Sensitivity analysis of acoustic channel characteristics to sea surface spectral uncertainty.

2010 ◽  
Vol 127 (3) ◽  
pp. 1820-1820
Author(s):  
Allan Rosenberg ◽  
Qinqing Zhang
Keyword(s):  
Sensitivity Analysis ◽  
Sea Surface ◽  
Acoustic Channel ◽  
Channel Characteristics

scholarly journals Array Gain of a Linear Array in an Ocean Waveguide

2021 ◽  
Vol 11 (11) ◽  
pp. 5046
Author(s):  
Zong-Wei Liu ◽  
Chun-Mei Yang ◽  
Ying Jiang ◽  
Lei Xie ◽  
Jin-Yan Du ◽  
...  
Keyword(s):  
Transmission Loss ◽  
Spatial Coherence ◽  
Correlation Coefficients ◽  
Channel Correlation ◽  
Acoustic Channel ◽  
Physical Mechanisms ◽  
Channel Characteristics ◽  
Special Case ◽  
Array Gain ◽  
Insight Into

Array gain is investigated based on the acoustic channel characteristics manifested by the fluctuant transmission loss and decrease in the acoustic channel spatial coherence. An analytical expression is derived as the summation of the products of the acoustic channel correlation coefficients and root-mean-square pressures. The formula provides insight into the physical mechanisms of the gain degradation in the ocean waveguide. Furthermore, this formula provides a new method to study array gain in the ocean waveguide from underwater acoustic field. The obtained expression is a more general formula that is applicable to shallow water, deep sea, and continental slope, with the traditional methods as a special case. Numerical results show that the array gain calculated by previous formulas are generally overestimated, caused by ignoring the effect of transmission loss fluctuation.

scholarly journals Global sensitivity analysis of the Indian monsoon during the Pleistocene

2015 ◽  
Vol 11 (1) ◽  
pp. 45-61 ◽  
Author(s):  
P. A. Araya-Melo ◽  
M. Crucifix ◽  
N. Bounceur
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Mixed Layer ◽  
Global Sensitivity Analysis ◽  
Indian Monsoon ◽  
Mixed Layer Depth ◽  
Sea Surface ◽  
Layer Depth ◽  
Global Sensitivity

Abstract. The sensitivity of the Indian monsoon to the full spectrum of climatic conditions experienced during the Pleistocene is estimated using the climate model HadCM3. The methodology follows a global sensitivity analysis based on the emulator approach of Oakley and O'Hagan (2004) implemented following a three-step strategy: (1) development of an experiment plan, designed to efficiently sample a five-dimensional input space spanning Pleistocene astronomical configurations (three parameters), CO2 concentration and a Northern Hemisphere glaciation index; (2) development, calibration and validation of an emulator of HadCM3 in order to estimate the response of the Indian monsoon over the full input space spanned by the experiment design; and (3) estimation and interpreting of sensitivity diagnostics, including sensitivity measures, in order to synthesise the relative importance of input factors on monsoon dynamics, estimate the phase of the monsoon intensity response with respect to that of insolation, and detect potential non-linear phenomena. By focusing on surface temperature, precipitation, mixed-layer depth and sea-surface temperature over the monsoon region during the summer season (June-July-August-September), we show that precession controls the response of four variables: continental temperature in phase with June to July insolation, high glaciation favouring a late-phase response, sea-surface temperature in phase with May insolation, continental precipitation in phase with July insolation, and mixed-layer depth in antiphase with the latter. CO2 variations control temperature variance with an amplitude similar to that of precession. The effect of glaciation is dominated by the albedo forcing, and its effect on precipitation competes with that of precession. Obliquity is a secondary effect, negligible on most variables except sea-surface temperature. It is also shown that orography forcing reduces the glacial cooling, and even has a positive effect on precipitation. As regards the general methodology, it is shown that the emulator provides a powerful approach, not only to express model sensitivity but also to estimate internal variability and detect anomalous simulations.

scholarly journals Application of Multivariate Sensitivity Analysis Techniques to AGCM-Simulated Tropical Cyclones

2018 ◽  
Vol 146 (7) ◽  
pp. 2065-2088 ◽  
Author(s):  
Fei He ◽  
Derek J. Posselt ◽  
Naveen N. Narisetty ◽  
Colin M. Zarzycki ◽  
Vijayan N. Nair
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Tropical Cyclones ◽  
Radiative Forcing ◽  
Initial Conditions ◽  
Sea Surface ◽  
Precipitation Rate ◽  
Analysis Framework ◽  
Control Factors

Abstract This work demonstrates the use of Sobol’s sensitivity analysis framework to examine multivariate input–output relationships in dynamical systems. The methodology allows simultaneous exploration of the effect of changes in multiple inputs, and accommodates nonlinear interaction effects among parameters in a computationally affordable way. The concept is illustrated via computation of the sensitivities of atmospheric general circulation model (AGCM)-simulated tropical cyclones to changes in model initial conditions. Specifically, Sobol’s variance-based sensitivity analysis is used to examine the response of cyclone intensity, cloud radiative forcing, cloud content, and precipitation rate to changes in initial conditions in an idealized AGCM-simulated tropical cyclone (TC). Control factors of interest include the following: initial vortex size and intensity, environmental sea surface temperature, vertical lapse rate, and midlevel relative humidity. The sensitivity analysis demonstrates systematic increases in TC intensity with increasing sea surface temperature and atmospheric temperature lapse rates, consistent with many previous studies. However, there are nonlinear interactions among control factors that affect the response of the precipitation rate, cloud content, and radiative forcing. In addition, sensitivities to control factors differ significantly when the model is run at different resolution, and coarse-resolution simulations are unable to produce a realistic TC. The results demonstrate the effectiveness of a quantitative sensitivity analysis framework for the exploration of dynamic system responses to perturbations, and have implications for the generation of ensembles.

scholarly journals The role of atmospheric boundary layer-surface interactions on the development of coastal fronts

2007 ◽  
Vol 25 (2) ◽  
pp. 341-360 ◽  
Author(s):  
D. Malda ◽  
J. Vilà-Guerau de Arellano ◽  
W. D. van den Berg ◽  
I. W. Zuurendonk
Keyword(s):  
Sensitivity Analysis ◽  
Surface Roughness ◽  
Land Surface ◽  
Mesoscale Model ◽  
Sea Surface ◽  
Precipitation Pattern ◽  
Surface Conditions ◽  
Frictional Convergence ◽  
Coastal Front ◽  
Thermal Difference

Abstract. Frictional convergence and thermal difference between land and sea surface are the two surface conditions that govern the intensity and evolution of a coastal front. By means of the mesoscale model MM5, we investigate the influence of these two processes on wind patterns, temperature and precipitation amounts, associated with a coastal front, observed on the west coast of The Netherlands in the night between 12 and 13 August 2004. The mesoscale model MM5 is further compared with available observations and the results of two operational models (ECMWF and HIRLAM). HIRLAM is not capable to reproduce the coastal front, whereas ECMWF and MM5 both calculate precipitation for the coastal region. The precipitation pattern, calculated by MM5, agrees satisfactorily with the accumulated radar image. The failure of HIRLAM is mainly due to a different stream pattern at the surface and consequently, a different behaviour of the frictional convergence at the coastline. The sensitivity analysis of frictional convergence is carried out with the MM5 model, by varying land surface roughness length (z0). For the sensitivity analysis of thermal difference between sea and land surface, we changed the sea surface temperature (SST). Increasing surface roughness implies stronger convergence near the surface and consequently stronger upward motions and intensification of the development of the coastal front. Setting land surface roughness equal to the sea surface roughness means an elimination of frictional convergence and results in a diminishing coastal front structure of the precipitation pattern. The simulation with a high SST produces much precipitation above the sea, but less precipitation in the coastal area above land. A small increment of the SST results in larger precipitation amounts above the sea; above land increments are calculated for areas near the coast. A decrease of the SST shifts the precipitation maxima inland, although the precipitation amounts diminish. In the situation under study, frictional convergence is the key process that enhances the coastal front intensity. A thermal difference between land and sea equal to zero still yields the development of the coastal front. A lower SST than land surface temperature generates a reversed coastal front. This study emphasizes the importance of accurate prescription of surface conditions as input of the numerical weather prediction model to improve coastal front predictability.

scholarly journals Use of a Mixed-Layer Model to Investigate Problems in Operational Prediction of Return Flow

2007 ◽  
Vol 135 (7) ◽  
pp. 2610-2628 ◽  
Author(s):  
John M. Lewis
Keyword(s):  
Sensitivity Analysis ◽  
Gulf Of Mexico ◽  
Boundary Conditions ◽  
Mixed Layer ◽  
Initial Conditions ◽  
Sea Surface ◽  
Return Flow ◽  
Control Vector ◽  
Layer Model ◽  
Mixed Layer Model

Abstract Inaccuracy in the numerical prediction of the moisture content of return-flow air over the Gulf of Mexico continues to plague operational forecasters. At the Environmental Modeling Center/National Centers for Environmental Prediction in the United States, the prediction errors have exhibited bias—typically too dry in the early 1990s and too moist from the mid-1990s to present. This research explores the possible sources of bias by using a Lagrangian formulation of the classic mixed-layer model. Justification for use of this low-order model rests on careful examination of the upper-air thermodynamic structure in a well-observed event during the Gulf of Mexico Experiment. The mixed-layer constraints are shown to be appropriate for the first phase of return flow, namely, the northerly-flow or outflow phase. The theme of the research is estimation of sensitivity—change in the model output (at termination of outflow) in response to inaccuracies or uncertainties in the elements of the control vector (the initial conditions, the boundary conditions, and the physical and empirical parameters). The first stage of research explores this sensitivity through a known analytic solution to a reduced form of the mixed-layer equations. Numerically calculated sensitivity (via Runge–Kutta integration of the equations) is compared to the exact values and found to be most credible. Further, because the first- and second-order terms in the solution about the base state can be found exactly for the analytic case, the degree of nonlinearity in the dynamical system can be determined. It is found that the system is “weakly nonlinear”; that is, solutions that result from perturbations to the control vector are well approximated by the first-order terms in the Taylor series expansion. This bodes well for the sensitivity analysis. The second stage of research examines sensitivity for the general case that includes moisture and imposed subsidence. Results indicate that uncertainties in the initial conditions are significant, yet they are secondary to uncertainties in the boundary conditions and physical/empirical parameters. The sea surface temperatures and associated parameters, the saturation mixing ratio at the sea surface and the turbulent transfer coefficient, exert the most influence on the moisture forecast. Uncertainty in the surface wind speed is also shown to be a major source of systematic error in the forecast. By assuming errors in the elements of the control vector that reflect observational error and uncertainties in the parameters, the bias error in the moisture forecast is estimated. These bias errors are significantly greater than random errors as explored through Monte Carlo experiments. Bias errors of 1–2 g kg−1 in the moisture forecast are possible through a variety of systematic errors in the control vector. The sensitivity analysis also makes it clear that judiciously chosen incorrect specifications of the elements can offset each other and lead to a good moisture forecast. The paper ends with a discussion of research approaches that hold promise for improved operational forecasts of moisture in return-flow events.

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