scholarly journals What Can Thermal Imagery Tell Us About Glacier Melt Below Rock Debris?

2021 ◽  
Vol 9 ◽  
Author(s):  
Sam Herreid
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Surface Temperature ◽  
Thermal Emission ◽  
Model Parameters ◽  
Thermal Imagery ◽  
Thermal Images ◽  
Ice Melt ◽  
Glacier Melt ◽  
Thermal Data

Rock debris on the surface of a glacier can dramatically reduce the local melt rate, where the primary factor governing melt reduction is debris layer thickness. Relating surface temperature to debris thickness is a recurring approach in the literature, yet demonstrations of reproducibility have been limited. Here, I present the results of a field experiment conducted on the Canwell Glacier, Alaska, United States to constrain how thermal data can be used in glaciology. These datasets include, 1) a measured sub-daily “Østrem curve” time-series; 2) a time-series of high resolution thermal images capturing several segments of different debris thicknesses including the measurements from 1); 3) a thermal profile through a 38 cm debris cover; and 4) two Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite thermal images acquired within 2 and 3 min of a field-based thermal camera image. I show that, while clear sky conditions are when space-borne thermal sensors can image a glacier, this is an unfavorable time, limiting the likelihood that different thicknesses of debris will have a unique thermal signature. I then propose an empirical approach to estimate debris thickness and compare it to two recently published methods. I demonstrate that instantaneous calibration is essential in the previously published methods, where model parameters calibrated only 1 h prior to a repeat thermal image return diminished debris thickness estimates, while the method proposed here remains robust through time and does not appear to require re-calibration. I then propose a method that uses a time-series of surface temperature at one location and debris thickness to estimate bare-ice and sub-debris melt. Results show comparable cumulative melt estimates to a recently published method that requires an explicit/external estimate of bare ice melt. Finally, I show that sub-pixel corrections to ASTER thermal imagery can enable a close resemblance to high resolution, field-based thermal imagery. These results offer a deeper insight into what thermal data can and cannot tell us about surface debris properties and glacier melt.

scholarly journals The Global Trend in Sea Surface Temperature from 20 Years of Advanced Very High Resolution Radiometer Data

2007 ◽  
Vol 20 (7) ◽  
pp. 1255-1264 ◽  
Author(s):  
S. A. Good ◽  
G. K. Corlett ◽  
J. J. Remedios ◽  
E. J. Noyes ◽  
D. T. Llewellyn-Jones
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
El Nino ◽  
Sea Surface ◽  
Mount Pinatubo ◽  
Global Trend ◽  
Very High ◽  
Linear Trends

Abstract The trend in sea surface temperature has been determined from 20 yr of Advanced Very High Resolution Radiometer Pathfinder data (version 5). The data span the period from January 1985 to December 2004, inclusive. The linear trends were calculated to be 0.18° ± 0.04° and 0.17° ± 0.05°C decade−1 from daytime and nighttime data, respectively. However, the measured trends were found to be somewhat smaller if version 4.1 of the Pathfinder data was used, or if the time series of data ended earlier. The influence of El Niño on global temperatures can be seen clearly in the data. However, it was not found to affect the trend measurements significantly. Evidence of cool temperatures after the eruption of Mount Pinatubo in 1991 was also observed.

scholarly journals Downscaling Aster Land Surface Temperature over Urban Areas with Machine Learning-Based Area-To-Point Regression Kriging

2020 ◽  
Vol 12 (7) ◽  
pp. 1082 ◽  
Author(s):  
Jianhui Xu ◽  
Feifei Zhang ◽  
Hao Jiang ◽  
Hongda Hu ◽  
Kaiwen Zhong ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Thermal Emission ◽  
Independent Variables

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

scholarly journals SPATIOTEMPORAL FUSION OF HIGH RESOLUTION LAND SURFACE TEMPERATURE USING THERMAL SHARPENED IMAGES FROM REGRESSION-BASED URBAN INDICES

2020 ◽  
Vol V-3-2020 ◽  
pp. 247-254
Author(s):  
M. Kim ◽  
K. Cho ◽  
H. Kim ◽  
Y. Kim
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Absolute Difference ◽  
Landsat 8 ◽  
Thermal Images ◽  
Urban Regions

Abstract. Obtaining spatially continuous, high resolution thermal images is crucial in order to effectively analyze heat-related phenomena in urban areas and the inherent high spatial and temporal variations. Spatiotemporal Fusion (STF) methods can be applied to enhance spatial and temporal resolutions simultaneously, but most STF approaches for the generation of Land Surface Temperature (LST) have not focused specifically on urban regions. This study therefore proposes a two-phase approach using Landsat 8 and MODIS images acquired on a study area in Beijing to first, investigate the sharpening of the fine resolution image input with urban-related spectral indices and second, to explore the potential of implementing the sharpened results into the Spatiotemporal Adaptive Data Fusion Algorithm for Temperature Mapping (SADFAT) to generate high spatiotemporal resolution LST images in urban areas. For this test, five urban indices were selected based on their correlation with brightness temperature. In the thermal sharpening phase, the Fractional Urban Cover (FUC) index was able to delineate spatial details in urban regions whilst maintaining its correlation with the original brightness temperature image. In the STF phase however, FUC sharpened results returned relatively high levels of correlation coefficient values up to 0.689, but suffered from the highest Root Mean Squared Error (RMSE) and Average Absolute Difference (AAD) values of 4.260 K and 2.928 K, respectively. In contrast, Normalized Difference Building Index (NDBI) sharpened results recorded the lowest RMSE and AAD values of 3.126 K and 2.325 K, but also the lowest CC values. However, STF results were effective in delineating fine spatial details, ultimately demonstrating the potential of using sharpened urban or built-up indices as a means to generate sharpened thermal images for urban areas, as well as for input images in the SADFAT algorithm. The results from this study can be used to further improve STF approaches for daily and spatially continuous mapping of LST in urban areas.

scholarly journals Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of observed and projected daily rainfall time series

2013 ◽  
Vol 17 (7) ◽  
pp. 2487-2500 ◽  
Author(s):  
D. Lisniak ◽  
J. Franke ◽  
C. Bernhofer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Global Climate ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Time Slice ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Rainfall Characteristics ◽  
Hourly Rainfall

Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar results of both MRCs in the validation period of 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

scholarly journals The Effect of Rain–No Rain Intermittency on the Estimation of the Universal Multifractals Model Parameters

2009 ◽  
Vol 10 (2) ◽  
pp. 493-506 ◽  
Author(s):  
L. de Montera ◽  
L. Barthès ◽  
C. Mallet ◽  
P. Golé
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Rain Rate ◽  
Scientific Literature ◽  
Model Parameters ◽  
New Model ◽  
Dual Beam ◽  
Event Basis

Abstract The multifractal properties of rain are investigated within the framework of universal multifractals. The database used in this study includes measurements performed over several months in different locations by means of a disdrometer, the dual-beam spectropluviometer (DBS). An assessment of the effect of the rain–no rain intermittency shows that the analysis of rain-rate time series may lead to a spurious break in the scaling and to erroneous parameters. The estimation of rain multifractal parameters is, therefore, performed on an event-by-event basis, and they are found to be significantly different from those proposed in scientific literature. In particular, the parameter H, which has often been estimated to be 0, is more likely to be 0.53, thus meaning that rain is a fractionally integrated flux (FIF). Finally, a new model is proposed that simulates high-resolution rain-rate time series based on these new parameters and on a simple threshold.

scholarly journals Circulation pattern based parameterization of a multiplicative random cascade for disaggregation of daily rainfall under nonstationary climatic conditions

2012 ◽  
Vol 9 (9) ◽  
pp. 10115-10149 ◽  
Author(s):  
D. Lisniak ◽  
J. Franke ◽  
C. Bernhofer
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Climatic Conditions ◽  
Statistical Properties ◽  
Rainfall Data ◽  
Time Slice ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Rainfall Characteristics ◽  
Hourly Rainfall

Abstract. The use of multiplicative random cascades (MRCs) for temporal rainfall disaggregation has been extensively studied in the past. MRCs are appealing for rainfall disaggregation due to their formal simplicity and the possibility to extract the model parameters directly from observed high resolution rainfall data. These parameters, however, represent the rainfall characteristics of the observation period. Since rainfall characteristics of different time slices are changing due to climate variability, we propose a parameterization approach for MRCs to adjust the parameters according to past (observed) or future (projected) time series. This is done on the basis of circulation patterns (CPs) by extracting a distinct MRC parameterization from high resolution rainfall data, as observed on days governed by each individual CP. The parameterization approach is tested by comparing the statistical properties of disaggregated rainfall time series of two time slices, 1969–1979 and 1989–1999, to the results obtained by two other disaggregation methods (a conceptually similar MRC without CP-based parameterization and a recombination approach) and to the statistical properties of observed hourly rainfall data. In this context, all three approaches use rainfall data of the time slice 1989–1999 for parameterization. We found that the inclusion of CPs into the parameterization of a MRC yields hourly time series that better reproduce the properties of observed rainfall in time slice 1989–1999, as compared to the simple MRC. Despite similar properties of both MRCs for the time slice 1969–1979, we can conclude that the CP-based parameterization approach is applicable for temporal rainfall disaggregation in time slices distinct from the parameterization period. This approach accounts for changes in rainfall characteristics due to changes in the frequency of occurrence of the CPs and allows generating hourly rainfall from daily data, as often provided by a statistical downscaling of global climate change.

scholarly journals Future temperature and salinity in Puget Sound, Washington State, under CMIP6 climate change scenarios

2021 ◽  
Author(s):  
Stevie Walker ◽  
Hem Nalini Morzaria-Luna ◽  
Isaac Kaplan ◽  
David Petatán-Ramírez
Keyword(s):  
Climate Change ◽  
Time Series ◽  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Puget Sound ◽  
Washington State ◽  
Trophic Levels ◽  
Sea Surface

Abstract In Washington State, climate change will reshape the Puget Sound marine ecosystem through bottom-up and top-down processes, directly affecting species at all trophic levels. To better understand future climate change effects on sea surface temperature and salinity in Puget Sound, we used empirical downscaling to derive high-resolution time series of future sea surface temperature and salinity. Downscaling was based on scenario outputs of two coarse-resolution General Circulation Models, GFDL-CM4 and CNRM-CM6-1-HR, developed as part of the Coupled Model Intercomparison Project Phase 6 (CMIP6). We calculated 30-year climatologies for historical and future simulations, calculated the anomalies between historical and future projections, interpolated to a high resolution, and applied the resulting downscaled anomalies to a Regional Ocean Modeling System (ROMS) time series, yielding short-term (2020–2050) and long-term (2070–2100) delta-downscaled forecasts. Downscaled output for Puget Sound showed temperature and salinity variability between scenarios and models, but overall, there was strong model agreement. Model variability and uncertainty was higher for long-term projections. Spatially, we found regional differences for both temperature and salinity, including higher temperatures in the South Basin of Puget Sound and higher salinity in the North Basin. This study is a first step to translating CMIP6 outputs to higher resolution predictions of future conditions in Puget Sound. Interpreting downscaled projections of temperature and salinity in Puget Sound will help inform future ecosystem-based management decisions, such as supporting end-to-end ecosystem modeling simulations and assessing local-scale exposure risk to climate change.

scholarly journals Lake surface water temperatures of European Alpine lakes (1989–2013) based on the Advanced Very High Resolution Radiometer (AVHRR) 1 km data set

2015 ◽  
Vol 7 (1) ◽  
pp. 1-17 ◽  
Author(s):  
M. Riffler ◽  
G. Lieberherr ◽  
S. Wunderle
Keyword(s):  
Time Series ◽  
Surface Water ◽  
High Resolution ◽  
Surface Temperature ◽  
Water Temperature ◽  
Lake Surface ◽  
Data Set ◽  
Simulation Based ◽  
Very High

Abstract. Lake water temperature (LWT) is an important driver of lake ecosystems and it has been identified as an indicator of climate change. Consequently, the Global Climate Observing System (GCOS) lists LWT as an essential climate variable. Although for some European lakes long in situ time series of LWT do exist, many lakes are not observed or only on a non-regular basis making these observations insufficient for climate monitoring. Satellite data can provide the information needed. However, only few satellite sensors offer the possibility to analyse time series which cover 25 years or more. The Advanced Very High Resolution Radiometer (AVHRR) is among these and has been flown as a heritage instrument for almost 35 years. It will be carried on for at least ten more years, offering a unique opportunity for satellite-based climate studies. Herein we present a satellite-based lake surface water temperature (LSWT) data set for European water bodies in or near the Alps based on the extensive AVHRR 1 km data record (1989–2013) of the Remote Sensing Research Group at the University of Bern. It has been compiled out of AVHRR/2 (NOAA-07, -09, -11, -14) and AVHRR/3 (NOAA-16, -17, -18, -19 and MetOp-A) data. The high accuracy needed for climate related studies requires careful pre-processing and consideration of the atmospheric state. The LSWT retrieval is based on a simulation-based scheme making use of the Radiative Transfer for TOVS (RTTOV) Version 10 together with ERA-interim reanalysis data from the European Centre for Medium-range Weather Forecasts. The resulting LSWTs were extensively compared with in situ measurements from lakes with various sizes between 14 and 580 km2 and the resulting biases and RMSEs were found to be within the range of −0.5 to 0.6 K and 1.0 to 1.6 K, respectively. The upper limits of the reported errors could be rather attributed to uncertainties in the data comparison between in situ and satellite observations than inaccuracies of the satellite retrieval. An inter-comparison with the standard Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature product exhibits RMSEs and biases in the range of 0.6 to 0.9 and −0.5 to 0.2 K, respectively. The cross-platform consistency of the retrieval was found to be within ~ 0.3 K. For one lake, the satellite-derived trend was compared with the trend of in situ measurements and both were found to be similar. Thus, orbital drift is not causing artificial temperature trends in the data set. A comparison with LSWT derived through global sea surface temperature (SST) algorithms shows lower RMSEs and biases for the simulation-based approach. A running project will apply the developed method to retrieve LSWT for all of Europe to derive the climate signal of the last 30 years. The data are available at doi:10.1594/PANGAEA.831007.

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