High Temporal Resolution Data Research Articles

Data Fusion for Estimating High-Resolution Urban Heatwave Air Temperature

High-resolution air temperature data is indispensable for analysing heatwave-related non-accidental mortality. However, the limited number of weather stations in urban areas makes obtaining such data challenging. Multi-source data fusion has been proposed as a countermeasure to tackle such challenges. Satellite products often offered high spatial resolution but suffered from being temporally discontinuous due to weather conditions. The characteristics of the data from reanalysis models were the opposite. However, few studies have explored the fusion of these datasets. This study is the first attempt to integrate satellite and reanalysis datasets by developing a two-step downscaling model to generate hourly air temperature data during heatwaves in London at 1 km resolution. Specifically, MODIS land surface temperature (LST) and other satellite-based local variables, including normalised difference vegetation index (NDVI), normalized difference water index (NDWI), modified normalised difference water index (MNDWI), elevation, surface emissivity, and ERA5-Land hourly air temperature were used. The model employed genetic programming (GP) algorithm to fuse multi-source data and generate statistical models and evaluated using ground measurements from six weather stations. The results showed that our model achieved promising performance with the RMSE of 0.335 °C, R-squared of 0.949, MAE of 1.115 °C, and NSE of 0.924. Elevation was indicated to be the most effective explanatory variable. The developed model provided continuous, hourly 1 km estimations and accurately described the temporal and spatial patterns of air temperature in London. Furthermore, it effectively captured the temporal variation of air temperature in urban areas during heatwaves, providing valuable insights for assessing the impact on human health.

Using home monitoring technology to study the effects of traumatic brain injury in older multimorbid adults.

Internet of things (IOT) based in-home monitoring systems can passively collect high temporal resolution data in the community, offering valuable insight into the impact of health conditions on patients' day-to-day lives. We used this technology to monitor activity and sleep patterns in older adults recently discharged after traumatic brain injury (TBI). The demographics of TBI are changing, and it is now a leading cause of hospitalisation in older adults. However, research in this population is minimal. We present three cases, showcasing the potential of in-home monitoring systems in understanding and managing early recovery in older adults following TBI.

First results from the Solar Orbiter Heavy Ion Sensor

Aims. Solar Orbiter launched in February 2020 with the goal of revealing the connections between the Sun’s interior, atmosphere, and the heliosphere. The Solar Orbiter Heavy Ion Sensor (HIS) is a time-of-flight ion mass spectrometer dedicated to measuring heavy ions in the solar wind. Methods. We present an overview of the first measurements of heavy ion composition from HIS, reviewing the methods used to transform the spectra obtained on board into scientific data products and examining two solar wind case studies as well as the statistical properties of the heavy ion composition observed by HIS. We also carried out a comparison with prior measurements of heavy ions at L1. Results. The HIS data set provides the first mass- and charge-resolved heavy ion measurements in the inner heliosphere. Conclusions. These high temporal resolution data have the potential to transform our understanding of the connections between the solar wind and its origin at the Sun, as well as the interaction between the solar wind and the environment around planets, comets, and in the interstellar medium.

Hybrid model for daily streamflow and phosphorus load prediction.

Environmental factors, such as climate change and land use changes, affect water quality drastically. To consider these, various predictive models, both process-based and data-driven, have been used. However, each model has distinct limitations. In this study, a hybrid model combining the soil and water assessment tool and the reverse time attention mechanism (SWAT-RETAIN) was proposed for predicting daily streamflow and total phosphorus (TP) load of a watershed. SWAT-RETAIN was applied to Hwangryong River, South Korea. The hybrid model uses the SWAT output as input data for the RETAIN. Spatial, meteorological, and hydrological data were collected to develop the SWAT to generate high temporal resolution data. RETAIN facilitated effective simultaneous prediction. The SWAT-RETAIN exhibited high accuracy in predicting streamflow (Nash-Sutcliffe efficiency (NSE): 0.45, root mean square error (RMSE): 27.74, percent bias (PBIAS): 22.63 for test sets), and TP load (NSE: 0.50, RMSE: 423.93, PBIAS: 22.09 for test sets). This result was evident in the performance evaluation using flow duration and load duration curves. The SWAT-RETAIN provides enhanced temporal resolution and performance, enabling the simultaneous prediction of multiple variables. It can be applied to predict various water quality variables in larger watersheds.

Application of a deep learning-based discrete weather data continuousization model in ship route optimization

When ships sail across oceans, weather and sea conditions are the key factors affecting the safety and economy of ship navigation. Thus, obtaining accurate weather forecast data is very important, which can help design voyage routes. However, most weather forecast data sources only provide sparse spatio-temporal low-resolution discrete data; furthermore, as the time span increases, the resolution decreases, seriously affecting route optimization accuracy. In addition, due to maritime communication conditions, ships cannot obtain high-resolution temporal and spatial weather forecast data. Therefore, this paper introduces a transformer-based time series model to recover spatio-temporal discrete weather forecast data to improve voyage optimization accuracy. Specifically, a convolutional neural network extracts multi-dimensional spatial features from weather forecast data. The extracted features are then input into a transformer attention mechanism along the time dimension to analyze the time characteristics of the weather data. The trained model can obtain the spatiotemporal continuous model of the weather forecast data. Experimentally, the effectiveness and accuracy of the reconstructed weather forecast data were confirmed by comparing it with the observed weather data. Furthermore, the proposed method demonstrated that it is conducive to route design and optimization.

Energy Repartition and Entropy Generation across the Earth’s Bow Shock: MMS Observations

The evolution of plasma entropy and the process of plasma energy redistribution at the collisionless plasma shock front are evaluated based on the high temporal resolution data from the four Magnetospheric Multiscale spacecraft during the crossing of the terrestrial bow shock. The ion distribution function has been separated into the populations with different characteristic behaviors in the vicinity of the shock: the upstream core population, the reflected ions, the gyrating ions, the ions trapped in the vicinity of the shock, and the downstream core population. The values of ion and electron moments (density, bulk velocity, and temperature) have been determined separately for these populations. It is shown that the solar wind core population bulk velocity slows down mainly in the ramp with the electrostatic potential increase but not in the foot region as it was supposed. The reflected ion population determines the foot region properties, so the proton temperature peak in the foot region is an effect of the relative motion of the different ion populations, rather than an actual increase in the thermal speed of any of the ion population. The ion entropy evaluated showed a significant increase across the shock: the enhancement of the ion entropy occurs in the foot of the shock front and at the ramp, where the reflected ions are emerging in addition to the upstream solar wind ions, the anisotropy growing to generate the bursts of ion-scale electrostatic waves. The entropy of electrons across the shock does not show a significant change: electron heating goes almost adiabatically.

The evolution and preservation potential of englacial eskers: An example from Breiðamerkurjökull, SE Iceland

AbstractDirectly observing glacial drainage systems (englacial and subglacial) is challenging. The distribution, morphology and internal structure of eskers can provide valuable information about the glacial drainage system and meltwater processes. This work presents the annual evolution (meltout) and internal structure of an esker emerging from the Breiðamerkurjökull ice margin, southeast Iceland. Changes in esker morphology have been repeatedly mapped over a 1‐year period using high temporal and spatial resolution data acquired by an uncrewed aerial vehicle (UAV). The internal architecture of the esker was investigated using ground‐penetrating radar (GPR) surveys. These data are used to identify the dominant processes driving the formation of this englacial esker and to evaluate the preservation potential. The englacial esker was up to 2.6 m thick and ice‐cored. A large moulin upglacier of the esker, which evolved into an englacial conduit, supplied meltwater to the englacial channel. Upglacier dipping debris‐filled basal hydrofractures, formed by pressurised subglacial meltwater rising up the retrograde bed slope, likely supplied sediment to the englacial conduit. Over the 1‐year period of observation the crest morphology evolved from flat‐ to sharp‐crested and the esker footprint increased by a factor of 5.7 in response to post‐depositional processes. The findings presented here indicate that englacial eskers may have low preservation potential due to post‐depositional reworking such as slumping through ice‐core meltout and erosion by later meltwater flow. As englacial eskers may not be preserved in the landscape, they could represent important glacial drainage system components that are not currently captured in palaeo‐ice sheet reconstructions. This work highlights the value of creating a time series of high‐temporal resolution data to quantify morphological evolution and improve glacial process‐form models.

Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays.

Low-cost air quality monitors are increasingly being deployed in various indoor environments. However, data of high temporal resolution from those sensors are often summarized into a single mean value, with information about pollutant dynamics discarded. Further, low-cost sensors often suffer from limitations such as a lack of absolute accuracy and drift over time. There is a growing interest in utilizing data science and machine learning techniques to overcome those limitations and take full advantage of low-cost sensors. In this study, we developed an unsupervised machine learning model for automatically recognizing decay periods from concentration time series data and estimating pollutant loss rates. The model uses k-means and DBSCAN clustering to extract decays and then mass balance equations to estimate loss rates. Applications on data collected from various environments suggest that the CO2 loss rate was consistently lower than the PM2.5 loss rate in the same environment, while both varied spatially and temporally. Further, detailed protocols were established to select optimal model hyperparameters and filter out results with high uncertainty. Overall, this model provides a novel solution to monitoring pollutant removal rates with potentially wide applications such as evaluating filtration and ventilation and characterizing indoor emission sources.

High-Precision In Situ Total Alkalinity Analyzer Capable of Month-Long Observations in Seawaters.

Total alkalinity (TA) is an essential variable for the study of physical and biogeochemical processes in coastal and oceanic systems, and TA data obtained at high spatiotemporal resolutions are highly desired. The performance of the current in situ TA analyzers/sensors, including precision, accuracy, and deployment duration, cannot fully meet most research requirements. Here, we report on a novel high-precision in situ analyzer for surface seawater TA (ISA-TA), based on an automated single-point titration with spectrophotometric pH detection, and capable of long-term field observations. The titration was carried out in a circulating loop, where the titrant (a mixture of HCl and bromocresol green) and seawater sample were mixed in a constant volume ratio. The effect of ambient temperature on the TA measurement was corrected with an empirical formula. The weight, height, diameter, and power consumption of ISA-TA were 8.6 kg (in air), 33 cm, 20 cm, and 7.3 W, respectively. A single measurement required ∼7 min of running time, ∼32 mL of seawater, and ∼0.6 mL of titrant. ISA-TA was able to operate continuously in the field for up to 30 days, and its accuracies in the laboratory and field were 0.5 ± 1.7 μmol kg-1 (n = 13) and 10.3 ± 2.8 μmol kg-1 (n = 29) with precisions of 0.6-0.8 μmol kg-1 (n = 51) and 0.2-0.7 μmol kg-1 (n = 8), respectively. This study provides the research community with a new tool to obtain seawater TA data of high temporal resolution.

Application of UAV for Village Mapping in Pangambatan, Sumatra Utara

Pangambatan Village, as part of the Lake Toba National Strategic Area and a tourist destination, still lacks facilities, infrastructure, and professional human resources to manage its cultural and tourism potential. An initial mapping of the conditions and potential of the area is needed to drive tourism in Pangambatan Village towards sustainable tourism. This can be done by mapping environmental conditions and tourism potential in Pangambatan Village, providing recommendations for sustainable tourism management, and providing environmental management facilities. The study was carried out using Unmanned Aerial Vehicle (UAV) technology. UAV technology offers new opportunities for fast and efficient remote environmental monitoring and the collection of high spatial and temporal resolution data. The benefits of this mapping can be used to see environmental conditions and environmental pollution that may occur because of tourist activities. In addition, the use of this technology is expected to help related parties in making decisions and planning.

Improvement of CYGNSS soil moisture retrieval model considering water and surface temperature

The key to retrieving soil moisture (SM) with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) data is to correct the effects of soil surface temperature (SST), roughness, water, and vegetation. In this study, Cyclone Global Navigation Satellite System (CYGNSS) data are used to calculate surface reflectivity and surface roughness is characterized based on the statistical moments of Delay-Doppler Maps (DDM). After removing open water, a multiple linear regression model is created to retrieve SM by combining the SST and vegetation optical depth (VOD) parameters provided by Soil Moisture Active Passive (SMAP) data. With a correlation coefficient of 0.815 and a root-mean-square error of 0.066 cm3cm−3, an experimental analysis reveals decent consistency between the CYGNSS SM and the referenced SMAP SM. In addition, a time-series analysis between the CYGNSS SM and the International Soil Moisture Network (ISMN) referenced SM data shows a good correlation. Since surface reflectivity is significantly affected by water and because there is a coupling relationship between SST and SM, the differences of CYGNSS SM when the two factors are simultaneously considered or ignored are also analyzed. The experimental results show that after removing water and incorporating SST into the linear regression model, the accuracy of CYGNSS SM has been improved significantly, with the root mean square error, mean absolute error, and Bias increasing by 6%, 7%, and 11.3%, respectively. This study demonstrates the necessity of considering water and SST in SM retrieval and provides a novel approach for SM retrieval using high accuracy and high spatial and temporal resolution data.

Assessing the impact of heavy rainfall on the Newcastle upon Tyne transport network using a geospatial data infrastructure

Extreme weather conditions can adversely impact transport networks and driver behaviour, leading to variations in traffic volumes and travel times and increased accident rates. Emergency services that need to navigate to an accident site in the shortest possible time require real-time location-based weather and traffic information to coordinate their response.We therefore require historical and high-resolution temporal real-time data to identify districts and roads that are prone to different types of incidents during inclement weather and to better support emergency services in their decision-making. However, real-time assessment of the current transport network requires a dense sensor network that can provide high-resolution data using internet-enabled technology.In this research, we demonstrate how we obtain historical time-series and real-time data from sensors operated by the Tyne and Wear Urban Traffic and Management Control Centre and the Urban Observatory based at Newcastle upon Tyne, UK. In the study, we assess the impact of rainfall on traffic volume and travel time, and the cascading impacts during a storm event in Newcastle during early October 2021. We also estimate the economic cost of the storm, with regards to transport disruption, as the cost of travel, using the “value of time” based on Department for Transport guidelines (2021).Using spatial-temporal analysis, we chose three locations to demonstrate how traffic parameters varied at different times throughout the storm. We identified increases in travel times of up to 600% and decreases in traffic volume of up to 100% when compared to historical data. Further, we assessed cascading impacts at important traffic locations and their broader implications for city areas. We estimated that the storm's economic impact on one sensor location increased by up to 370% of the reference value.By analysing historical and real-time data, we detected and explained patterns in the data that would have remained uncovered if they had been examined individually. The combination of different data sources, such as traffic and weather, helps explain temporal fluctuations at locations where incidents were recorded near traffic detectors.We anticipate our study to be a starting point for stakeholders involved in incident response to identify bottleneck locations in the network to help prepare for similar future events.

ANALYSIS OF FACTORS FOR FORMATION OF THE CHUMYSH RIVER MAXIMUM RUNOFF (WESTERN SIBERIA)

Link for citation: Samoilova S.Yu., Lovtskaya O.V., Kudishin A.V., Arnaut D.V. Analysis of factors for formation of the Chumysh river maximum runoff (Western Siberia). Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 5, рр.116-128. In Rus. The relevance of the study stems from the necessity to refine the methods of medium-term flood forecasts when sufficient hydrometeorological data are not available. The purpose of the work is to assess probable application of spatially distributed precipitation models to forecast runoff volume of flood, to develop stochastic models for predicting flood volume and its maximum discharge using hydrometeorological observation data, distributed precipitation datasets from reanalysis and remote sensing data of high spatial and temporal resolution. Methods include geoinformation, complex geographical and hydrometeorological analysis, statistical methods (correlation and regression analysis). Results. The Chumysh basin moistening was estimated due to the data from spatially distributed precipitation models and hydrometeorological observation data; the relationship of total precipitation with runoff volume and maximum flood discharge was analyzed. A comparative evaluation of the obtained dependencies made it possible to identify key predictors for deriving the multiple linear regression equation. The statistical model was developed for predicting volumes and maximum discharges of Chumysh flood at Talmenka settlement using hydrometeorological observation data and reanalysis ones of high spatial and temporal resolution.

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

The study of surface vegetation monitoring in the “Three-River Headwaters” Region (TRHR) relies on satellite data with high spatial and temporal resolutions. The spatial and temporal fusion method for multiple data sources can effectively overcome the limitations of weather, the satellite return period, and funding on research data to obtain data higher spatial and temporal resolutions. This paper explores the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the flexible spatiotemporal data fusion (FSDAF) method applied to Sentinel-2 and MODIS data in a typical area of the TRHR. In this study, the control variable method was used to analyze the parameter sensitivity of the models and explore the adaptation parameters of the Sentinel-2 and MODIS data in the study area. Since the spatiotemporal fusion model was directly used in the product data of the vegetation index, this study used NDVI fusion as an example and set up a comparison experiment (experiment I first performed the band spatiotemporal fusion and then calculated the vegetation index; experiment II calculated the vegetation index first and then performed the spatiotemporal fusion) to explore the feasibility and applicability of the two methods for the vegetation index fusion. The results showed the following. (1) The three spatiotemporal fusion models generated high spatial resolution and high temporal resolution data based on the fusion of Sentinel-2 and MODIS data, the STARFM and FSDAF model had a higher fusion accuracy, and the R2 values after fusion were higher than 0.8, showing greater applicability. (2) The fusion accuracy of each model was affected by the model parameters. The errors between the STARFM, ESTARFM, and FSDAF fusion results and the validation data all showed a decreasing trend with an increase in the size of the sliding window or the number of similar pixels, which stabilized after the sliding window became larger than 50 and the similar pixels became larger than 80. (3) The comparative experimental results showed that the spatiotemporal fusion model can be directly fused based on the vegetation index products, and higher quality vegetation index data can be obtained by calculating the vegetation index first and then performing the spatiotemporal fusion. The high spatial and temporal resolution data obtained using a suitable spatial and temporal fusion model are important for the identification and monitoring of surface cover types in the TRHR.

Rainfall erosivity estimation models for the Tibetan Plateau

Many models have been developed internationally to estimate rainfall erosivity. However, the effectiveness of these pre-existing models has not been verified for the Tibetan Plateau with large spatiotemporal heterogeneity of rainfall due to lack of high-temporal resolution rainfall data. The purpose of this study was to develop models to accurately estimate daily and semi-monthly rainfall erosivity values in the Tibetan Plateau. We used one-minute resolution rainfall data from 1,182 weather stations to develop rainfall erosivity models over the Tibetan Plateau for the first time. The particle swarm optimization (PSO) method was performed to determine the newly developed model parameters. The newly developed models were compared with three internationally common existing models (exponentially seasonal1Exponentially seasonal model denoted Richardson model (1983) that was revised as R =αP1.7265 by Xie et al. (2016).1, cosine2Cosine model denoted Yu and Rosewell model (1996) that was revised as R=0.26861+0.5412cosπ6M-7π6P1.7265 by Xie et al. (2016).2, and exponentially semi-month3Exponentially semi-month model denoted Zhang model (2002) that was expressed as R=α∑j=1mPjβ,α= 21.586β- 7.1891 and β=0.8363+18.177Pd12+24.455Py12.3) to evaluate their effectiveness. Our results showed that (1) the three existing models all overestimated the values of mean annual rainfall erosivity in the Tibetan Plateau because limited rainfall data in the area were used to develop these models; (2) the new models greatly reduced computational error values of mean annual rainfall erosivity (the symmetric mean absolute percentage error (SMAPE) of ≤ 23%), relative to the three existing models (SMAPE of ≥ 40%); (3) the new semi-monthly models (SMAPE of 17.7 – 29.2%) generally outperformed the new daily models (SMAPE of 17.9 – 30.7%) because of their better ability to estimate the values of extreme rainfall erosivity. It is advisable to use the semi-monthly model rather than daily model to compute rainfall erosivity when using universal soil loss equation (USLE) and its derived models to calculate soil erosion. That will benefit to reduce the time-scale conversion errors between event-based and daily data when lacking high-temporal resolution data and using daily rainfall data to analyze the influence of rainfall on soil erosion.

Cold homes in Australia: Questioning our assumptions about prevalence

Australia is considered by many to be a warm climate country and hence winter cold and its health effects are often overlooked. The majority of the Australian population live in temperate climate regions, which are heating-dominated and experience cold wintertime conditions. The prevalence of cold in Australian homes has to date been rarely measured or estimated, and the few studies that do are based on proxy data that estimate a low prevalence (around 5 %). This paper tests these proxy measures, using high resolution temperature data on indoor temperature from 100 homes across temperate Australia. The data were assessed using the World Health Organization's Housing and Health guideline for minimum indoor temperature (18 °C), which provides an internationally recognised benchmark for defining cold in homes. Across the sampled homes, 81 % were below 18 °C on average across the whole of winter (June–August 2022). Average winter indoor temperatures were 16.5 ± 2.7 °C across all homes, with no significant difference between locations. These early findings suggest that the problem of unhealthily cold homes in Australia is likely to be significantly more prevalent than previously estimated. Far from affecting 5 % of Australian households, the affected population may be 10 times this value. These initial findings have important implications for how we model health impacts and develop policy. This early release data from a recently commenced large environmental monitoring project is of timely importance. It challenges our understanding of the prevalence of cold housing in Australia context, flagging the pressing need to increase policy attention in advance of winter.

Estimating stocking weights for Atlantic salmon to grow to market size at novel aquaculture sites with extreme temperatures

Land-based hatcheries are now capable of growing large Atlantic salmon (Salmo salar) post-smolts (approximately 150 – 1000 g), which means that marine net-pens can be stocked with substantially larger fish compared to traditional stocking sizes (< 150 g). This stocking strategy typically aims to reduce the time required for fish to grow to market size in the marine environment and limit risks (e.g., exposure to pathogens and diseases, opportunities for escapes). This study investigates another potential application of this strategy: the use of novel sites in areas previously considered unsuitable for aquaculture due to seasonally cold temperatures. The thermal-unit growth coefficient (TGC) model was applied to estimate the stocking weight needed to reach a harvest size of 5.5 kg, based on observed degree days for three sites. High resolution, depth-partitioned temperature time series from coastal locations in Atlantic Canada were used to represent a short, medium, and long growing season, as constrained by seasonal temperature extremes. Growing days for model inputs were defined as temperatures > 4 °C and trending up for stocking, < 18 °C to account for heat stress, and > -0.7 °C to avoid superchill conditions. Different TGC values were applied to simulate remedial, average, and elite growth performance. There was a range of model stocking weight estimates for each site (1.5 – 2.5 kg, 0.94 – 2.8 kg, and < 0.1 – 0.52 kg, for the short, medium, and long season sites, respectively). Results were sensitive to the number of degree days, heat stress threshold, and TGC value. At the two sites where season length was constrained by superchill, fish with a stocking weight of approximately 1.5 kg could grow to market size in shallow water depths (< 15 m), assuming elite growth performance. This investigation suggests that with appropriate growth performance assumptions and high-resolution temperature data, large post-smolt stocking strategies could enable the use of novel sites in coastal areas previously considered unsuitable for aquaculture.

Multimodal deep neural decoding reveals highly resolved spatiotemporal profile of visual object representation in humans

Perception and categorization of objects in a visual scene are essential to grasp the surrounding situation. Recently, neural decoding schemes, such as machine learning in functional magnetic resonance imaging (fMRI), has been employed to elucidate the underlying neural mechanisms. However, it remains unclear as to how spatially distributed brain regions temporally represent visual object categories and sub-categories. One promising strategy to address this issue is neural decoding with concurrently obtained neural response data of high spatial and temporal resolution. In this study, we explored the spatial and temporal organization of visual object representations using concurrent fMRI and electroencephalography (EEG), combined with neural decoding using deep neural networks (DNNs). We hypothesized that neural decoding by multimodal neural data with DNN would show high classification performance in visual object categorization (faces or non-face objects) and sub-categorization within faces and objects. Visualization of the fMRI DNN was more sensitive than that in the univariate approach and revealed that visual categorization occurred in brain-wide regions. Interestingly, the EEG DNN valued the earlier phase of neural responses for categorization and the later phase of neural responses for sub-categorization. Combination of the two DNNs improved the classification performance for both categorization and sub-categorization compared with fMRI DNN or EEG DNN alone. These deep learning-based results demonstrate a categorization principle in which visual objects are represented in a spatially organized and coarse-to-fine manner, and provide strong evidence of the ability of multimodal deep learning to uncover spatiotemporal neural machinery in sensory processing.

The Long-term variability of Global Typical Oceanic Fronts

Using high-resolution NOAA Optimum Interpolation sea surface temperature (OISST) data, the temporal and spatial characteristics of typical oceanic fronts in the past 39 years, including Oyashio front, Gulf Stream front, Peru current front and southern Indian Ocean polar front, are analyzed. The results show that the intensities of the four typical fronts have all strengthened, among which the Peru current is the most significant and the South Indian Ocean polar front is the weakest. The movement of the fronts are quite different: the Oyashio front has a significant poleward movement, and the Gulf Stream front extending into the open sea part has a marginal poleward movement; while the south Indian Ocean polar front and Peru current front have no obvious movements.

The Expectations and Prospects for Quantitative Volcanology in the Upcoming Surface Biology and Geology (SBG) Era

AbstractThe Surface Biology and Geology (SBG) mission is part of the National Aeronautics and Space Administration's planned Earth System Observatory. The mid/thermal infrared (MIR/TIR) portion of SBG will deliver high spatial and temporal resolution data designed (in part) to answer fundamental volcano science questions, as outlined in the 2018 Decadal Survey (DS). The planned 60 m spatial resolution, high TIR saturation temperature (∼500 K), and addition of two high temperature MIR bands (∼800–1200 K) will improve thermal feature and spatial variability detection across all volcanic surfaces, as well as limit data saturation over high temperature targets such as lava flows. However, the MIR/TIR part of the mission also has design aspects that will hinder volcanic science. The ability to derive accurate spectral information of volcanic, and indeed other geologic surfaces, is a function of the number and placement of the TIR bands. The originally proposed five TIR bands do not advance these diagnostic investigations beyond what is currently available. In fact, it may hinder them. Six or more TIR bands are vital for accurate assessment of mineral and glass abundances in volcanic deposits. The SBG MIR/TIR mission temporal resolution (3–5 days) will improve repeat coverage of volcanic systems, aiding in longer‐term monitoring, but observations of highly dynamic processes will not be possible, despite their importance in the 2018 DS. Most problematic is the proposed local overpass time of 12:30, which will result in cloudier scenes (especially in tropical/lower latitude regions) and complicate atmospheric corrections compared to the traditional morning overpass times of earlier TIR missions.