Westward-propagating Rossby modes in idealized GCMs

This work investigates the characteristics of westward-propagating Rossby modes in idealized global general circulation models. Using a nonlinear smoothing algorithm to estimate the background spectrum and an objective method to extract the spectral peaks, the 4 leading meridional modes can be identified for each of the first 3 zonal wavenumbers, with frequencies close to the predictions from the Hough modes obtained by linearizing about a state of rest. Variations in peak amplitude for different modes, both within a simulation and across simulations, may be understood under the assumption that the forcing of the modes scales with the background spectrum. Surface friction affects the amplitude and width of the peaks but both remain finite as friction goes to zero, which implies that some other mechanism, arguably nonlinear, must also contribute to the damping of the modes. Although spectral peaks are also observed for the precipitation field with idealized moist physics, there is no evidence of mode enhancement by the convective heating. Subject to the same friction, the amplitude of the peaks are very similar in the dry and moist models when both are normalized by the background spectra.

Backward nonlinear smoothing diffusions

Мы представляем обратный диффузионный поток (т.е. обратное по времени стохастическое дифференциальное уравнение), маргинальное распределение которого в любой (более ранний) момент времени равно сглаживающему распределению, когда конечное состояние (в заключительный момент) распределено согласно распределению фильтра. Это новая интерпретация сглаживающего решения в терминах нелинейного диффузионного (стохастического) потока. Это решение контрастирует и дополняет (обратный) детерминированный поток вероятностных распределений (т.е. разновидность уравнения сглаживания Кушнера), изучавшегося в ряде предшествующих работ. Приведен ряд следствий нашего основного результата, включая вывод стохастического дифференциального уравнения с обращенным временем и вывод классических уравнений сглаживания Рауха-Тунга-Стрибела в линейной постановке.

UNSUPERVISED METHODOLOGY TO IN-SEASON MAPPING OF SUMMER CROPS IN URUGUAY WITH MODIS EVI’S TEMPORAL SERIES AND MACHINE LEARNING

This paper presents a new methodology for mapping summer crops in Uruguay, during the season, based on time-series analysis of the EVI vegetation index derived from the MODIS sensor. Time-series were processed with the k-means unsupervised machine learning algorithm. For this algorithm, the ideal number of clusters was estimated using the elbow method. Once the clusters were obtained, for each one, the average phenological signature was adjusted using a nonlinear smoothing spline regression technique. Additionally, using the derivative analysis, the key points of the curve were estimated (minimum, maximum and inflection points). When analyzing the average signature of each cluster, those whose signature follows the seasonal pattern of an agricultural crop (similar to a Gaussian function) were selected to generate a binary map of crops/non-crops. The estimated crop area is 2,336,525 hectares, higher than the official statistics of 1,667,400 hectares for the 2014–15 season. This overestimation can be explained by the resolution of the MODIS pixel (250 meters), where each has a different degree of purity; and commission errors. The methodology was validated with 5,317 ground truth points, with a general accuracy of 95.8%, kappa index of 85.6, production and user accuracy of 85.1% and 91.3% for crops/non-crops.

Application of generalized method of least modules in problems of processing and analysing images

The article describes the use of nonlinear smoothing filter for image processing and analysis. Description of the model of the smoothing filter based on the generalized method of the least absolute values is given. The filter constructed on the basis of the offered model efficiently reduces the noise on brightness difference. Along with noise reduction in the contrast images, this method can be used for the solving problems of machine vision, medical diagnostics, etc. It has been found that nonlinear filtration on the basis of the generalized method of the least modules allows to solve such problems as clarification of the boundaries of contrast objects and segmentation of the image. There has been shown the possibility of recovering the boundaries of the images in which the contrast borders were blurry. X-ray image of an animal hand with defocusing was used as an example. After filtering, the contrast boundary was restored to the place where it was originally located. When processing a fluorography image, the filter removed various artifacts from the image and increased the contrast. Removal of artifacts along with the recoveries of the boundaries of contrast objects improves the overall “readability” of the fluorography image and also allows seeing earlier not distinguishable details on the image. Examples of the filter application in the clustering problem using the k-means algorithm are given. Due to the lack of this algorithm, applying it directly to the image does not give an acceptable result. However, after processing the original image with a nonlinear filter, the application of the k-means algorithm yields the desired result.

Research on the Influence of Environmental Regulation on Social Employment—An Empirical Analysis Based on the STR Model

Environmental regulation will affect social employment through corporate costs, technological innovation, industrial upgrading, and industrial transfer. To verify the effect of environmental regulation on social employment in different periods and under the intensity of environmental regulation, in this paper, environmental regulation is introduced as an influencing factor of social employment levels, based on China’s urban registration unemployment data from 1987 to 2017. A nonlinear smoothing autoregressive model is used to analyze the nonlinear long-term effect relationship between environmental regulation and social employment. The research results show that the relationship between environmental regulation and social employment does exhibit the characteristics of nonlinear transformation under different mechanisms, and the transformation speed is fast. The specific manifestation is that the environmental regulation has a restraining effect on social employment in the short term, and the environmental regulation has a promoting effect on social employment in the long term. Continued high-level environmental regulations will exacerbate the adverse impact of environmental regulations on social employment.