Estimation of Turbulent Triplet Covariances for Bora Flows

Bora is a strong or severe, relatively cold, gusty wind that usually blows from the northastern quadrant at the east coast of the Adriatic Sea. In this study bora’s turbulence triplet covariances were analysed, for the first time, for bora flows. The measurements used were obtained from the measuring tower on Pometeno brdo (“Swept-Away Hill”), in the hinterland of the city of Split, Croatia. From April 2010 until June 2011 three components of wind speed and sonic temperature were measured. The measurements were performed on three heights, 10, 22 and 40 m above the ground with the sampling frequency of 5 Hz. During the observed period, total of 60 bora episodes were isolated. We analyse the terms in prognostic equations for turbulence variances. In that respect, the viscous dissipation term was calculated using two approaches: (i) inertial dissipation method (εIDM) and (ii) direct approach from the prognostic equations for variances of turbulence (εEQ). We determine that the direct approach can successfully reproduce the shape of the curve, but the values are for several orders of magnitudes smaller compared to the real data. Further, linear relationship between εIDM and εEQ was obtained. Using the results for εEQ, viscous dissipation rate in longitudinal, transversal and vertical direction was determined. It is shown that viscous dissipation has the greatest impact on bora’s longitudinal direction. The focus is on the turbulence transport term, i.e., the triplet covariance term. For the first time, it is found that turbulence transport is very significant for the intensity of near−surface bora flows. Furthermore, turbulence transport can be both positive and negative, yet intensive. It is mostly negative at the upper levels and positive at the lower levels. Therefore, turbulence transport, in most cases, takes away turbulence variance from the upper levels and brings it down to the lower ones. This is one of the main findings of this study; it adds to the understanding of peculiarities of bora wind, and perhaps some other severe winds.

Aggregate Efficiency Dynamics in Lithuanian Dairy Farms

In this paper, we attempt to identify the major groups of decision making units (dairy farms) contributing to the aggregate efficiency change. We also suggest identifying influential peers in order to gain more insights into possible development strategies within a sector. The empirical application focuses on specialist dairy farms in Lithuania. The farm-level data cover the period 2004-2016. The results indicate the presence of structural changes and resulting shifts in the aggregate efficiency. Based on the results of decomposition of the covariance term and identification of the influential peers, two models can be followed by Lithuanian dairy farms, namely “pure” family farms with lower operational scale and large farms involving hired labour.

Simplified Fréchet Distance for Generative Adversarial Nets

We introduce a distance metric between two distributions and propose a Generative Adversarial Network (GAN) model: the Simplified Fréchet distance (SFD) and the Simplified Fréchet GAN (SFGAN). Although the data generated through GANs are similar to real data, GAN often undergoes unstable training due to its adversarial structure. A possible solution to this problem is considering Fréchet distance (FD). However, FD is unfeasible to realize due to its covariance term. SFD overcomes the complexity so that it enables us to realize in networks. The structure of SFGAN is based on the Boundary Equilibrium GAN (BEGAN) while using SFD in loss functions. Experiments are conducted with several datasets, including CelebA and CIFAR-10. The losses and generated samples of SFGAN and BEGAN are compared with several distance metrics. The evidence of mode collapse and/or mode drop does not occur until 3000k steps for SFGAN, while it occurs between 457k and 968k steps for BEGAN. Experimental results show that SFD makes GANs more stable than other distance metrics used in GANs, and SFD compensates for the weakness of models based on BEGAN-based network structure. Based on the experimental results, we can conclude that SFD is more suitable for GAN than other metrics.

Comparison of straight line curve fit approaches for determining parameter variances and covariances

Pressure balances are known to have a linear straight line equation of the form y = ax + b that relates the applied pressure x to the effective area y, and recent work has investigated the use of Ordinary Least Squares (OLS), Weighted Least Squares (WLS), and Generalized Least Squares (GLS) regression schemes in order to quantify the expected values of the zero-pressure area A0 = b and distortion coefficient λ = a/b in pressure balance models of the form y = A0(1 + λx). The limitations with conventional OLS, WLS and GLS approaches is that whilst they may be used to quantify the uncertainties u(a) and u(b) and the covariance cov(a, b), it is technically challenging to analytically quantify the covariance term cov(A0, λ) without additional Monte Carlo simulations. In this paper, we revisit an earlier Weighted Total Least Squares with Correlation (WTLSC) algorithm to determine the variances u2(a) and u2(b) along with the covariance cov(a, b), and develop a simple analytical approach to directly infer the corresponding covariance cov(A0, λ) for pressure metrology uncertainty analysis work. Results are compared to OLS, WLS and GLS approaches and indicate that the WTLSC approach may be preferable as it avoids the need for Monte Carlo simulations and additional numerical post-processing to fit and quantify the covariance term, and is thus simpler and more suitable for industrial metrology pressure calibration laboratories. Novel aspects is that a Gnu Octave/Matlab program for easily implementing the WTLSC algorithm to calculate parameter expected values, variances and covariances is also supplied and reported.

The Quanto Theory of Exchange Rates

We present a new identity that relates expected exchange rate appreciation to a risk-neutral covariance term, and use it to motivate a currency forecasting variable based on the prices of quanto index contracts. We show via panel regressions that the quanto forecast variable is an economically and statistically significant predictor of currency appreciation and of excess returns on currency trades. Out of sample, the quanto variable outperforms predictions based on uncovered interest parity, on purchasing power parity, and on a random walk as a forecaster of differential (dollar-neutral) currency appreciation. (JEL C53, E43, F31, F37, G12, G15)

Modeling the determinants of dry bulk FFA trading volume from a cross-market perspective of spot and forward

Purpose This study aims to propose a theoretical model to characterize the optimal forward freight agreement (FFA) procurement strategies and investigate the determinants of FFA trading activities from a new cross-market perspective. Findings A two-step model specification is used to empirically test the theoretical results for the Capesize, Panamax and Supramax sectors. It is found that spot demand has a positive relation with FFA trading volume for all three sectors. Moreover, spot demand volatility has a negative relation, while the correlation between spot demand and spot rate has a positive relation with FFA trading volume for the Capesize and Panamax sectors. Originality/value The results show that the expected spot demand is scaled by a “quantity premium,” which is the product of a demand covariance term, a demand riskiness term and a demand volatility term. This can be used by the traders in the FFA market to construct their hedging strategies.

Relationships among Four-Dimensional Hybrid Ensemble–Variational Data Assimilation Algorithms with Full and Approximate Ensemble Covariance Localization

Ensemble–variational data assimilation algorithms that can incorporate the time dimension (four-dimensional or 4D) and combine static and ensemble-derived background error covariances (hybrid) are formulated in general forms based on the extended control variable and the observation-space-perturbation approaches. The properties and relationships of these algorithms and their approximated formulations are discussed. The main algorithms discussed include the following: 1) the standard ensemble 4DVar (En4DVar) algorithm incorporating ensemble-derived background error covariance through the extended control variable approach, 2) the 4DEnVar neglecting the time propagation of the extended control variable (4DEnVar-NPC), 3) the 4D ensemble–variational algorithm based on observation space perturbation (4DEnVar), and 4) the 4DEnVar with no propagation of covariance localization (4DEnVar-NPL). Without the static background error covariance term, none of the algorithms requires the adjoint model except for En4DVar. Costly applications of the tangent linear model to localized ensemble perturbations can be avoided by making the NPC and NPL approximations. It is proven that En4DVar and 4DEnVar are mathematically equivalent, while 4DEnVar-NPC and 4DEnVar-NPL are mathematically equivalent. Such equivalences are also demonstrated by single-observation assimilation experiments with a 1D linear advection model. The effects of the non-flow-following or stationary localization approximations are also examined through the experiments. All of the above algorithms can include the static background error covariance term to establish a hybrid formulation. When the static term is included, all algorithms will require a tangent linear model and an adjoint model. The first guess at appropriate time (FGAT) approximation is proposed to avoid the tangent linear and adjoint models. Computational costs of the algorithms are also discussed.

Estimating Quebec provincial forest resources using ICESat/GLAS

Ground plots, airborne profiling and space lidar (light detection and ranging) measurements of canopy height and crown closure, space radar topographic data, a Landsat cover type map, and a vegetation zone map were used in a model-assisted, two-phase sampling design to estimate the aboveground biomass and carbon resources of Quebec. It was determined that a simple random sampling estimator, with covariance terms added, could be used to quantify the variability of regional Geoscience Laser Altimeter System (GLAS) biomass estimates where interorbit distances are, on average, ≥15 km apart. Prediction error increased standard errors, on average, 24.4%, 4.6%, and 2.8% at the cover type, vegetation zone, and provincial levels, respectively. Inclusion of the covariance term in the calculation of grouped cover type variances increased the vegetation zone standard errors up to 3.7 times and the provincial standard errors 15.6 times. In the southern commercial forests of Quebec, GLAS underestimated ground-based biomass values by 7.3% (stratified linear model) and 10.2% (nonstratified linear model). Quebec forests support 2.57 ± 0.33 gigatonnes of carbon (nonstratified linear model). Approximately 25% of that carbon was found to be located in two southern vegetation zones (northern hardwood and mixedwood), another 25% in two northern vegetation zones (taiga and treed tundra), and the remaining 50% in the boreal zone.

On the Theoretical Equivalence of Differently Proposed Ensemble–3DVAR Hybrid Analysis Schemes

Hybrid ensemble–three-dimensional variational analysis schemes incorporate flow-dependent, ensemble-estimated background-error covariances into the three-dimensional variational data assimilation (3DVAR) framework. Typically the 3DVAR background-error covariance estimate is assumed to be stationary, nearly homogeneous, and isotropic. A hybrid scheme can be achieved by 1) directly replacing the background-error covariance term in the cost function by a linear combination of the original background-error covariance with the ensemble covariance or 2) through augmenting the state vector with another set of control variables preconditioned upon the square root of the ensemble covariance. These differently proposed hybrid schemes are proven to be equivalent. The latter framework may be a simpler way to incorporate ensemble information into operational 3DVAR schemes, where the preconditioning is performed with respect to the background term.