Application of satellite data to identification and mapping high temperature oil producing areas in Western Siberia

2016 ◽  
Vol 29 (6) ◽  
Keyword(s):  
High Temperature ◽  
Satellite Data ◽  
Western Siberia

Isolation and characterization of Thermococcus sibiricus sp. nov. from a Western Siberia high-temperature oil reservoir

Extremophiles ◽  
2001 ◽  
Vol 5 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Margarita Miroshnichenko ◽  
Hans Hippe ◽  
Erko Stackebrandt ◽  
Nadezhda Kostrikina ◽  
Nikolai Chernyh ◽  
...  
Keyword(s):  
High Temperature ◽  
Western Siberia ◽  
Oil Reservoir ◽  
Isolation And Characterization

Analysis of the landscape structure in the tundra zone for western Siberia based on satellite data

2015 ◽  
Vol 51 (9) ◽  
pp. 969-978 ◽  
Author(s):  
L. L. Golubyatnikov ◽  
E. A. Zarov ◽  
V. S. Kazantsev ◽  
I. V. Filippov ◽  
G. O. Gavrilov
Keyword(s):  
Satellite Data ◽  
Landscape Structure ◽  
Western Siberia ◽  
Tundra Zone

scholarly journals Estimating Soil Wetness from the GOES Sounder

2006 ◽  
Vol 23 (7) ◽  
pp. 991-1003 ◽  
Author(s):  
Robert M. Rabin ◽  
Timothy J. Schmit
Keyword(s):  
United States ◽  
Soil Moisture ◽  
High Temperature ◽  
Satellite Data ◽  
Temperature Rise ◽  
Clear Sky ◽  
Average Temperature ◽  
Radiative Temperature ◽  
The Relationship ◽  
High Temperature Rise

Abstract In this note, the relationship between the observed daytime rise in surface radiative temperature, derived from the Geostationary Operational Environmental Satellites (GOES) sounder clear-sky data, and modeled soil moisture is explored over the continental United States. The motivation is to provide an infrared (IR) satellite–based index for soil moisture, which has a higher resolution than possible with the microwave satellite data. The daytime temperature rise is negatively correlated with soil moisture in most areas. Anomalies in soil moisture and daytime temperature rise are also negatively correlated on monthly time scales. However, a number of exceptions to this correlation exist, particularly in the western states. In addition to soil moisture, the capacity of vegetation to generate evapotranspiration influences the amount of daytime temperature rise as sensed by the satellite. In general, regions of fair to poor vegetation health correspond to the relatively high temperature rise from the satellite. Regions of favorable vegetation match locations of lower-than-average temperature rise.

Parameters of Different Cloud Types over the Natural Zones of Western Siberia According to MODIS Satellite Data

2020 ◽  
Vol 33 (5) ◽  
pp. 512-518
Author(s):  
V. G. Astafurov ◽  
A. V. Skorokhodov ◽  
K. V. Kur’yanovich ◽  
Ya. K. Mitrofanenko
Keyword(s):  
Satellite Data ◽  
Western Siberia ◽  
Natural Zones

Occurrence and geochemical activity of microorganisms in high‐temperature, water‐flooded oil fields of Kazakhstan and Western Siberia

1995 ◽  
Vol 13 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Tamara N. Nazina ◽  
Anna E. Ivanova ◽  
Igor A. Borzenkov ◽  
Sergei S. Belyaev ◽  
Michail V. Ivanov
Keyword(s):  
High Temperature ◽  
Western Siberia ◽  
Oil Fields ◽  
High Temperature Water ◽  
Temperature Water

scholarly journals THE DETERMINATION OF CLOUD COVER PARAMETERS USING SATELLITE DATA PROCESSING SYSTEMS

2020 ◽  
pp. 124-134
Author(s):  
Elena Viktorovna Volkova ◽  
◽  
Anzhelika Andreevna Kostornaya ◽  
Ruslana Aleksandrovna Amikishieva ◽  
◽  
...  
Keyword(s):  
Satellite Data ◽  
Cloud Cover ◽  
Western Siberia ◽  
Cloud Parameters ◽  
Cloud Characteristics ◽  
Avhrr Data ◽  
Polar Orbiting Satellite ◽  
Cloud Mask

The paper discusses the results of comparing cloud cover properties determined by using polar orbiting satellite data (AVHRR/NOAA and MSU-MR/Meteor-M No. 2) for the European territory of Russia and Western Siberia. The cloud characteristics were computed by two threshold techniques: Complex Threshold Technique (CTT) (developed at the European Centre of the State Research Center ‗Planeta‘) and Cloud Cover Detection Technique (CCDT) (developed at the Siberian Centre of ‗Planeta‘). Pixel-by-pixel comparison was performed for very close in time satellite observations, and it showed that in spite of technical similarity of the two radiometers and little difference between both techniques used for the classifications, the results were not the same. The quality of the MSU-MR classification is significantly worse than that of the two AVHRR classifications. In fact, the MSU-MR derivation of cloud parameters fails in optically thin cirrus and altocumulus clouds, thus underestimating the cloud top height for multilayered clouds. As a result, the cloud top is found to be lower, warmer and less iced in comparison with both AVHRR estimates, regardless of the region and other conditions; on the contrary, the cloud top of low and middle clouds appears to be colder, higher and more iced according to MSU-MR data. The MSU-MR cloud mask is strongly underestimated at night during the cold period of the year. The CTT and CCDT‘s cloud top height, temperature and water phase retrieved by AVHRR data are quite close for both regions.

scholarly journals Open database of snow-measuring observations in the south of Western Siberia (2011-2021) and its comparison with data from stationary meteorological observations and satellite monitoring

2021 ◽  
pp. 1-18
Author(s):  
Dmitry Konstantinovich Pershin ◽  
Liliya Fedorovna Lubenets ◽  
Dmitry Vladimirovich Chernykh ◽  
Roman Yur'evich Biryukov ◽  
Dmitrii Vladimirovich Zolotov
Keyword(s):  
Satellite Data ◽  
Western Siberia ◽  
Mean Squared Error ◽  
River Basins ◽  
Winter Period ◽  
The South ◽  
Squared Error ◽  
The Mean ◽  
Weather Stations ◽  
Snow Mass

This article provides a database of the local snow-measuring observations for three river basins in the south of Western Siberia, reviews the methodological peculiarities of the conduct of measurements, and compares the acquired data with the observations at weather stations and available satellite data (CGLS SWE). Observations were carried out in several stages over the period of ten years (2011-2021) in small river basins of Kuchuk, Kasmala, and Mayma Rivers, and reflect the transition from the West Siberian Plain to the Altai lowlands. Total of 25,000 measurements of the parameters of snow deposits (snow mass and snow storage) were made over the years. The database of snow measuring observations is accessible to public. The comparison with station and satellite data indicated significant variances, but also fairly good coherence in some reservoir basins. According to satellite data, the common to basins mean squared error of snow storages was 42.9 mm, which is slightly higher than the claimed by the product engineers (37.4 mm). The time-limited observation data on the permanent routes of weather stations demonstrated the mean squared error of snow storages of 43.5 mm. In winter period, the mean squared error of satellite data gradually increased to the period of maximum accumulation of snow in reservoir basins. Moreover, the errors of satellite data did not depend on the snowiness of the winter period; and most likely, are associated with the parameters of microstructure of the snow mass in separate seasons.

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