Study on spacial-temporal analysis of ground surface temperature for global warming countermeasures

Impact ◽  
2020 ◽  
Vol 2020 (6) ◽  
pp. 32-34
Author(s):  
Tomoko Matsui
Keyword(s):  
Surface Temperature ◽  
Heat Waves ◽  
Ground Surface ◽  
Temporal Analysis ◽  
Observation Time ◽  
Extreme Weather Events ◽  
Ground Surface Temperature ◽  
Weather Patterns ◽  
Statistical Mathematics ◽  
Prediction Systems

Extreme weather events can arrive unannounced and cause immense harm for communities. Especially in cities where many people live in close proximity, events like flash flooding, windstorms or even heat waves can cause property damage, overworking of the emergency infrastructure and death. Unfortunately, because climate change continues to alter weather patterns, from subtle local variations to changes in global factors like ocean currents, these events are occurring with increased frequency. There is a great need for accurate monitoring and prediction systems that can help forecast these catastrophes. Monitoring overall changes in the patterns of these events will also help governments and citizens better adapt and plan measures to protect themselves from climate change's inevitable impact. Professor Tomoko Matsui is an expert in the field of statistical spatial-temporal modelling. Matsui is heading up an international team of researchers at the Institute of Statistical Mathematics in Tokyo, Heriot-Watt University in UK and the National Institute for Environmental Studies in Tsukuba to find ways for using a variety of data including low-resolution surface meteorological observation, time-series measurements of ground surface temperature by high-resolution satellite and social media.

scholarly journals Modeling the Temperature Field in the Ground with an Installed Slinky-Coil Heat Exchanger

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 4010
Author(s):  
Monika Gwadera ◽  
Krzysztof Kupiec
Keyword(s):  
Temperature Field ◽  
Heat Exchanger ◽  
Surface Temperature ◽  
Ground Surface ◽  
Heat Sources ◽  
Ground Surface Temperature ◽  
Coil Heat Exchanger ◽  
The Relationship ◽  
Temperature Responses ◽  
Coil Heat

In order to find the temperature field in the ground with a heat exchanger, it is necessary to determine temperature responses of the ground caused by heat sources and the influence of the environment. To determine the latter, a new model of heat transfer in the ground under natural conditions was developed. The heat flux of the evaporation of moisture from the ground was described by the relationship taking into account the annual amount of rainfall. The analytical solution for the equations of this model is presented. Under the conditions for which the calculations were performed, the following data were obtained: the average ground surface temperature Tsm = 10.67 °C, the ground surface temperature amplitude As = 13.88 K, and the phase angle Ps = 0.202 rad. This method makes it possible to easily determine the undisturbed ground temperature at any depth and at any time. This solution was used to find the temperature field in the ground with an installed slinky-coil heat exchanger that consisted of 63 coils. The results of calculations according to the presented model were compared with the results of measurements from the literature. The 3D model for the ground with an installed heat exchanger enables the analysis of the influence of miscellaneous parameters of the process of extracting or supplying heat from/to the ground on its temperature field.

On the factors contributing to heat waves in Europe. A global large ensemble approach with MPAS

2021 ◽  
Author(s):  
Gerhard Smiatek ◽  
Harald Kunstmann
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Heat Waves ◽  
Circulation Pattern ◽  
Sea Surface ◽  
Maximum Temperature ◽  
Large Ensemble ◽  
Sea Ice Extent ◽  
Weather Patterns ◽  
Potential Factors

<p>The European summer heat wave of 2003 with record-breaking temperature anomalies was brought into connection with a blocking Omega circulation pattern, soil moisture deficit and high sea surface temperature, especially in the Mediterranean Sea. <sup> </sup>We investigate the potential factors influencing extreme heat waves in Europe with a very large ensemble obtained from multiple global integrations of the Model for Prediction Across Scales (MPAS). The global MPAS runs are performed in approximately 60 km resolution with sea surface temperature (SST) and sea ice extent from ERA-Interim data as boundary condition initialized on different days.</p><p>The contribution investigates the results obtained from a total of 540 simulations. It concentrates on the regional SST and weather patterns and moisture obtained in simulations contributing to the upper 10% of the resulting probability density function (PDF) of the summer daily mean and maximum temperature. The investigation considers in total eight standard evaluation domains in Europe as defined in the PRUDENCE project.</p>

Relation Between The Ground Surface Temperature

2005 ◽  
Author(s):  
R. Yokoyama ◽  
Chang Ming Zhou ◽  
S. Tanba ◽  
H. Ihara
Keyword(s):  
Surface Temperature ◽  
Ground Surface ◽  
Ground Surface Temperature

scholarly journals Past climate changes and permafrost depth at the Lake El'gygytgyn site: implications from data and thermal modeling

2013 ◽  
Vol 9 (1) ◽  
pp. 119-133 ◽  
Author(s):  
D. Mottaghy ◽  
G. Schwamborn ◽  
V. Rath
Keyword(s):  
Surface Temperature ◽  
Thermal Regime ◽  
Climate Changes ◽  
Ground Surface ◽  
Temperature Data ◽  
Ice Age ◽  
Drilling Process ◽  
Past Climate ◽  
Ground Surface Temperature ◽  
Recent Climate

Abstract. This study focuses on the temperature field observed in boreholes drilled as part of interdisciplinary scientific campaign targeting the El'gygytgyn Crater Lake in NE Russia. Temperature data are available from two sites: the lake borehole 5011-1 located near the center of the lake reaching 400 m depth, and the land borehole 5011-3 at the rim of the lake, with a depth of 140 m. Constraints on permafrost depth and past climate changes are derived from numerical simulation of the thermal regime associated with the lake-related talik structure. The thermal properties of the subsurface needed for these simulations are based on laboratory measurements of representative cores from the quaternary sediments and the underlying impact-affected rock, complemented by further information from geophysical logs and data from published literature. The temperature observations in the lake borehole 5011-1 are dominated by thermal perturbations related to the drilling process, and thus only give reliable values for the lowermost value in the borehole. Undisturbed temperature data recorded over more than two years are available in the 140 m deep land-based borehole 5011-3. The analysis of these observations allows determination of not only the recent mean annual ground surface temperature, but also the ground surface temperature history, though with large uncertainties. Although the depth of this borehole is by far too insufficient for a complete reconstruction of past temperatures back to the Last Glacial Maximum, it still affects the thermal regime, and thus permafrost depth. This effect is constrained by numerical modeling: assuming that the lake borehole observations are hardly influenced by the past changes in surface air temperature, an estimate of steady-state conditions is possible, leading to a meaningful value of 14 ± 5 K for the post-glacial warming. The strong curvature of the temperature data in shallower depths around 60 m can be explained by a comparatively large amplitude of the Little Ice Age (up to 4 K), with low temperatures prevailing far into the 20th century. Other mechanisms, like varying porosity, may also have an influence on the temperature profile, however, our modeling studies imply a major contribution from recent climate changes.

scholarly journals Forest floor temperature and greenness link significantly to canopy attributes in South Africa’s fragmented coastal forests

2018 ◽  
Author(s):  
Marion Pfeifer ◽  
Michael JW Boyle ◽  
Stuart Dunning ◽  
Pieter Olivier
Keyword(s):  
Land Use ◽  
Food Security ◽  
Surface Temperature ◽  
Habitat Quality ◽  
Ground Surface ◽  
Quality Metrics ◽  
Ground Vegetation ◽  
Ground Surface Temperature ◽  
Forest Models ◽  
Random Forest Models

Tropical landscapes are changing rapidly due to changes in land use and land management. Being able to predict and monitor land use change impacts on species for conservation or food security concerns requires the use of habitat quality metrics, that are consistent, can be mapped using above - ground sensor data and are relevant for species performance. Here, we focus on ground surface temperature (Thermalground) and ground vegetation greenness (NDVIdown) as potentially suitable metrics of habitat quality. We measure both across habitats differing in tree cover (natural grassland to forest edges to forests and tree plantations) in the human-modified coastal forested landscapes of Kwa-Zulua Natal, South Africa. We show that both habitat quality metrics decline linearly as a function of increasing canopy closure (FCover, %) and canopy leaf area index (LAI). Opening canopies by about 20% or reducing canopy leaf area by 1% would result in an increase of temperatures on the ground by more than 1°C, and an increase in ground vegetation greenness by 0.2 and 0.14 respectively. Upscaling LAI and FCover to develop maps from Landsat imagery using random forest models allowed us to map Thermalground and NDVIdown using the linear relationships. However, map accuracy was constrained by the predictive capacity of the random forest models predicting canopy attributes and the linear models linking canopy attributes to the habitat quality metrics. Accounting for micro-scale variation in temperature is seen as essential to improve biodiversity impact predictions. Our upscaling approach suggests that mapping ground surface temperature based on radiation and vegetation properties might be possible, and that canopy cover maps could provide a useful tool for mapping habitat quality metrics that matter to species. However, we need to increase sampling of surface temperature spatially and temporally to improve and validate upscaled models. We also need to link surface temperature maps to demographic traits of species of different threat status or functions in landscapes with different disturbance and management histories testing for generalities in relationships. The derived understanding could then be exploited for targeted landscape restoration that benefits biodiversity conservation and food security sustainably at the landscape scale.

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