Small-Scale In Situ Measurements of S-H Velocity in Surficial Sedimentary Deposits: Localised Textural and Biological Controls

1991 ◽  
pp. 313-320 ◽  
Author(s):  
S. E. Jones ◽  
C. F. Jago
2021 ◽  
Author(s):  
Karolin S. Ferner ◽  
K. Heinke Schlünzen ◽  
Marita Boettcher

<p>Urbanisation locally modifies the regional climate: an urban climate develops. For example, the average wind speed in cities is reduced, while the gustiness is increased. Buildings induce vertical winds, which influence the falling of rain. All these processes lead to heterogeneous patterns of rain at ground and on building surfaces. The small-scale spatial rain heterogeneities may cause discomfort for people. Moreover, non-uniform wetting of buildings affects their hydrothermal performance and durability of their facades.</p><p>Measuring rain heterogeneities between buildings is, however, nearly impossible. Building induced wind gusts negatively influence the representativeness of in-situ measurements, especially in densely urbanised areas. Weather radars are usually too coarse and, more importantly, require an unobstructed view over the domain and thus do not measure ground precipitation in urban areas. Consequently, researchers turn to numerical modelling in order to investigate small-scale precipitation heterogeneities between buildings.</p><p>In building science, numerical models are used to investigate rain heterogeneities typically focussing on single buildings and vertical facades. Only few studies were performed for more than a single building or with inclusion of atmospheric processes such as radiation or condensation. In meteorology, increasing computational power now allows the use of small-scale obstacle-resolving models resolving atmospheric processes while covering neighbourhoods.</p><p>In order to assess rain heterogeneities between buildings we extended the micro-scale and obstacle-resolving transport- and stream model MITRAS (Salim et al. 2019). The same cloud microphysics parameterisation as in its mesoscale sister model METRAS (Schlünzen et al., 2018) was applied and boundary conditions for cloud and rain water content at obstacle surfaces were introduced. MITRAS results are checked for plausibility using radar and in-situ measurements (Ferner et al., 2021). To our knowledge MITRAS is the first numerical urban climate model that includes rain and simulates corresponding processes.</p><p>Model simulations were initialised for various wind speeds and mesoscale rain rates to assess their influence on the heterogeneity of falling rain in a domain of 1.9 x 1.7 km² around Hamburg City Hall. We investigated how wind speed or mesoscale rain rate influence the precipitation patterns at ground and at roof level. Based on these results we assessed the height dependence of precipitation. First analyses show that higher buildings receive more rain on their roofs than lower buildings; the results will be presented in detail in our talk.</p><p>Ferner, K.S., Boettcher, M., Schlünzen, K.H. (2021): Modelling the heterogeneity of rain in an urban neighbourhood. Publication in preparation</p><p>Salim, M.H., Schlünzen, K.H., Grawe, D., Boettcher, M., Gierisch, A.M.U., Fock B.H. (2018): The microscale obstacle-resolving meteorological model MITRAS v2.0: model theory. Geosci. Model Dev., 11, 3427–3445, https://doi.org/10.5194/gmd-11-3427-2018.</p><p>Schlünzen, K.H., Boettcher, M., Fock, B.H., Gierisch, A.M.U., Grawe, D., and Salim, M. (2018): Scientific Documentation of the Multiscale Model System M-SYS. Meteorological Institute, Universität Hamburg. MEMI Technical Report 4</p>


2013 ◽  
Vol 30 (11) ◽  
pp. 2689-2694 ◽  
Author(s):  
Nadya T. Vinogradova ◽  
Rui M. Ponte

Abstract Calibration and validation efforts of the Aquarius and Soil Moisture and Ocean Salinity (SMOS) satellite missions involve comparisons of satellite and in situ measurements of sea surface salinity (SSS). Such estimates of SSS can differ by the presence of small-scale variability, which can affect the in situ point measurement, but be averaged out in the satellite retrievals because of their large footprint. This study quantifies how much of a difference is expected between in situ and satellite SSS measurements on the basis of their different sampling of spatial variability. Maps of sampling error resulting from small-scale noise, defined here as the root-mean-square difference between “local” and footprint-averaged SSS estimates, are derived using a solution from a global high-resolution ocean data assimilation system. The errors are mostly <0.1 psu (global median is 0.05 psu), but they can be >0.2 psu in several regions, particularly near strong currents and outflows of major rivers. To examine small-scale noise in the context of other errors, its values are compared with the overall expected differences between monthly Aquarius SSS and Argo-based estimates. Results indicate that in several ocean regions, small-scale variability can be an important source of sampling error for the in situ measurements.


2020 ◽  
Author(s):  
Marc Buckley ◽  
Jochen Horstmann

<p>Small-scale turbulent dynamics within the coupled atmospheric and oceanic wave boundary layers control air-sea fluxes of momentum and scalars. However measuring and understanding small-scale dynamics very close to the rapidly moving ocean surface remains technically challenging.</p><p>We present novel in situ measurements of small-scale motions in the airflow above, and in the water below the wavy air-water interface. A high resolution, large field of view PIV system (Particle Image Velocimetry) was developed for in situ air-water measurements within the first millimeters to meters above and below the wavy surface. The system was recently deployed on a single pile platform in the Szczecin lagoon (Baltic Sea coast, Germany). We will show first results and we will discuss the influence of waves on the partitioning of momentum flux within the coupled air-water wave boundary layers.</p>


2021 ◽  
Author(s):  
Clovis Thouvenin-Masson ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Dimitry Khvorostyanov ◽  
Xavier Perrot ◽  
...  

<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>


2022 ◽  
Vol 924 (2) ◽  
pp. 43
Author(s):  
Yu Chen ◽  
Qiang Hu

Abstract We report small-scale magnetic flux ropes via the in situ measurements from the Parker Solar Probe during the first six encounters, and present additional analyses to supplement our prior work in Chen et al. These flux ropes are detected by the Grad–Shafranov-based algorithm, with their durations and scale sizes ranging from 10 s to ≲1 hr and from a few hundred kilometers to 10−3 au, respectively. They include both static structures and those with significant field-aligned plasma flows. Most structures tend to possess large cross helicity, while the residual energy is distributed over wide ranges. We find that these dynamic flux ropes mostly propagate in the antisunward direction relative to the background solar wind, with no preferential signs of magnetic helicity. The magnetic flux function follows a power law and is proportional to scale size. We also present case studies showing reconstructed two-dimensional (2D) configurations, which confirm that both the static and dynamic flux ropes have a common configuration of spiral magnetic field lines (also streamlines). Moreover, the existence of such events hints at interchange reconnection as a possible mechanism for generating flux rope-like structures near the Sun. Lastly, we summarize the major findings, and discuss the possible correlation between these flux rope-like structures and turbulence due to the process of local Alfvénic alignment.


2021 ◽  
Vol 14 (4) ◽  
pp. 3119-3130
Author(s):  
Nicholas M. Deutscher ◽  
Travis A. Naylor ◽  
Christopher G. R. Caldow ◽  
Hamish L. McDougall ◽  
Alex G. Carter ◽  
...  

Abstract. Open-path measurements of atmospheric composition provide spatial averages of trace gases that are less sensitive to small-scale variations and the effects of meteorology. In this study we introduce improvements to open-path near-infrared (OP-NIR) Fourier transform spectrometer measurements of CO2 and CH4. In an extended field trial, the OP-NIR achieved measurement repeatability 6 times better for CO2 (0.28 ppm) and 10 times better for CH4 (2.1 ppb) over a 1.55 km one-way path than its predecessor. The measurement repeatability was independent of path length up to 1.55 km, the longest distance tested. Comparisons to co-located in situ measurements under well-mixed conditions characterise biases of 1.41 % for CO2 and 1.61 % for CH4 relative to in situ measurements calibrated to World Meteorological Organisation – Global Atmosphere Watch (WMO-GAW) scales. The OP-NIR measurements can detect signals due to local photosynthesis and respiration, and local point sources of CH4. The OP-NIR is well-suited for deployment in urban or rural settings to quantify atmospheric composition on kilometre scales.


2009 ◽  
Vol 9 (17) ◽  
pp. 6581-6595 ◽  
Author(s):  
J.-F. Gayet ◽  
G. Mioche ◽  
A. Dörnbrack ◽  
A. Ehrlich ◽  
A. Lampert ◽  
...  

Abstract. Airborne measurements in Arctic boundary-layer stratocumulus were carried out near Spitsbergen on 9 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign. A unique set of co-located observations is used to describe the cloud properties, including detailed in situ cloud microphysical and radiation measurements along with airborne and co-located spaceborne remote sensing data (CALIPSO lidar and CloudSat radar). CALIPSO profiles indicate cloud top levels at temperature between −24°C and −21°C. In situ measurements confirm that the cloud-top lidar attenuated backscatter signal along the aircraft trajectory is linked with the presence of liquid water, a common feature observed in Arctic mixed-phase stratocumulus clouds. A low concentration of large ice crystals is also observed up to the cloud top resulting in significant CloudSat radar echoes. Since the ratio of the extinction of liquid water droplets to ice crystals is high, broadband radiative effects near the cloud top are mostly dominated by water droplets. CloudSat observations and in situ measurements reveal high reflectivity factors (up to 15 dBZ) and precipitation rates (1 mm h−1). This feature results from efficient ice growth processes. About 25% of the theoretically available liquid water is converted into ice water with large precipitating ice crystals. Using an estimate of mean cloud cover, a considerable value of 106 m3 h−1 of fresh water could be settled over the Greenland sea pool. European Centre for Medium-Range Weather Forecast (ECMWF) operational analyses reproduces the boundary layer height variation along the flight track. However, small-scale features in the observed cloud field cannot be resolved by ECMWF analysis. Furthermore, ECMWF's diagnostic partitioning of the condensed water into ice and liquid reveals serious shortcomings for Arctic mixed-phased clouds. Too much ice is modelled.


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