Numerical simulation on ground vibration caused by the demolition of a 200 m high chimney

A chimney-soil model was built using finite element method to simulate the demolition of a chimney and the subsequent ground vibration. The acceleration history of ground vibration at observed point was obtained. The simulated results were compared with on-site measured data and good agreement was found with errors of less than 2.88 % for maximum acceleration amplitudes. It was also demonstrated that the element disappearance in the model did not affect the vibration response

ERAstar: A high-resolution ocean forcing product

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksTo address the growing demand for accurate high-resolution ocean wind forcing from the ocean modeling community, we develop a new forcing product, ERA*, by means of a geolocated scatterometer-based correction applied to the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis or ERA-interim (hereafter referred to as ERAi). This method successfully corrects for local wind vector biases present in the ERAi output globally. Several configurations of the ERA* are tested using complementary scatterometer data [advanced scatterometer (ASCAT)-A/B and oceansat-2 scatterometer (OSCAT)] accumulated over different temporal windows, verified against independent scatterometer data [HY-2A scatterometer (HSCAT)], and evaluated through spectral analysis to assess the geophysical consistency of the new stress equivalent wind fields (U10S). Due to the high quality of the scatterometer U10S, ERA* contains some of the physical processes missing or misrepresented in ERAi. Although the method is highly dependent on sampling, it shows potential, notably in the tropics. Short temporal windows are preferred, to avoid oversmoothing of the U10S fields. Thus, corrections based on increased scatterometer sampling (use of multiple scatterometers) are required to capture the detailed forcing errors. When verified against HSCAT, the ERA* configurations based on multiple scatterometers reduce the vector root-mean-square difference about 10% with respect to that of ERAi. ERA* also shows a significant increase in small-scale true wind variability, observed in the U10S spectral slopes. In particular, the ERA* spectral slopes consistently lay between those of HSCAT and ERAi, but closer to HSCAT, suggesting that ERA* effectively adds spatial scales of about 50 km, substantially smaller than those resolved by global numerical weather prediction (NWP) output over the open ocean (about 150 km).Peer ReviewedPostprint (author's final draft

On buoys, scatterometers and reanalyses for globally representative winds

15 pages, 3 figures, 2 tablesMoored buoy winds are of high quality and our only absolute reference for satellite wind calibration and monitoring. General Circulation Models (GCMs) and satellites lack absolute calibration otherwise. Maintaining a long-term data record of surface wind measurements is thus critical to the cross-calibration of satellite winds from different satellite missions and different satellite sensor types (e.g., the SSM/I series microwave radiometers, Ku- vs C- vs L-band scatterometers). The current non-uniform distribution of moored buoys makes them rather unsuitable for global change metrics. The geographical distribution of moored buoys points to a glaring hole in the southern hemisphere. With 60m of global water level stored in the southern hemisphere, scientific misjudgement may have rather drastic consequences. However, buoy monitoring in the SH extratropics is essentially missing and should be recommended in our view. It would be much appreciated if (particularly southern hemisphere governments) would take responsibility in this area. We perform triple collocation (TC) with moored buoys, scatterometers and GCMs to establish the accuracy and calibration of the scatterometer winds and the GCMs at the moored buoy positions. By physical inference, we assume that the spatial sample of buoys is sufficient to obtain a globally representative absolute calibration. This can obviously not be proven, as no globally representative in situ wind network is available. However, given such plausible inference, it appears possible to reach the 0.1 m/s per decade stability in a representative global metric. Moreover, randomly reducing the density of the current spatial distribution of moored buoys, does not appear too harmful. We note that different global metrics provide different trends though, as they cover different spatio-temporal domains, e.g., at all global buoy measurement positions (as in TC), at model grid positions (either regular or uniformly spaced), or at all satellite measurement points (after QC usually). The satellite or GCM representations of the global waters appear clearly the most faithful (see above). The IOVWST community currently converges in the understanding that stress-equivalent wind (U10S) is the most practical retrieval quantity for scatterometers and radiometers, as it may be well validated by GCM and buoy data. This implies that for an accurate computation of U10S from buoys, we ideally need continuous buoy series of: the 10-m wind, SST, air temperature, air humidity, air pressure and ocean current. These variables are used to respectively take out effects of atmospheric stratification, air mass density and ocean mean motion (as the sensed ocean roughness depends on the mean relative difference between water and air motion). As less of this information would become available at the buoys, it will be harder to stay within the climate requirement of 0.1 m/s per decade in the more representative global metrics. Recent publications suggest that observation of OSVW variability in the tropics is quite relevant, e.g., Sherwood et al. (2014), Lin et al. (2015), King et al. (2014) or Sandu et al. (2011), suggesting that spread in climate model sensitivity and model bias can be related to subtle dynamical model aspects, such as moist convection. Another question is thus how dynamical meteorological and oceanographic interaction processes, relevant for the realism of climate models should be addressed by measurement capability in the satellite era. This question is not further addressed in this report.This documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement dated 16 December, 2003, between EUMETSAT and the Met Office, UK, by one or more partners within the NWP SAF. The partners in the NWP SAF are the Met Office, ECMWF, KNMI and Météo FrancePeer Reviewe

On mesoscale analysis and ASCAT ambiguity removal

45 pages, 17 figures, 7 tablesIn the so-called two-dimensional variational ambiguity removal (2DVAR) scheme [Vogelzanget al., 2010], the scatterometer observations and the model background (fromthe European Centre for Medium-range Weather Forecasts, ECMWF) are combined using a two-dimensional variational approach, similar to that used in meteorological data assimilation, to provide an analyzed wind field. Since scatterometers provide unique mesoscale information on the wind field, mesoscale analysis is a common challenge for 2DVAR and for mesoscale data assimilation in 4D-var or 3D-var, such as applied using the Integrated Forecasting System (IFS) at ECMWF, Meteo France or in the HIRLAM project (www.hirlam.org). This study elaborates on the common problem of specifying the observation and background error covariances in data assimilationThis documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement dated 29 June 2011, between EUMETSAT and the Met Office, UK, by one or more partners within the NWP SAF. The partners in the NWP SAF are the Met Office, ECMWF, KNMI and Météo FrancePeer Reviewe

RapidScat winds from the OSI SAF

2015 EUMETSAT Meteorological Satellite Conference, 21-25 September 2015, Toulouse.-- 1 page, 2 figures, 3 tablesThe RapidScat scatterometer instrument is a speedy and cost-effective replacement for the National Aeronautics and Space Administration (NASA) QuikSCAT satellite, which provided a decade-long ocean vector wind observations. RapidScat was launched on 20 September 2014 and mounted on the International Space Station (ISS). The use of generic algorithms for Ku-band scatterometer wind processing allowed us to develop a good quality wind product in a very short time. The wind products with development status are available to users since early December 2014, only one month after the level 2a data became available. Operational status was achieved in March 2015. The good quality of the winds is confirmed by comparisons of RapidScat with NWP, buoy and ASCAT windsPeer Reviewe

Measurements of Air-Sea Interaction from the HY-2A Scatterometer

International Ocean Vector Wind Science Team Meeting (IOVWST), 2-4 June 2014, Brest, France.-- 21 pagesPeer Reviewe