Earth and Space Science最新文献

Mesoscale convective systems (MCSs) that occur in the Baiu frontal zone (BFZ) can cause devastating flash floods during early summer in Japan; however, the environmental conditions necessary for their development require further investigation. High-frequency atmospheric soundings, conducted using multiple marine vessels in the East China Sea on 19 June 2022, captured the detailed environmental conditions pertaining to the development of an MCS within the BFZ. The MCS, which developed rapidly without any remarkable preceding synoptic or mesoscale disturbance in the mid- or upper troposphere, caused intense precipitation exceeding 80 mm/hr. The MCS persisted for approximately 6 hr, and it intensified when the influx of nearly saturated air near the sea surface toward a weak surface front overlapped with the influx of free-tropospheric moist air. The influx of nearly saturated air near the sea surface ensured conditional instability within the lower troposphere. The influx of moist air in the free troposphere contributed to the near-saturation conditions above the boundary layer, a feature inherent to the BFZ, and played an important role in minimizing the reduction in the buoyancy of air parcels. The results of this study indicate that a better forecast of the horizontal distribution of free tropospheric moist air is beneficial for limiting the potential area of genesis of MCS in the BFZ, and a more comprehensive understanding of the vertical variations in moisture transport contributes to an improved forecast skill for MCS in the BFZ.

We explore the effects of lower thermospheric water vapor deposited by launch vehicle plumes on polar mesospheric cloud (PMC) frequencies at 80°N. We use July-averaged PMC frequencies from 2007 to 2022 from the Cloud Imaging and Particle Size (CIPS) instrument on NASA's Aeronomy of Ice in the Mesosphere (AIM) satellite. Launch sites worldwide are typically located near northern mid-latitudes. Using the orbital launch record for the same time period, we find that the number of launches correlates with PMC frequencies with a coefficient of r = 0.60, which increases to r = 0.75 when only selecting launches from 2.5 to 21.5 local time (LT), indicating a weak LT dependence on global-scale transport to 80°N. To support our findings, we use meridional winds from the Michelson Interferometer for Global High-resolution Imaging experiment on NASA's Ionospheric Connection Explorer satellite and winds from the Horizontal Wind Model climatology to interpret the northward motion of air parcels at 105 km. We find the launch LT window that maximizes the correlation coefficient to be consistent with the expected maximum northward motion from the diurnal variation of mid-latitude meridional winds. Comparisons with Microwave Limb Sounder satellite observations of upper mesospheric temperature and water vapor reveal a strong dependence of cloud frequency on water vapor (r = 0.86) but not on temperature (r = −0.26), indicating that water vapor is the primary source of PMC variability for the bright PMCs at 80°N. We therefore find that launch vehicle plumes originating primarily from northern mid-latitudes modulate PMC frequency at 80°N in July.

In recent years, synthetic aperture radar (SAR) interferometry (InSAR) has emerged as a valuable tool for measuring water level change (WLC) to study hydrodynamic processes in coastal wetlands. However, the highly dynamic wet atmosphere conditions common in these areas have a significant impact on InSAR observations, producing errors in the derived values. Standard methods for estimating atmospheric noise in InSAR time series lack the spatial or temporal resolution needed to adequately correct for wet tropospheric delays. In this study, we utilize the Independent Component Analysis (ICA) signal decomposition technique to identify the likely WLC signal and eliminate atmospheric noise in a time series derived from rapid repeat measurements made with the L-band uninhabited aerial vehicle synthetic aperture radar airborne instrument. The method compares in-situ water level measurements with the independent components (IC) to identify the ICA components corresponding to WLC. The signal-to-noise ratio between the WLC after the ICA-based filtering and in situ water level gauges used for validation reaches 16 dB compared to an average of 2.6 dB before filtering. The excluded IC are used to generate maps showing a time series of likely atmospheric features. The identified features in the maps generally correspond to atmospheric features identifiable in Next Generation Weather Radar (NEXRAD) S-band ground weather radar reflectivity maps collected during the SAR acquisitions. The method is sufficiently general to be applied to any InSAR-derived surface displacement time series.

The Mars Reconnaissance Orbiter and its Context Camera (CTX) have acquired more than 100,000 separate panchromatic images that capture nearly the entire surface of Mars at ∼5–6 m/pixel. This paper describes a data processing workflow used to generate the first contiguous global mosaic of CTX data, which represents a large improvement in spatial resolution over existing 100 m/pixel contiguous global mosaics. We describe the overarching strategy for the mosaic's construction, which was to maximize the scientific utility of a continuous mosaic that is 5.7 trillion pixels in size. The pipeline used for data processing prioritized traceability and reproducibility of the final mosaic, such that the provenance of all pixels is reported, equipping scientists with information to differentiate mosaic artifacts from surface landforms and to incorporate critical image metadata into their analyses. The CTX data set synthesized into a global CTX mosaic facilitates ready analysis and provides a new capability in transitioning global studies of Mars from high-resolution investigations of individual images to systematic studies of the entire Martian surface at outcrop-resolving quality without regard to image boundaries.

In the field of coastal geomorphology, advancements in space technology have revolutionized coastal mapping and understanding. Satellite-derived bathymetry (SDB) research has progressed, focusing on various estimation methods using high-resolution satellite imagery and in-situ data. Challenges arise when applying these methods to the Indian coastline due to its turbid waters and intricate features such as creeks and deltas, laden with sediment and submerged rocks. A study aims to assess multivariate machine learning (ML) regression techniques for estimating bathymetric data. The study employs ground-truth data and imagery from Aster, Landsat-8, and Sentinel-2 at distinct sites known for complex underwater landscapes. Several algorithms including Multiple Linear Regression, Support Vector Regressor, Gaussian Process Regression (GPR), Decision Tree Regression, K-Neighbors Regressor, k-fold cross-validation with Decision Tree Regression, and Random Forest (RF) are evaluated for SDB. Results from the Vengurla Site show that using the Landsat-8 data set with the GPR algorithm achieves R² 0.94, root mean squared error (RMSE) 1.53 m, and MAE 1.14 m, utilizing visible spectrum bands. At Mormugao, the Sentinel-2 data set with GPR and RF algorithms attains R² 0.97 and RMSE 1.23 m, with GPR outperforming RF, having an MAE of 1.05 m compared to RF's 1.22 m. This study underscores the potential of ML regression techniques in overcoming challenges with using SDB for mapping coastal geomorphology, particularly in intricate underwater terrains and turbid waters by assimilating sophisticated algorithms and their refined cartographic representation.

Tropical cyclone (TC) intensification is influenced by environmental conditions, inner-core dynamics, and interactions with upper-ocean layers. Rapid intensification (RI) is a significant threat that is difficult to predict, prompting multiple institutions to collaborate. However, the accuracy still needs further improvements. It is well-known that a warm upper ocean is conducive to RI, but the role of salinity stratification in this process is not well understood, particularly under different TC translation speeds. This study reveals that rapidly intensifying TCs are related to large salinity stratification, especially when TC moves slowly. To develop a predictive model, several machine learning (ML) algorithms are used, with the most appropriate parameters and weights for each algorithm being determined. Our final ML model, which incorporates salinity stratification as a predictor and TC translation speed as a weight parameter, demonstrates superior performance across various predictive metrics, including the probability of detection (POD), false alarm ratio (FAR), and Peirce Skill Score (PSS) over the Western North Pacific during 2004–2022 compared to the model without these two factors. The most significant enhancement is observed for intense RI episodes. The improvements are up to 14% for both in POD; 7% and 13% in FAR; and 19% and 16% in PSS for 12.75 and 15.3 m s⁻¹ RI thresholds, respectively. These results highlight the importance of including salinity stratification as a new predictor and TC translation speed as a weighted parameter using ML techniques in RI prediction models.

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large-scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half-hourly precipitation in northeastern China. We assess it by compared to operational near-real-time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite-derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research.

China's first Mars sampling return mission (Tianwen-3) is designed to launch and retrieve samples around 2030. Three tentative landing areas (TLAs) (Amazonis, Chryse and Utopia Planitiae, i.e., TLA-A, TLA-C and TLA-U) are selected based on elevation <−2,000 m and latitude between 17° and 30°N. As a dominant feature of Martian meteorology, dust storms manifest in all seasons and affect the accuracy and safety of Mars exploration missions. Tianwen-3's landing, sampling and ascent phases are in the dust storm season. Therefore, analyzing dust storm activity around landing areas is significant for the Tianwen-3 mission. According to Mars Daily Global Maps taken by Mars Orbiter Camera spanning Martian years 24–28, 2,476 dust storm events around the three TLAs were identified in this research. Dust storm temporal probabilities within TLA-A, TLA-C and TLA-U were calculated as 0%–44.69%, 0%–66.29% and 0%–33.64%, separately. Dust storm spatial probabilities around the TLA-A, TLA-C and TLA-U were computed, with ranges of 0%–10.71%, 0%–14.55% and 0%–19.96% during the T₁ period (Ls = 161–309°), and 0%–6.75%, 0%–7.65% and 0%–8.26% during the T₂ period (Ls = 342-55°), respectively. Finally, considering the temporal and spatial distribution of dust storms, we recommend the T₂ period as the launch scenario. Three safe periods (Ls = 2–18°, 4–12°, and 356–4°) were assigned for the entry-descent-landing (EDL) phase, along with one period (Ls = 45–55°) for the take-off and ascent phase. Five circular landing zones with dust storm spatial probability <3% were selected for the Tianwen-3 mission.

Drought is associated with adverse environmental and societal impacts across various regions. Therefore, drought monitoring based on a single variable may lead to unreliable information, especially about the onset and persistence of drought. Previous studies show vapor pressure deficit (VPD) data can detect drought onset earlier than other drought indicators such as precipitation. On the other hand, soil moisture (SM) is a robust indicator for assessing drought persistence. This study introduces a nonparametric multivariate drought index Vapor Pressure Deficit Soil moisture standardized Drought Index (VPDSDI) which is developed by combining VPD with SM information. The performance of the multivariate index in terms of drought onset detection is compared with the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) for six major drought events across the United States including three rapidly developing drought events (this term refers to flash droughts that develop on monthly scales) and three conventional drought events. Additionally, the performance of the proposed index in detecting drought persistence is compared with the Standardized Soil moisture Index (SSI), which is an agricultural drought index. Results indicate the multivariate index detects drought onset always earlier than SPI for conventional events, but VPDSDI detects drought onset earlier than or about the same time as SPEI for rapidly developing droughts. In terms of persistence, VPDSDI detects persistence almost identical to SSI for both rapidly developing and conventional drought events. The results also show that combining VPD with SM reduces the high variability of VPD and produces a smoother index which improves the onset and persistence detection of drought events leveraging VPD and SM information.

The particle size of sediments below the seabed is a crucial factor affecting the formation and enrichment of gas hydrates. Apart from the formation and enrichment law of gas hydrate in coarse-grained sediments (dominated by a sandy-sized fraction), in the fine-grained sediments (<62.5 μm) which accounts for more than 90% of offshore gas hydrate resources globally, the control effect of sediment particle size on gas hydrate is still unclear. Therefore, understanding the relationship between the fine-grained sediment particle size and gas hydrate enrichment is essential for revealing the global distribution and dynamic evolution of gas hydrates. Here, we analyzed the vertical gas hydrate saturation, particle size parameters of sediments, whole-rock minerals, and clay mineral components based on drilling data and sediment samples from fine-grained gas hydrate reservoirs (GHRs) in the Shenhu area of the northern South China Sea. The results show that in fine-grained sediments, the coarse particles cannot improve the reservoir quality or enrich the gas hydrate because many fine particles fill the intergranular pores formed by the coarse particles. Meanwhile, the fine particles were dominated by clay minerals, especially in the illite/smectite mixed layer, which significantly reduced the permeability of the sediment layer and was not conducive to the enrichment of gas hydrates. Moreover, sedimentary processes directly control the sediment particle size and mineral composition, which play an essential role in controlling GHRs at the macroscale. In the fine-grained sediments, very fine sediments (<8 μm) have a more significant negative impact on gas hydrate enrichment.