Ambient noise seismic data are widely used by geophysicists to explore subsurface properties at crustal and exploration scales. Two-step dispersion inversion schema is the dominant method used to invert the surface wave data generated by the cross-correlation of ambient noise signals. However, the two-step methods have a 1-D layered model assumption, which does not account for the complex wave propagation. To overcome this limitation, we employ a 2-D wave-equation dispersion inversion (WD) method which reconstructs the subsurface shear (S) velocity model in one step, and elastic wave-equation modeling is used to simulate the subsurface wave propagation. In the WD method, the optimal S velocity model is obtained by minimizing the dispersion curve differences between the observed and predicted surface wave data. This dispersion curve misfit makes the WD method less prone to getting stuck to local minima compared with full waveform inversion. In our study, the observed Scholte waves are generated by cross-correlating continuous ambient noise signals recorded by ocean-bottom nodes (OBN) in the 3-D Gorgon OBN survey, Western Australia. For every two OBN lines, the WD method is used to retrieve the 2-D S velocity structure beneath the first line. We then use a robust neural network based method to interpolate the inverted 2-D velocity slices to a continuous 3-D velocity model and also obtain a corresponding 3-D uncertainty model. Overall, a robust waveform and dispersion match between the observed and predicted data is observed across all of the Gorgon OBN lines both on inverted and interpolated velocity models.

Scientists and regulators commonly use benzo[a]pyrene concentrations to assess cancer risk from complex mixtures of atmospheric polycyclic aromatic hydrocarbons (PAHs). Here, we show that benzo[a]pyrene is a poor indicator of PAH risk distribution and management: nearly 90% of cancer risk worldwide results from other PAHs, including unregulated degradation products of emitted PAHs. We develop and apply a global-scale atmospheric model and conduct health impact analyses to estimate human cancer risk from 16 PAHs and their N-PAH degradation products. We find that benzo[a]pyrene is a minor contributor to the total cancer risks of PAHs (11%); the remaining risk comes from other directly-emitted PAHs (73%) and N-PAHs (15%). We show that assessment and policy-making that relies solely on benzo[a]pyrene exposure provides misleading estimates of risk distribution, the importance of chemical processes, and the prospects for risk mitigation. We conclude that researchers and decision-makers should consider additional PAHs as well as degradation products.

Urban Green Spaces (UGSs) are proving to be most important part of urban area of a city. These green spaces not only provide psychological comfort to humans but also affect heat impact on city to a vast level. In a developing country like India, where urban growth is happening in a very fast and haphazard manner a little consideration is given to Green Spaces in city. Such a study has been conducted over Lucknow metro-city of Uttar Pradesh state of India. In this study, the relation between Land Use\Land Cover (LULC) and Land surface temperature (LST) has been tried to find. Using Landsat-8 OLI data (Band 3, 4 & 5) and Maximum Likelihood Classification algorithm, 6 Land Use classes are obtained, which are “Built-up”, “Vegetation”, “Shrub”, “Water”, “Fallow Land” and “Other”. In addition to these 4 Land Cover Indices namely Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Barren Index (NDBaI) are also generated for same areas. LST is obtained using TIRS data (Band 10 & 11) and Split Window Algorithm by Radiative Transfer Equation. Ancillary data is used for digitization of 5 assembly constituencies (ACs) of Lucknow parliamentary constituency (PC) of Lucknow district namely, Lucknow Cantonment, Lucknow North, Lucknow East, Lucknow West and Lucknow Central has been done. River and Canal passing through these areas, are also considered in Urban Green Spaces as per Urban Atlas code 14100. 250m radius buffer is generated around Built-up pixels for analysis of impact of UGSs on LST. Cantonment and East Lucknow area having highest amount of UGSs in terms of “Vegetation”, “Shrub” & “Water” pixels due to presence of Forest area in both ACs. It is found that LST is positively related with all indices except for NDVI with strong negative correlation and R2 of 0.47 and highest R2 of 0.53 with NDBI. Among all 5 ACs best correlation between all 4 LC and LST values is found in Lucknow East AC with R2 > 0.64 for NDVI, NDWI and NDBI. Lucknow East AC is having least LST but there is very little difference between LST values of Built-up pixels having minimum UGSs present in 250m radius buffer around built-ups and Built-up pixels having no UGSs around. AC Lucknow Central is having 1 °C difference in LST values of such different Built-ups.

In order to examine the spatial variability in plankton metabolic rates and their balance, deck incubation experiments were conducted in the Indian Ocean during spring intermonsoon. Nutrients in the mixed layer is low in the South Indian Ocean (SIO) compared to Arabian Sea (AS) and Bay of Bengal (BoB) associating with stratification in the former region suggesting that existence of oligotrophic conditions. Picophytoplankton biomass contributed to 70-90% of total biomass in the upper 50m of water column followed by nanoplankton. Dominant net autotrophy (positive NCP) was noticed in the AS whereas net heterotrophy (negative NCP) in the BoB and SIO. Higher community respiration (CR) was observed in the SIO followed by BoB and AS. NCP displayed inverse (linear) relationship with temperature (salinity) in the BoB, Andaman Sea and SIO suggesting that stratification driven by river discharge reduced nutrients inputs through vertical mixing resulting in formation of net heterotrophy. The photic zone integrated nutrients displayed linear relationship with NCP and GPP indicating that availability of nutrients controlled the plankton metabolic rates in the Indian Ocean. The threshold of GPP for plankton metabolic balance (6.1 mmol O2 m-3 d-1) was higher than global mean and such higher threshold GPP may be caused by warmer waters resulting in high CR. This study suggests that plankton metabolic balance is noticed in the AS and BoB and dominant heterotrophy in the SIO may be supported by excess carbon produced in the northern Indian Ocean.

For catchment-scale streamflow estimation, recently, the preference is shifting towards the use of innovative remote sensing (RS)-based approaches at the remote gauging stations. In this context, this study evaluates the performances of three RS-based models designed with the near-infrared (NIR) bands of Landsat images at the Jenapur outlet of the Brahmani River basin in eastern India. These RS-based models are designed using the spectral behavior of land (C) and water (M) pixels in the NIR region of the electromagnetic spectrum in the presence and absence of water surrounding the streamflow gauging station. Further, the computed pixel ratio (C/M) is used as a parameter for the discharge estimation, in which four years (2009-2013) of Landsat images are used during calibration and three years (2014-2016) of these images are used during validation. Model-I uses the C/M method in which a box-matrix is conceptualized to analyze the optimal location of the land pixel (C0); and subsequently, the time series of C/M is calibrated with the in-situ discharge (Q) time series. The best pixel ratio (C0/M) time series is preprocessed with an exponential smoothing filter to derive the best filtered-pixel ratio (C0/M*) time series, which is used in the regression model to estimate the river discharge. Model-II corresponds to the multi-pixel ratio (MPR) method, where a 3×3 window is used to calculate the average reflectance of both the C and M pixels within the box-matrix, and subsequently, to obtain the best pixel (C0ʹ/Mʹ) ratio as in the case of Model-I to develop the spectral relationship between C0ʹ/Mʹ and Q time series. Model-III uses both the C/M and water width-based function to estimate the streamflow. The performance evaluation of the models is carried out using the Nash-Sutcliffe efficiency, Percentage bias, and Mean absolute error, which reveals that the model performance varies in the order: Model-III > Model-II > Model-I. This proposed RS-based discharge estimation model framework has the potential to be used in many world- rivers with varying cross-sections.

Palmer Deep Canyon is one of the biological hotspots associated with deep bathymetric features along the Western Antarctic Peninsula. The upwelling of nutrient-rich Upper Circumpolar Deep Water to the surface mixed layer in the submarine canyon has been hypothesized to drive increased phytoplankton biomass productivity, attracting krill, penguins and other top predators to the region. However, observations in Palmer Deep Canyon lack a clear in-situ upwelling signal, lack a physiological response by phytoplankton to Upper Circumpolar Deep Water in laboratory experiments, and surface residence times that are too short for phytoplankton populations to reasonably respond to any locally upwelled nutrients. This suggests that enhanced local upwelling may not be the mechanism that links canyons to increased biological activity. Previous observations of isopycnal doming within the canyon suggested that a subsurface recirculating feature may be present. Here, using in-situ measurements and a circulation model, we demonstrate that the presence of a recirculating eddy may contribute to maintaining the biological hotspot by increasing the residence time at depth and retaining a distinct layer of biological particles. Neutrally buoyant particle simulations showed that residence times increase to upwards of 175 days with depth within the canyon during the austral summer. In-situ particle scattering, flow cytometry, and water samples from within the subsurface eddy suggest that retained particles are detrital in nature. Our results suggest that these seasonal, retentive features of Palmer Deep Canyon are important to the establishment of the biological hotspot.

A major problem with calculating the uncertainties of measurements with weather satellites is the fact that a full characterisation and calibration of their instruments can only be carried out before launch. The Moon, however, makes at least some of these activities possible in flight as well by providing a reliable flux reference at a well-defined position. We used serendipitous observations of the Moon with AMSU-B and MHS on eight different satellites to measure pointing accuracy, spectral channels coregistration, and beamwidth with unprecedented accuracy in flight. In addition we compared these findings with the corresponding values obtained on ground. By analysing more than a hundred Moon intrusions in the deep space view we could determine the radiance of the Moon as a function of its phase angle and distance from the Sun. The difference in average brightness temperature of the lunar disk between perihelion and aphelion amounts to $4.4 \pm 2.3$ K at 183 GHz. We compare the measured brightness temperature of the Moon as a function of phase angle between $-85^{\circ}$ (waxing) and $+76^{\circ}$ (waning) with the predictions from two models and find that one of them reproduces the shape of this function very well.

Aeolus is the first Doppler wind lidar in space. It provides unique high-resolution measurements of horizontal wind in the sparsely-observed upper-troposphere/lower-stratosphere (UTLS), with global coverage. In this study, Aeolus’ ability to resolve atmospheric gravity waves (GWs) is demonstrated. The accurate representation of these small-scale waves is vital to properly simulate dynamics in global weather and climate models. In a case study over the Andes, Aeolus GW measurements show coherent phase structure from the surface to the lower stratosphere, with wind perturbations >10 m/s, a vertical wavelength ~8 km and an along-track horizontal wavelength ~900 km. Good agreement is found between Aeolus and colocated satellite, ground-based lidar and reanalysis data sets for this example. Our results show that data from satellites of this type can provide unique information on GW sources and propagation in the UTLS, filling a key knowledge gap that underlies known major deficiencies in weather and climate modelling.

We assess the ability of neural network emulators of physical parametrization schemes in numerical weather prediction models to aid in the construction of linearised models required by 4D-Var data assimilation. Neural networks can be differentiated trivially, and so if a physical parametrization scheme can be accurately emulated by a neural network then its tangent-linear and adjoint versions can be obtained with minimal effort, compared with the standard paradigms of manual or automatic differentiation of the model code. Here we demonstrate this idea by emulating the non-orographic gravity wave drag parametrization scheme in an atmospheric model with a neural network, and deriving its tangent-linear and adjoint models. We demonstrate that these neural network-derived tangent-linear and adjoint models not only pass the standard consistency tests but also can be used successfully to do 4D-Var data assimilation. This technique holds the promise of significantly easing maintenance of tangent-linear and adjoint codes in weather forecasting centres, if accurate neural network emulators can be constructed.

We present a characterization of transient-large-amplitude (TLA) geomagnetic disturbances that occurred at six stations of the Magnetometer Array for Cusp and Cleft Studies throughout 2015. TLA events are defined as one or more short-timescale (< 60 seconds) dB/dt signature with magnitude > 6 nT/s. A semi-automated dB/dt search algorithm was developed to identify TLA events in ground magnetometer data and used to identify 40 TLA dB/dt events. We demonstrate the existence of large-amplitude dB/dt with timescale less than 10 seconds in nine of the events. The association of these events to sudden commencements is relatively weak, rather the events are more likely to occur in relation to substorm onsets. However, 15% of TLA events show no direct association to geomagnetic storms, substorms or nighttime magnetic impulse events.

Arctic precipitation (PG) that occurs as rainfall (Pr) or snowfall (Ps) depending on the prevailing climatic conditions results in seasonally specific hydrological events. Climate change can affect the PG- and permafrost-originated water (Pi) regimes, resulting in change to ecohydrological processes. However, the relative influences of source waters (i.e., Pr, Ps, and Pi) on terrestrial hydrological processes have not yet been fully established. Here, we report the development and implementation of a numerical water tracer model designed to quantify changes in the storages and fluxes of the source waters and the hydrogen and oxygen isotopic tracers associated with hydrometeorological events. The presented tracer model was used to illustrate the spatiotemporal variability of the tracers in the surface–subsurface system of a deciduous needleleaf boreal forest, and to separate the contribution rates of the tracer waters to evapotranspiration (ET). Although Ps accounted for 14%–40% of ET and the subcomponents, the contribution rates to soil evaporation and transpiration were significant only during the spring season. The major source water for soil moisture was Pr, which accounted for 80.1% of ET and showed an increasing trend. Additionally, Pr also accounted for 85.7% of transpiration. Under the present conditions of warming permafrost, Pi demonstrated negligibly low impact on ET. The tracer model was shown capable of quantifying the contribution rates of tracer waters to ET, highlighting the advantages of the tracer model for similar quantitative separation regarding future climate change.

A method is presented for retrieving temperature and composition from 150-350 km in Earth’s thermosphere using total number density measurements made via EUV solar occultations by the PROBA2/LYRA instrument. Systematic and random uncertainties are calculated and found to be less than 5% for the temperature measurements and 5-20% for the composition measurements. Regression coefficients relating both temperature and the [O]/[N2] abundance ratio with EUV irradiance at 150, 275 and 350 km are reported. Additionally, it is shown that the altitude where [O] equals [N2] decreases with increasing solar EUV irradiance, an effect attributed to thermal expansion. Temperatures from 2010 to 2017 are compared with estimates from the MSIS empirical model and show good agreement at the dawn terminator but LYRA is markedly cooler at the dusk terminator, with the MSIS-LYRA temperature difference increasing with solar activity. Anthropogenic cooling can explain this discrepancy at periods of lower solar activity, but the divergence of temperature with increasing solar activity remains unexplained. LYRA measurements of the exospheric sensitivity to EUV irradiance are compared with contemporaneous measurements made at Mars, showing that the exospheric temperature at Mars is approximately half as sensitive to EUV variability as that of Earth.

We explore the mantle density structure of the northeast Atlantic region using constrained linear inversion of the satellite gravity gradient data based on statistical prior information and assuming a Gaussian model. The uncertainty of residual gravity gradient signal is characterized by covariance matrix obtained using geostatistical analysis of controlled-source seismic data. The forward modelling of the gravity gradients in the 3D reference crustal model is performed using a global spherical harmonics analysis. We estimate the model covariance function in the radial and angular directions using a variogram method. We compute volumetric gravity gradient kernels for a spherical shell covering the northeast Atlantic region down to the upper limit of the mantle transition zone (410 km depth). The solution of the linear inverse problem in the form the mean density model follows a least-squares approach. The results indicate a direct relationship between the seismic velocity and density anomalies in the Iceland-Jan Mayen region, Greenland and the Norwegian passive margin. The predicted low-density anomalies at the depth of 100-150 km underneath the northeast Atlantic Ocean are correlated with the distribution of Cenozoic underwater volcanoes and seamount-like features of the seafloor.

In this letter, we present for the first time simultaneously measured electron and gamma ray spectra of TGE. We also demonstrate how the measurement of the energy spectra of TGE particles can help to understand the charge structure of the thundercloud. We introduce two main scenarios that can support the initiation of the relativistic runaway avalanches (RREA) in the thundercloud. One of the scenarios includes the emergence of the lower positive charged layer (LPCR). The LPCR is a short-living charge structure sitting on hydrometeors, which exhibit accidental charge reversals depending on temperature changes. LPCR can sustain a positive (downwards directed) near-surface electric field for several minutes, before falling down with precipitation. Comparison of energy spectra of electrons and gamma rays makes it possible to scrutinize the LPCR emergence and estimate the height where the strong electric field that originates the RREA declines.

In this study, a new high-latitude empirical model is introduced, named for Auroral energy Spectrum and High-Latitude Electric field variabilitY (ASHLEY). This model aims to improve specifications of soft electron precipitations and electric field variability that are not well represented in existing high-latitude empirical models. ASHLEY consists of three components, ASHLEY-A, ASHLEY-E and ASHLEY-Evar, which are developed based on the electron precipitation and bulk ion drift measurements from the Defense Meteorological Satellite Program (DMSP) satellites during the most recent solar cycle. On the one hand, unlike most existing high-latitude electron precipitation models, which have assumptions about the energy spectrum of incident electrons, the electron precipitation component of ASHLEY, ASHLEY-A, provides the differential energy fluxes in the 19 DMSP energy channels under different geophysical conditions without making any assumptions about the energy spectrum. It has been found that the relaxation of spectral assumptions significantly improves soft electron precipitation specifications with respect to a Maxwellian spectrum (up to several orders of magnitude). On the other hand, ASHLEY provides consistent mean electric field and electric field variability under different geophysical conditions by ASHLEY-E and ASHLEY-Evar components, respectively. This is different from most existing electric field models which only focus on the large-scale mean electric field and ignore the electric field variability. Furthermore, the consistency between the electric field and electron precipitation is better taken into account in ASHLEY.

Tropopause-penetrating overshooting convection (OC) can transport tropospheric air into and affect the composition of the lower stratosphere. During the warm season, OC occurs frequently over the contiguous United States, and the transport of plumes from these events is modulated by the flow over North America, which throughout June to August is characterized by a large-scale anticyclone in the upper troposphere and lower stratosphere. This study uses data from the Next Generation Weather Radar (NEXRAD) and the ERA5 reanalysis to locate OC during May–August of 2008 to 2020. Evidence of convective transport is found well above the 380 K isentrope, which is the top of the “lowermost stratosphere” and also the top of the stratospheric middleworld. By initializing massless particles within the volume of OC above the tropopause, we perform trajectory calculations to simulate the transport of OC plumes. With three-dimensional diabatic trajectory modeling in isentropic coordinates using winds from ERA5, we quantify the confinement within the anticyclone and the number of trajectories transported into the tropical and extratropical stratosphere. By evaluating the trajectory residence time in the North American region, we find that July exhibits the strongest confinement, with about a quarter of trajectories staying in the region for more than 11 days. It is shown that, together with sufficient injection height, convective injection that occurs south of the jet and/or into anticyclonic regimes increases the chances of air remaining in the stratosphere. After 30 days, 45% of all air masses injected above the tropopause remain in the global stratosphere.

High resolution and accurate rainfall information is essential to modeling and predicting hydrological processes. Crowdsourced personal weather stations (PWSs) have become increasingly popular in recent years and can provide dense spatial and temporal resolution in rainfall estimates. However, their usefulness is limited due to a lack of trust in crowdsourced data compared to traditional data sources. Using crowdsourced PWSs data without an evaluation of its trustworthiness can result in inaccurate rainfall estimates as PWSs may be poorly maintained or incorrectly sited. In this study, we advance the Reputation System for Crowdsourced Rainfall Networks (RSCRN) to bridge this trust gap by assigning dynamic trust scores to the PWSs. Using rainfall data collected from 18 PWSs in two dense clusters in Houston, Texas USA as a case study, the results show that using RSCRN-derived trust scores can increase the accuracy of 15-min PWS rainfall estimates when compared to rainfall observations recorded at city’s high-fidelity rainfall stations. Overall, RSCRN rainfall estimates improved for 77% (48 out of 62) of the analyzed storm events, with a median RMSE improvement of 27.3%. Compared to an existing PWS quality control method, results showed that while 13 (21%) storm events had the same performance, RSCRN improved rainfall estimates for 78% of the remaining storm events (38 out of 49), with a median RMSE improvement of 13.4%. Using RSCRN-derived trust scores can make the rapidly growing network of PWSs a more useful resource for urban flood management, greatly improving knowledge of rainfall patterns in areas with dense PWSs.

From the heated debates over the airborne transmission of the novel coronavirus to the abrupt Earth system changes caused by the sudden lockdowns, the dire circumstances resulting from the coronavirus disease 2019 (COVID-19) pandemic has brought the field of GeoHealth to the forefront of visibility in science and policy. The pandemic has inadvertently provided an opportunity to study how human response has impacted the Earth system, how the Earth system may impact the pandemic, and the capacity of GeoHealth to inform real-time policy. The lessons learned throughout our responses to the COVID-19 pandemic are shaping the future of GeoHealth.