Using High-Resolution Seismic and GPR Methods in Sedimentological Studies of the Geneva Bay Area (Switzerland)

2006 ◽  
Author(s):  
M. Beres ◽  
M. Fuchs ◽  
S. Girardclos ◽  
P. Corboud ◽  
V. Sastre
Keyword(s):  
High Resolution ◽  
Bay Area

scholarly journals On the Uncertainty of High-Resolution Hourly Quantitative Precipitation Estimates in California

2020 ◽  
Vol 21 (5) ◽  
pp. 865-879
Author(s):  
Janice L. Bytheway ◽  
Mimi Hughes ◽  
Kelly Mahoney ◽  
Rob Cifelli
Keyword(s):  
High Resolution ◽  
Complex Terrain ◽  
Heavy Rain ◽  
Reference Dataset ◽  
Wet Season ◽  
Large Variability ◽  
Operational Forecast ◽  
Bay Area ◽  
Precipitation Estimates ◽  
Quantitative Precipitation Estimates

AbstractThe Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area.

scholarly journals Depth Contours and Coastline Generalization for Harbour and Approach Nautical Charts

2021 ◽  
Vol 10 (4) ◽  
pp. 197
Author(s):  
Andriani Skopeliti ◽  
Lysandros Tsoulos ◽  
Shachak Pe’eri
Keyword(s):  
New York ◽  
Information System ◽  
High Resolution ◽  
Best Practices ◽  
Bay Area ◽  
Depth Contour ◽  
Digital Elevation ◽  
Suitable Generalization ◽  
Gis Environment ◽  
Critical Process

Generalization of nautical charts and electronic nautical charts (ENCs) is a critical process which aims at the safety of navigation and clear cartographic presentation. This paper elaborates on the problem of depth contours and coastline generalization—natural and artificial—for medium-scale charts (harbour and approach) taking into account International Hydrographic Organization (IHO) standards, hydrographic offices’ (HOs) best practices and cartographic literature. Additional factors considered are scale, depth, and seafloor characteristics. The proposed method for depth contour generalization utilizes contours created from high-resolution digital elevation models (DEMs) or those already portrayed on nautical charts. Moreover, it ensures consistency with generalized soundings. Regarding natural coastline generalization, the focus was on managing the resolution, while maintaining the shape, and on the islands. For the provision of a suitable generalization solution for the artificial shoreline, it was preprocessed in order to automatically recognize the shape of each structure as perceived by humans (e.g., a pier that looks like a T). The proposed generalization methodology is implemented with custom-developed routines utilizing standard geo-processing functions available in a geographic information system (GIS) environment and thus can be adopted by hydrographic agencies to support their ENC and nautical chart production. The methodology has been tested in the New York Lower Bay area in the U.S.A. Results have successfully delineated depth contours and coastline at scales 1:10 K, 1:20 K, 1:40 K and 1:80 K.

scholarly journals Beyond Measurement: Extracting Vegetation Height from High Resolution Imagery with Deep Learning

2020 ◽  
Vol 12 (22) ◽  
pp. 3797
Author(s):  
David Radke ◽  
Daniel Radke ◽  
John Radke
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
San Francisco ◽  
Aerial Imagery ◽  
Surface Model ◽  
Vegetation Growth ◽  
Vegetation Height ◽  
Bay Area ◽  
Model Complex ◽  
Elevation Data

Measuring and monitoring the height of vegetation provides important insights into forest age and habitat quality. These are essential for the accuracy of applications that are highly reliant on up-to-date and accurate vegetation data. Current vegetation sensing practices involve ground survey, photogrammetry, synthetic aperture radar (SAR), and airborne light detection and ranging sensors (LiDAR). While these methods provide high resolution and accuracy, their hardware and collection effort prohibits highly recurrent and widespread collection. In response to the limitations of current methods, we designed Y-NET, a novel deep learning model to generate high resolution models of vegetation from highly recurrent multispectral aerial imagery and elevation data. Y-NET’s architecture uses convolutional layers to learn correlations between different input features and vegetation height, generating an accurate vegetation surface model (VSM) at 1×1 m resolution. We evaluated Y-NET on 235 km2 of the East San Francisco Bay Area and find that Y-NET achieves low error from LiDAR when tested on new locations. Y-NET also achieves an R2 of 0.83 and can effectively model complex vegetation through side-by-side visual comparisons. Furthermore, we show that Y-NET is able to identify instances of vegetation growth and mitigation by comparing aerial imagery and LiDAR collected at different times.

High-resolution polarimetric radar network for improving urban resilience to natural disasters in a complex environment

2020 ◽  
Author(s):  
Chandrasekar V. Chandra ◽  
Haonan Chen ◽  
Rob Cifelli
Keyword(s):  
High Resolution ◽  
Natural Disasters ◽  
Sea Level ◽  
San Francisco ◽  
Northern California ◽  
Complex Environment ◽  
Mountainous Regions ◽  
Bright Band ◽  
Bay Area ◽  
Radar Network

<p>The operational Weather Surveillance Radar - 1988 Doppler (WSR-88D) network is an efficient tool for observing hydrometeorology processes and it forms the cornerstone of national weather forecast and warning systems. However, the observation performance of the WSR-88D network is severely hampered over the western U.S., due to 1) the radar network density is not as high as that over the eastern U.S.; 2) WSR-88D radar beams are often partially or fully blocked by the mountainous terrain in the western U.S. </p><p>For example, the San Francisco Bay Area in Northern California, which supports one of the most prosperous economies in the U.S., is expected to be covered by two WSR-88D radars: KMUX and KDAX. The KMUX radar is located in the Santa Cruz Mountains at an elevation of over 1000 m above mean sea level (AMSL) compared with the densely populated valley regions which are near the sea level. Typically, the storms in Northern California have freezing levels approximately 1–2 km AMSL. As the distance from the radar increases, the KMUX radar beam can easily overshoot the mixed-phase hydrometeors in the bright band or snowflakes above the bright band, even if it is raining at the ground. The KDAX radar is located near the sea level in Davis, California. However, the KDAX radar beams are partially blocked by the Coast Ranges at low elevation angles. The coverage limitations of the KMUX and KDAX radars are further compounded by the complex precipitation microphysics as a result of land-ocean interaction in the coastal regions and orographic enhancement in the mountainous regions. As a result, it is still challenging to monitor and predict the changing atmospheric conditions using operational radars in the Bay Area, which will make the Bay Area particularly susceptible to catastrophic flooding that disrupts transportation, threatens public safety, and negatively impacts water quality. </p><p>In this paper, we present an Advanced Quantitative Precipitation Information (AQPI) system built by NOAA and collaborating partners to improve monitoring and forecasting of precipitation and coastal flooding in the Bay Area. The high-frequency (i.e., C and X band) high-resolution gap-filling radars deployed as part of the AQPI program are detailed. A radar-based rainfall system is designed to improve real-time precipitation estimation over the Bay Area. The sensitivity of rainfall products on the occurrence of hydrologic extremes is investigated through a distributed hydrological model to improve the streamflow forecast. The performance of rainfall and associated hydrological impacts during the 2018-2019 and 2019-2020 winter storm seasons is quantified in the context of improving urban resiliency to natural disasters in such a complex environment. </p>

scholarly journals STUDY ON THE COASTLINE CHANGE OF JIAOZHOU BAY BASED ON HIGH RESOLUTION REMOTE SENSING IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 2591-2597
Author(s):  
H. Zhu ◽  
B. Xing ◽  
S. Ni ◽  
P. Wei
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Coastal Area ◽  
Jiaozhou Bay ◽  
Remote Sensing Image ◽  
Average Increase ◽  
Decrease Rate ◽  
Average Decrease ◽  
Bay Area ◽  
Increase Rate

In recent years, with the rapid development of the Jiaozhou Bay area of Qingdao, the influence of human activities on the coastline of Jiaozhou Bay is becoming more and more serious. Based on the high resolution remote sensing image data of 10 periods from 2001 to 2017 in the Jiaozhou Bay area, and combined with the data of on-the-spot survey and expert knowledge, this paper have completed the interpretation and extraction of coastline data of each year, and analyzed the distribution, size, rate of change, and trend of the increase and decrease of the coastal area of Jiaozhou Bay in different time periods, combined with the economic construction and the marine hydrodynamic environment of the region to analyze the reasons for the change of the coastline of Jiaozhou Bay. The results show that the increase and reduction of the coastal area of Jiaozhou Bay was mainly affected by human activities such as sea reclamation and marine aquaculture, resulting in a gradual change in the rate of increase and decrease with human development. For coastal advance part,2001&amp;ndash;2013, the average increase rate on the coastal area of Jiaozhou Bay was 2.30&amp;thinsp;km<sup>2</sup>/a, showing a trend of rapid growth, 2013&amp;ndash;2017 the average increase rate of 0.53&amp;thinsp;km<sup>2</sup>/a, and the growth rate slowed down. For coastal retreat part, 2001&amp;ndash;2013, the average decrease rate was 2.58&amp;thinsp;&amp;times;&amp;thinsp;10<sup>&amp;minus;3</sup>&amp;thinsp;km<sup>2</sup>/a. 2013&amp;ndash;2014, the decrease rate reached a peak value of 1.11&amp;thinsp;km<sup>2</sup>/a. 2014&amp;ndash;2017, the average decrease rate was 0.14&amp;thinsp;km<sup>2</sup>/a. The decrease rate shows a trend of increasing first and then slowing down.

scholarly journals Anthropogenic CO<sub>2</sub> flux constraints in the Tokyo Bay Area from Lagrangian diffusive backward trajectories and high resolution in situ measurements

2012 ◽  
Vol 12 (4) ◽  
pp. 10623-10649
Author(s):  
I. Pisso ◽  
P. Patra ◽  
M. Takigawa ◽  
T. Machida ◽  
H. Matsueda ◽  
...  
Keyword(s):  
High Resolution ◽  
Hot Spot ◽  
A Priori ◽  
In Situ Measurements ◽  
Tokyo Bay ◽  
Bay Area ◽  
Time Variations ◽  
Carbon Dioxide Co2 ◽  
Tall Tower

Abstract. In order to use high resolution in-situ measurements to constrain regional emissions of carbon dioxide (CO2), we use a Lagrangian methodology based on diffusive backward trajectory tracer reconstructions. We use aircraft, tall tower and ground sites for CO2 data collected nearby the CO2 emission hot spot of the Tokyo Bay Area during the CONTRAIL campaign, from the MRI/JMA Tsukuba tall tower and from the World Data Centre for Greenhouse Gases (WDCGG). We calculated Bayesian inversions based on EDGAR 4 and CDIAC a priori fluxes. Estimated fluxes for the Tokyo Bay Area for the analyzed period between 2005 and 2009 range between 4.80×10−7 and 3.45×10−6 kgCO2 m2 s−1 with significant time variations. Significant differences in retrieved fluxes of up to 21% were found when CONTRAIL measurements were added to the dataset. No significant trend was found in the time series of spatially averaged retrieved fluxes.

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