Revisiting the validity of Braak’s equation on altitudinal temperature lapse rate using thermal-infrared bands of Landsat 8

2018 ◽  
Author(s):  
Desi Suyamto ◽  
Lilik B. Prasetyo ◽  
Yudi Setiawan
Keyword(s):  
Thermal Infrared ◽  
Lapse Rate ◽  
Landsat 8 ◽  
Temperature Lapse Rate

scholarly journals MODELING THE IMPACT OF SURFACE CHARACTERISTICS ON THE NEAR SURFACE TEMPERATURE LAPSE RATE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 395-399 ◽  
Author(s):  
M. K. Firozjaei ◽  
S. Fathololuomi ◽  
S. K. Alavipanah ◽  
M. Kiavarz ◽  
A. Vaezi ◽  
...  
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Feature Space ◽  
Surface Characteristics ◽  
Lapse Rate ◽  
Landsat 8 ◽  
Near Surface ◽  
Temperature Lapse Rate ◽  
Topographic Characteristics ◽  
The Impact

Abstract. Modeling of Near-Surface Temperature Lapse Rate (NSTLR) is very important in various environmental applications. The Land Surface Temperature (LST) is influenced by many properties and conditions including surface biophysical and topographic characteristics. Some researches have considered the LST - Digital Elevation Model (DEM) feature space to model NSTLR. However, the influence of detailed surface characteristics is rare. This study investigated the impact of surface characteristics on the LST-DEM feature space for NSTLR modeling. A set of remote sensing data including Landsat 8 images, MODIS products, and surface features including DEM and land use of the Balikhli-Chay on 01/07/2018, 18/08/2018 and 03/09/2018 were collected and used in this study. First, Split Window (SW) algorithm was used to estimate LST, and spectral indices were employed to model surface biophysical characteristics. Owing to the impact of surface biophysical and topographic characteristics on the LST-DEM feature space, the NSTLR was calculated for different classes of surface biophysical characteristics, land use, and solar local incident angle. The modeled NSTLR values based on the LST-DEM feature space on 01/07/2018, 18/08/2018 and 03/09/2018 were 8.5, 1.5 and 2.4 °C Km−1; respectively. The NSTLR in different classes of surface biophysical characteristics, land use type and topographical parameters were variable between 0.5 to 14 °C Km−1. This clearly showed the dependence of NSTLR on topographic and biophysical conditions. This provides a new way of calculating surface characteristic specific NSTLR.

scholarly journals Analysis of Near-Surface Temperature Lapse Rates in Mountain Ecosystems of Northern Mexico Using Landsat-8 Satellite Images and ECOSTRESS

2021 ◽  
Vol 14 (1) ◽  
pp. 162
Author(s):  
Marcela Rosas-Chavoya ◽  
Pablito Marcelo López-Serrano ◽  
José Ciro Hernández-Díaz ◽  
Christian Wehenkel ◽  
Daniel José Vega-Nieva
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Linear Models ◽  
Lapse Rate ◽  
Stress Index ◽  
Landsat 8 ◽  
Near Surface ◽  
Mountain Ecosystems ◽  
Temperature Lapse Rate ◽  
Environmental Goods

Mountain ecosystems provide environmental goods, which can be threatened by climate change. Near-Surface Temperature Lapse Rate (NSTLR) is an essential factor used for thermal and hydrological analysis in mountain ecosystems. The aims of the present study were to estimate NSTLR and to identify its relationship with aspect, Local solar zenith angle (LSZA) and Evaporative Stress Index (ESI) for two seasons of the year in a mountain ecosystem at the North of Mexico. Normalized Land Surface Temperature (NLST) was estimated using environmental and topographical variables. LSZA was calculated from slope to consider the effect of solar position. NSTLR was estimated through simple linear models. Observed NSTLR was 9.4 °C km−1 for the winter and 14.3 °C km−1 for the summer. Our results showed variation in NSTLR by season. In addition, aspect, LSZA and ESI also influenced NSTLR regulation. In addition, Northwest and West aspects exhibited the highest NSTLR. LSZA angles closest to 90° were related with a decrease in NSTLR for both seasons. Finally, ESI values associated with less evaporative stress were related to lower NSTLR. These results suggest potential of Landsat-8 LST and ECOSTRESS ESI to capture interactions of temperature, topography, and water stress in complex ecosystems.

scholarly journals Information-based mid-upper tropospheric methane derived from Atmospheric Infrared Sounder (AIRS) and its validation

2009 ◽  
Vol 9 (4) ◽  
pp. 16331-16360 ◽  
Author(s):  
X. Xiong ◽  
C. Barnet ◽  
J. Wei ◽  
E. Maddy
Keyword(s):  
Temporal Variation ◽  
High Latitude ◽  
A Priori ◽  
Thermal Infrared ◽  
Lapse Rate ◽  
Spatial And Temporal Variation ◽  
Aircraft Measurements ◽  
Infrared Sensors ◽  
Atmospheric Infrared Sounder ◽  
Temperature Lapse Rate

Abstract. Atmospheric Infrared Sounder (AIRS) measurements of methane (CH4) generally contain about 1.0 degree of freedom and are therefore dependent on a priori assumptions about the vertical methane distribution as well as the temperature lapse rate and the amount of moisture. Thus it requires that interpretation and/or analysis of the CH4 spatial and temporal variation based on the AIRS retrievals need to use the averaging kernels (AK). To simplify the use of satellite retrieved products for scientific analysis, a method based on the information content of the retrievals is developed, in which the AIRS retrieved CH4 in the layer from 50 to 250 hPa below the tropopause is used to characterize the mid-upper tropospheric CH4 in the mid-high latitude regions. The basis of this method is that in the mid-high latitude regions the maximum sensitive layers of AIRS to CH4 have a good correlation with the tropopause heights, and these layers are usually between 50 and 250 hPa below the tropopause. Validation using the aircraft measurements from NOAA/ESRL/GMD and the campaigns INTEX-A and -B indicated that the correlation of AIRS mid-upper tropospheric CH4 with aircraft measurements is ~0.6–0.7, and its the bias and rms difference are less than ±1% and 1.2%, respectively. Further comparison of the CH4 seasonal cycle indicated that the cycle from AIRS mid-upper tropospheric CH4 is in a reasonable agreement with NOAA aircraft measurements. This method provides a simple way to use the thermal infrared sounders data to approximately analyze the spatial and temporal variation CH4 in the upper free tropospere without referring the AK. This method is applicable to derive tropospheric CH4 as well as other trace gases for any thermal infrared sensors.

scholarly journals Thermal structure of intense convective clouds derived from GPS radio occultations

2012 ◽  
Vol 12 (12) ◽  
pp. 5309-5318 ◽  
Author(s):  
R. Biondi ◽  
W. J. Randel ◽  
S.-P. Ho ◽  
T. Neubert ◽  
S. Syndergaard
Keyword(s):  
Thermal Structure ◽  
Deep Convection ◽  
Lapse Rate ◽  
Orthogonal Polarization ◽  
Cold Temperatures ◽  
Temperature Lapse Rate ◽  
Convective Systems ◽  
Convective Clouds ◽  
Strong Inversion ◽  
Cloud Tops

Abstract. Thermal structure associated with deep convective clouds is investigated using Global Positioning System (GPS) radio occultation measurements. GPS data are insensitive to the presence of clouds, and provide high vertical resolution and high accuracy measurements to identify associated temperature behavior. Deep convective systems are identified using International Satellite Cloud Climatology Project (ISCCP) satellite data, and cloud tops are accurately measured using Cloud-Aerosol Lidar with Orthogonal Polarization (CALIPSO) lidar observations; we focus on 53 cases of near-coincident GPS occultations with CALIPSO profiles over deep convection. Results show a sharp spike in GPS bending angle highly correlated to the top of the clouds, corresponding to anomalously cold temperatures within the clouds. Above the clouds the temperatures return to background conditions, and there is a strong inversion at cloud top. For cloud tops below 14 km, the temperature lapse rate within the cloud often approaches a moist adiabat, consistent with rapid undiluted ascent within the convective systems.

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 A Method for Calculating the Height of Overshooting Convective Cloud Tops Using Satellite-Based IR Imager and CloudSat Cloud Profiling Radar Observations

2016 ◽  
Vol 55 (2) ◽  
pp. 479-491 ◽  
Author(s):  
Sarah M. Griffin ◽  
Kristopher M. Bedka ◽  
Christopher S. Velden
Keyword(s):  
Brightness Temperature ◽  
Characteristic Temperature ◽  
Weather Prediction ◽  
Convective Cloud ◽  
Detection Algorithm ◽  
Lapse Rate ◽  
Temperature Lapse Rate ◽  
Cloud Tops ◽  
Height Assignment ◽  
Vertical Level

AbstractAssigning accurate heights to convective cloud tops that penetrate into the upper troposphere–lower stratosphere (UTLS) region using infrared (IR) satellite imagery has been an unresolved issue for the satellite research community. The height assignment for the tops of optically thick clouds is typically accomplished by matching the observed IR brightness temperature (BT) with a collocated rawinsonde or numerical weather prediction (NWP) profile. However, “overshooting tops” (OTs) are typically colder (in BT) than any vertical level in the associated profile, leaving the height of these tops undetermined using this standard approach. A new method is described here for calculating the heights of convectively driven OTs using the characteristic temperature lapse rate of the cloud top as it ascends into the UTLS region. Using 108 MODIS-identified OT events that are directly observed by the CloudSat Cloud Profiling Radar (CPR), the MODIS-derived brightness temperature difference (BTD) between the OT and anvil regions can be defined. This BTD is combined with the CPR- and NWP-derived height difference between these two regions to determine the mean lapse rate, −7.34 K km−1, for the 108 events. The anvil height is typically well known, and an automated OT detection algorithm is used to derive BTD, so the lapse rate allows a height to be calculated for any detected OT. An empirical fit between MODIS and geostationary imager IR BT for OTs and anvil regions was performed to enable application of this method to coarser-spatial-resolution geostationary data. Validation indicates that ~75% (65%) of MODIS (geostationary) OT heights are within ±500 m of the coincident CPR-estimated heights.

scholarly journals Multi-Sensor Fusion of Landsat 8 Thermal Infrared (TIR) and Panchromatic (PAN) Images

Sensors ◽  
2014 ◽  
Vol 14 (12) ◽  
pp. 24425-24440 ◽  
Author(s):  
Hyung-Sup Jung ◽  
Sung-Whan Park
Keyword(s):  
Sensor Fusion ◽  
Thermal Infrared ◽  
Landsat 8
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