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The Coastal Data Information Program (CDIP) is an extensive network for monitoring waves and beaches along the coastlines of the United States. The buoy primarily collects wave and sea surface temperature information. These data are collected as a part of an observational database to support the floating offshore wind research and industry along the coast of California within the ORACLE project funded by DOE-WETO.
To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.
This dataset contains bathymetric data for the Block Island region used in acoustic propagation modeling, including water depth measurements, coastline boundaries, and processed bathymetry datasets from multiple sources and resolutions.
To explore the integrated effects of ocean and ocean surface wave related physical processes on TC simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data.
To explore the integrated effects of ocean and ocean surface wave related physical processes on tropical cyclone simulations, a set of three model simulations is performed. * In experiment 'A', the event is modeled using the Weather Research Forecasting (WRF) model alone with prescribed Sea Surface Temperature (SST) at 6-hour intervals. * In experiment ‘AO,’ WRF is coupled with the Finite Volume Community Ocean Model (FVCOM), enabling variable exchange between atmosphere and ocean, but without considering ocean surface wave-related physical processes. * In experiment ‘AOW’, WRF, FVCOM, and Simulating WAves Nearshore (SWAN) exchange variables with each other every hour through the OASIS3-MCT Coupler to allow direct and indirect atmosphere-ocean-wave interactions. * Observational data are also included in this dataset (Dropsonde, HRD-Radar, NDBC_wave). All simulations are initialized at 18:00 UTC on August 19, 2021, within a domain encompassing the western North Atlantic Ocean. The atmospheric domain features a horizontal resolution of 3 km. The ocean domain, which covers a substantial portion of the WRF ocean domain, employs an unstructured triangular grid with resolutions ranging from 3 km near the coast to 9 km in the open ocean, effectively resolving the complex coastline of the U.S. Northeast Coast. Initial and boundary conditions for the atmosphere model are obtained from the 6-hourly 0.25° NCEP (National Centers for Environmental Prediction) Global Forecast System (GFS; NCEP, 2015) data. These CSV files are derived from the NetCDF files in the c0 dataset. Unlike the original format, where geographic coordinates were stored in a separate file, each CSV now embeds the corresponding latitude and longitude values alongside the measured or simulated variables.
Coastal areas are highly influenced by terrestrial runoffs and anthropogenic disturbances, commonly leading to ecological gradients from bay, nearshore, to offshore areas. Although the occurrence and distribution of sediment antibiotic resistome are explored in various coastal environments, little information is available regarding geographic patterns and determinants of coastal sediment antibiotic resistomes across ecological gradients at the regional scale. Here, using high-throughput quantitative PCR, we investigated the geographic patterns of 285 antibiotic resistance genes (ARGs) in coastal sediments across a ~ 200 km scale in the East China Sea. Sediment bacterial communities and physicochemical properties were characterized to identify the determinants of sediments antibiotic resistome. Higher richness and abundance of ARGs were detected in the bay samples compared with those in nearshore and offshore samples, and significant negative correlations between the richness and/or abundance of ARGs and the distance to coastline (DTC) were identified, whereas different types of ARGs showed inconsistency in their relationships with DTC. The composition of antibiotic resistome showed significant correlations with nutrition-related variables (including NH 4 + -N, NO 3 − -N, and total phosphorus) and metals/metalloid (including As, Cu, Ni, and Zn), suggesting that terrestrial disturbances largely shape the antibiotic resistome. The Bipartite network showed strong associations between ARGs and mobile genetic elements (MGEs), and Partial Least Squares Path Modeling further revealed that terrestrial disturbance strength (as indicated by DTC) directly affected abiotic environmental conditions and bacterial community composition, and indirectly affected antibiotic resistome via MGEs. These findings provide insights into regional variability of sediment antibiotic resistome and its shaping path across complex ecological gradients, highlighting terrestrial disturbances as determinative forces in shaping coastal sediment antibiotic resistomes.
Wave-generated power has potential as a valuable coastal resource, but the wave climate needs to be mapped for feasibility before wave energy converters are installed. Numerical models are used for wave resource assessments to quantify the amount of available power and its seasonality. Alaska is the U.S. state with the longest coastline and has extensive wave resources, but it is affected by seasonal sea ice that dampens the wave energy and the full extent of this dampening is unknown. To accurately characterize the wave resource in regions that experience seasonal sea ice, coastal wave models must account for these effects. The aim of this study is to determine how the dampening effects of sea ice change wave energy resource assessments in the nearshore. Here, we show that by combining high-resolution sea ice imagery with a sea ice/wave dampening parameterization in an unstructured grid, the Simulating Waves Nearshore (SWAN) model improves wave height predictions and demonstrates the extent to which wave power decreases when sea ice is present. The sea ice parametrization decreases the bias and root mean square errors of wave height comparisons with two wave buoys and predicts a decrease in the wave power of up to 100 kW/m in areas around Prince William Sound, Alaska. The magnitude of the improvement of the model/buoy comparison depends on the coefficients used to parameterize the wave–ice interaction.
Two years of high-resolution simulations conducted with the Weather Research and Forecasting (WRF) model are used to characterize the frequency, intensity and height of low-level jets (LLJ) over the U.S. Atlantic coastal zone. Meteorological conditions and the occurrence and characteristics of LLJs are described for (i) the centroids of thirteen of the sixteen active offshore wind energy lease areas off the U.S. east coast and (ii) along two transects extending east from the U.S. coastline across the northern lease areas (LA). Flow close to the nominal hub-height of wind turbines is predominantly northwesterly and southwesterly and exhibits pronounced seasonality, with highest wind speeds in November, and lowest wind speeds in June. LLJs diagnosed using vertical profiles of modeled wind speeds from approximately 20 to 530 m above sea level exhibit highest frequency in LA south of Massachusetts, where LLJs are identified in up to 12% of hours in June. LLJs are considerably less frequent further south along the U.S. east coast and outside of the summer season. LLJs frequently occur at heights that intersect the wind turbine rotor plane, and at wind speeds within typical wind turbine operating ranges. LLJs are most frequent, intense and have lowest core heights under strong horizontal temperature gradients and lower planetary boundary layer heights.
Ecosystems at coastal terrestrial–aquatic interfaces play a significant role in global biogeochemical cycles. In this study, we aimed to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region. We investigated the heterogeneity of coastal ecosystem structures as a function of along-shore distance and transverse distance, based on the spatial data layers, including topography, wetland vegetation cover, and the time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. Results showed that the topographic metrics (elevation and slope), soil texture, and plant productivity influence the spatial distribution of wetland land-covers (emergent and phragmites). These results highlight a natural organization along the transverse axis, where the elevation and the EVI increase further away from the coastline. In addition, the clustering analysis allowed us to identify regions with distinct environmental characteristics, as well as the ones that are more sensitive to interannual lake-level variations.
Abstract. The Southern Ocean region is one of the most pristine in the world and serves as an important proxy for the pre-industrial atmosphere. Improving our understanding of the natural processes in this region is likely to result in the largest reductions in the uncertainty of climate and earth system models. While remoteness from anthropogenic and continental sources is responsible for its clean atmosphere, this also results in the dearth of atmospheric observations in the region. Here we present a statistical summary of the latitudinal gradient of aerosol (condensation nuclei larger than 10 nm, CN10) and cloud condensation nuclei (CCN at various supersaturations) concentrations obtained from five voyages spanning the Southern Ocean between Australia and Antarctica from late spring to early autumn (October to March) of the 2017/18 austral seasons. Three main regions of influence were identified: the northern sector (40–45∘ S), where continental and anthropogenic sources coexisted with background marine aerosol populations; the mid-latitude sector (45–65∘ S), where the aerosol populations reflected a mixture of biogenic and sea-salt aerosol; and the southern sector (65–70∘ S), south of the atmospheric polar front, where sea-salt aerosol concentrations were greatly reduced and aerosol populations were primarily biologically derived sulfur species with a significant history in the Antarctic free troposphere. The northern sector showed the highest number concentrations with median (25th to 75th percentiles) CN10 and CCN0.5 concentrations of 681 (388–839) cm−3 and 322 (105–443) cm−3, respectively. Concentrations in the mid-latitudes were typically around 350 cm−3 and 160 cm−3 for CN10 and CCN0.5, respectively. In the southern sector, concentrations rose markedly, reaching 447 (298–446) cm−3 and 232 (186–271) cm−3 for CN10 and CCN0.5, respectively. The aerosol composition in this sector was marked by a distinct drop in sea salt and increase in both sulfate fraction and absolute concentrations, resulting in a substantially higher CCN0.5/CN10 activation ratio of 0.8 compared to around 0.4 for mid-latitudes. Long-term measurements at land-based research stations surrounding the Southern Ocean were found to be good representations at their respective latitudes; however this study highlighted the need for more long-term measurements in the region. CCN observations at Cape Grim (40∘39′ S) corresponded with CCN measurements from northern and mid-latitude sectors, while CN10 observations only corresponded with observations from the northern sector. Measurements from a simultaneous 2-year campaign at Macquarie Island (54∘30′ S) were found to represent all aerosol species well. The southernmost latitudes differed significantly from both of these stations, and previous work suggests that Antarctic stations on the East Antarctic coastline do not represent the East Antarctic sea-ice latitudes well. Further measurements are needed to capture the long-term, seasonal and longitudinal variability in aerosol processes across the Southern Ocean.
In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81°N. Excellent agreement (difference less than 1%) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45% and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73°N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5.
Shallow convection plays an important role in Earth's climate system by regulating the vertical transport of heat, moisture, and momentum in the lower troposphere. Aerosols, large-scale meteorology, and low-level convergence influence the spatiotemporal variability in shallow convection, and the coastal urban area of Houston, Texas, is an ideal laboratory to investigate these complex interactions. Here, geostationary satellite and ground-based radar observations from June to September 2022 during the TRacking Aerosol Convection interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) field campaigns are used to characterize the spatial coverage, vertical extent, and precipitation fraction of shallow convective clouds. The fused operational remote sensing datasets over a 250 km × 250 km domain are evaluated against profiling observations. The domain-wide diurnal shallow cloud fractions are used to identify four distinct modes of shallow convection. In all clusters, the domain-wide cloud fractions are consistently higher than the domain-wide precipitation fractions, and shallow cloud fractions are higher over water than they are over land, while the shallow precipitation fractions show the opposite behavior. In the two modes with minimal deep cloud activity, shallow cloud frequency is highest over ocean in the early morning, and there is a transition to higher shallow cloud frequency over land by the afternoon in one cluster or to high shallow cloud frequencies everywhere by the afternoon in the other. Lastly, we find regions with higher shallow cloud top heights and a large region along the coastline where shallow clouds are more likely to precipitate.
Ice nucleating particles (INPs) play a critical role in cloud microphysics and precipitation formation, yet long-term, spatially extensive observational datasets remain limited. Here, we present one of the most comprehensive publicly available datasets of immersion-mode INP concentrations using a single analytical method, generated through the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility. INP filter samples have been collected across a broad range of environments – including agricultural plains, Arctic coastlines, high-elevation mountain sites, marine regions, and urban areas – via fixed observatories, mobile facility deployments, and vertically-resolved tethered balloon system operations. We describe the standardized processing and quality assurance pipeline, from filter collection and processing using the Ice Nucleation Spectrometer to final data products archived on the ARM Data Discovery portal. The dataset includes both total INP concentrations and selectively treated samples, allowing for classification of biological, organic, and inorganic INP types. It features a continuous 5-year record of INP measurements from a central U.S. site, with data collection still ongoing. Seasonal and site-specific differences in INP concentrations are illustrated through intercomparisons at −10 and −20 °C, revealing distinct regional sources and atmospheric drivers. We also outline mechanisms for researchers to access existing data, request additional sample analyses, and propose future field campaigns involving ARM INP measurements. This dataset supports a wide range of scientific applications, from observational and mechanistic studies to model development, and provides critical constraints on aerosol-cloud interactions across diverse atmospheric regimes (Creamean et al., 2024, 2020b; https://doi.org/10.5439/1770816).
Abstract. We present MPAS-Seaice, a sea-ice model which uses the Model for Prediction Across Scales (MPAS) framework and spherical centroidal Voronoi tessellation (SCVT) unstructured meshes. As well as SCVT meshes, MPAS-Seaice can run on the traditional quadrilateral grids used by sea-ice models such as CICE. The MPAS-Seaice velocity solver uses the elastic–viscous–plastic (EVP) rheology and the variational discretization of the internal stress divergence operator used by CICE, but adapted for the polygonal cells of MPAS meshes, or alternatively an integral (“finite-volume”) formulation of the stress divergence operator. An incremental remapping advection scheme is used for mass and tracer transport. We validate these formulations with idealized test cases, both planar and on the sphere. The variational scheme displays lower errors than the finite-volume formulation for the strain rate operator but higher errors for the stress divergence operator. The variational stress divergence operator displays increased errors around the pentagonal cells of a quasi-uniform mesh, which is ameliorated with an alternate formulation for the operator. MPAS-Seaice shares the sophisticated column physics and biogeochemistry of CICE and when used with quadrilateral meshes can reproduce the results of CICE. We have used global simulations with realistic forcing to validate MPAS-Seaice against similar simulations with CICE and against observations. We find very similar results compared to CICE, with differences explained by minor differences in implementation such as with interpolation between the primary and dual meshes at coastlines. We have assessed the computational performance of the model, which, because it is unstructured, runs with 70 % of the throughput of CICE for a comparison quadrilateral simulation. The SCVT meshes used by MPAS-Seaice allow removal of equatorial model cells and flexibility in domain decomposition, improving model performance. MPAS-Seaice is the current sea-ice component of the Energy Exascale Earth System Model (E3SM).
Abstract. Predicting future climate change over a region of complex terrain, such as the western United States (US), remains challenging due to the low resolution of global climate models (GCMs). Yet the climate extremes of recent years in this region, such as floods, wildfires, and drought, are likely to intensify further as climate warms, underscoring the need for high-quality and high-resolution predictions. Here, we present an ensemble of dynamically downscaled simulations over the western US from 1980–2100 at 9 km grid spacing, driven by 16 latest-generation GCMs. This dataset is titled the Western US Dynamically Downscaled Dataset (WUS-D3). We describe the challenges of producing WUS-D3, including GCM selection and technical issues, and we evaluate the simulations' realism by comparing historical results to temperature and precipitation observations. The future downscaled climate change signals are shaped in physically credible ways by the regional model's more realistic coastlines and topography. (1) The mean warming signals are heavily influenced by more realistic snowpack. (2) Mean precipitation changes are often consistent with wetting on the windward side of mountain complexes, as warmer, moister air masses are uplifted orographically during precipitation events. (3) There are large fractional precipitation increases on the lee side of mountain complexes, leading to potentially significant changes in water resources and ecology in these arid landscapes. (4) Increases in precipitation extremes are generally larger than in the GCMs, driven by locally intensified atmospheric updrafts tied to sharper, more realistic gradients in topography. (5) Changes in temperature extremes are different from what is expected by a shift in mean temperature and are shaped by local atmospheric dynamics and land surface feedbacks. Because of its high resolution, comprehensiveness, and representation of relevant physical processes, this dataset presents a unique opportunity to evaluate societally relevant future changes in western US climate.
A performance test of a night image intensification system for use as a visual aid by the crewmen in Gemini is reported. The equipment package consisted of (1) image intensification camera; (2) camera control unit; (3) viewing monitor; (4) recording monitor and photographic recorder; and (5) monitor electronics and equipment control unit. Representative photographs are predominantly of lights and clouds. Photographs of three different sections of coastline reveal a contrast between the images of land and ocean. These images range in quality from good to poor.
The author has identified the following significant results. Multispectral scanner image quality is considered excellent. It is immediately apparent that details of the coastline, water courses, and many other features readily apparent in the imagery will permit important updating and correction of available maps of Thailand.
The author has identified the following significant results. This catalogue was prepared as a part of the joint ERTS-1 New Jersey coastal mapping experiment. First look analysis of ERTS-1 images indicates that numerous coastal oceanographic patterns can be mapped on a sequential basis using ERTS-1 images. Analysis of imagery indicates a predominant southwesterly drift of dumped wasted in the surface waters. Initial analysis of imagery indicates that the effects of tidal flushing of New York harbor extend as far south as Long Branch, New Jersey. Analysis of imagery from 3 September, 1972, indicates a wide band of turbid water extending several miles offshore around Barnegat Inlet. First look analysis of imagery from 10 October, 1972, illustrates the increased reflectance of turbid waters within the bays, sounds, and thoroughfares behind the barrier islands in the southern New Jersey shore area. The estuarine waters emanating from both Brigantine and Absecon Inlets are very turbid relative to the waters further offshore and to the north and south. The tidal prism appears to be quite large but the movement of water once outside the inlets is not very rapid. The waters are not moving away from the coastline but rather along the coast.