The Arctic and Antarctic Research Institute, or AARI (Russian: Арктический и антарктический научно-исследовательский институт, abbreviated as ААНИИ) is the oldest and largest Russian research institute in the field of comprehensive studies of Arctic and Antarctica. It is located in Saint Petersburg.The AARI has numerous departments, such as those of oceanography, glaciology, meteorology, hydrology or Arctic river mouths and water resources, geophysics, polar geography, and others. It also has its own computer center, ice research laboratory, experimental workshops, and a museum (the Arctic and Antarctic Museum).Scientists, such as Alexander Karpinsky, Alexander Fersman, Yuly Shokalsky, Nikolai Knipovich, Lev Berg, Otto Schmidt, Rudolf Samoylovich, Vladimir Vize, Nikolai Zubov, Pyotr Shirshov, Nikolai Urvantsev, and Yakov Gakkel have all made their valuable contributions to the work of the AARI.Throughout its history, the AARI has organized more than a thousand Arctic expeditions, including dozens of high-latitude aerial expeditions, which transported 34(?) manned drifting ice stations Severniy Polyus ("Северный полюс", or North Pole) to Central Arctic.
This study investigates machine learning methods for approximating the temperature difference between urban and rural areas (urban heat island intensity) using examples from Moscow and St. Petersburg. Predictors consist of characteristics of large-scale meteorological conditions derived from long-term, regionally averaged observational data from rural weather stations and global ERA5 reanalysis data from 2012 to 2023. A key feature of meteorological data is the delayed dependencies between processes, where the value of a target variable is influenced by factors that act with a time lag. Two approaches were explored to account for these dependencies: explicit feature engineering to generate lag-related features for the CatBoost regression model, and application of the long short-term memory recurrent neural network (LSTM), for sequence modeling. The dependence of modeling results on the length of the lookback period was investigated. Experimental results showed that LSTM did not exceed the accuracy of CatBoost with expert-designed temporal features. The most informative data for modeling urban heat island corresponded to a lookback depth of 3 time steps (9-h history). The study revealed the critical importance of accounting for temporal dependencies in modeling urban heat islands.
Monitoring of Arctic environmental changes and study of bottom sediments yielded new data on modern microfaunal assemblages in the Kara and Laptev seas. The paper describes the distribution of benthic calcareous and agglutinated, as well as planktic, foraminifers in bottom sediments around the Severnaya Zemlya Archipelago and their relationship to environmental parameters including the Atlantic derived waters.
The results of existing publicly available automatic ice lead identification algorithms have been verified using expert identification of ice cover leads in the Laptev and East Siberian seas based on optical satellite imagery. It has been found that none of these algorithms can be used to obtain data that allows for calculating lead characteristics such as orientation, length, and spatial density. For automatic ice lead identification, it has been proposed to develop an algorithm using a convolutional neural network trained on data from the AARI electronic archive of sea ice leads.
The stability and high yield potential of sugarcane is of great importance for economic survival of farmers and sugar industry. It warrants to develop site specific new clones across various climatic conditions. A study was carried out at Sugarcane Research Institute, AARI, Faisalabad during 2023 with the objected to evaluate the impact of G×E interaction on stability and adaptability of sugarcane clones by two ways i.e. AMMI and GGE-biplot analysis. To achieve this objective, fourteen sugarcane varieties, along with one control, were planted at three distinct climatic locations in Punjab during autumn 2023. The design of experiment was randomized complete block design and (RCBD) replicated thrice under each environment. The analysis of the data displayed significant G × E interaction. The varieties CPF-250, CPF-253, SA-111 depicted stable performers across all environments. The results revealed that CPF-251, CPF-249 and CPF-252 have more specific environmental adaptations and performed better in Jaranwala, Sargodha and Chilianwala respectively. CPF-246 and CPF-248 have produced the low average cane yield across all three environments. The most productive varieties, as indicated by the stability analysis, were G9 (CPF-252) and G10 (CPF-253). These varieties demonstrated stability and were recommended for the future commercial cultivation.
An Erratum to this paper has been published: https://doi.org/10.1134/S0001437026020013
