Temporal Coverage Research Articles

Knowledge Gaps and Impact of Future Satellite Missions to Facilitate Monitoring of Changes in the Arctic Ocean

Polar-orbiting satellite observations are of fundamental importance to explore the main scientific challenges in the Arctic Ocean, as they provide information on bio-geo-physical variables with a denser spatial and temporal coverage than in-situ instruments in such a harsh and inaccessible environment. However, they are limited by the lack of coverage near the North Pole (Polar gap), the polar night, and frequent cloud cover or haze over the ocean and sea ice, which prevent the use of optical satellite instruments, as well as by the limited availability of external validation data. The satellite sensors’ coverage and repeat cycles may also have limitations in properly identifying and resolving the dominant spatial and temporal scales of atmospheric, ocean, cryosphere and land variability and their interactive processes and feedback mechanisms. In this paper, we provide a state of the art of contribution of satellite observations to the understanding of the polar environment and climate scientific challenges tackled within the Arktalas Hoavva project funded by the European Space Agency. We identify the current limitations to the wider use of polar orbiting remote sensing data, as well as the observational gaps of the existing satellite missions. A comprehensive overview of all satellite missions and applications is given provided with a primary focus on the European satellites. Finally, we assess the expected capability of the approved future satellite missions to answer today’s scientific challenges in the Arctic Ocean.

Physical-biological drivers modulating phytoplankton seasonal succession along the Northern Antarctic Peninsula

The Northern Antarctic Peninsula (NAP) shows shifts in phytoplankton distribution and composition along its warming marine ecosystems. However, despite recent efforts to mechanistically understand these changes, little focus has been given to the phytoplankton seasonal succession, remaining uncertainties regarding to distribution patterns of emerging taxa along the NAP. To fill this gap, we collected phytoplankton (pigment and microscopy analysis) and physico-chemical datasets during spring and summer (November, February and March) of 2013/2014 and 2014/2015 off the NAP. Satellite measurements (sea surface temperature, sea ice concentration and chlorophyll-a) were used to extend the temporal coverage of analysis associated with the in situ sampling. We improved the quantification and distribution pattern of emerging taxa, such as dinoflagellates and cryptophytes, and described a contrasting seasonal behavior and distinct fundamental niche between centric and pennate diatoms. Cryptophytes and pennate diatoms preferentially occupied relatively shallower mixing layers compared with centric diatoms and dinoflagellates, suggesting differences between these groups in distribution and environment occupation over the phytoplankton seasonal succession. Under colder conditions, negative sea surface temperature anomalies were associated with positive anomalies of sea ice concentration and duration. Therefore, based on sea ice–phytoplankton growth relationship, large phytoplankton biomass accumulation was expected during the spring/summer of 2013/2014 and 2014/2015 along the NAP. However, there was a decoupling between sea ice concentration/duration and phytoplankton biomass, characterizing two seasonal periods of low biomass accumulation (negative chlorophyll-a anomalies), associated with the top-down control in the region. These results provide an improved mechanistic understanding on physical-biological drivers modulating phytoplankton seasonal succession along the Antarctic coastal waters.

Controlling factor analysis of oceanic surface pCO2 in the South China Sea using a three-dimensional high-resolution biogeochemical model

The oceanic surface pressure of CO2 (pCO2) is an essential parameter for understanding the global and regional carbon cycle and the oceanic carbon uptake capacity. We constructed a three-dimensional physical-biogeochemical model with a high resolution of 1/30° for the South China Sea (SCS) to compensate for the limited temporal coverage and limited spatial resolution of the observations and numerical models. The model simulated oceanic surface pCO2 from 1992 to 2021, and the empirical orthogonal function analysis (EOF) of the model results is conducted for a better understanding of the seasonal and interannual variations of oceanic surface pCO2 in this region. The model results showed that the SCS serves as an atmospheric CO2 source from March to October and a sink from November to February, with a domain-averaged climatological oceanic surface pCO2 value that varies between 357 and 408 μatm, and the temporal variation was positively correlated with the variation of sea surface temperature (SST). The majority of the SCS showed a long-term increasing trend for oceanic surface pCO2 with a value of (1.19±0.60) μatm/a, which is in response to the continuously rising atmospheric CO2 concentration. The first EOF mode is positively correlated with the Niño 3 index with a correlation coefficient of 0.51 when the Niño 3 leads 5 months, and the second EOF mode is correlated with the PDO index when the PDO leads 7 months, which suggests an influence of climate variability on the carbonate system. Moreover, it was found that the long-term trend rate of oceanic surface pCO2 was mainly controlled by total CO2 (TCO2) through the decomposition of influence factors, and SST variation took a dominant role in seasonal variations of pCO2. With rapid global warming and continuous release of CO2, the carbonate system in the SCS may change leading to calcite and aragonite saturation.

Using qPCR of environmental DNA (eDNA) to estimate the biomass of juvenile Pacific salmon (Oncorhynchus spp.)

AbstractDuring the outmigration of Pacific Salmon, the early marine phase is a critical period when high mortality can occur. Traditional sampling and monitoring of juvenile salmon migration can be limited by logistically intensive gear requirements, accessibility, and cost. Improved understanding of the early marine phase, for example, migration duration and habitat use, requires innovative techniques that can improve the spatial and temporal coverage of monitoring. Environmental DNA (eDNA) is genetic fragments present in the environment that can be used as a proxy for organism presence and can be effectively and efficiently collected through water samples. Estimating fish abundance or biomass from eDNA concentration data would provide a valuable fisheries tool but remains challenging to calibrate. To quantify the relationship between eDNA abundance and fish biomass, we used a controlled mesocosm experiment, in which eDNA samples were collected from 15 aquaria (340 L) with varying densities of juvenile Chinook salmon per tank (0, 5, 10, 20, and 30). The concentration of eDNA was obtained by qPCR scaled with fish biomass (ANOVA, p < 0.05). However, we also observed that variability of eDNA concentrations among replicates of the same treatment positively scaled biomass (ANOVA, p < 0.05). Therefore, higher biomasses of fish can yield more challenging data to interpret. This study lays important groundwork for the application of eDNA for monitoring juvenile salmonids yet highlights caveats for the applicability of eDNA as a stand‐alone method to assess biomass in a field setting.

An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A

Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-related studies to be more effectively directed. However, accurate classification of clouds is challenging and often requires certain manual involvement due to the complex cloud forms and dispersion. To address this challenge, this paper proposes an improved cloud classification method based on a densely connected hybrid convolutional network. A dense connection mechanism is applied to hybrid three-dimensional convolutional neural network (3D-CNN) and two-dimensional convolutional neural network (2D-CNN) architectures to use the feature information of the spatial and spectral channels of the FY-4A satellite fully. By using the proposed network, cloud categorization solutions with a high temporal resolution, extensive coverage, and high accuracy can be obtained without the need for any human intervention. The proposed network is verified using tests, and the results show that it can perform real-time classification tasks for seven different types of clouds and clear skies in the Chinese region. For the CloudSat 2B-CLDCLASS product as a test target, the proposed network can achieve an overall accuracy of 95.2% and a recall of more of than 82.9% for all types of samples, outperforming the other deep-learning-based techniques.

Magnetostratigraphy of Early Oligocene-Middle Miocene Deposits in the Xunhua Basin on the Tibet Plateau, China, and Their Paleoclimate Significance

The growth history of the Tibetan Plateau provides a valuable natural laboratory to understand tectonic processes of the India–Asia collision and their impact on and interactions with Asian and global climate change. However, both Tibetan Plateau growth and Asian paleoenvironments are generally poorly documented in pre-Pliocene times and reflect limited temporal coverage for different parts of the plateau. In this paper, the 238 m thick Cenozoic sediments in the Hongzhuang section of the Xunhua Basin were tested and analyzed via paleomagnetic and environmental magnetic methods. The formation age was determined, and the evolution history of the regional climate environment was analyzed. The magnetostratigraphy study shows that the sediments record a continuous sequence of geomagnetic polarity changes from C5ACn to C10r, which spans an interval of approximately 30~14.3 Ma from the early Oligocene to the middle Miocene. The magnetic susceptibility of the Hongzhuang section is basically similar to the deep-sea oxygen isotope fluctuation, indicating that the monsoon climate change indicated by the magnetic susceptibility is affected by global temperature. It is worth noting that at ~27 Ma and ~15 Ma, there is a negative correlation between magnetic susceptibility and deep-sea oxygen isotope, and magnetic susceptibility lags behind the increase in deep-sea oxygen isotope. Combined with the change in the sedimentary rate curve, we explain the asynchrony between the magnetic susceptibility and the deep-sea oxygen isotope around ~27 Ma and ~15 Ma. As the uplift of the plateau leads to the enhancement of the East Asian summer monsoon, the soil formation in the region is strengthened, resulting in an increase in magnetic susceptibility. At the same time, the rapid uplift of the plateau caused the erosion of the surrounding mountains to strengthen, and the input of near-source materials may promote the increase in magnetic susceptibility.

Challenges and Opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As the most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation to mass losses brought on by salinization. These issues affect agricultural productivity and ecological balance, undermining sustainability and food security. Therefore, timely monitoring and accurate mapping of salinization processes are crucial, especially in semi-arid and arid regions where climate variability impacts have already reached alarming levels. Salt-affected soil mapping has enormous potential thanks to recent progress in remote sensing. This paper comprehensively reviews the potential of remote sensing to assess soil salinization. The review demonstrates that large-scale soil salinity estimation based on remote sensing tools remains a significant challenge, primarily due to data resolution and acquisition costs. Fundamental trade-offs constrain practical remote sensing applications in salinization mapping between data resolution, spatial and temporal coverage, acquisition costs, and high accuracy expectations. This article provides an overview of research work related to soil salinization mapping and monitoring using remote sensing. By synthesizing recent research and highlighting areas where further investigation is needed, this review helps to steer future efforts, provides insight for decision-making on environmental sustainability and soil resource management, and promotes interdisciplinary collaboration.

Vehicular Crowdsensing with High-Mileage Vehicles: Investigating Spatiotemporal Coverage Dynamics in Historical Cities with Complex Urban Road Networks

Background. Vehicular crowdsensing (VCS) can be a cost-effective solution to gather data in urban environments, leveraging the onboard sensors of modern vehicles moving around the city. Many experimental studies have proven that high-mileage vehicles, such as taxis, can be effectively used for VCS. However, these studies have been mostly carried out in cities with regular, grid-based, road networks. Conversely, little work has been conducted to assess the suitability of VCS in cities with more complex urban road networks, such as historical ones. Goal. As a step towards filling this gap, the present study investigates the feasibility of using different-sized fleets of taxis to crowdsense information in the urban areas of the historical cities of Porto (Portugal) and Rome (Italy), whose road networks evolved over the centuries and feature a complex topology. Data and Methodology. This work leverages massive real-world datasets of taxi trajectories collected over three contiguous weeks in the cities of Porto and Rome to estimate the spatiotemporal coverage achievable by different-sized fleets of taxis if they were used for VCS. Indeed, using these trajectories, several simulations were conducted, considering four sizes of taxi fleets, ranging from 50 to 400 vehicles, for both cities. The achievable spatiotemporal road network coverage metrics were computed at a fine-grained scale of single road segments. Results. Results show that the achievable coverage in both historical cities exhibits very similar trends, with as few as 50 vehicles being capable of visiting a relevant part of the road network at least once in the considered time frame. As expected, increasing the number of involved vehicles improves spatial and temporal coverage. Still, time gaps between subsequent visits can be possibly inadequate for some VCS use cases. As a consequence, recruiting more vehicles and/or devising specialized routing/incentivization mechanisms might be necessary to achieve more comprehensive coverage of the urban road network.

A comparative study on CNN-based semantic segmentation of intertidal mussel beds

Mussels are globally distributed and abundant species on intertidal habitats such as rocky shores where they form dense beds and provide a variety of ecosystem services. The ability to measure mussel abundance on shores with high efficiency and accuracy will enhance assessment of their facilitative services to intertidal ecosystems. Traditional sampling methods, however, are labour intensive and consequently limited in spatial and temporal coverage. To advance such monitoring and quantification processes, four state-of-the-art convolutional neural networks (CNN); namely U-Net, FastSCNN, DeepLab v3+, and HRNet, were compared in their performance to segment mussels at the pixel level either with or without Cityscapes pre-trained weights. All proposed CNN were trained on a photographic dataset of 1455 images generated from field images of mussel beds. CNN trained with the Cityscapes pre-trained weights showed an overall increased model performance as illustrated by mean intersection-over-union (mIoU), loss, overall accuracy (oAcc), and Cohen's kappa coefficient. DeepLab v3+ and HRNet with the combination of Cityscapes pre-trained weights achieved over 95% mIoU and 97% oAcc for assessing distribution and abundance of these important intertidal species. Such combination of approaches is, therefore, recommended for ecologists who wish to conduct image-driven surveys of intertidal ecosystems and the code for this study is available at: https://github.com/Vicellken/CNN-SS-mussel.

Inland Water Greenhouse Gas Budgets for RECCAP2: 1. State‐Of‐The‐Art of Global Scale Assessments

AbstractInland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) to the atmosphere. In the framework of the 2nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP‐2) initiative, we review the state of the art in estimating inland water GHG budgets at global scale, which has substantially advanced since the first phase of RECCAP nearly 10 years ago. The development of increasingly sophisticated upscaling techniques, including statistical prediction and process‐based models, allows for spatially explicit estimates that are needed for regionalized assessments of continental GHG budgets such as those established for RECCAP. A few recent estimates also resolve the seasonal and/or interannual variability in inland water GHG emissions. Nonetheless, the global‐scale assessment of inland water emissions remains challenging because of limited spatial and temporal coverage of observations and persisting uncertainties in the abundance and distribution of inland water surface areas. To decrease these uncertainties, more empirical work on the contributions of hot‐spots and hot‐moments to overall inland water GHG emissions is particularly needed.

Evaluating Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

Synergistic use of Sentinel-1, Sentinel-2, and Landsat 8 in predicting forest variables

Forest variables measurement is indispensable to supervising the dynamic of forest carbon storage. As commonly quantified forest variables, canopy cover (CC), diameter at breast height (DBH), and tree height (TH) were the predicted objects in our study. Recently, remote sensing technology, with high efficiency and wide temporal and spatial coverage, has been extensively used in practice. However, integrating open access satellite resources Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 (L8) to quantify forest CC, DBH, and TH has not been extensively examined in the literature. In this study, we explored the potential of individual satellite bands/indices and the integration of different sensors in predicting mean CC, mean DBH, and mean TH in the plots dominated by four tree species (total area 102,887.6 ha). We acquired the satellite patterns of S1, S2, and L8 covered our research plots in 2017, and extracted the mean pixel values of the spectral images for every plot we detected. Specifically, seven sets of features derived from the satellite images of S1, S2, and L8 were used to predict CC, DBH, and TH for the survey plots. The results mainly indicated that group S2 + L8 (RMSEr (the relative root mean square error) = 17.9%, 21.0%, 23.0%) or S2 + L8 + S1 (RMSEr = 17. 8%, 21.0%, 23.1%) achieved the best accuracy for CC, DBH, and TH of all plots. Additionally, group S2 + L8 outperformed the other groups within two sensors and was superior to the individual sensors. Adding S1 synthetic aperture radar (SAR, the sensor carried by S1) had no evident improvement or deviation for most models, nevertheless, numerous bands/indices of SAR occupied important positions in variable importance and were been selected by stepwise regression, which denoted that it is available and valuable for forest parameters of some dominant tree species. Moreover, shortwave infrared (SWIR) bands played a crucial role in the three forest parameters prediction during the rainy season. SWIR bands (RMSEr = 20.5%), NIR bands (RMSEr = 24.1%), and red-edge bands (RMSEr = 27.1%) exerted the most significant implication for predicting CC, DBH, and TH apiece as univariate models. Green bands and NIR also occupied more positions of higher variable importance in multivariate models for estimating DBH and TH, while SWIR took up more for CC in all dominant species. Furthermore, non-parametric models outperformed the parametric model on the whole. In the meanwhile, the best models also were influenced by the data analysis methods.In a nutshell, a combination of multiple satellite sensors was proposed for forest variable selection, especially different optical sensors. SAR data were also inspired to be pitched in related forest measurement work, but it requires some data processing before applying to the research in many cases.

Key Science Goals for the Next-Generation Event Horizon Telescope

The Event Horizon Telescope (EHT) has led to the first images of a supermassive black hole, revealing the central compact objects in the elliptical galaxy M87 and the Milky Way. Proposed upgrades to this array through the next-generation EHT (ngEHT) program would sharply improve the angular resolution, dynamic range, and temporal coverage of the existing EHT observations. These improvements will uniquely enable a wealth of transformative new discoveries related to black hole science, extending from event-horizon-scale studies of strong gravity to studies of explosive transients to the cosmological growth and influence of supermassive black holes. Here, we present the key science goals for the ngEHT and their associated instrument requirements, both of which have been formulated through a multi-year international effort involving hundreds of scientists worldwide.

Spatial extreme model for rainfall depth: application to the estimation of IDF curves in the Basque country

Intensity-duration-frequency (IDF) curves are commonly used in engineering practice for the hydraulic design of flood protection infrastructures and flood risk management. IDF curves are simple functions between the rainfall intensity, the timescale at which the rainfall process is studied, and the return period. This work proposes and tests a new methodological framework for the spatial analysis of extreme rainfall depth at different timescales, taking advantage of two different precipitation datasets: local observational and gridded products. On the one hand, the proposed method overcomes or reduces known issues related to observational datasets (missing data and short temporal coverage, outliers, systematic biases and inhomogeneities, etc.). On the other hand, it allows incorporating appropriately terrain dependencies on the spatial distribution of the extreme precipitation regime. Finally, it allows to estimate the IDF curves at regional level overcoming the deficiencies of the classical regional approaches commonly used in practice. The method has been tested to compute IDF curves all over the Basque Country, contrasting results with respect to local analyses. Results show the method robustness against outliers, missing data, systematic biases and short length time series. Moreover, since generalized extreme value (GEV)-parameters from daily gridded dataset are used as covariates, the proposed approach allows coherent spatial interpolation/extrapolation of IDF curves properly accounting for the influence of orographic factors. In addition, due to the current coexistence of local observations and gridded datasets at regional (e.g. The Alps), national (e.g. Spain, France, etc.) or international scale (e.g. E-OBS for Europe or Daymet for the United States of America), the proposed methodology has a wide range of applicability in order to fulfill the known gaps of the observational datasets and reduce the uncertainty related to analysis and characterization of the extreme precipitation regime.

Spatial reconstruction of long-term (2003–2020) sea surface pCO2 in the South China Sea using a machine-learning-based regression method aided by empirical orthogonal function analysis

Abstract. The South China Sea (SCS) is the largest marginal sea of the North Pacific Ocean, where intensive field observations, including mappings of the sea surface partial pressure of CO2 (pCO2), have been conducted over the last 2 decades. It is one of the most studied marginal seas in terms of carbon cycling and could thus be a model system for marginal sea carbon research. However, the cruise-based sea surface pCO2 datasets are still temporally and spatially sparse. Using a machine-learning-based method facilitated by empirical orthogonal function (EOF) analysis, this study provides a reconstructed dataset of the monthly sea surface pCO2 in the SCS with a reasonably high spatial resolution (0.05∘ × 0.05∘) and temporal coverage between 2003 and 2020. The data input to our model includes remote-sensing-derived sea surface salinity, sea surface temperature, and chlorophyll, the spatial pattern of pCO2 constrained by EOF, atmospheric pCO2, and time labels (month). We validated our reconstruction with three independent testing datasets that are not involved in the model training. Among them, Test 1 includes 10 % of our in situ data, Test 2 contains four independent in situ datasets corresponding to the four seasons, and Test 3 is an in situ monthly dataset available from 2003–2019 at the South East Asia Time-series Study (SEATs) station located in the northern basin of the SCS. Our Test 1 validation demonstrated that the reconstructed pCO2 field successfully simulated the spatial and temporal patterns of sea surface pCO2 observations. The root mean square error (RMSE) between our reconstructed data and in situ data in Test 1 averaged ∼10 µatm, which is much smaller (by ∼50 %) than that between the remote-sensing-derived data and in situ data. Test 2 verified the accuracy of our retrieval algorithm in months lacking observations, showing a relatively small bias (RMSE of ∼8 µatm). Test 3 evaluated the accuracy of the reconstructed long-term trend, showing that, at the SEATs station, the difference between the reconstructed pCO2 and in situ data ranged from −10 to 4 µatm (−2.5 % to 1 %). In addition to the typical machine learning performance metrics, we assessed the uncertainty resulting from reconstruction bias and its feature sensitivity. These validations and uncertainty analyses strongly suggest that our reconstruction effectively captures the main spatial and temporal features of sea surface pCO2 distributions in the SCS. Using the reconstructed dataset, we show the long-term trends of sea surface pCO2 in five subregions of the SCS with differing physicobiogeochemical characteristics. We show that mesoscale processes such as the Pearl River plume and China coastal currents significantly impact sea surface pCO2 in the SCS during different seasons. While the SCS is overall a weak source of atmospheric CO2, the northern SCS acts as a sink, showing a trend of increasing strength over the past 2 decades. The data used in this article are available at https://doi.org/10.57760/sciencedb.02050 (Wang and Dai, 2022).

What Makes Public Sector Data Valuable for Development?

Abstract Data produced by the public sector can have transformational impacts on development outcomes through better targeting of resources, improved service delivery, cost savings, increased accountability, and more. Around the world, the amount of data produced by the public sector is increasing rapidly, but we argue the full potential of data to improve development outcomes has not been realized yet. We outline 12 features needed for data to generate greater value for development and present case studies substantiating these features. We argue that a key reason why the transformational value of data has not yet been realized is that suboptimal data—data not satisfying these 12 features—are being supplied. The features are that the data should be of adequate spatial and temporal coverage (complete, frequent, and timely), should be of high quality (accurate, comparable, and granular), should be easy to use (accessible, understandable, and interoperable), and should be safe to use (impartial, confidential, and appropriate).

Quantifying uncertainty in coastal salinity regime for biological application using quantile regression

AbstractSalinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m3/s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simpleRroutine, and is less computationally‐intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.

A SmallSat Concept to Resolve Diurnal and Vertical Variations of Aerosols, Clouds, and Boundary Layer Height

Abstract A SmallSat mission concept is formulated here to carry out Time-varying Optical Measurements of Clouds and Aerosol Transport (TOMCAT) from space while embracing low-cost opportunities enabled by the revolution in Earth science observation technologies. TOMCAT’s “around-the-clock” measurements will provide needed insights and strong synergy with existing Earth observation satellites to 1) statistically resolve diurnal and vertical variation of cirrus cloud properties (key to Earth’s radiation budget), 2) determine the impacts of regional and seasonal planetary boundary layer (PBL) diurnal variation on surface air quality and low-level cloud distributions, and 3) characterize smoke and dust emission processes impacting their long-range transport on the subseasonal to seasonal time scales. Clouds, aerosol particles, and the PBL play critical roles in Earth’s climate system at multiple spatiotemporal scales. Yet their vertical variations as a function of local time are poorly measured from space. Active sensors for profiling the atmosphere typically utilize sun-synchronous low-Earth orbits (LEO) with rather limited temporal and spatial coverage, inhibiting the characterization of spatiotemporal variability. Pairing compact active lidar and passive multiangle remote sensing technologies from an inclined LEO platform enables measurements of the diurnal and vertical variability of aerosols, clouds, and aerosol-mixing-layer (or PBL) height in tropical-to-midlatitude regions where most of the world’s population resides. TOMCAT is conceived to bring potential societal benefits by delivering its data products in near–real time and offering on-demand hazard-monitoring capabilities to profile fire injection of smoke particles, the frontal lofting of dust particles, and the eruptive rise of volcanic plumes.

Evolution of Solar Eruptive Events: Investigating the Relationships among Magnetic Reconnection, Flare Energy Release, and Coronal Mass Ejections

We study the evolution of solar eruptive events by investigating the temporal relationships among magnetic reconnection, flare energy release, and the acceleration of coronal mass ejections (CMEs). Leveraging the optimal viewing geometry of the Solar TErrestrial RElations Observatory (STEREO) relative to the Solar Dynamics Observatory (SDO) and the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) during 2010–2013, we identify 12 events with sufficient spatial and temporal coverage for a detailed examination. STEREO and SDO data are used to measure the CME kinematics and the reconnection rate, respectively, and hard X-ray (HXR) measurements from RHESSI provide a signature of the flare energy release. This analysis expands upon previous solar eruptive event timing studies by examining the fast-varying features, or “bursts,” in the HXR and reconnection rate profiles, which represent episodes of energy release. Through a time lag correlation analysis, we find that HXR bursts occur throughout the main CME acceleration phase for most events, with the HXR bursts lagging the acceleration by 2 ± 9 minutes for fast CMEs. Additionally, we identify a nearly one-to-one correspondence between bursts in the HXR and reconnection rate profiles, with HXRs lagging the reconnection rate by 1.4 ± 2.8 minutes. The studied events fall into two categories: events with a single dominant HXR burst and events with a train of multiple HXR bursts. Events with multiple HXR bursts, indicative of intermittent reconnection and/or particle acceleration, are found to correspond with faster CMEs.

Meandering periods and asymmetries in light curves of Miras: Observational evidence for low mass-loss rates

Context. Some Miras (long-period variables in late evolutionary stages) have meandering pulsation periods and light-curve asymmetries, the causes of which are still unclear. Aims. We aim to better understand the origin of meandering periods and light-curve asymmetries by investigating a sample of Miras in the solar neighbourhood. We characterised this group of stars and related their variability characteristics to other stellar parameters. Methods. We analysed observations from several databases to obtain light curves with maximum time span and temporal coverage for a sample of 548 Miras. We determined their pulsation-period evolution over a time span of many decades, searched for changes in the periods, and determined the amplitude of the period change. We also analysed the Fourier spectra with respect to possible secondary frequency maxima. The sample was divided into two groups with respect to the presence of light-curve asymmetries (‘bumps’). Infrared colours and indicators of the third dredge-up were collected to study the mass loss and deep mixing properties of the stars of our sample. Results. Our analysis reveals one new star, T Lyn, with a continuously changing period. The group of Miras with meandering period changes is exclusively made up of M-type stars. The Fourier spectra of the meandering-period Miras have no prominent additional peaks, suggesting that additional pulsation modes are not the cause of the meandering periods. We confirm that light-curve bumps are more common among S and C Miras and show, for the first time, that Miras with such bumps have lower mass-loss rates than those with regular, symmetric light curves. Also, Miras with meandering period changes have relatively little mass loss. Conclusions. We conclude that Miras with strongly changing periods (including meandering periods) or asymmetries in their light curves have relatively low dust mass-loss rates. Meandering period changes and light-curve asymmetries could be connected to He-shell flashes and third dredge-up episodes.