Synthetic aperture radar (SAR) is an important means of obtaining battlefield information. Correct identification of SAR images is essential. Hence, we propose a new method of SAR image recognition based on multi-feature fusion. Convolutional neural networks (CNNs) are based on local pixels and fuse this information at deep layers to obtain deep features. The moment method focuses on the entire image and obtains global moment features of different orders. We fuse the two feature types and choose a suitable feature fusion. First, we propose a Q_sigmoid function to enhance SAR image contrast, and then we separate the target area and remove noise interference. Next, we design a convolutional neural network to obtain the deep features of the target from the separated image. Then, we use four moment methods to produce the moment features of the image and sort them according to recognition rate. Finally, the above features are fused and sorted according to recognition rate to find the optimal combination. The recognition rate of our optimal fusion is 98.63%, which is 2.77% higher than is obtained by CNNs and 6.32% higher than moment methods.

Radio environment map (REM) is an efficient enabler for practical cognitive radio networks by sensing the electromagnetic information within regions of interest dynamically. Most of works on Kriging-based method have proven that separate estimation for pathloss and shadowing can obtain more accurate REM construction. But these methods have some shortcomings that prior information is required for construction or disability for multiple transmitters scenario. In order to overcome the problems of urban REM construction mentioned above, this paper propose a residual Kriging algorithm based on generalized regression neural network (GRNN-RK) for that. The performance of proposed algorithm has been evaluated by the analysis of simulation results, and experiments show that GRNN is capable of improving Kriging in accuracy. Additionally, the influence of spread on REM construction is also experimented.

Gravity waves (GWs) are key drivers of atmospheric dynamics, with major impacts on climate and weather processes. However, they are challenging to measure in observational data, and as a result no large-area multi-decadal GW time series yet exist. This has prevented us from quantifying the interactions between GWs and long-timescale climate processes. Here, we exploit temperatures measured by commercial aircraft since 1994 as part of the IAGOS atmospheric chemistry research programme to produce a novel 26-year time series of upper troposphere/lower stratosphere (UTLS) GW measurements across most of the northern hemisphere. We analyse 90\,342 flight-hours (76.2 million flight-kilometres) of data, typically at a temporal resolution of seconds and with high temperature precision. We show that GW activity in the northern-hemisphere UTLS is consistently strongest north of and above the upper tropospheric jet. We also show that GW sources not typically observed in stratospheric data but assumed in model schemes, such as the Rocky Mountains, are visible at these altitudes, suggesting that wave momentum from these sources is deposited specifically between $\sim$200–50\,hPa. Our data shows no significant impact of the Quasi Biennial Oscillation, the Northern Annular Mode, or climate change. However, we do see strong evidence of links with the El Ni\ no-Southern Oscillation, which modulates the measured GW signal by $\sim$25\%, and weak evidence of links with the 11-year solar cycle. These results have important implications for atmospheric process modelling and for understanding large-scale climate teleconnections.

Much of the water that once flowed on the surface of Mars was lost to space long ago, and the total amount lost remains unknown. Clues to the amount lost can be found by studying hydrogen (H) and its isotope deuterium (D), both of which are produced when atmospheric water molecules H$_2$O and HDO dissociate. The freed H and D atoms then escape to space at different rates due to their different masses, leaving an enhanced D/H ratio. The rate of change of D/H is referred to as the fractionation factor $f$. Both the D/H ratio and $f$ are necessary to estimate water loss; thus, if we can constrain the range of $f$, we will be able to estimate water loss more accurately. In this study, we use a 1D photochemical model of the Martian atmosphere to determine how $f$ depends on assumed temperature and water vapor profiles. We find that for most Martian atmospheric conditions, $f$ varies between $10^{-1}$ and $10^{-5}$; for the standard Martian atmosphere, $f=0.002$ for thermal escape processes, and $f\approxeq0.06$ when both thermal and non-thermal escape are considered. Using these results, we estimate that Mars has lost at minimum 66-123 m GEL of water. Our results demonstrate that the value of $f$ is almost completely controlled by the amount of non-thermal escape of D, and that photochemical modeling studies that include fractionation must thus model both neutral and ion processes throughout the atmosphere.

Lake Powell is a major reservoir responsible for providing water resources throughout the Colorado River Basin. Here we develop a hybrid model to perform 1-4 month forecasts of the inflow to Lake Powell. First, observational data are used in early December for a binary prediction of high- or low-flow based on peak monthly inflow or April-July inflow, and our prediction is much better than the Colorado Basin River Forecast Center (CBRFC)’s forecasts for the 1982-2016 study period. Second, we calibrate the NCEP Climate Forecast System seasonal forecasts of temperature, precipitation, and snow mass, and use them to perform 1-4 month inflow prediction through a regression equation, with the coefficients determined for the high- and low-flow years separately. Our early April forecasts for the critical April-July inflow are better than those of the CBRFC from 1982-2016. Sensitivity tests have also demonstrated the value of including snow mass in our prediction.

At Jupiter, part of the auroral radio emissions are induced by the Galilean moons Io, Europa and Ganymede. Until now, they have been remotely detected, using ground-based radio-telescopes or electric antennas aboard spacecraft. The polar trajectory of the Juno orbiter allows the spacecraft to cross the magnetic flux tubes connected to these moons, or their tail, and gives a direct measure of the characteristics of these decametric moon–induced radio emissions. In this study, we focus on the detection of a radio emission during the crossing of the Ganymede flux tube. Using electromagnetic waves (Juno/Waves) and in-situ electron measurements (Juno/JADE-E), we estimate the flux tube width to be a few 100 km, a growth rate of the radio emission >3x10-4, an electron population of energy E=4-15 keV and an emission beaming angle of θ=76°-83°, at a frequency 1.005-1.021xfce. We also confirmed that radio emission is associated with Ganymede’s down-tail far-ultraviolet emission.

Aurora displays provides an essential diagnostic to spatial and temporal variations of terrestrial space environment and is also an important proxy of solar activity. Contemporary auroral observations have just continued for more than half a century. In the long history prior to modern era, visual auroral observations can dates back to 1450 AD in mid-latitudes and polar regions in Europe. In mid- and low-latitude regions in East Asia, official historical books in China, Korea, and Japan also recorded numerous visual auroral phenomena began from 1000 AD until modern times. In this study, we compiled a new auroral catalogue from ancient Korean historical books, including 2013 auroral records with day-level resolution from 1012 to 1811 AD, especially for the records searched from the . The occurrence of the aurora in the new catalogue is generally consistent with previous datasets. This extended dataset provides valuable support for various studies related to solar-terrestrial space weather and ancient climates.

Lessons from the strong global response to the coronavirus disease 2019 (COVID-19) pandemic and a renewed call for “One health” approach to health systems management in “The Lancet” parallel climate change emergencies. The weakened health - climate change nexus, perceived largely within public health need to engage how the Earth system (i.e. relationships between air, land, life and water on earth) in shaping the etiologies, incidences and transmission dynamics of diseases. The question “What are the drivers of the drivers of diseases?” using the context of diarrheal diseases is posed. Subsequently, we need to understand how (i) climatic risks drive biological health hazards, (ii) shifts in disease control services of ecosystems regulate diseases, (iii) climate change within Earth systems modify disease pathogens and species hosts relationships. Hence, safeguarding Earth system-related disease dynamics would inform pluralistic approaches beyond “One health”.

The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to lessen the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the LES-like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Besides the improvement of low-level cloud amount, the skill scores of mid- and high-level cloud amounts are not negatively impacted partly because FIVE can avoid negative consequences of running deep convection parameterization at high vertical resolution.

Mathematical formulation of the diffusion phenomenon might be described through a differential equation, which takes into account complementary and different effects with respect to the physical processes simulated with the support of the Fick´s equation, which is usually adopted to represent the diffusion process. In particular, diffusion applied to spatio-temporal retention problems with bimodal mass transmission are highlighted. To better understand this physical phenomenon, the proper use of the analytical Green function (GF) or the steady-state fundamental solution was investigated. In this case, we use the Boundary Element Method formulation is presented for the solution of the anomalous diffusion equation for one-dimensional problems. The formulation employs the steady-state fundamental solution. Besides the basic integral equation, another one is required, due to the fourth-order differential operator in the differential equation of the problem. The domain discretization employs linear cells. The first order time derivative is approximated by a backward finite difference scheme. Two examples are presented. Numerical results are compared with analytical solutions, showing good agreement between them.

Decomposing the electric field (E) into the contributions from generalized Ohm’s law provides key insight into both nonlinear and dissipative dynamics across the full range of scales within a plasma. Using high-resolution, multi-spacecraft measurements of three intervals in Earth’s magnetosheath from the Magnetospheric Multiscale mission, the influence of the magnetohydrodynamic, Hall, electron pressure, and electron inertia terms from Ohm’s law, as well as the impact of a finite electron mass, on the turbulent spectrum are examined observationally for the first time. The magnetohydrodynamic, Hall, and electron pressure terms are the dominant contributions to over the accessible length scales, which extend to scales smaller than the electron inertial length at the greatest extent, with the Hall and electron pressure terms dominating at sub-ion scales. The strength of the non-ideal electron pressure contribution is stronger than expected from linear kinetic Alfvén waves and a partial anti-alignment with the Hall electric field is present, linked to the relative importance of electron diamagnetic currents in the turbulence. The relative contribution of linear and nonlinear electric fields scale with the turbulent fluctuation amplitude, with nonlinear contributions playing the dominant role in shaping for the intervals examined in this study. Overall, the sum of the Ohm’s law terms and measured agree to within ~20% across the observable scales. These results both confirm general expectations about the behavior of in turbulent plasmas and highlight features that should be explored further theoretically.

We apply unsupervised machine learning to three years of continuous seismic data to unravel the evolution of seismic wavefield properties in the period of the 2009 L’Aquila earthquake. To obtain sensible representations of wavefield properties variations, wavefield features, i.e. entropy, coherency, eigenvalue variance, and first eigenvalue, are extracted from the covariance matrix analysis of continuous array seismic data. The defined wavefield features are insensitive to site-dependent local noise, and can inform the spatiotemporal properties of seismic waves generated by sources inside the array. We perform a sensitivity analysis of these wavefield features and build unsupervised learning based on the uncorrelated features to track the evolution of source properties. By clustering the wavefield features, our unsupervised analysis avoids explicit physical modeling (e.g. location of events, magnitude estimation) and can naturally separate peculiar patterns solely from continuous seismic data. The unsupervised learning of wavefield features reveals distinct clusters well correlated with different periods of the seismic cycle.

We use continuous and survey GNSS data to image the spatial and temporal evolution of afterslip during the two-months following the Mw8.3 Illapel earthquake. Our approach solves for the incremental daily slip on the subduction interface using non-negative least-squares and spatial/temporal minimum Laplacian constraints. We find that afterslip developed at three specific areas at the megathrust, surrounding the coseismic rupture. The largest patch takes place at shallow depth north of the coseismic rupture. Smaller patches occur at greater depth north and south of the rupture, but no afterslip is found downdip. In addition, well resolved afterslip also occurs within the coseismic area that experienced 3-5 meters of seismic slip. Our afterslip model shows striking correlations with the spatial distribution of aftershocks and repeating earthquakes. A Mw6.8 aftershock occur on November 7 at the deep patch of enhanced afterslip and our inversion captures the triggered afterslip. Two Mw6.9 events occurred 100 km north of the rupture 55 days after the mainshock. The enhanced shallow afterslip developing northward possibly triggered these remote and delayed events. Enhanced afterslip spatially correlates with areas having experienced regular seismic swarms observed during the years prior to the Illapel earthquake. This correlation supports the view of localized fluid high-pore pressure areas behaving aseismically and surrounding a highly locked asperity, preventing the seismic rupture to propagate into them. The similarity with the behavior observed for the Mw7.8 2016 Ecuador and Mw7.6 2012 Costa Rica, suggests a common behaviour for heterogeneous subduction interfaces.

Aerosols are postulated to alter moist convection by increasing cloud droplet number concentration (Nd). Cloud-resolving model simulations of radiative-convective equilibrium show that higher Nd leads to stronger convective mass flux, seemingly in line with a hypothesis that links the convective invigoration to delayed rain formation allowing more cloud liquid condensate to be frozen. Yet, the invigoration is also present in an alternative model configuration with warm-rain microphysics only, suggesting that ice microphysics is not central to the phenomenon. The key dynamical mechanism lies in the different vertical distributions of the increases in water vapor condensation and in cloud liquid re-evaporation, causing a dipole pattern favoring convection. This is further supported by a pair of mechanism-denial experiments in which an imposed weakening of cloud liquid re-evaporation tends to mute invigoration.

The properties of Southern Ocean (SO) liquid phase non precipitating clouds (hereafter clouds) are examined using shipborne data collected during the Measurements of Aerosols, Radiation and Clouds over the Southern Ocean (MARCUS) and the Clouds Aerosols Precipitation Radiation and atmospheric Composition Over the SoutheRN ocean (CAPRICORN) I and II campaigns that took place in the Southern Ocean south of Australia during 2016 and late 2017 into early 2018. The cloud properties are derived using W-band radar, lidar, and microwave radiances using an optimal estimation algorithm. The SO clouds tended to have larger liquid water paths (LWP, 115 ±117 g m-2), smaller effective radii (, 8.7 ±3um), and higher number concentrations (, 90 ±107 cm), than typical values of eastern ocean basin stratocumulus. The clouds demonstrated a tendency for the LWP to increase with presumably due to precipitation suppression up to of approximately 100 cm when mean LWP decreased with increasing . Due to higher optical depth, cloud albedos were less susceptible to changes in compared to subtropical stratocumulus. The high latitude clouds observed along and near the Antarctic coast presented a distinctly bimodal character. One mode had the properties of marine clouds further north. The other mode occurred in an aerosol environment characterized by high cloud condensation nuclei concentrations and elevated sulfate aerosol without any obvious continental aerosol markers that had much higher , smaller and overall higher LWP suggesting distinct sensitivity of the clouds to seasonal biogenic aerosol production in the high latitude regions.

Approximating the three-dimensional structure of a fault network at depth in the subsurface is key for robust estimates of fluid flow. However, only observations of two-dimensional outcrops are often available. To shed light on the relationship between two- and three-dimensional measurements of fracture networks, we examine data from a unique set of eleven X-ray synchrotron triaxial compression experiments that reveal the evolving three-dimensional fracture network throughout loading. Using machine learning, we derive relationships between the two- and three-dimensional measurements of three properties that control fluid flow: the porosity, and volume and tortuosity of the largest fracture at a particular differential stress step. The models predict the porosity and volume of the largest fracture with R2 scores of >0.99, but predict the tortuosity with maximum R2 scores of 0.68. To test the assumption that different rock types may require different equations between the two- and three-dimensional properties, we develop models for both individual rock types (granite, monzonite, marble, sandstone) and all of the experiments. Models developed using all of the experiments perform better than models developed for individual rock types, suggesting fundamental similarities between fracture networks in rocks often analyzed separately. Models developed with several parallel two-dimensional observations perform similarly to models developed with several perpendicular two-dimensional observations. When the models are developed with statistics of the two-dimensional observations, the models primarily depend on the mean and median when they predict the porosity, and minimum when they predict the volume and tortuosity.

The largest volcanic eruption of this century, which was submarine, led to a dramatic phytoplankton bloom north of the island of Tongatapu, in the Kingdom of Tonga. In the absence of shipboard observations, we reconstructed the dynamics of this event by using a suite of satellite observations. Two independent bio-optical approaches confirmed that the phytoplankton bloom was a robust observation and not an optical artifact due to volcanogenic material. Furthermore, the timing, size, and position of the phytoplankton bloom suggest that plankton growth was primarily stimulated by nutrients released from volcanic ash rather than by nutrients upwelled through submarine volcanic activity. The appearance of a large region with high chlorophyll a concentrations less than 48 hours after the largest eruptive phase indicates a fast ecosystem response to nutrient fertilization. However, net phytoplankton growth probably initiated before the main eruption, when weaker volcanism had already fertilized the ocean.

Pedogenic carbonates document a wealth of environmental information, but their seasonal variations may obscure long-term trends. Here we report evidence of changing seasonality of pedogenic carbonate growth from the Chinese Loess Plateau during the Quaternary glacial cycles, using the d18O of pedogenic carbonates (d18Oc). The glacial and interglacial d18Oc show negative and positive correlations with proxy-inferred rainfall amount, respectively. We explain this pattern using modern observations and modeling results, which show opposite correlations between d18Oc and rainfall amount in growing versus non-growing seasons. In glacial episodes under weak monsoon, pedogenic carbonate growth occurred within the growing season, inheriting a negative d18Oc-rainfall correlation. Conversely, pedogenic carbonate growth likely extended into the non-growing season during interglacials due to intensified monsoonal rainfall, incorporating a positive d18Oc-rainfall correlation. Our work links seasonal fluctuations of pedogenic carbonates with their long-term records, shedding new light on interpreting this paleoarchive.