Earth and Space Science最新文献

There are many problems in space debris monitoring with ground-based telescopes, such as too many stars in the same field of view, uneven background and optical distortion in the optical system. We propose a two-stage weak debris detection algorithm. In the first stage, wavelet transform is used to extract different components of three frames of images, and the median of corresponding components of the images is taken respectively to eliminate the influence of stars. In the second stage, an improved version of the faint space target extraction based on principal component analysis. The algorithm uses a smooth-detection idea to extract target information. Based on a 150 mm aperture telescope, we improved the existing method of faint space debris extraction based on principal component analysis by introducing the smooth-detection idea, and transformed the target detection problem into the separation problem of sparse matrix and low-rank matrix. We applied a certain preprocessing consisting of wavelet-based star removal and median pre-filtering to keep as little noise and other contaminants as possible. After experimental measurements by observers, the algorithm demonstrated advanced detection capabilities on multiple indicators.

The estimation of the wet refractivity indices is crucial for applications like weather predictions or improving the accuracy of real-time positioning techniques. Traditionally, solving the inverse tomography problem to estimate these atmospheric parameters has been challenging due to its ill-posed nature and high computational demands, necessitating additional constraints. To overcome these challenges, the data assimilation method is proposed here to integrate Global Navigation Satellite System (GNSS) observations into a background model. In this study, the Ensemble Kalman Filter (EnKF) was served as the assimilation core to reduce the computational load and to enable the epoch-wise estimation of wet refractivity indices. The Global Pressure and Temperature 3 (GPT3w) model was utilized as the background, and wet refractivity indices at each epoch were transformed into B-spline coefficients, representing state vector parameters. Subsequently, GNSS derived zenith wet delay (ZWD) values were integrated into the model using the EnKF method. The study's region encompassed the western parts of Europe and incorporated approximately 893 GNSS stations. Evaluation spanned from 1 January 2017 to 31 December 2017. The estimated wet refractivity indices from the proposed method were compared with observations from 16 existing radiosonde stations, radio occultation data, and ZWD values from the 47 selected GNSS test stations. Additionally, calculated ZWD values, resulting from the integration of wet refractivity indices, were compared to the ZWD values from 47 test stations in the study region. The numerical results demonstrated that the proposed method achieved a root mean square error value of approximately 2.6 ppm, which was nearly 49% and 18% lower than that of the considered empirical and numerical atmospheric models, respectively.

The stochastic EXtended finite-fault ground-motion SIMulation algorithm (EXSIM) has been widely applied in simulating and predicting broadband strong ground-motion. However, an increasingly number of researchers have found that EXSIM may overestimate ground-motions at low frequencies for some large-magnitude earthquakes and/or thrust earthquakes, for which the far-field source model has been explained by a double-corner-frequency model. Despite controversy, the double-corner-frequency model is now being accepted as one of the main categories of the far-field source model. This study demonstrated the limited applicability of EXSIM to earthquakes explained by the double-corner-frequency source model, by presenting the equivalence between motions generated by EXSIM and those generated by EXSIM's point-source version, SMSIM, which adopts the ω-square single-corner-frequency model. Furthermore, two improvements to EXSIM have been proposed: (a) the incorporation of the asperity-distributed stress-drop compound faults model and (b) the hybrid application of EXSIM with the proposed model. The effects of the two improvements have been verified by comparing EXSIM-generating motions with recorded ground-motions for the 2013 M_w 6.7 Lushan thrust earthquake. Significantly, consistent simulation accuracy has been achieved across high- and low-frequency bands as well as in far- and near-fields. The consistent accuracy of the improved EXSIM in simulating high- and low-frequency ground motions enables its direct and independent application to broadband ground motion simulations. Moreover, the first validation of this consistent accuracy in both near- and far-field scenarios further enhances its application in earthquake engineering practices.

In the past few years, the remarkable progress of commercially operated spacecrafts, the success with reusable rocket engines, as well as the international competition to explore space, has led to a substantial acceleration of activities in the design and preparation of ambitious future lunar missions. In the search for ice and/or cavities imaging the shallow subsurface structure is of vital importance. Hereby, previous studies have shown that seismic interferometry is a promising method to investigate the subsurface properties from passive lunar data. In this study, we want to evaluate the potential of this method further by examining the required duration of seismic measurements and the influence of scattering on the Green's function retrieval. Therefore, we applied seismic interferometry to both measured Apollo 17 data and synthetic data. Our findings indicate that, under optimal conditions, a few hours of data are sufficient when using the method of time-scaled phase-weighted stack (ts-PWS). However, this strongly depends on the inter-station distance, the orientation toward the principal noise sources, and the timing of the measurement during the lunar cycle. Additionally, we were able to reproduce the measured data using numerical simulations in 2D. The synthetic results show that scattering effects clearly influence the Green's function extraction, especially for larger station distances.

Observational data inherently contain noise which manifests as uncertainties in the measured parameters and creates positive biases or noise floors in second-order products like variances, fluxes, and spectra. Historical methods estimate and subsequently subtract noise floors, but struggle with accuracy. Gardner and Chu (2020, doi.org/10.1364/AO.400375) proposed an interleaved data processing method, which inherently eliminates biases from variances and fluxes, and suggested that the method could also eliminate noise floors of power spectra. We investigate the interleaved method for spectral analysis of atmospheric waves through theoretical studies, forward modeling, and demonstration with lidar data. Our work shows that calculating the cross-power spectral density (CPSD) from two interleaved subsamples does reduce the spectral noise floor significantly. However, only the Co-PSD (the real part of CPSD) eliminates the noise floor completely, while taking the absolute magnitude of CPSD adds a reduced noise floor back to the spectrum when the sample number is finite. This reduced noise floor can be further minimized through averaging over more observations, completely different from traditional spectrum calculations whose noise floor cannot be reduced by incorporating more samples. We demonstrate the first application of the interleaved method to spectral data, successfully eliminating the noise floor using the Co-PSD in a forward model and in lidar observations of the vertical wavenumber of gravity waves at McMurdo, Antarctica. This high accuracy is gained by sacrificing precision due to photon-count splitting, requiring additional observations to counter this effect. We provide quantitative assessment of accuracy and precision as well as application recommendations.

The long-wavelength correction (LWC) of SWOT data is intended to reduce errors related to the stability of the SWOT antenna and its attitude in orbit. The algorithms used to compute the LWC utilize SWOT KaRIn sea surface-height (SSH) measurements and additional data, and the LWC may absorb geophysical SSH into the correction. Different LWC algorithms are used on the L2 and L3 SWOT products, which are analyzed here during the 1 day repeat (Cal/Val) mission phase lasting approximately 100 days. During this mission phase the SSH anomaly (SSHA) computed using the L3 LWC is much more realistic than the L2 LWC, as shown here by comparing spatial statistics of the L2 and L3 products. The L3 LWC algorithm is nonlinear insofar as it depends on second-order statistics of the SSHA and multi-satellite SSHA differences, making it difficult to quantify the extent to which it could absorb baroclinic tidal signals. To overcome this difficulty, a proxy L3 LWC algorithm is developed which mimics the L3 LWC but is strictly linear in the SSHA. The proxy LWC is applied to both idealized waveforms and to the predicted internal tide available on the products, and it is found to absorb 1% or less of the signal variance, leading to corresponding pointwise errors of 10% or less. Because the errors are at longer wavelengths and are significantly smaller amplitude than internal tide signals, the LWC impact on the measurement and interpretation of internal tides with SWOT is expected to be negligible in most applications.

The Trans-Alboran Shear Zone is one of the most seismically active areas in the westernmost Mediterranean, where a wide variety of tectonic domains have developed within the context of oblique convergence between Eurasia and Africa plates. In this region, earthquakes occur close to seismogenic structures, some of them large enough to cause damaging events. In addition, the diversity of tectonic domains implies a lateral variation of seismic wave propagation, which could affect the hypocenter reliability if not addressed during the location procedure. We present mTAB3D, a new 3D P-wave velocity created after data collection, geometry modeling and velocity estimation in our study area. To test this model, we used arrival times from the Spanish Seismic Network catalog and performed two non-linear absolute hypocenter inversions: the first comprises all the seismicity detected during 2018–2022 in the Eastern Betics Shear Zone; the second one consists of the earthquakes recorded during the Al-Hoceima seismic sequence (2016). We compare our results against hypocenters computed with a 1D velocity model of the region (mIGN1D) and observe that mTAB3D achieves better clustering near active structures and lower epicentral uncertainties (up to 11% lower). Moreover, hypocenters obtained with mTAB3D show notable reliability even in scenarios of a low azimuthal gap, such as the 2016 Al-Hoceima sequence. The new catalogs computed with our model help us to infer possible genetic relations between seismicity and source faults within our study area and can be used as an additional tool when looking into prior seismic sequences.

Antarctic sea ice, a key component in the complex Antarctic climate system, is an important driver and indicator of the global climate. In the relatively short satellite-observed period from 1979 to 2022 the sea ice extent has continuously increased (contrasting a major decrease in Arctic sea ice) up to a dramatic decrease between 2014 and 2017. Recent years have seen record sea ice lows in February 2022–February 2023. We use a statistical ensemble reconstruction of Antarctic sea ice to put the observed changes into the historical context of the entire 20th century. We propose a seasonal Vector Auto-Regressive Moving Average (VARMA) model fit in a Bayesian framework using regularized horseshoe priors on the regression coefficients to create a stochastic ensemble reconstruction of monthly Antarctic Sea ice extent from 1900 to 1979. This novel model produces a set of 2,500 plausible sea ice extent reconstructions for the sea ice by sector that incorporate the autocorrelation structure of sea ice over time as well as the dependence of sea ice between the sectors. These fully observed reconstructions exhibit plausible month-to-month changes in reconstructed sea ice as well as plausible interactions between the sectors and the total. We reconstruct an overall higher sea ice extent earlier in the 20th century with a relatively sharp decline in the 1970s. These trends agree well with previous reconstructions of Antarctic sea ice based on ice core data, whaling locations, and climatological data, as well as early satellite observations in the reconstruction period.

Deficiencies in upper ocean vertical mixing parameterizations contribute to tropical upper ocean biases in global coupled general circulation models, affecting their simulated ocean heat uptake and ENSO variability. To better understand these deficiencies, we develop a suite of ocean model experiments including both idealized single column models and realistic global simulations. The vertical mixing parameterizations are first evaluated using large eddy simulations as a baseline to assess uncertainties and evaluate their implied turbulent mixing. Global models are then developed following NOAA/GFDL's 0.25° nominal horizontal grid spacing OM4 (uncoupled) configuration of the MOM6 ocean model, with various modifications that target biases in the original model. We test several enhancements to the existing mixing schemes and evaluate them against observational constraints from Tropical Atmosphere Ocean moorings and Argo floats. In particular, we find that we can improve the diurnal variability of mixing in OM4 via modifications to its surface boundary layer mixing scheme, and can improve the net mixing in the upper thermocline by reducing the background vertical viscosity, allowing for more realistic, less diffuse currents. The improved OM4 model better represents the mixing, leading to improved diurnal deep-cycle variability, a more realistic time-mean tropical thermocline structure, and a better Pacific Equatorial Undercurrent.