develops an algorithm to select valid vectors from candidates detected by multiple cross-correlation peaks based on validations with large-scale sea ice displacements extracted from 19- and 37-GHz data after questionable vectors are prefiltered by comparing them with reanalysis surface wind and neighboring vectors. The algorithm selects a vector corresponding to large-scale motion as the optimal vector. The retrieved results from 2013 to 2020 show that by replacing outlier vectors with valid ones detected by second or third cross-correlation peaks, validation with simultaneous observations enables retrieval of more than 60% of the Arctic motion field from 89-GHz data in winter but only 10% in summer; therefore, lower-frequency data are employed for retrievals. The uncertainty assessment using in situ data from acoustic measurements from ocean moorings shows that the algorithm provides daily SIMVs with root-mean-square errors of only 1–2 cm s–1 in idealized winter conditions with the absence of diurnal brightness temperature (Tb) changes that make tracking of the similarity of Tb fields difficult. The analysis also illustrates the applicability limit of the algorithm for summer retrievals. Significance Statement An algorithm was developed to validate sea ice motion vectors retrieved from AMSR2 89-GHz data by those from lower-frequency data. The validation via simultaneous observations enabled that valid vectors are sorted from invalid ones resulting from weather effects.

Validation of IMERG Oceanic Precipitation over Kwajalein

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellite-based retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B to V06B. IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR + morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG.