Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS

Coccia, Gabriele; Siemann, Amanda; Pan, Ming; Wood, Eric F.

doi:10.1016/j.rse.2015.09.010

Cited by 31 publications

(32 citation statements)

References 32 publications

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“…From the WACMOS-ET models, only GLEAM uses observations of precipitation and surface soil moisture as input. In the reference input data set, precipitation data come from the Climate Forecast System Reanalysis for Land (CFSRLand; Coccia et al, 2015), which uses the Climate Prediction Center (CPC, Chen et al, 2008) and the Global Precipitation Climatology Project (GPCP, Huffman et al, 2001) daily data sets and applies a temporal downscaling based on the CFSR (Saha et al, 2010). For soil moisture, we use the satellite product of combined active-passive microwave surface soil moisture by Liu et al (2012), which combines information from scatterometers and radiometers from different platforms, and was developed as part of the ESA Climate Change Initiative (CCI).…”

Section: Input Datamentioning

confidence: 99%

“…The global contribution of transpiration to total average evaporation has been extensively debated recently (Schlesinger and Jasechko, 2014;. Studies have reported values ranging between 35 and 90 %, based on isotopes (Jasechko et al, 2013;Coenders-Gerrits et al, 2015), sapflow measurements (Moran et al, 2009), satellite data (Miralles et al, 2011a;Mu et al, 2011;Zhang et al, 2016), and modelling (Wang-Erlandsson et al, 2014). Consequently, this large range of uncertainty is also expected in the relative contribution from other evaporation sources.…”

Section: Partitioning Of Evaporation Into Separate Componentsmentioning

confidence: 99%

“…While the global contribution of transpiration has received much attention in the literature (Jasechko et al, 2013;Coenders-Gerrits et al, 2015), the flux of interception loss has seldom been explored globally (Miralles et al, 2010;Vinukollu et al, 2011b;Wang-Erlandsson et al, 2014). The physical process of interception loss differs from that of transpiration on its sensitivity to environmental and climatic variables: the rates and magnitude of interception are dictated by the aerodynamic properties of the vegetation stand and the occurrence and characteristics of rainfall (Horton, 1919).…”

Section: Partitioning Of Evaporation Into Separate Componentsmentioning

confidence: 99%

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The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets

Miralles

Jiménez²,

Jung

et al. 2016

Hydrol. Earth Syst. Sci.

293

214

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Abstract. The WAter Cycle Multi-mission Observation Strategy -EvapoTranspiration (WACMOS-ET) project aims to advance the development of land evaporation estimates on global and regional scales. Its main objective is the derivation, validation, and intercomparison of a group of existing evaporation retrieval algorithms driven by a common forcing data set. Three commonly used process-based evaporation methodologies are evaluated: the PenmanMonteith algorithm behind the official Moderate Resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Global Land Evaporation Amsterdam Model (GLEAM), and the Priestley-Taylor Jet Propulsion Laboratory model (PT-JPL). The resulting global spatiotemporal variability of evaporation, the closure of regional water budgets, and the discrete estimation of land evaporation components or sources (i.e. transpiration, interception loss, and direct soil evaporation) are investigated using river discharge data, independent global evaporation data sets and results from previous studies. In a companion article (Part 1), Michel et al. (2016) inspect the performance of these three models at local scales using measurements from eddy-covariance towers and include in the assessment the Surface Energy Balance System (SEBS) model. In agreement with Part 1, our results indicate that the Priestley and Taylor products (PT-JPL and GLEAM) perform best overall for most ecosystems and climate regimes. While all three evaporation products adequately represent the expected average geographical patterns and seasonality, there is a tendency in PM-MOD to underestimate the flux in the tropics and subtropics. Overall, results from GLEAM and PT-JPL appear more realistic when compared to surface water balances from 837 globally distributed catchments and to separate evaporation estimates from ERAInterim and the model tree ensemble (MTE). Nonetheless, all products show large dissimilarities during conditions of water stress and drought and deficiencies in the way evaporation is partitioned into its different components. This observed inter-product variability, even when common forcing is used, suggests that caution is necessary in applying a single data set for large-scale studies in isolation. A general finding that different models perform better under different conditions highlights the potential for considering biome-or climatespecific composites of models. Nevertheless, the generation of a multi-product ensemble, with weighting based on validation analyses and uncertainty assessments, is proposed as the Published by Copernicus Publications on behalf of the European Geosciences Union. D. G. Miralles et al.: The WACMOS-ET project -Part 2best way forward in our long-term goal to develop a robust observational benchmark data set of continental evaporation.

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Section: Input Datamentioning

confidence: 99%

Section: Partitioning Of Evaporation Into Separate Componentsmentioning

confidence: 99%

Section: Partitioning Of Evaporation Into Separate Componentsmentioning

confidence: 99%

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The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets

Miralles

Jiménez²,

Jung

et al. 2016

Hydrol. Earth Syst. Sci.

293

214

View full text Add to dashboard Cite

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“…The concept of PU applies to precipitation, temperature or any variable (re-)forecast by atmospheric reanalysis. In this context, a recent application has been presented [28] in which consistent datasets of Land Surface Temperature (LST) were generated and data gaps closed with respective uncertainty estimates by combining multiple satellite LST estimates with LST reanalyses in a Bayesian framework. Missing data were filled with the aid of the National Centre of Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR) product [29] by using reanalyzed LST as the predictor.…”

Section: Introductionmentioning

confidence: 99%

Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

Reggiani

Coccia

Mukhopadhyay

2016

Water

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Abstract:The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Range is difficult to access and, therefore, remains scarcely monitored. In situ precipitation and temperature measurements are only available at valley locations. High-altitude observations exist only for very limited periods. Gridded precipitation and temperature data generated from the spatial interpolation of in situ observations are unreliable for this region because of the extreme topography. Besides satellite measurements, which offer spatial coverage, but underestimate precipitation in this area, atmospheric reanalyses remain one of the few alternatives. Here, we apply a proven approach to quantify the uncertainty associated with an ensemble of monthly precipitation and temperature reanalysis data for 1979-2009 in Shigar Basin, Central Karakoram. A Model-Conditional Processor (MCP) of uncertainty is calibrated on precipitation and temperature in situ data measured in the proximity of the study region. An ensemble of independent reanalyses is processed to determine the predictive uncertainty of monthly observations. As to be expected, the informative gain achieved by post-processing temperature reanalyses is considerable, whereas significantly less gain is achieved for precipitation post-processing. The proposed approach indicates a systematic assessment procedure for predictive uncertainty through probabilistic weighting of multiple re-forecasts, which are bias-corrected on ground observations. The approach also supports an educated reconstruction of gap-filling for missing in situ observations.

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“…The inter-calibrated data allow for studies of global temperatures including surface and air temperatures over the last several decades. For example, HIRS-derived land surface temperature has been combined with land surface model estimates to build a consistent dataset from 1979 to 2009 [9].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of HIRS Surface Air Temperature with USCRN Data

Stegall

Shi

2016

Remote Sensing

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Abstract:The surface air temperature retrievals from the High Resolution Infrared Radiation Sounder (HIRS) are evaluated by using observations from the U.S. Climate Reference Network (USCRN) for the period of 2006 to 2013. One year of the USCRN data is also used as ground truth in calibrating retrieval biases. The final retrieval results show that mean biases of HIRS retrievals from comparisons to all surface stations for each year are mostly in the range of˘0.2˝C, and the root mean square difference (RMSD) values are 3.2-3.5˝C. Results for biases of individual stations are mostly within 2˝C. In average, RMSDs are smaller over the eastern U.S. than over the western U.S., smaller at nighttime than at daytime, and smaller at lower elevations. The comparison patterns are consistent from year to year and for different satellites, showing the potential of HIRS data for long-term studies.

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Creating consistent datasets by combining remotely-sensed data and land surface model estimates through Bayesian uncertainty post-processing: The case of Land Surface Temperature from HIRS

Cited by 31 publications

References 32 publications

The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets

The WACMOS-ET project – Part 2: Evaluation of global terrestrial evaporation data sets

Predictive Uncertainty Estimation on a Precipitation and Temperature Reanalysis Ensemble for Shigar Basin, Central Karakoram

An Assessment of HIRS Surface Air Temperature with USCRN Data

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