scholarly journals Regional Swell Transformation by Backward Ray Tracing and SWAN

2019 ◽  
Vol 36 (2) ◽  
pp. 217-229 ◽  
Author(s):  
Sean C. Crosby ◽  
N. Kumar ◽  
W. C. O’Reilly ◽  
R. T. Guza
Keyword(s):  
High Resolution ◽  
Ray Tracing ◽  
Spatial Resolution ◽  
Deep Water ◽  
Beach Erosion ◽  
Linear Wave ◽  
Energy Propagation ◽  
Nearshore Processes ◽  
O 10

Beach erosion and wave-induced flooding models are often initialized in O(10)-m depth, seaward of the surfzone, with wave conditions estimated from regional nonlinear spectral wave models [e.g., Simulating Waves Nearshore (SWAN)]. These models are computationally expensive for high-resolution, long-term regional O(100)-km hindcasts, and they limit examination of the effect of different climate scenarios on nearshore processes. Alternatively, computationally fast models with reduced linear wave physics enable long-term hindcasts at high spatial (<100 m) resolution. Linear models, that efficiently transform complete spectral details from deep water through complex offshore bathymetry, are appropriate for low-frequency swell wave energy propagation. Here, two numerically different linear methods are compared: backward ray-tracing and stationary linear SWAN simulations. The methods yield similar transformations from deep water (seaward of offshore islands in Southern California) to the nearshore, O(10)-m depth. However, SWAN is sensitive to model spatial resolution, especially in highly sheltered regions, where with typical (1–2 km) resolution SWAN estimates of nearshore energy vary by over a factor of 2 relative to ray tracing. Alongshore radiation stress estimates from SWAN and ray tracing also differ, and in some cases the climatological means have opposite signs. Increasing the SWAN resolution to 90 m, higher than usually applied to regional models, yields the nearshore transforms most similar to ray tracing. Both accurate rays and high-resolution SWAN require significant computation time; however, ray tracing is more efficient if transforms are needed at relatively few locations (compared with every grid point), or if computer memory is limited. Though presently less user friendly than SWAN, ray tracing is not affected by numerical diffusion or limited by model domain size or spatial resolution.

scholarly journals Assimilating Global Wave Model Predictions and Deep-Water Wave Observations in Nearshore Swell Predictions

2017 ◽  
Vol 34 (8) ◽  
pp. 1823-1836 ◽  
Author(s):  
Sean C. Crosby ◽  
Bruce D. Cornuelle ◽  
William C. O’Reilly ◽  
Robert T. Guza
Keyword(s):  
Wave Propagation ◽  
High Resolution ◽  
Wave Energy ◽  
Propagation Model ◽  
Linear Wave ◽  
Time Lags ◽  
Energy Propagation ◽  
Nearshore Processes ◽  
Model Predictions ◽  
Linear Wave Propagation

AbstractNearshore wave predictions with high resolution in space and time are needed for boating safety, to assess flood risk, and to support nearshore processes research. This study presents methods for improving regional nearshore predictions of swell-band wave energy (0.04–0.09 Hz) by assimilating local buoy observations into a linear wave propagation model with a priori guidance from global WAVEWATCH III (WW3) model predictions. Linear wave propagation, including depth-induced refraction and shoaling, and travel time lags, is modeled with self-adjoint backward ray tracing techniques. The Bayesian assimilation yields smooth, high-resolution offshore wave directional spectra that are consistent with WW3, and with offshore and local buoy observations. Case studies in the Southern California Bight (SCB) confirm that the nearshore predictions at independent (nonassimilated) buoy sites are improved by assimilation compared with predictions driven with WW3 or with a single offshore buoy. These assimilation techniques, valid in regions and frequency bands where wave energy propagation is mostly linear, use significantly less computational resources than nonlinear models and variational methods, and could be a useful component of a larger regional assimilation program. Where buoy locations have historically been selected to meet local needs, these methods can aid in the design of regional buoy arrays by quantifying the regional skill improvement for a given buoy observation and identifying both high-value and redundant observations. Assimilation techniques also identify likely forward model error in the Santa Barbara Channel, where permanent observations or model corrections are needed.

scholarly journals Merging ground-based sunshine duration observations with satellite cloud and aerosol retrievals to produce high-resolution long-term surface solar radiation over China

2021 ◽  
Vol 13 (3) ◽  
pp. 907-922
Author(s):  
Fei Feng ◽  
Kaicun Wang
Keyword(s):  
Standard Deviation ◽  
High Resolution ◽  
Solar Radiation ◽  
Spatial Resolution ◽  
Sunshine Duration ◽  
Ordinary Least Squares ◽  
Cloud Fraction ◽  
Surface Solar Radiation ◽  
The Impact

Abstract. Although great progress has been made in estimating surface solar radiation (Rs) from meteorological observations, satellite retrieval, and reanalysis, getting best-estimated long-term variations in Rs are sorely needed for climate studies. It has been shown that Rs data derived from sunshine duration (SunDu) can provide reliable long-term variability, but such data are available at sparsely distributed weather stations. Here, we merge SunDu-derived Rs with satellite-derived cloud fraction and aerosol optical depth (AOD) to generate high-spatial-resolution (0.1∘) Rs over China from 2000 to 2017. The geographically weighted regression (GWR) and ordinary least-squares regression (OLS) merging methods are compared, and GWR is found to perform better. Based on the SunDu-derived Rs from 97 meteorological observation stations, which are co-located with those that direct Rs measurement sites, the GWR incorporated with satellite cloud fraction and AOD data produces monthly Rs with R2=0.97 and standard deviation =11.14 W m−2, while GWR driven by only cloud fraction produces similar results with R2=0.97 and standard deviation =11.41 W m−2. This similarity is because SunDu-derived Rs has included the impact of aerosols. This finding can help to build long-term Rs variations based on cloud data, such as Advanced Very High Resolution Radiometer (AVHRR) cloud retrievals, especially before 2000, when satellite AOD retrievals are not unavailable. The merged Rs product at a spatial resolution of 0.1∘ in this study can be downloaded at https://doi.org/10.1594/PANGAEA.921847 (Feng and Wang, 2020).

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

scholarly journals A Long-Term, High-Resolution Wetland Dataset over the Amazon Basin, Downscaled from a Multiwavelength Retrieval Using SAR Data

2013 ◽  
Vol 14 (2) ◽  
pp. 594-607 ◽  
Author(s):  
Filipe Aires ◽  
Fabrice Papa ◽  
Catherine Prigent
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Land Surface ◽  
Amazon Basin ◽  
Near Infrared ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Sar Data ◽  
Ocean Topography

Abstract A climatology of wetlands has been derived at a low spatial resolution (0.25° × 0.25° equal-area grid) over a 15-yr period by combining visible and near-infrared satellite observations and passive and active microwaves. The objective of this study is to develop a downscaling technique able to retrieve wetland estimations at a higher spatial resolution (about 500 m). The proposed method uses an image-processing technique applied to synthetic aperture radar (SAR) information about the low and high wetland season. This method is tested over the densely vegetated basin of the Amazon. The downscaling results are satisfactory since they respect the spatial hydrological features of the SAR data and the temporal evolution of the low-resolution wetland estimates. A new long-term and high-resolution wetland dataset has been generated for 1993–2007 for the Amazon basin. This dataset represents a new and unprecedented source of information for climate and land surface modeling of the Amazon and for the definition of future hydrology-oriented satellite missions such as Surface Water and Ocean Topography (SWOT).

scholarly journals Merging ground-based sunshine duration with satellite cloud and aerosol data to produce high resolution long-term surface solar radiation over China

2020 ◽  
Author(s):  
Fei Feng ◽  
Kaicun Wang
Keyword(s):  
Standard Deviation ◽  
High Resolution ◽  
Solar Radiation ◽  
Spatial Resolution ◽  
Sunshine Duration ◽  
Ordinary Least Squares ◽  
Cloud Fraction ◽  
Surface Solar Radiation ◽  
The Impact

Abstract. Although great progress has been made in estimating surface solar radiation (Rs) from meteorological observations, satellite retrieval and reanalysis, getting best estimated of long-term variations in Rs are sorely needed for climate studies. It has been shown that sunshine duration (SunDu)-derived Rs data can provide reliable long-term Rs variation. Here, we merge SunDu-derived Rs data with satellite-derived cloud fraction and aerosol optical depth (AOD) data to generate high spatial resolution (0.1°) Rs over China from 2000 to 2017. The geographically weighted regression (GWR) and ordinary least squares regression (OLS) merging methods are compared, and GWR is found to perform better. Whether or not AOD is taken as input data makes little difference for the GWR merging results. Based on the SunDu-derived Rs from 97 meteorological observation stations, the GWR incorporated with satellite cloud fraction and AOD data produces monthly Rs with R2 = 0.97 and standard deviation = 11.14 W/m2, while GWR driven by only cloud fraction produces similar results with R2 = 0.97 and standard deviation = 11.41 w/m2. This similarity is because SunDu-derived Rs has included the impact of aerosols. This finding can help to build long-term Rs variations based on cloud data, such as Advanced Very High Resolution Radiometer (AVHRR) cloud retrievals, especially before 2000, when satellite AOD retrievals are not unavailable. The merged Rs product at a spatial resolution of 0.1° in this study can be downloaded at https://doi.pangaea.de/10.1594/PANGAEA.921847 (Feng and Wang, 2020).

scholarly journals SPREAD: A high-resolution daily gridded precipitation dataset for Spain

2017 ◽  
Author(s):  
Roberto Serrano-Notivoli ◽  
Santiago Beguería ◽  
Miguel Ángel Saz ◽  
Luis Alberto Longares ◽  
Martín de Luis
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Extreme Precipitation ◽  
Daily Precipitation ◽  
Original Data ◽  
Extreme Precipitation Events ◽  
Complete Dataset ◽  
Extreme Precipitation Indices ◽  
Precipitation Indices

Abstract. A high-resolution daily gridded precipitation dataset was built from raw data of 12,858 observatories covering a period from 1950 to 2012 in peninsular Spain and 1971 to 2012 in Balearic and Canary Islands. The original data were quality controlled and gaps were filled on each day and location independently. Using the serially-complete dataset, a grid with a 5 x 5 kilometres spatial resolution was constructed by estimating daily precipitation amounts and their corresponding uncertainty at each grid node. Daily precipitation estimations were compared to original observations to assess the quality of the gridded dataset. Four daily precipitation indices were computed to characterize the spatial distribution of daily precipitation and nine extreme precipitation indices were used to describe the frequency and intensity of extreme precipitation events. The use of the total available data in Spain, the independent estimation of precipitation for each day and the high spatial resolution of the grid allowed for a precise spatial and temporal assessment of daily precipitation that are difficult to achieve when using other methods, pre- selected long-term stations or global gridded datasets. SPREAD dataset is publicly available at http://dx.doi.org/10.20350/digitalCSIC/7393.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

High Resolution Current Imaging by Direct Magnetic Field Sensing

2003 ◽  
Author(s):  
S.I. Woods ◽  
Nesco M. Lettsome ◽  
A.B. Cawthorne ◽  
L.A. Knauss ◽  
R.H. Koch
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Spatial Resolution ◽  
Great Promise ◽  
Magnetoresistive Sensor ◽  
Magnetic Sensitivity ◽  
Current Lines ◽  
Scanning Squid ◽  
Scanning Fiber ◽  
Ferromagnetic Probe

Abstract Two types of magnetic microscopes have been investigated for use in high resolution current mapping. The scanning fiber/SQUID microscope uses a SQUID sensor coupled to a nanoscale ferromagnetic probe, and the GMR microscope employs a nanoscale giant magnetoresistive sensor. Initial scans demonstrate that these microscopes can resolve current lines less than 10 µm apart with edge resolution of 1 µm. These types of microscopes are compared with the performance of a standard scanning SQUID microscope and with each other with respect to spatial resolution and magnetic sensitivity. Both microscopes show great promise for identifying current defects in die level devices.

National coastal management challenges and needs

Shore & Beach ◽  
2020 ◽  
pp. 34-43
Author(s):  
Nicole Elko ◽  
Tiffany Roberts Briggs
Keyword(s):  
Coastal Management ◽  
Harmful Algal Blooms ◽  
Algal Blooms ◽  
Local Community ◽  
Extreme Event ◽  
Technical Information ◽  
Interdisciplinary Approach ◽  
Beach Erosion ◽  
The U.S

In partnership with the U.S. Geological Survey Coastal and Marine Hazards and Resources Program (USGS CMHRP) and the U.S. Coastal Research Program (USCRP), the American Shore and Beach Preservation Association (ASBPA) has identified coastal stakeholders’ top coastal management challenges. Informed by two annual surveys, a multiple-choice online poll was conducted in 2019 to evaluate stakeholders’ most pressing problems and needs, including those they felt most ill-equipped to deal with in their day-to-day duties and which tools they most need to address these challenges. The survey also explored where users find technical information and what is missing. From these results, USGS CMHRP, USCRP, ASBPA, and other partners aim to identify research needs that will inform appropriate investments in useful science, tools, and resources to address today’s most pressing coastal challenges. The 15-question survey yielded 134 complete responses with an 80% completion rate from coastal stakeholders such as local community representatives and their industry consultants, state and federal agency representatives, and academics. Respondents from the East, Gulf, West, and Great Lakes coasts, as well as Alaska and Hawaii, were represented. Overall, the prioritized coastal management challenges identified by the survey were: Deteriorating ecosystems leading to reduced (environmental, recreational, economic, storm buffer) functionality, Increasing storminess due to climate change (i.e. more frequent and intense impacts), Coastal flooding, both Sea level rise and associated flooding (e.g. nuisance flooding, king tides), and Combined effects of rainfall and surge on urban flooding (i.e. episodic, short-term), Chronic beach erosion (i.e. high/increasing long-term erosion rates), and Coastal water quality, including harmful algal blooms (e.g. red tide, sargassum). A careful, systematic, and interdisciplinary approach should direct efforts to identify specific research needed to tackle these challenges. A notable shift in priorities from erosion to water-related challenges was recorded from respondents with organizations initially formed for beachfront management. In addition, affiliation-specific and regional responses varied, such as Floridians concern more with harmful algal blooms than any other human and ecosystem health related challenge. The most common need for additional coastal management tools and strategies related to adaptive coastal management to maintain community resilience and continuous storm barriers (dunes, structures), as the top long-term and extreme event needs, respectively. In response to questions about missing information that agencies can provide, respondents frequently mentioned up-to-date data on coastal systems and solutions to challenges as more important than additional tools.

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