A Revised Force–Restore Model for Land Surface Modeling

2004 ◽  
Vol 43 (11) ◽  
pp. 1768-1782 ◽  
Author(s):  
Diandong Ren ◽  
Ming Xue
Keyword(s):  
Soil Temperature ◽  
Skin Temperature ◽  
Land Surface ◽  
Deep Layer ◽  
Additional Term ◽  
Prediction Equation ◽  
Lapse Rate ◽  
Temperature Prediction ◽  
Land Surface Modeling ◽  
Definition Of

Abstract To clarify the definition of the equation for the temperature toward which the soil skin temperature is restored, the prediction equations in the commonly used force–restore model for soil temperature are rederived from the heat conduction equation. The derivation led to a deep-layer temperature, commonly denoted T2, that is defined as the soil temperature at depth πd plus a transient term, where d is the e-folding damping depth of soil temperature diurnal oscillations. The corresponding prediction equation for T2 has the same form as the commonly used one except for an additional term involving the lapse rate of the “seasonal mean” soil temperature and the damping depth d. A term involving the same also appears in the skin temperature prediction equation, which also includes a transient term. In the literature, T2 was initially defined as the short-term (over several days) mean of the skin temperature, but in practice it is often used as the deep-layer temperature. Such inconsistent use can lead to drift in T2 prediction over a several-day period, as is documented in this paper. When T2 is properly defined and initialized, large drift in T2 prediction is avoided and the surface temperature prediction is usually improved. This is confirmed by four sets of experiments, each for a period during each season of 2000, that are initialized using and verified against measurements of the Oklahoma Atmospheric Surface-Layer Instrumentation System (OASIS) project.

scholarly journals A qualitative description of shallow groundwater effect on surface temperature of bare soil

2009 ◽  
Vol 13 (9) ◽  
pp. 1749-1756 ◽  
Author(s):  
F. Alkhaier ◽  
R. J. Schotting ◽  
Z. Su
Keyword(s):  
Numerical Model ◽  
Soil Temperature ◽  
Skin Temperature ◽  
Soil Profile ◽  
Land Surface ◽  
Shallow Groundwater ◽  
Soil Surface ◽  
Qualitative Description ◽  
Groundwater Depth ◽  
Daily Minimum Temperature

Abstract. Whether or not shallow groundwater affects skin temperature (the temperature of soil surface) is important to detect depth and extent of shallow groundwater by dint of remote sensing and important for land surface modelling studies. Although few studies have been conducted to investigate that effect, they have yielded contradicting conclusions and they stopped in 1982. To determine that shallow groundwater affects skin temperature, we measured soil temperature at two different depths (5 and 10 cm) in seven places with variable water table depths every ten minutes and for six days. After that, we correlated the minimum, maximum and average daily temperatures to average groundwater depth. We also built a simple numerical model using a differential equations solver, Flex PDE, to simulate heat transfer into soil profile and used it to simulate groundwater effect on skin temperature. We found quite high negative correlation between the maximum and average daily soil temperature and groundwater depth. Contrarily, we could hardly find any correlation between the daily minimum temperature and groundwater depth. Numerical simulations, though simple, were useful in showing that groundwater shifted skin temperature curves up in the winter and down in the summer without affecting the shape of the curve. We conclude that shallow groundwater affects skin temperature directly by its distinctive thermal properties in the soil profile and indirectly by affecting soil moisture which in turn has many different and contradictory effects on skin temperature. This study recommends building a comprehensive numerical model that simulates the effect of shallow groundwater on skin temperature and on the different energy fluxes at land surface.

scholarly journals A qualitative description of shallow groundwater effect on surface soil temperature

2009 ◽  
Vol 6 (2) ◽  
pp. 2129-2152
Author(s):  
F. Alkhaier ◽  
R. J. Schotting ◽  
Z. Su
Keyword(s):  
Numerical Model ◽  
Soil Temperature ◽  
Skin Temperature ◽  
Soil Profile ◽  
Land Surface ◽  
Shallow Groundwater ◽  
Qualitative Description ◽  
Groundwater Depth ◽  
Daily Minimum Temperature ◽  
Modelling Studies

Abstract. Whether or not shallow groundwater affects skin temperature is important to detect depth and extent of shallow groundwater by dint of remote sensing and important for land surface modelling studies. Although few studies have been conducted to investigate that effect, they have yielded contradicting conclusions and they stopped in 1982. To determine that shallow groundwater affects skin temperature, we measured soil temperature at two different depths (5 and 10 cm) in seven places with variable water table depths every ten minutes and for six days. After that, we correlated the minimum, maximum and average daily temperatures to average groundwater depth. We also built a simple numerical model using a differential equations solver, Flex PDE, to simulate heat transfer into soil profile and used it to simulate groundwater effect on skin temperature. We found quite high negative correlation between the maximum and average daily soil temperature and groundwater depth. Contrarily, we could hardly find any correlation between the daily minimum temperature and groundwater depth. Numerical simulations, though simple, were useful in showing that groundwater shifted skin temperature curve up in the winter and down in the summer without affecting the shape of the curve. We conclude that shallow groundwater affects skin temperature directly by its distinctive thermal properties in the soil profile and indirectly by affecting soil moisture which in turn has many different and contradictory effects on skin temperature. This study recommends building comprehensive numerical model that simulate the effect of shallow groundwater on skin temperature and on the different energy fluxes at land surface.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

The Stratification of the Atmosphere1 (I)

1950 ◽  
Vol 31 (3) ◽  
pp. 71-78 ◽  
Author(s):  
H. Flohn ◽  
R. Penndorf
Keyword(s):  
Thermal Structure ◽  
Mixing Layer ◽  
Sudden Change ◽  
Atomic Layer ◽  
Bottom Layer ◽  
Lapse Rate ◽  
New Classification ◽  
Isothermal Layer ◽  
Outer Atmosphere ◽  
Definition Of

A suitable nomenclature for atmospheric strata as well as a clear definition of the boundaries is proposed. The necessity of such a new classification is stressed. The atmosphere is divided into an inner and an outer atmosphere; from the latter particles may escape. The inner atmosphere is divided into three spheres—troposphere, stratosphere, and ionosphere—with each sphere in turn being subdivided into 3 or 4 layers. The new classification is based upon the thermal structure of the atmosphere.' Boundaries of each layer are fixed by a sudden change of lapse rate. The bottom layer, the ground layer, the advection layer, and the tropopause layer are subdivisions of the troposphere. The advantages gained by defining a separate tropopause layer as part of the troposphere are discussed in detail. Its upper boundary is assumed to be situated at 12 km over temperate latitudes. The stratosphere, consisting of an isothermal layer, a warm layer, and an upper mixing layer, extends from 12 to 80 km. The atmosphere between 80 and 800 km is occupied by the ionosphere, the subdivisions of which are the E-layer, the Flayer and the atomic layer. Above that height the exosphere exists.

scholarly journals A New Methodology for Estimating the Surface Temperature Lapse Rate Based on Grid Data and Its Application in China

2018 ◽  
Vol 10 (10) ◽  
pp. 1617 ◽  
Author(s):  
Yun Qin ◽  
Guoyu Ren ◽  
Tianlin Zhai ◽  
Panfeng Zhang ◽  
Kangmin Wen
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
North China ◽  
Seasonal Pattern ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Vertical Gradient ◽  
Lapse Rate ◽  
Grid Data ◽  
Temperature Lapse Rate

Land surface temperature (LST) is an important parameter in the study of the physical processes of land surface. Understanding the surface temperature lapse rate (TLR) can help to reveal the characteristics of mountainous climates and regional climate change. A methodology was developed to calculate and analyze land-surface TLR in China based on grid datasets of MODIS LST and digital elevation model (DEM), with a formula derived on the basis of the analysis of the temperature field and the height field, an image enhancement technique used to calculate gradient, and the fuzzy c-means (FCM) clustering applied to identify the seasonal pattern of the TLR. The results of the analysis through the methodology showed that surface temperature vertical gradient inversion widely occurred in Northeast, Northwest, and North China in winter, especially in the Xinjiang Autonomous Region, the northern and the western parts of the Greater Khingan Mountains, the Lesser Khingan Mountains, and the northern area of Northwest and North China. Summer generally witnessed the steepest TLR among the four seasons. The eastern Tibetan Plateau showed a distinctive seasonal pattern, where the steepest TLR happened in winter and spring, with a shallower TLR in summer. Large seasonal variations of TLR could be seen in Northeast China, where there was a steep TLR in spring and summer and a strong surface temperature vertical gradient inversion in winter. The smallest seasonal variation of TLR happened in Central and Southwest China, especially in the Ta-pa Mountains and the Qinling Mountains. The TLR at very high altitudes (>5 km) was usually steeper than at low altitudes, in all months of the year.

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

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