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The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

Change detection based on remote sensing images plays an important role in the field of remote sensing analysis, and it has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. In recent years, it has aroused widespread interest due to the explosive development of artificial intelligence (AI) technology, and change detection algorithms based on deep learning frameworks have made it possible to detect more delicate changes (such as the alteration of small buildings) with the help of huge amounts of remote sensing data, especially high-resolution (HR) data. Although there are many methods, we still lack a deep review of the recent progress concerning the latest deep learning methods in change detection. To this end, the main purpose of this paper is to provide a review of the available deep learning-based change detection algorithms using HR remote sensing images. The paper first describes the change detection framework and classifies the methods from the perspective of the deep network architectures adopted. Then, we review the latest progress in the application of deep learning in various granularity structures for change detection. Further, the paper provides a summary of HR datasets derived from different sensors, along with information related to change detection, for the potential use of researchers. Simultaneously, representative evaluation metrics for this task are investigated. Finally, a conclusion of the challenges for change detection using HR remote sensing images, which must be dealt with in order to improve the model’s performance, is presented. In addition, we put forward promising directions for future research in this area.

Purpose A high‐energy‐resolution whole‐body SPECT‐CT device (NM/CT 870 CZT; C‐SPECT) equipped with a CZT detector has been developed and is being used clinically. A MEHRS collimator has also been developed recently, with an expected improvement in imaging accuracy using medium‐energy radionuclides. The objective of this study was to compare and analyze the accuracies of the following devices: a WEHR collimator and the MEHRS collimator installed on a C‐SPECT, and a NaI scintillation detector‐equipped Anger‐type SPECT (A‐SPECT) scanner, with a LEHR and LMEGP. Methods A line phantom was used to measure the energy resolutions including collimator characteristics in the planar acquisition of each device using 99mTc and 123I. We also measured the system's sensitivity and high‐contrast resolution using a lead bar phantom. We evaluated SPECT spatial resolution, high‐contrast resolution, radioactivity concentration linearity, and homogeneity, using a basic performance evaluation phantom. In addition, the effect of scatter correction was evaluated by varying the sub window (SW) employed for scattering correction. Results The energy resolution with 99mTc was 5.6% in C‐SPECT with WEHR and 9.9% in A‐SPECT with LEHR. Using 123I, the results were 9.1% in C‐SPECT with WEHR, 5.5% in C‐SPECT with MEHRS, and 10.4% in A‐SPECT with LMEGP. The planar spatial resolution was similar under all conditions, but C‐SPECT performed better in SPECT acquisition. High‐contrast resolution was improved in C‐SPECT under planar condition and SPECT. The sensitivity and homogeneity were improved by setting the SW for scattering correction to 3% of the main peak in C‐SPECT. Conclusion C‐SPECT demonstrates excellent energy resolution and improved high‐contrast resolution for each radionuclide. In addition, when using 123I, careful attention should be paid to SW for scatter correction. By setting the appropriate SW, C‐SPECT with MEHRS has an excellent scattered ray removal effect, and highly homogenous imaging is possible while maintaining the high‐contrast resolution.

High-resolution hyperspectral imaging is becoming indispensable, enabling the precise detection of spectral variations across complex, spatially intricate targets. However, despite these significant benefits, currently available high-resolution set-ups are typically prohibitively expensive, significantly limiting their user base and accessibility. These limitations can have wider implications, limiting data collection opportunities, and therefore our knowledge, across a wide range of environments. In this article we introduce a low-cost alternative to the currently available instrumentation. This instrument provides hyperspectral datasets capable of resolving spectral variations in mm-scale targets, that cannot typically be resolved with many existing low-cost hyperspectral imaging alternatives. Instrument metrology is provided, and its efficacy is demonstrated within a mineralogy-based environmental monitoring application highlighting it as a valuable addition to the field of low-cost hyperspectral imaging.

Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

High‐resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high‐resolution (approximately 3 km grid‐spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic‐scale pattern and thereby to the steering flow. Our results suggest that optimizing small‐scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (∼10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high‐resolution models, and its often overlooked role in affecting larger‐scale circulations and hurricane track prediction. Plain Language Summary High‐resolution models (approximately 3 km grid spacing or finer) covering a large domain are emerging powerful tools for hurricane prediction. However, the use of high resolution in the model can be a double‐edged sword—while it helps improve hurricane intensity prediction, it can also make the model more prone to develop errors in the prediction of steering flow and hurricane tracks due to the possible impact of prevalent small‐scale features resolved by the model. Our results suggest that regulating small‐scale convection activity in a high‐resolution model can significantly improve hurricane track predictions at days 4 and 5. Key Points Better regulation of explicit convection reduces North Atlantic hurricane track errors in a high‐resolution model by 10% at days 4 and 5 Improved track forecasts are related to a more realistic representation of the North Atlantic subtropical ridge Explicit convection is modulated by implicit diffusivity in the model's advection scheme

Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.

Atmospheric blocking is a key dynamical phenomenon in the mid‐ and high latitudes, able to drive day‐to‐day weather changes and meteorological extremes such as heatwaves, droughts and cold waves. Current global circulation models struggle to fully capture observed blocking frequencies, likely because of their coarse horizontal resolution. Here we use convection permitting, nested idealized model simulations for quantifying changes in blocking frequency and Rossby wave breaking compared to a coarser resolution reference. We find an increase in blocking frequency poleward and downstream of the area with increased resolution, while the exact regions depend on the blocking index. These changes are probably due to a more accurate representation of small‐scale processes such as diabatic heating, which affect Rossby wave breaking and blocking formation downstream. Our results thus suggest an improved representation of blocking in the next generation of high‐resolution global climate models. Plain Language Summary Atmospheric blocking is a persistent weather pattern associated with high‐pressure anomalies that is able to drive meteorological extremes such as heatwaves and drought in summer, and cold waves in winter. Having blocking well represented in state‐of‐the‐art climate models is of paramount importance, however these models fail in simulating the frequency of blocking events, likely because their grid resolution is not high enough for resolving small scale physical processes important for the development of blocking episodes. Here we use very‐high resolution model simulations for quantifying blocking frequencies and the mechanisms driving these episodes. Our simulations are idealized, in the sense that they do not fully represent the Earth's system but allow us to focus on key physical mechanisms driving the blocking events. Our results show that using a very‐high resolution enhances blocking frequencies when compared to a lower resolution grid. The findings point toward the importance that unresolved physical processes play in generating blocking events that can only be simulated at very‐high resolution and can be of importance for the next generation of climate models. Key Points Blocking frequency increases downstream and poleward of sea‐surface temperature front with convection permitting atmospheric resolution The specific region of increased blocking depends on the blocking index Changes in diabatic heating and Rossby wave breaking play a fundamental role for the blocking enhancement

Diffraction and spectroscopy instruments using a filtering process with several analyser crystals have existed for about 30 years at synchrotron radiation sources, but they are difficult to use on laboratory sources. Several diffraction multi‐filtering systems for powder diffraction experiments have been studied and optimized, in order to show the relevance, simplicity and efficiency of their implementation. Optical filter systems containing one or many diffracting elements, precisely positioned in a rigid manner on a logarithmic spiral surface and having a stability that allows high resolution and high sensitivity to powder diffraction experiments, have been developed. After having tested prototypes with various geometries, we present in particular the realization of a small rigid‐compact multi‐analyser comb that allows 20–50 measurements on synchrotron radiation sources to be filtered in parallel, but also and especially that can be adapted on laboratory X‐ray sources (Ag Kα1) to increase by an order of magnitude the intensities and resolutions of the measurements. Such a rigid‐compact multi‐analyser block can advantageously be associated with `photon‐counting' 1D and 2D detectors in order to drastically improve the detection thresholds of powder diffraction measurements to better than 0.1%, which allows the detection/quantification/analysis of minor phases in studies of `real' complex materials. Principles and implementation of a compact‐rigid multi‐analyser for energy and angle filtering of high‐resolution X‐ray experiments are presented. Several systems containing one or many diffracting elements positioned in a rigid manner on a logarithmic spiral surface are discussed and developed in order to detect/analyze minor phases of complex materials.