scholarly journals Analysis of the horizontal two-dimensional near-surface structure of a winter tornadic vortex using high-resolution in situ wind and pressure measurements

2015 ◽  
Vol 120 (12) ◽  
pp. 5879-5894 ◽  
Author(s):  
Ryohei Kato ◽  
Kenichi Kusunoki ◽  
Eiichi Sato ◽  
Wataru Mashiko ◽  
Hanako Y. Inoue ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Structure ◽  
Pressure Measurements ◽  
Two Dimensional ◽  
Near Surface

Apparent Resistivity Calculations for Three Two-dimensional Conductivity Models with Vertical Discontinuities

1971 ◽  
Vol 8 (8) ◽  
pp. 961-966 ◽  
Author(s):  
F. W. Jones
Keyword(s):  
Surface Charge ◽  
Numerical Method ◽  
Surface Structure ◽  
Electromagnetic Induction ◽  
Apparent Resistivity ◽  
Two Dimensional ◽  
Near Surface ◽  
A Value ◽  
Surface Contact ◽  
Conductivity Models

Electromagnetic induction in three two-dimensional models of conductors with two regions of different conductivity is considered. Solutions are obtained for both the H-polarization and E-polarization cases by a numerical method. Apparent resistivity as a function of period is plotted for various locations relative to the surface contact. For the H-polarization case, the apparent resistivity values calculated for points near the surface contact are affected by the varying surface charge on the interface between the regions, and a value different from the expected value is obtained. In the E-polarization case the apparent resistivity curves exhibit a "dip" characteristic when the apparent resistivity is calculated for surface points near a shelf or step structure. In both cases the value of apparent resistivity calculated at or near surface contacts between different conducting regions depends greatly on the sub-surface structure.

Surface Structure of LiNi0.8Co0.2O2: a New Experimental Technique Using in Situ X-ray Diffraction and Two-Dimensional Epitaxial Film Electrodes

2009 ◽  
Vol 21 (13) ◽  
pp. 2632-2640 ◽  
Author(s):  
Kazuyuki Sakamoto ◽  
Masaaki Hirayama ◽  
Noriyuki Sonoyama ◽  
Daisuke Mori ◽  
Atsuo Yamada ◽  
...  
Keyword(s):  
Surface Structure ◽  
Experimental Technique ◽  
Epitaxial Film ◽  
Two Dimensional ◽  
X Ray Diffraction ◽  
X Ray

Temperature monitoring in mountain regions using reanalyses: Lessons from the Alps

2020 ◽  
Author(s):  
Simon C. Scherrer ◽  
Sven Kotlarski
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Reanalysis Data ◽  
Swiss Alps ◽  
Mountain Regions ◽  
Near Surface ◽  
The Alps ◽  
Regional Reanalysis ◽  
High Elevations

<p>The monitoring of near-surface temperature is a fundamental task of climatology that remains especially challenging in mountain regions. Here we assess the regional monitoring capabilities of modern reanalysis products in the well-monitored northern Swiss Alps during the last 20 to almost 60 years. Monthly and seasonal 2 m air temperature (T2m) anomalies of the global ERA5 and the three regional reanalysis products HARMONIE, MESCAN-SURFEX and COSMO-REA6 are evaluated against high quality in situ observational data for a low elevation (foothills) mean, and a high elevation (Alpine) mean. All reanalysis products show a good year-round performance for the foothills with the global reanalysis ERA5 showing the best overall performance. The high-resolution regional reanalysis COSMO-REA6 clearly performs best for the Alpine mean, especially in winter. Most reanalysis data sets show deficiencies at high elevations in winter and considerably overestimate recent T2m trends in winter. This stresses the fact that even in the most recent decades utmost care is required when using reanalysis data for near-surface temperature trend assessments in mountain regions. Our results indicate that a high-resolution model topography is an important prerequisite for an adequate monitoring of winter T2m using reanalysis data at high elevations in the Alps. Assimilating T2m remains challenging in highly complex terrain. The remaining shortcomings of modern reanalyses also highlight the continued need for a reliable and dense in situ observational monitoring network in mountain regions.</p><p> </p>

scholarly journals Salinity variability in satellite subpixels: impact on satellite in-situ comparisons.

2021 ◽  
Author(s):  
Clovis Thouvenin-Masson ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Dimitry Khvorostyanov ◽  
Xavier Perrot ◽  
...  
Keyword(s):  
Sampling Error ◽  
In Situ Measurements ◽  
Global Ocean ◽  
Small Scale ◽  
Two Dimensional ◽  
Surface Salinity ◽  
Near Surface ◽  
One Dimensional ◽  
Ship Tracks

<p>Sea Surface Salinity (SSS) are retrieved from SMOS and SMAP L-band radiometers at a spatial resolution of about 50km.</p><p> </p><p>Traditionally, satellite SSS products validation is based on comparisons with in-situ near surface salinity measurements.</p><p> </p><p>In-situ measurements are performed on moorings, argo floats and along ship tracks[JB1] , which provide punctual or one-dimensional (along ship tracks) estimations of the SSS.</p><p> </p><p>The sampling difference between one-dimensional or punctual in-situ measurements and two-dimensional satellite products results in a sampling error that must be separated from measurement errors for the validation of satellite products.</p><p> </p><p>We use a small-scale resolution field (1/12° Mercator Global Ocean Physics Analysis and Forecast) to estimate the expected sampling error of each kind of in-situ measurements, by comparing punctual, [JB2] one-dimensional and two-dimensional SSS variability.</p><p> </p><p>The better understanding of sampling errors allows a more accurate validation of satellite SSS and of the errors estimated by satellite retrieval algorithms. The improvement is quantified by considering the standard deviation of satellite minus in-situ salinities differences normalized by the sampling and retrieval errors. This quantity should be equal to one if all the error contributions are correctly considered. This methodology will be applied to SMOS SSS and to merged SMOS and SMAP SSS products.</p>

scholarly journals Near-surface diurnal warming simulations: validation with high resolution profile measurements

2012 ◽  
Vol 9 (6) ◽  
pp. 3851-3878
Author(s):  
B. Scanlon ◽  
G. A. Wick ◽  
B. Ward
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Wave Breaking ◽  
Solar Absorption ◽  
Near Surface ◽  
Annual Cycles ◽  
Diurnal Warming ◽  
In Situ Methods ◽  
Critical Requirement

Abstract. Sea surface temperature (SST) is an important property for governing the exchange of energy between the ocean and the atmosphere. Common in-situ methods of measuring SST often require a cool-skin and warm-layer adjustment in the presence of diurnal warming effects. A critical requirement for an ocean sub-model is that it can simulate the change in SST over diurnal, seasonal, and annual cycles. In this paper we use high-resolution near-surface profiles of SST to validate simulated near-surface temperature profiles from a modified version of the Kantha and Clayson 1-D mixed layer model. Additional model enhancements such as the incorporation of a parameterisation of turbulence generated by wave breaking and a solar absorption model are also validated. The model simulations show a strong variability in highly stratified conditions, with different models providing the best results depending on the specific criteria and conditions. In general, the models with enhanced wave breaking effects tended to underestimate the temperature profile measurements while the more coarse baseline and blended approaches produced the most accurate comparisons with the in-situ SST data.

scholarly journals Importance of satellite observations for high-resolution mapping of near-surface NO2 by machine learning

2021 ◽  
Author(s):  
Minsu Kim ◽  
Gerrit Kuhlmann ◽  
Lukas Emmenegger ◽  
Dominik Brunner
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
High Resolution ◽  
Information Gain ◽  
Traffic Density ◽  
Population Exposure ◽  
Data Repositories ◽  
Near Surface ◽  
The Impact

<p>Nitrogen oxides (NO<sub>x  </sub>= NO<sub></sub>+ NO<sub>2</sub>) are harmful to human health and are precursors of other key air pollutants like ozone (O<sub>3</sub>) and particulate matter (PM). Since the lifetime of NO<sub>x</sub> is short and its main sources are anthropogenic emissions like fuel combustion from traffic and industry, near-surface NO<sub>x </sub>concentrations are highly variable in space and time. To assess the impact of NO<sub>2 </sub>on public health, maps of high spatial and temporal resolution are critical. In this study, we present hourly near-surface NO<sub>2</sub> concentrations at 100 m resolution for Switzerland and northern Italy that are produced using machine learning, specifically an extreme gradient-boosted tree ensemble. The model was trained with <em>in situ </em>observations from European Air Quality e-Reporting data repositories (Airbase). Satellite NO<sub>2</sub> observations from the TROPospheric Monitoring Instrument (TROPOMI) were compiled together with land use data, meteorological data and topography as covariates. Evaluation against <em>in situ</em> observations not used for the training shows that the dynamic maps produced in this study reproduce the spatio-temporal variation in near-surface NO<sub>2</sub> concentrations with high accuracy (R<sup>2</sup> = 0.59, MAE = 7.69 µg/m<sup>3</sup>). In addition, we demonstrate how public health studies can utilize such high-resolution maps for unbiased assessment of population exposure that can account for home addresses and mobility of individuals. Comparing the relative importance of the different covariates based on two different metrics, total information gain and averaged local feature importance, show a leading contribution of the TROPOMI observations despite their rather coarse resolution (3.5 km × 5.5 km) and daily update. TROPOMI NO<sub>2 </sub>observations were particularly important for the quality of the NO<sub>2</sub> maps during periods of unusual NO<sub>2 </sub>reductions (e.g., during COVID19 lockdown) and when detailed emission-related covariates like traffic density, that may not be available in other regions of the globe, were not included in the model. Since all data used in our study are publicly available, our approach can be readily extended to other regions in Europe or applied worldwide.</p>

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