High-Resolution Temporal Sampling of the Nearshore Vertical Structure of Bioluminescence

2001 ◽  
Author(s):  
Mark A. Moline
Keyword(s):  
High Resolution ◽  
Vertical Structure ◽  
Temporal Sampling

scholarly journals Investigating Hector Convective Development and Microphysical Structure Using High-Resolution Model Simulations, Ground-Based Radar Data, and TRMM Satellite Data

2014 ◽  
Vol 71 (4) ◽  
pp. 1353-1370 ◽  
Author(s):  
Sabrina Gentile ◽  
Rossella Ferretti ◽  
Frank Silvio Marzano
Keyword(s):  
High Resolution ◽  
Conceptual Model ◽  
Vertical Structure ◽  
Mesoscale Model ◽  
Tropical Rainfall Measuring Mission ◽  
Sea Breeze ◽  
Radar Data ◽  
Full Life Cycle ◽  
Resolution Model ◽  
High Resolution Model

Abstract One event of a tropical thunderstorm typically observed in northern Australia, known as Hector, is investigated using high-resolution model output from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) observations from a ground-based weather radar located in Berrimah (Australia) and data from the Tropical Rainfall Measuring Mission (TRMM) satellite. The analysis is carried out by tracking the full life cycle of Hector from prestorm stage to the decaying stage. In both the prestorm stage, characterized by nonprecipitating cells, and the triggering stage, when the Hector storm is effectively initiated, an analysis is performed with the aid of high-spatial-and-temporal-resolution MM5 output and the Berrimah ground-based radar imagery. During the mature (“old”) stage of Hector, considering the conceptual model for tropical convection suggested by R. Houze, TRMM Microwave Imager satellite-based data were added to ground-based radar data to analyze the storm vertical structure (dynamics, thermodynamics, and hydrometeor contents). Model evaluation with respect to observations (radar reflectivity and TRMM data) suggests that MM5 performed fairly well in reproducing the dynamics of Hector, providing support to the assertion that the strength of convection, in terms of vertical velocity, largely contributes to the vertical distribution of hydrometeors. Moreover, the stages of the storm and its vertical structure display good agreement with Houze’s aforementioned conceptual model. Finally, it was found that the most important triggering mechanisms for this Hector event are topography, the sea breeze, and a gust front produced by previous convection.

scholarly journals High resolution modeling of CO<sub>2</sub> over Europe: implications for representation errors of satellite retrievals

2009 ◽  
Vol 9 (5) ◽  
pp. 20599-20630
Author(s):  
D. Pillai ◽  
C. Gerbig ◽  
J. Marshall ◽  
R. Ahmadov ◽  
R. Kretschmer ◽  
...  
Keyword(s):  
High Resolution ◽  
Horizontal Resolution ◽  
Co2 Fluxes ◽  
Modeling Framework ◽  
Global Models ◽  
Near Surface ◽  
Temporal Sampling ◽  
Inverse Models ◽  
Representation Error ◽  
Satellite Retrievals

Abstract. Satellite retrievals for column CO2 with better spatial and temporal sampling are expected to improve the current surface flux estimates of CO2 via inverse techniques. However, the spatial scale mismatch between remotely sensed CO2 and current generation inverse models can induce representation errors, which can cause systematic biases in flux estimates. This study is focused on estimating these representation errors associated with utilization of satellite measurements in global models with a horizontal resolution of about 1 degree or less. For this we used simulated CO2 from the high resolution modeling framework WRF-VPRM, which links CO2 fluxes from a diagnostic biosphere model to a weather forecasting model at 10×10 km2 horizontal resolution. Sub-grid variability of column averaged CO2, i.e. the variability not resolved by global models, reached up to 1.2 ppm. Statistical analysis of the simulation results indicate that orography plays an important role. Using sub-grid variability of orography and CO2 fluxes as well as resolved mixing ratio of CO2, a linear model can be formulated that could explain about 50% of the spatial patterns in the bias component of representation error in column and near-surface CO2 during day- and night-times. These findings give hints for a parameterization of representation error which would allow for the representation error to taken into account in inverse models or data assimilation systems.

scholarly journals A simple insect removal algorithm for 35-GHz cloud radar measurements

2017 ◽  
Author(s):  
Madhu Chandra R. Kalapureddy ◽  
Sukanya Patra ◽  
Subrata K. Das ◽  
Sachin M. Deshpande ◽  
Kaustav Chakravarty ◽  
...  
Keyword(s):  
High Resolution ◽  
Vertical Structure ◽  
Cloud Droplet ◽  
Radar Reflectivity ◽  
Noise Floor ◽  
Cloud Radar ◽  
Cloud Height ◽  
Radar Echoes ◽  
Sensitivity Curves ◽  
Radar Measurements

Abstract. One of the key parameters that must be included in the analysis of atmospheric constituents (gases and particles) and clouds is the vertical structure of the atmosphere. Therefore high-resolution vertical profile observations of the atmospheric targets are required for both theoretical and practical evaluation and as inputs to increase accuracy of atmospheric models. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure in a resourceful way. However, extracting intended meteorological cloud content from the overall measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work a technique is proposed to identify and separate cloud and non-hydrometeor returns from a cloud radar measurements. Firstly the observed cloud reflectivity profile must be evaluated against the theoretical radar sensitivity curves. This step helps to determine the range of receiver noise floor above which it can be identified as signal or an atmospheric echo. However it should be noted that the signal above the noise floor may be contaminated by the air-borne non-meteorological targets such as insects, birds, or airplanes. The second step in this analysis statistically reviews the continual radar echoes to determine the signal de-correlation period. Cloud echoes are observed to be temporally more coherent, homogenous and have a longer de-correlation period than insects and noise. This step critically helps in separating the clouds from insects and noise which show shorter de-correlation periods. The above two steps ensure the identification and removal of non-hydrometeor contributions from the cloud radar reflectivity profile which can then be used for inferring unbiased vertical cloud structure. However these two steps are insufficient for recovering the weakly echoing cloud boundaries associated with the sharp reduction in cloud droplet size and concentrations. In the final step in order to obtain intact cloud height information, identified cloud echo peak(s) needs to be backtracked along the either sides on the reflectivity profile till its value falls close to the mean noise floor. The proposed algorithm potentially identify cloud height solely through the characterization of high resolution cloud radar reflectivity measurements with the theoretical echo sensitivity curves and observed echo statistics for the cloud tracking (TEST). This technique is found to be more robust in identifying and filtering out the contributions due to insects and noise which may contaminate a cloud reflectivity profile. With this algorithm it is possible to improve monsoon tropical cloud characterization using cloud radar.

scholarly journals High-resolution vertical total electron content maps based on multi-scale B-spline representations

2019 ◽  
Vol 37 (4) ◽  
pp. 699-717 ◽  
Author(s):  
Andreas Goss ◽  
Michael Schmidt ◽  
Eren Erdogan ◽  
Barbara Görres ◽  
Florian Seitz
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Sampling Interval ◽  
Basis Functions ◽  
Electron Content ◽  
Vertical Total Electron Content ◽  
Spectral Content ◽  
Total Electron ◽  
Multi Scale ◽  
Temporal Sampling

Abstract. For more than 2 decades the IGS (International GNSS Service) ionosphere associated analysis centers (IAACs) have provided global maps of the vertical total electron content (VTEC). In general, the representation of a 2-D or 3-D function can be performed by means of a series expansion or by using a discretization technique. While in the latter case, pixels or voxels are usually chosen for a spherical function such as VTEC, for a series expansion spherical harmonics (SH) are primarily used as basis functions. The selection of the best suited approach for ionosphere modeling means a trade-off between the distribution of available data and their possibility of representing ionospheric variations with high resolution and high accuracy. Most of the IAACs generate global ionosphere maps (GIMs) based on SH expansions up to the spectral degree n=15 and provide them with a spatial resolution of 2.5∘×5∘ with respect to the latitudinal and longitudinal directions, respectively, and a temporal sampling interval of 2 h. In recent years, it has frequently been claimed that the spatial resolution of the VTEC GIMs has to be increased to a spatial resolution of 1∘×1∘ and to a temporal sampling interval of about 15 min. Enhancing the grid resolution means an interpolation of VTEC values for intermediate points but with no further information about variations in the signal. n=15 in the SH case, for instance, corresponds to a spatial sampling of 12∘×12∘. Consequently, increasing the grid resolution concurrently requires an extension of the spectral content, i.e., to choose a higher SH degree value than 15. Unlike most of the IAACs, the VTEC modeling approach at Deutsches Geodätisches Forschungsinstitut der Technischen Universität München (DGFI-TUM) is based on localizing basis functions, namely tensor products of polynomial and trigonometric B-splines. In this way, not only can data gaps be handled appropriately and sparse normal equation systems be established for the parameter estimation procedure, a multi-scale representation (MSR) can also be set up to determine GIMs of different spectral content directly, by applying the so-called pyramid algorithm, and to perform highly effective data compression techniques. The estimation of the MSR model parameters is finally performed by a Kalman filter driven by near real-time (NRT) GNSS data. Within this paper, we realize the MSR and create multi-scale products based on B-spline scaling, wavelet coefficients and VTEC grid values. We compare these products with different final and rapid products from the IAACs, e.g., the SH model from CODE (Berne) and the voxel solution from UPC (Barcelona). In contrast to the abovementioned products, DGFI-TUM's products are based solely on NRT GNSS observations and ultra-rapid orbits. Nevertheless, we can conclude that the DGFI-TUM's high-resolution product (“othg”) outperforms all products used within the selected time span of investigation, namely September 2017.

Mapping tectonic strain in the central Alpine-Himalayan Belt with Sentinel-1 InSAR and GNSS observations

2021 ◽  
Author(s):  
Chris Rollins ◽  
Tim Wright ◽  
Jonathan Weiss ◽  
Andrew Hooper ◽  
Richard Walters ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Hazard ◽  
Strain Rate ◽  
Crustal Deformation ◽  
Large Scale ◽  
Surface Deformation ◽  
Processing System ◽  
Temporal Sampling ◽  
Himalayan Belt ◽  
Automated Processing

&lt;p&gt;Geodetic measurements of crustal deformation provide crucial constraints on a region&amp;#8217;s tectonics, geodynamics and seismic hazard. However, such geodetic constraints have traditionally been hampered by poor spatial and/or temporal sampling, which can result in ambiguities about how the lithosphere accommodates strain in space and time, and therefore where and how often earthquakes might occur. High-resolution surface deformation maps address this limitation by imaging (rather than presuming or modelling) where and how deformation takes place. These maps are now within reach for the Alpine-Himalayan Belt thanks to the COMET-LiCSAR InSAR processing system, which performs large-scale automated processing and time-series analysis of Sentinel-1 InSAR data. Expanding from our work focused on Anatolia, we are combining LiCSAR products with GNSS data to generate high-resolution maps of tectonic strain rates across the central Alpine-Himalayan Belt. Then, assuming that the buildup rate of seismic moment (deficit) from this geodetically-derived strain is balanced over the long term by the rate of moment release in earthquakes, we pair these strain rate maps with seismic catalogs to estimate the recurrence intervals of large, moderate and small earthquakes throughout the region. We also use arguments from dislocation modeling to identify two key signatures of a locked fault in a strain rate field, allowing us to convert the strain maps to &amp;#8220;effective fault maps&amp;#8221; and assess the contribution of individual fault systems to crustal deformation and seismic hazard. Finally, we address how to expand these approaches to the Alpine-Himalaya Belt as a whole.&lt;/p&gt;

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