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Books on the topic 'Land Surface Temperatures'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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1

Prata, A. J. Validation data for land surface temperature determination from satellites. Commonwealth Scientific and Industrial Research Organization, 1994.

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2

Tan, Kok Chooi. Land cover changes and their relationship with land surface temperature using remote sensing technique. Penerbit Universiti Sains Malaysia, 2013.

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3

Mass, Clifford. A next-generation land surface model for the prediction of pavement temperature. Washington State Dept. of Transportation, 2003.

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4

Labgaa, Rachid R. A model of the CO2 exchanges between biosphere and atmosphere in the tundra. Earth-Space Research Group, CRSEO -- Ellison Hall, University of California Santa Barbara, 1994.

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5

Carlson, Toby N. A remotely sensed index of deforestation/urbanization for use in climate models: Annual performance report for the period 1 January 1995 - 31 December 1995. Pennsylvania State University, 1995.

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6

Carlson, Toby N. A remotely sensed index of deforestation/urbanization for use in climate models: Annual performance report for the period 1 January 1995 - 31 December 1995. Pennsylvania State University, 1995.

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7

A Validation Study of the SSM/I Temperature Algorithm and Comparison with the CAL/VAL Land Surface Temperatures. Storming Media, 1997.

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8

Dicks, Steven E. Satellite-derived surface temperatures and their relationships to land cover, land use, soils and physiography of North-Central Florida. 1986.

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9

United States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data. National Aeronautics and Space Administration, 1994.

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10

Remote Sensing Monitoring of Land Surface Temperature (LST). MDPI, 2021. http://dx.doi.org/10.3390/books978-3-0365-1427-7.

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11

Nichols, William E. Land surface energy balance and surface soil moisture variation in HAPEX-MOBILHY. 1989.

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12

D, Conner Mark, and United States. National Aeronautics and Space Administration., eds. Identification and classification of transient signatures in over-land SSM/I imagery. National Aeronautics and Space Administration, 1994.

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13

Land surface temperature measurements form EOS MODIS data: Semi-annual report ... for July - December, 1996, contract number: NAS5-31370. National Aeronautics and Space Administration, 1996.

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14

United States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for January-June, 1995. National Aeronautics and Space Administration, 1995.

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15

Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for January-June, 1994. National Aeronautics and Space Administration, 1994.

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16

Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for July-December, 1993. National Aeronautics and Space Administration, 1993.

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17

Goswami, B. N., and Soumi Chakravorty. Dynamics of the Indian Summer Monsoon Climate. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.613.

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Lifeline for about one-sixth of the world’s population in the subcontinent, the Indian summer monsoon (ISM) is an integral part of the annual cycle of the winds (reversal of winds with seasons), coupled with a strong annual cycle of precipitation (wet summer and dry winter). For over a century, high socioeconomic impacts of ISM rainfall (ISMR) in the region have driven scientists to attempt to predict the year-to-year variations of ISM rainfall. A remarkably stable phenomenon, making its appearance every year without fail, the ISM climate exhibits a rather small year-to-year variation (the standard deviation of the seasonal mean being 10% of the long-term mean), but it has proven to be an extremely challenging system to predict. Even the most skillful, sophisticated models are barely useful with skill significantly below the potential limit on predictability. Understanding what drives the mean ISM climate and its variability on different timescales is, therefore, critical to advancing skills in predicting the monsoon. A conceptual ISM model helps explain what maintains not only the mean ISM but also its variability on interannual and longer timescales.The annual ISM precipitation cycle can be described as a manifestation of the seasonal migration of the intertropical convergence zone (ITCZ) or the zonally oriented cloud (rain) band characterized by a sudden “onset.” The other important feature of ISM is the deep overturning meridional (regional Hadley circulation) that is associated with it, driven primarily by the latent heat release associated with the ISM (ITCZ) precipitation. The dynamics of the monsoon climate, therefore, is an extension of the dynamics of the ITCZ. The classical land–sea surface temperature gradient model of ISM may explain the seasonal reversal of the surface winds, but it fails to explain the onset and the deep vertical structure of the ISM circulation. While the surface temperature over land cools after the onset, reversing the north–south surface temperature gradient and making it inadequate to sustain the monsoon after onset, it is the tropospheric temperature gradient that becomes positive at the time of onset and remains strongly positive thereafter, maintaining the monsoon. The change in sign of the tropospheric temperature (TT) gradient is dynamically responsible for a symmetric instability, leading to the onset and subsequent northward progression of the ITCZ. The unified ISM model in terms of the TT gradient provides a platform to understand the drivers of ISM variability by identifying processes that affect TT in the north and the south and influence the gradient.The predictability of the seasonal mean ISM is limited by interactions of the annual cycle and higher frequency monsoon variability within the season. The monsoon intraseasonal oscillation (MISO) has a seminal role in influencing the seasonal mean and its interannual variability. While ISM climate on long timescales (e.g., multimillennium) largely follows the solar forcing, on shorter timescales the ISM variability is governed by the internal dynamics arising from ocean–atmosphere–land interactions, regional as well as remote, together with teleconnections with other climate modes. Also important is the role of anthropogenic forcing, such as the greenhouse gases and aerosols versus the natural multidecadal variability in the context of the recent six-decade long decreasing trend of ISM rainfall.
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18

Cook, Kerry H. Climate Change Scenarios and African Climate Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.545.

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Accurate projections of climate change under increasing atmospheric greenhouse gas levels are needed to evaluate the environmental cost of anthropogenic emissions, and to guide mitigation efforts. These projections are nowhere more important than Africa, with its high dependence on rain-fed agriculture and, in many regions, limited resources for adaptation. Climate models provide our best method for climate prediction but there are uncertainties in projections, especially on regional space scale. In Africa, limitations of observational networks add to this uncertainty since a crucial step in improving model projections is comparisons with observations. Exceeding uncertainties associated with climate model simulation are uncertainties due to projections of future emissions of CO2 and other greenhouse gases. Humanity’s choices in emissions pathways will have profound effects on climate, especially after the mid-century.The African Sahel is a transition zone characterized by strong meridional precipitation and temperature gradients. Over West Africa, the Sahel marks the northernmost extent of the West African monsoon system. The region’s climate is known to be sensitive to sea surface temperatures, both regional and global, as well as to land surface conditions. Increasing atmospheric greenhouse gases are already causing amplified warming over the Sahara Desert and, consequently, increased rainfall in parts of the Sahel. Climate model projections indicate that much of this increased rainfall will be delivered in the form of more intense storm systems.The complicated and highly regional precipitation regimes of East Africa present a challenge for climate modeling. Within roughly 5º of latitude of the equator, rainfall is delivered in two seasons—the long rains in the spring, and the short rains in the fall. Regional climate model projections suggest that the long rains will weaken under greenhouse gas forcing, and the short rains season will extend farther into the winter months. Observations indicate that the long rains are already weakening.Changes in seasonal rainfall over parts of subtropical southern Africa are observed, with repercussions and challenges for agriculture and water availability. Some elements of these observed changes are captured in model simulations of greenhouse gas-induced climate change, especially an early demise of the rainy season. The projected changes are quite regional, however, and more high-resolution study is needed. In addition, there has been very limited study of climate change in the Congo Basin and across northern Africa. Continued efforts to understand and predict climate using higher-resolution simulation must be sustained to better understand observed and projected changes in the physical processes that support African precipitation systems as well as the teleconnections that communicate remote forcings into the continent.
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19

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application. National Aeronautics and Space Administration, 1994.

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20

A, Maslanik James, Steffen Konrad, and United States. National Aeronautics and Space Administration., eds. Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application : semi-annual report, year 2. National Aeronautics and Space Administration, 1994.

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21

James, Maslanik, Steffen Konrad, and United States. National Aeronautics and Space Administration., eds. Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application : semi-annual report, year 2. National Aeronautics and Space Administration, 1994.

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22

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application. National Aeronautics and Space Administration, 1994.

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23

United States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for July-December, 1997 : contract number NAS5-31370. National Aeronautics and Space Administration, 1998.

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24

United States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for January-June, 1997 : contract number: NAS5-31370. National Aeronautics and Space Administration, 1997.

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25

Xue, Yongkang, Yaoming Ma, and Qian Li. Land–Climate Interaction Over the Tibetan Plateau. Oxford University Press, 2017. http://dx.doi.org/10.1093/acrefore/9780190228620.013.592.

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The Tibetan Plateau (TP) is the largest and highest plateau on Earth. Due to its elevation, it receives much more downward shortwave radiation than other areas, which results in very strong diurnal and seasonal changes of the surface energy components and other meteorological variables, such as surface temperature and the convective atmospheric boundary layer. With such unique land process conditions on a distinct geomorphic unit, the TP has been identified as having the strongest land/atmosphere interactions in the mid-latitudes.Three major TP land/atmosphere interaction issues are presented in this article: (1) Scientists have long been aware of the role of the TP in atmospheric circulation. The view that the TP’s thermal and dynamic forcing drives the Asian monsoon has been prevalent in the literature for decades. In addition to the TP’s topographic effect, diagnostic and modeling studies have shown that the TP provides a huge, elevated heat source to the middle troposphere, and that the sensible heat pump plays a major role in the regional climate and in the formation of the Asian monsoon. Recent modeling studies, however, suggest that the south and west slopes of the Himalayas produce a strong monsoon by insulating warm and moist tropical air from the cold and dry extratropics, so the TP heat source cannot be considered as a factor for driving the Indian monsoon. The climate models’ shortcomings have been speculated to cause the discrepancies/controversies in the modeling results in this aspect. (2) The TP snow cover and Asian monsoon relationship is considered as another hot topic in TP land/atmosphere interaction studies and was proposed as early as 1884. Using ground measurements and remote sensing data available since the 1970s, a number of studies have confirmed the empirical relationship between TP snow cover and the Asian monsoon, albeit sometimes with different signs. Sensitivity studies using numerical modeling have also demonstrated the effects of snow on the monsoon but were normally tested with specified extreme snow cover conditions. There are also controversies regarding the possible mechanisms through which snow affects the monsoon. Currently, snow is no longer a factor in the statistic prediction model for the Indian monsoon prediction in the Indian Meteorological Department. These controversial issues indicate the necessity of having measurements that are more comprehensive over the TP to better understand the nature of the TP land/atmosphere interactions and evaluate the model-produced results. (3) The TP is one of the major areas in China greatly affected by land degradation due to both natural processes and anthropogenic activities. Preliminary modeling studies have been conducted to assess its possible impact on climate and regional hydrology. Assessments using global and regional models with more realistic TP land degradation data are imperative.Due to high elevation and harsh climate conditions, measurements over the TP used to be sparse. Fortunately, since the 1990s, state-of-the-art observational long-term station networks in the TP and neighboring regions have been established. Four large field experiments since 1996, among many observational activities, are presented in this article. These experiments should greatly help further research on TP land/atmosphere interactions.
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26

Remotely sensed index of deforestation/urbanization for use in climate models: Annual performance report for the period 1 January 1996 - 31 December 1996 for NASA grant no. NAGW-4250. Pennsylvania State University, Office of Sponsored Programs, 1996.

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