DISTRIBUTION OF TEMPERATURE INDICES OF EXTREME IN THE TERRITORY OF THE EUROPEAN PART OF RUSSIA IN THE PERIOD 1980-2019

The spatio-temporal variability of the average values of temperature indices of climate extremity in the territory of the European part of Russia (ER) in 1980-2019 is presented. To calculate the extremeness indices, we used hourly data on the maximum and minimum temperatures obtained using the ERA5 reanalysis on a 1°´1° spatial grid. Statistical processing of the index values revealed an increase in the temperature indices TNX, TNN, TXN, TXX, associated with the minimum and maximum temperatures, with the exception of the north and southeast of the region. An increase in the number of sunny days and a decrease in the number of frosty days were also revealed.

Residential Buildings Complex Boundaries Generation Based on Spatial Grid System

The automatic generation of building boundaries in contemporary research and engineering projects and practices is dominantly characterized by interior functional constraints. As a basis for the automated generation of various building boundaries, the solution presented in this paper is a novel approach that ignores the internal (functional) and focuses only on the external (non-functional) impacts. The primary orientation on external impacts may be, at any instance, extended by suitable complementary traditional methodology. The applied research methodology and presented method rely on a developed extendible rule-based system that simplifies floor plan creation by the recursive application of a formulated spatial grid generation algorithm. Based on starting parameter values (mainly the lot and building area spaces) the algorithm tends to create a set of grids that satisfy initial constraints by marking the individual grid cells as a part of the building or empty. The presented conceptual framework model served as a foundation for creating a prototype software application that supports the experimental generation of grid arrays that are transformed into readable images of residential building boundaries. For the initial validation of the developed methodology, method, and algorithm, the concrete parametric resolution is set to 1 m. The comparative analysis has shown that the presented approach overcomes some of the limitations of previous related research that generate building boundaries in simple rectangular form or with limited variability. The proposed method, in its current stage, outperforms discussed existing methods concerning complex shape boundary building plan generation. Besides that, there is a broad space for further enhancement directions concerning the interoperability with other, independently developed, frameworks, and software tools.

Monthly daily-mean rainfall forecast over Indonesia using machine learning and artificial intelligence ensemble

A daily mean rainfall in a month forecast method is presented in this paper. The method provides spatial forecast over Indonesia and employs ensemble of Machine Learning and Artificial Intelligence algorithms as its forecast models. Each spatial grid in the forecast output is processed as an individual dataset. Therefore, each location in the forecast output has different stacked ensemble models as well as their model parameter settings. Furthermore, the best ensemble model is chosen for each spatial grid. The input dataset of the model consists of eight climate data (i.e., East and West Dipole Mode Index, Outgoing Longwave Radiation, Southern Oscillation Index, and Nino 1.2, 3, 4, 3.4) and monthly rainfall reanalysis data, ranging from January 1982 until December 2019. There are four assessment procedures performed on the models: daily mean rainfall establishment as a response function of climate patterns, and one-up to three-month lead forecast. The results show that, based on their performance, these non-Physical models are considerable to complement the existing forecast models.

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.

Memory-efficient source wavefield reconstruction and its application to 3D reverse time migration

Reverse time migration (RTM) generally uses the zero-lag crosscorrelation imaging condition, requiring the source and receiver wavefields to be known at the same time step. However, the receiver wavefield is calculated in time-reversed order, opposite to the order of the forward-propagated source wavefield. The inconvenience can be resolved by storing the source wavefield on a computer memory/disk or by reconstructing the source wavefield on the fly for multiplication with the receiver wavefield. The storage requirements for the former approach can be very large. Hence, we have followed the latter route and developed an efficient source wavefield reconstruction method. During forward propagation, the boundary wavefields at N layers of the spatial grid points and a linear combination of wavefields at M − N layers of the spatial grid points are stored. During backward propagation, it reconstructs the source wavefield using the saved wavefields based on a new finite-difference stencil ( M is the operator length parameter, and 0 ≤ N ≤ M). Unlike existing methods, our method allows a trade-off between accuracy and storage by adjusting N. A maximum-norm-based objective function is constructed to optimize the reconstruction coefficients based on the minimax approximation using the Remez exchange algorithm. Dispersion and stability analyses reveal that our method is more accurate and marginally less stable than the method that requires storage of a combination of boundary wavefields. Our method has been applied to 3D RTM on synthetic and field data. Numerical examples indicate that our method with N = 1 can produce images that are close to those obtained using a conventional method of storing M layers of boundary wavefields. The memory usage of our method is ( N + 1)/ M times that of the conventional method.