Spectral characteristics of standing waves of the Bakar Bay

Provedena je analiza podataka s elektroničkog mareografa smještenog u Bakarskom zaljevu. Spektar snage izračunat iz dobivenih podataka pokazuje da se prvi mod javlja s periodima T11 = 26.53 min, T12 = 22.63 min, T13 = 19.92 min, zatim drugi mod s periodima T21 = 8.06 min, T22 = 7.79 min, T23 = 7.24 min te treći mod s periodom T3 = 4.27 min. Vidljivo je da modovi oscilacija Bakarskog zaljeva, izuzev trećeg, nemaju jedinstven period. Najveći rasap je vidljiv na prvom modu, gdje iznosi gotovo 7 min, dok je za drugi mod dobiven rasap s iznosom oko minute. Proveden je i račun za pojednostavljeni teorijski model kako bi se vidjelo slaganje s opaženim periodima i pokazao utjecaj položaja čvorne linije na ušću na periode. Čvorne linije postavljene su na presjeke ušća Rt Škrkovac Rt Lipica, Rt Oštro Rt Molnarić i Rt Srednji Rt Nirvana. Ovisno o duljini ušća, dobiveni su perodi prvog moda od 21.45 min, 19.05 min i 17.11 min, periodi drugog moda su 8.08 min, 7.73 min i 6.86 min te je za treći mod dobiveno 4.89 min, 4.95 min i 4.92 min. Za prvi mod su vidiljiva neslaganja izmedu teorijskih perioda i perioda dobivenih iz mjerenja, iako teorijski rezultat pokazuje rasap od oko 4 min što se donekle slaže s mjerenim vrijednostima. Za više modove slaganje teorijskih i mjerenih perioda je mnogo bolje

Evaluation of two common source estimation measurement strategies using large-eddy simulation of plume dispersion under neutral atmospheric conditions

This study uses large-eddy simulations (LESs) to evaluate two widely used observational techniques that estimate point source emissions. We evaluate the use of car measurements perpendicular to the wind direction and the commonly used Other Test Method 33A (OTM 33A). The LES study simulates a plume from a point source released into a stationary, homogeneous, and neutral atmospheric surface layer over flat terrain. This choice is motivated by our ambition to validate the observational methods under controlled conditions where they are expected to perform well since the sources of uncertainties are minimized. Three plumes with different release heights were sampled in a manner that mimics sampling according to car transects and the stationary OTM 33A. Subsequently, source strength estimates are compared to the true source strength used in the simulation. Standard deviations of the estimated source strengths decay proportionally to the inverse of the square root of the number of averaged transects, showing statistical independence of individual samples. The analysis shows that for the car transect measurements at least 15 repeated measurement series need to be averaged to obtain a source strength within 40 % of the true source strength. For the OTM 33A analysis, which recommends measurements within 200 m of the source, the estimates of source strengths have similar values close to the source, which is caused by insufficient dispersion of the plume by turbulent mixing close to the source. Additionally, the derived source strength is substantially overestimated with OTM 33A. This overestimation is driven by the proposed OTM 33A dispersion coefficients, which are too large for this specific case. This suggests that the conditions under which the OTM 33A dispersion constants were derived were likely influenced by motions with length scales beyond the scale of the surface layer. Lastly, our simulations indicate that, due to wind-shear effects, the position of the time-averaged centerline of the plumes may differ from the plume emission height. This mismatch can be an additional source of error if a Gaussian plume model (GPM) is used to interpret the measurement. In the case of the car transect measurements, a correct source estimate then requires an adjustment of the source height in the GPM.</p

Technical note: Interpretation of field observations of point-source methane plume using observation-driven large-eddy simulations

This study demonstrates the ability of large-eddy simulation (LES) forced by a large-scale model to reproduce plume dispersion in an actual field campaign. Our aim is to bring together field observations taken under non-ideal conditions and LES to show that this combination can help to derive point-source strengths from sparse observations. We analyze results from a single-day case study based on data collected near an oil well during the ROMEO campaign (ROmanian Methane Emissions from Oil and gas) that took place in October 2019. We set up our LES using boundary conditions derived from the meteorological reanalysis ERA5 and released a point source in line with the configuration in the field. The weather conditions produced by the LES show close agreement with field observations, although the observed wind field showed complex features due to the absence of synoptic forcing. In order to align the plume direction with field observations, we created a second simulation experiment with manipulated wind fields that better resemble the observations. Using these LESs, the estimated source strengths agree well with the emitted artificial tracer gas plume, indicating the suitability of LES to infer source strengths from observations under complex conditions. To further harvest the added value of LES, higher-order statistical moments of the simulated plume were analyzed. Here, we found good agreement with plumes from previous LES and laboratory experiments in channel flows. We derived a length scale of plume mixing from the boundary layer height, the mean wind speed and convective velocity scale. It was demonstrated that this length scale represents the distance from the source at which the predominant plume behavior transfers from meandering dispersion to relative dispersion