Application of a radiative cooling model to daily minimum temperature prediction

1985 ◽  
Vol 5 (6) ◽  
pp. 681-686 ◽  
Author(s):  
S. Gandia ◽  
J. Melia ◽  
D. Segarra
Keyword(s):  
Minimum Temperature ◽  
Daily Minimum Temperature ◽  
Radiative Cooling ◽  
Temperature Prediction ◽  
Daily Minimum ◽  
Cooling Model

scholarly journals LightGBM Low-Temperature Prediction Model Based on LassoCV Feature Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Shangqi Duan ◽  
Shuangde Huang ◽  
Wei Bu ◽  
Xingke Ge ◽  
Haidong Chen ◽  
...  
Keyword(s):  
Feature Selection ◽  
Low Temperature ◽  
Prediction Model ◽  
Minimum Temperature ◽  
Power Grid ◽  
Daily Minimum Temperature ◽  
Short Term ◽  
Temperature Prediction ◽  
Data Set ◽  
Daily Minimum

Icing disasters on power grid transmission lines can easily lead to major accidents, such as wire breakage and tower overturning, that endanger the safe operation of the power grid. Short-term prediction of transmission line icing relies to a large extent on accurate prediction of daily minimum temperature. This study therefore proposes a LightGBM low-temperature prediction model based on LassoCV feature selection. A data set comprising four meteorological variables was established, and time series autocorrelation coefficients were first used to determine the hysteresis characteristics in relation to the daily minimum temperature. Subsequently, the LassoCV feature selection method was used to select the meteorological elements that are highly related to minimum temperature, with their lag characteristics, as input variables, to eliminate noise in the original meteorological data set and reduce the complexity of the model. On this basis, the LightGBM low-temperature prediction model is established. The model was optimized through grid search and crossvalidation and validated using daily minimum surface temperature data from Yongshan County (station number 56489), Zhaotong City, Yunnan Province. The root mean square error, MAE, and MAPE of the model minimum temperature prediction after feature selection are shown to be 1.305, 0.999, and 0.112, respectively. These results indicate that the LightGBM prediction model is effective at predicting low temperatures and can be used to support short-term icing prediction.

scholarly journals Conditional probability of the daily minimum temperature in certain types of circulation on a seasonal basis in the Zywiec Valley in Southern Poland

2017 ◽  
Vol 13 ◽  
pp. 18-24
Author(s):  
Paulina Szczotka
Keyword(s):  
Atmospheric Circulation ◽  
Minimum Temperature ◽  
Extreme Values ◽  
Daily Minimum Temperature ◽  
Synoptic Situation ◽  
Local Conditions ◽  
Daily Minimum ◽  
Vistula River ◽  
Circulation Types ◽  
Average Minimum Temperature

Air minimum temperature is very important for the natural environment and human activity. This paper presents certain aspects related to the variability of daily minimum temperature of air in the winter (XII, I, II) in the Zywiec Valley, in relation to the synoptic situation in the valley. The analysis is based on the results of research carried out at one point node (the grid) obtained from the base Carpat Clim database. The node is located at the bottom of the Zywiec Valley in the period 1961-2010. The study was complemented with a comprehensive analysis of local conditions for atmospheric circulation and temporal variability over a 50 years period. For this purpose, the classification of types of atmospheric circulation  (Niedźwiedź 1981) was used for the upper Vistula river basin. Extreme temperatures included an average minimum temperature of air exceeding the 90th and 95th percentile. The relationship between the extremes of air temperature and atmospheric circulation types was examined by analyzing the frequency of occurrence of extreme values and their conditional occurrence in each particular type of atmospheric circulation.

Diverse Responses of Phenology in Multi-Grassland to Environmental Factors on Qinghai-Tibetan Plateau in China

2021 ◽  
Author(s):  
Gexia Qin ◽  
Benjamin Adu ◽  
Chunbin Li ◽  
Jing Wu
Keyword(s):  
Minimum Temperature ◽  
Alpine Meadow ◽  
Growing Season ◽  
Mean Value ◽  
Daily Minimum Temperature ◽  
Spring Phenology ◽  
Change Rate ◽  
Daily Minimum ◽  
Alpine Steppe ◽  
Yearly Maximum

Abstract Revealing grassland growing season spatial patterns and their climatic controls is crucial for the understanding of the productivity change mechanism in regional terrestrial ecosystem. However, the multi-grassland phenological factors are different, which has not been well studied. In this paper, the spatio-temporal patterns of the grassland start of the growing season (SOS) and the end of growing season (EOS) were investigated using MODIS Normalized Difference Vegetation Index (NDVI) on the Qinghai-Tibetan Plateau (QTP) during 2000 to 2019. At the same time, we analyzed the factors (including extreme and mean climate, drought, solar radiation, etc.) regulating grassland phenology under ongoing climate change. The results showed that the SOS appeared first in mountain meadow, shrub-tussock, temperature steppe and desert, then in alpine steppe and alpine meadow, showed a significant advancing tendency in all types. The EOS appeared first in temperature steppe, alpine steppe and alpine meadow, then in mountain meadow, shrub-tussock and desert. Further analysis indicated that the decrease of yearly minimum value of daily minimum temperature (TNN), yearly maximum value of daily minimum temperature (TNX), Temperature vegetation dryness index (TVDI) and the increase of yearly maximum consecutive five-day precipitation (RX5day) advance the grassland spring phenology, whereas the increase of solar radiation (SR) delay the grassland spring phenology. Meanwhile, SOS and its change rate showed the trend of significant delay and decline with the increase of altitude, respectively. We also found that the decrease of TVDI, TNN and the increase of yearly mean value of temperature (MAT_MEAN), yearly mean value of daily maximum temperature (MAT_MAX) and yearly mean value of daily minimum temperature (MAT_MIN) advanced the autumn phenology. The EOS and its change rate advance and increase with increasing altitude, respectively.

scholarly journals Subarctic shores without an ice foot: Low extremes in intertidal temperature during winter

2020 ◽  
Author(s):  
Ricardo Augusto Scrosati
Keyword(s):  
Nova Scotia ◽  
Minimum Temperature ◽  
Intertidal Zone ◽  
Freezing Point ◽  
Atlantic Coast ◽  
Daily Minimum Temperature ◽  
Absolute Minimum ◽  
Daily Minimum ◽  
Air Temperatures ◽  
Intertidal Habitats

On marine shores that freeze in winter, the intertidal zone becomes covered by an ice foot. Stable ice foots insulate intertidal organisms against highly negative air temperatures. On subpolar intertidal habitats that do not freeze, the periodic inundation with seawater at temperatures near its freezing point also prevents benthic organisms from experiencing highly negative temperatures. However, low tides do expose ice-free intertidal habitats to aerial conditions, but information on how negative temperature gets there during the winter is lacking. Using data loggers, this study measured the daily lowest temperature in rocky intertidal habitats on the Atlantic coast of Nova Scotia, Canada (which does not freeze), during the winter. As a control, temperature was also monitored above the intertidal zone (on tree branches). Intertidal temperature was almost as low as supratidal temperature, as the seasonal averages of daily minimum temperature were -4.2 °C and -6.4 °C (with absolute minima of -14.1 °C and -19.1 °C), respectively. The study site on the Atlantic coast is climatically similar to a site surveyed on the Gulf of St. Lawrence coast of Nova Scotia. However, the Gulf of St. Lawrence coast, which freezes in winter, showed milder intertidal temperatures, with a winter average of daily minimum temperature of -1.9 °C and an absolute minimum of only -6.8 °C. Therefore, despite tidal influences, the absence of an ice foot exposes subpolar intertidal habitats to highly negative air temperatures.

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