scholarly journals Evaluation of GPM IMERG Products and Estimation of Warm-Season Precipitation in Crimea

2021 ◽  
Vol 37 (4) ◽  
Author(s):  
A. E. Anisimov ◽  
V. V. Efimov ◽  
M. V. Lvova ◽  
◽  
◽  
...  
Keyword(s):  
Spatial Variability ◽  
Warm Season ◽  
Summer Precipitation ◽  
Ground Truth ◽  
Statistical Characteristics ◽  
Coastal Zones ◽  
Crimean Peninsula ◽  
Rainfall Frequency ◽  
Precipitation Estimates ◽  
The Crimean Peninsula

Purpose. The study was aimed at the evaluation of the Integrated MultisatellitE Retrievals from GPM (IMERG) remote sensing dataset using ground observations and estimation of the 2006–2018 warmseason precipitation in the Crimean Peninsula. Methods and Results. Evaluation of the IMERG dataset was performed using the meteorological station observations treated as the ground truth. We provided the multiyear statistical characteristics of precipitation amounts, frequency and intensity for different climate zones of the Crimean Peninsula. We considered the spatial variability of summer precipitation, bias and correlation between IMERG and the ground observations. Conclusions. IMERG has a weaker spatial variability compared to the ground observations. The warm-season IMERG bias is small in the central and mountainous parts of Crimea, whereas the precipitation estimates in the coastal zones are substantially overestimated. The IMERG wet bias is mostly caused by the excessive rainfall frequency. The temporal variability of IMERG is in good agreement with the observations with an average correlation coefficient 0.73. For most of the metrics considered, warm-season IMERG precipitation significantly outperforms the other datasets in the central and mountainous parts of Crimea and could be used for practical tasks with certain precautions. At the same time, due to the lack of calibration over the marine areas, the quality of IMERG precipitation estimates in the coastal zones is reduced.

scholarly journals Evaluation of GPM IMERG Products and Estimation of Warm-Season Precipitation in Crimea

2021 ◽  
Vol 28 (4) ◽  
Author(s):  
A. E. Anisimov ◽  
V. V. Efimov ◽  
M. V. Lvova ◽  
◽  
◽  
...  
Keyword(s):  
Spatial Variability ◽  
Warm Season ◽  
Summer Precipitation ◽  
Ground Truth ◽  
Statistical Characteristics ◽  
Coastal Zones ◽  
Crimean Peninsula ◽  
Rainfall Frequency ◽  
Precipitation Estimates ◽  
The Crimean Peninsula

Purpose. The study was aimed at the evaluation of the Integrated MultisatellitE Retrievals from GPM (IMERG) remote sensing dataset using ground observations and estimation of the 2006–2018 warm-season precipitation in the Crimean Peninsula. Methods and Results. Evaluation of the IMERG dataset was performed using the meteorological station observations treated as the ground truth. We provided the multiyear statistical characteristics of precipitation amounts, frequency and intensity for different climate zones of the Crimean Peninsula. We considered the spatial variability of summer precipitation, bias and correlation between IMERG and the ground observations. Conclusions. IMERG has a weaker spatial variability compared to the ground observations. The warm-season IMERG bias is small in the central and mountainous parts of Crimea, whereas the precipitation estimates in the coastal zones are substantially overestimated. The IMERG wet bias is mostly caused by the excessive rainfall frequency. The temporal variability of IMERG is in good agreement with the observations with an average correlation coefficient of 0.73. For most of the metrics considered, warm-season IMERG precipitation significantly outperforms the other datasets in the central and mountainous parts of Crimea and could be used for practical tasks with certain precautions. At the same time, due to the lack of calibration over the marine areas, the quality of IMERG precipitation estimates in the coastal zones is reduced.

Charophyta of the Crimean Peninsula

2001 ◽  
Vol 3 (4) ◽  
pp. 101-110
Author(s):  
G. M. Palamar-Mordvintseva
Keyword(s):  
Crimean Peninsula ◽  
The Crimean Peninsula

ANTHROPOGENIC AND ECONOMIC FACTORS OF THE СRIMEAN ECOLOGY

2020 ◽  
pp. 5-14
Author(s):  
N.P. Demchenko ◽  
N.Yu. Polyakova
Keyword(s):  
Business Owners ◽  
Crimean Peninsula ◽  
The North ◽  
Chemical Release ◽  
The Crimea ◽  
Russian Plant ◽  
The Republic ◽  
The Town ◽  
Integrated Indicators ◽  
The Crimean Peninsula

The situation in the ecology of the Crimean Peninsula in recent years was discussed in the article. The analysis of absolute and integrated indicators of the anthropogenic impact showed that the ecological situation remains difficult, and according to some indicators even continues to deteriorate. In summer 2018, the situation had worsened because of the large chemical release of titanium dioxide on the north of the Crimea from the holding pond of a large Russian plant that is situated near the town of Armyansk. This, in turn, led to the contamination of the large territory on the north of the peninsula. This fact indicates insufficient control by officials of the Republic of Crimea over the implementation of the RF laws for environmental protection by business owners of various forms of ownership, especially private ownership, the level of responsibility for the environment of which is very low.

scholarly journals Statistical Characteristics of Unsteady Storms in Radar Observations for the Beijing–Tianjin Region

2015 ◽  
Vol 54 (1) ◽  
pp. 106-116 ◽  
Author(s):  
Yu Wang ◽  
Hong-Qing Wang ◽  
Lei Han ◽  
Yin-Jing Lin ◽  
Yan Zhang
Keyword(s):  
Warm Season ◽  
Environmental Parameters ◽  
Statistical Characteristics ◽  
Movement Speed ◽  
Movement Direction ◽  
Upper Troposphere ◽  
Maximum Value ◽  
Dynamic Factors ◽  
The Mean ◽  
Wind Profiles

AbstractThis study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

scholarly journals Climatological and statistical characteristics of the Haines Index for North America

2007 ◽  
Vol 16 (2) ◽  
pp. 139 ◽  
Author(s):  
Julie A. Winkler ◽  
Brian E. Potter ◽  
Dwight F. Wilhelm ◽  
Ryan P. Shadbolt ◽  
Krerk Piromsopa ◽  
...  
Keyword(s):  
North America ◽  
Spatial Variability ◽  
Atmospheric Pressure ◽  
Statistical Characteristics ◽  
Potential Contribution ◽  
Extensive Analysis ◽  
Temperature And Humidity ◽  
Humidity Measurements ◽  
Operational Tool

The Haines Index is an operational tool for evaluating the potential contribution of dry, unstable air to the development of large or erratic plume-dominated wildfires. The index has three variants related to surface elevation, and is calculated from temperature and humidity measurements at atmospheric pressure levels. To effectively use the Haines Index, fire forecasters and managers must be aware of the climatological and statistical characteristics of the index for their location. However, a detailed, long-term, and spatially extensive analysis of the index does not currently exist. To meet this need, a 40-year (1961–2000) climatology of the Haines Index was developed for North America. The climatology is based on gridded (2.5° latitude × 2.5° longitude) temperature and humidity fields from the NCEP/NCAR reanalysis. The climatology illustrates the large spatial variability in the Haines Index both within and between regions using the different index variants. These spatial variations point to the limitations of the index and must be taken into account when using the Haines Index operationally.

A Middle Palaeolithic bone tool from Crimea (Ukraine)

Antiquity ◽  
2008 ◽  
Vol 82 (318) ◽  
pp. 843-852 ◽  
Author(s):  
Ariane Burke ◽  
Francesco d'Errico
Keyword(s):  
Middle Palaeolithic ◽  
Technological Properties ◽  
Crimean Peninsula ◽  
Bone Tool ◽  
The Crimean Peninsula

AbstractA fragment of equid tibia found with a Mousterian assemblage in a rockshelter in the Crimean peninsula is carefully examined. The authors show that it has been knapped like flint to produce a tool probably at a time when stone resources were becoming exhausted. This tool is thus the product of a Neanderthal response to a local need as well as proof that the technological properties of bone were known.

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