Melt Pond Scheme Parameter Estimation Using an Adjoint Model

Melt ponds significantly affect Arctic sea ice thermodynamic processes. The melt pond parameterization scheme in the Los Alamos sea ice model(CICE6.0) can predict the volume, area fraction(the ratio between melt pond area to sea ice area in a model grid), and depth of melt ponds. However, this scheme has some uncertain parameters that affect melt pond simulations. These parameters could be determined through a conventional parameter estimation method, which requires a large number of sensitivity simulations. The adjoint model can calculate the parameter sensitivity efficiently. In the present research, an adjoint model was developed for the CESM(Community Earth System Model) melt pond scheme. A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model, melt pond adjoint model,and L-BFGS(Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm. The parameter estimation algorithm was verified under idealized conditions. By using MODIS(Moderate Resolution Imaging Spectroradiometer)melt pond fraction observation as a constraint and the developed parameter estimation algorithm, the melt pond aspect ratio parameter in CESM scheme, which is defined as the ratio between pond depth and pond area fraction, was estimated every eight days during summertime for two different regions in the Arctic. One region was covered by multi-year ice(MYI) and the other by first-year ice(FYI). The estimated parameter was then used in simulations and the results show that:(1) the estimated parameter varies over time and is quite different for MYI and FYI;(2) the estimated parameter improved the simulation of the melt pond fraction.     

Mitigating parameter bias in hydrological modelling due to uncertainty in covariates

The uncertainty in hydrological model covariates, if ignored, introduces systematic bias in the parameters estimated. We introduce here a method to determine the true value of parameters given uncertainty in model inputs. This method, known as simulation extrapolation (SIMEX) operates on the basis of an empirical relationship between parameters and the level of input noise (or uncertainty). The method starts by generating a series of alternate model inputs by artificially adding white noise in increasing multiples of the known error variance. The resulting parameter sets allow us to formulate an empirical relationship between their values and the level of noise present. SIMEX is based on theory that the trend in alternate parameters can be extrapolated back to the notional error free zone.

We illustrate the strength of SIMEX in improving skills of predictive models that use uncertain sea surface temperature anomaly (SSTA) data over the NINO3 region as predictor to the southern oscillation index (SOI), an alternate measure of the strength of the El Nino southern oscillation. Our hypothesis is that the higher magnitude of noise in the pre 1960 data period introduces bias to model parameters where SSTA is the input variable. The relatively error invariant southern oscillation index (SOI) is regressed over SSTA and calibrated using a subset of the series from 1900 to 1960. We validate the resulting models using the less erroneous 1960–2003 data period. Overall the application of SIMEX is found to reduce the residual predictive errors during the validation period.     

The Determination of Relationship between Zooplankton and Abiotic Factors Using Canonical Correspondence Analysis (CCA) in the Ova Stream (Ankara/Turkey)

In this study totally 25 rotifer species, 3 cladocerus species, and 2 copepoda species were identified in Ova Stream. Among zooplankton rotifer species were dominant. Population densities of planktonic organisms were calculated as individual per cubic meter and the relationship of the planktonic organisms with physicochemical parameters was determined applying canonical correspondence analysis (CCA). Also diversity index has been calculated according to the sampling stations. According to the results, the diversity index changes between stations and sampling time. The CCA results show that the rotifer species Keratella, Notholca showed negative correlation with the increasing chemical parameters and temperature but Brachionus, Mytilina, Colurella, and Testudinella have positive correlation with increasing temperature.     

Multiscale adaptive estimation of the conductivity field from head and tracer data

Large scale fluctuations in the conductivity field are regionalized and estimated via a maximum likelihood, adjoint-state methodology. Small-scale fluctuations within each region are estimated adaptively via a Kalman-like stochastic filter. The variance and integral scale within each region are assumed to control the small-scale fluctuations. A Monte Carlo technique is used to examine the distribution of large-scale conductivity estimates.     

估算模式 AERSCREEN的参数敏感性研究

《环境影响评价技术导则大气环境》（HJ2.2-2018）推荐的估算模式AERSCREEN在气象和地形资料的处理以及建筑物下洗等多个方面做了改进。利用估算模式AERSCREEN,针对30 m左右高度的点源,进行了不同排放参数、不同气象条件下最大落地浓度的敏感性试验。结果表明：随着烟气出口流速的增大,地面浓度最大值逐渐减小;随着波文比的变化,地面浓度最大值没有明显的变化;随着地面粗糙度的增大,地面浓度最大值逐渐减小;随着烟气出口温度的增高,地面浓度最大值逐渐减小;当烟气温度为75℃,粗糙度达到1.3 m时,地面浓度达到最小;随着反照率的增大,地面浓度最大值逐渐减小;随着烟囱高度的增大,地面浓度最大值逐渐减小;在各种烟囱高度条件下,随着最高环境温度的增高,地面浓度最大值逐渐增大;而在各种环境温度条件下,随着烟囱高度的增高,地面浓度最大值在逐渐减小;模式中,随着最低环境温度的增高,地面浓度最大值没有变化;但随着最小风速的增大,模拟得到的地面浓度最大值会逐渐减小。     

Misfit functionals in Laplace‐Fourier domain waveform inversion,with application to wide‐angle ocean bottom seismograph data

In seismic waveform inversion, non‐linearity and non‐uniqueness require appropriate strategies. We formulate four types of L2 normed misfit functionals for Laplace‐Fourier domain waveform inversion: i) subtraction of complex‐valued observed data from complex‐valued predicted data (the ‘conventional phase‐amplitude’ residual), ii) a ‘conventional phase‐only’ residual in which amplitude variations are normalized, iii) a ‘logarithmic phase‐amplitude’ residual and finally iv) a ‘logarithmic phase‐only’ residual in which the only imaginary part of the logarithmic residual is used. We evaluate these misfit functionals by using a wide‐angle field Ocean Bottom Seismograph (OBS) data set with a maximum offset of 55 km. The conventional phase‐amplitude approach is restricted in illumination and delineates only shallow velocity structures. In contrast, the other three misfit functionals retrieve detailed velocity structures with clear lithological boundaries down to the deeper part of the model. We also test the performance of additional phase‐amplitude inversions starting from the logarithmic phase‐only inversion result. The resulting velocity updates are prominent only in the high‐wavenumber components, sharpening the lithological boundaries. We argue that the discrepancies in the behaviours of the misfit functionals are primarily caused by the sensitivities of the model gradient to strong amplitude variations in the data. As the observed data amplitudes are dominated by the near‐offset traces, the conventional phase‐amplitude inversion primarily updates the shallow structures as a result. In contrast, the other three misfit functionals eliminate the strong dependence on amplitude variation naturally and enhance the depth of illumination. We further suggest that the phase‐only inversions are sufficient to obtain robust and reliable velocity structures and the amplitude information is of secondary importance in constraining subsurface velocity models.     

Transient process and optimal design of receiver coil for small‐loop transient electromagnetics

Distributed parameters of the receiver coils greatly affect transient electromagnetic signals over short time periods, causing a delay in the signal's effective sampling time and the loss of shallow exploration information. This paper investigates the influence of transient process on apparent resistivity calculation and analyses the relations between the error of apparent resistivity and receiver coil design. We find that, under the same effective area, different radii of the receiver coils lead to different levels of impact on the estimation of the apparent resistivity. An optimization model is proposed to determine the optimal receiver coil size that gives rise to the smallest estimation error of the apparent resistivity. The relationship between the optimal radius and the effective areas is developed, which serves as a guideline for the optimal receiver coil design. The results may provide a useful means for improving the accuracy of the small loop transient electromagnetic instrumentation for shallow‐depth mapping.     

Inverse methods in hydrogeology: Evolution and recent trends

Parameter identification is an essential step in constructing a groundwater model. The process of recognizing model parameter values by conditioning on observed data of the state variable is referred to as the inverse problem. A series of inverse methods has been proposed to solve the inverse problem, ranging from trial-and-error manual calibration to the current complex automatic data assimilation algorithms. This paper does not attempt to be another overview paper on inverse models, but rather to analyze and track the evolution of the inverse methods over the last decades, mostly within the realm of hydrogeology, revealing their transformation, motivation and recent trends. Issues confronted by the inverse problem, such as dealing with multiGaussianity and whether or not to preserve the prior statistics are discussed.     

Research Note: Sparsity‐Aware Multiple Microseismic Event Localization Blind to the Source Time‐Function

We consider the problem of simultaneously estimating three parameters of multiple microseimic events, i.e., the hypocenter, moment tensor, and origin time. This problem is of great interest because its solution could provide a better understanding of reservoir behavior and can help to optimize the hydraulic fracturing process. The existing approaches employing spatial source sparsity have advantages over traditional full‐wave inversion‐based schemes; however, their validity and accuracy depend on the knowledge of the source time‐function, which is lacking in practical applications. This becomes even more challenging when multiple microseimic sources appear simultaneously. To cope with this shortcoming, we propose to approach the problem from a frequency‐domain perspective and develop a novel sparsity‐aware framework that is blind to the source time‐function. Through our simulation results with synthetic data, we illustrate that our proposed approach can handle multiple microseismic sources and can estimate their hypocenters with an acceptable accuracy. The results also show that our approach can estimate the normalized amplitude of the moment tensors as a by‐product, which can provide worthwhile information about the nature of the sources.