scholarly journals Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

2017 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gabriel Abramowitz ◽  
Jason Evans ◽  
Anna Ukkola
Keyword(s):  
Linear Combination ◽  
Energy Budgets ◽  
Weighted Mean ◽  
Weighting Method ◽  
Error Covariance ◽  
Optimal Linear Combination ◽  
Out Of Sample ◽  
Optimal Linear ◽  
Data Requirements ◽  
Better Than

Abstract. Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, as well as being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data, and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in three common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

scholarly journals Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate

2018 ◽  
Vol 22 (2) ◽  
pp. 1317-1336 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gab Abramowitz ◽  
Jason Evans ◽  
Anna Ukkola
Keyword(s):  
Linear Combination ◽  
Energy Budgets ◽  
Weighted Mean ◽  
Weighting Method ◽  
Error Covariance ◽  
Optimal Linear Combination ◽  
Out Of Sample ◽  
Optimal Linear ◽  
Data Requirements ◽  
Better Than

Abstract. Accurate global gridded estimates of evapotranspiration (ET) are key to understanding water and energy budgets, in addition to being required for model evaluation. Several gridded ET products have already been developed which differ in their data requirements, the approaches used to derive them and their estimates, yet it is not clear which provides the most reliable estimates. This paper presents a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. The weighting method is based on a technique that provides an analytically optimal linear combination of ET products compared to site data and accounts for both the performance differences and error covariance between the participating ET products. We examine the performance of the weighting approach in several in-sample and out-of-sample tests that confirm that point-based estimates of flux towers provide information on the grid scale of these products. We also provide evidence that the weighted product performs better than its six constituent ET product members in four common metrics. Uncertainty in the ET estimate is derived by rescaling the spread of participating ET products so that their spread reflects the ability of the weighted mean estimate to match flux tower data. While issues in observational data and any common biases in participating ET datasets are limitations to the success of this approach, future datasets can easily be incorporated and enhance the derived product.

scholarly journals Linear Optimal Runoff Aggregate (LORA): A global gridded synthesis runoff product

2018 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gab Abramowitz ◽  
Jason Evans ◽  
Hylke E. Beck
Keyword(s):  
Hydrological Models ◽  
Multiple Sources ◽  
Weighting Method ◽  
Error Covariance ◽  
Out Of Sample ◽  
Streamflow Data ◽  
Optimal Weighting ◽  
Gauged Basins ◽  
Climatic Characteristics ◽  
Better Than

Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available despite such a product being useful to meet the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach and we confirm that the weighted product performs better than its 11 constituents products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly time scales, and includes time variant uncertainty, for the period 1980–2012 on a 0.5° grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents well the seasonal runoff cycle for most of the globe. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/.

scholarly journals Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product

2019 ◽  
Vol 23 (2) ◽  
pp. 851-870 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gab Abramowitz ◽  
Jason Evans ◽  
Hylke E. Beck
Keyword(s):  
Hydrological Models ◽  
Multiple Sources ◽  
Weighting Method ◽  
Error Covariance ◽  
Out Of Sample ◽  
Streamflow Data ◽  
Optimal Weighting ◽  
Gauged Basins ◽  
Climatic Characteristics ◽  
Better Than

Abstract. No synthesized global gridded runoff product, derived from multiple sources, is available, despite such a product being useful for meeting the needs of many global water initiatives. We apply an optimal weighting approach to merge runoff estimates from hydrological models constrained with observational streamflow records. The weighting method is based on the ability of the models to match observed streamflow data while accounting for error covariance between the participating products. To address the lack of observed streamflow for many regions, a dissimilarity method was applied to transfer the weights of the participating products to the ungauged basins from the closest gauged basins using dissimilarity between basins in physiographic and climatic characteristics as a proxy for distance. We perform out-of-sample tests to examine the success of the dissimilarity approach, and we confirm that the weighted product performs better than its 11 constituent products in a range of metrics. Our resulting synthesized global gridded runoff product is available at monthly timescales, and includes time-variant uncertainty, for the period 1980–2012 on a 0.5∘ grid. The synthesized global gridded runoff product broadly agrees with published runoff estimates at many river basins, and represents the seasonal runoff cycle for most of the globe well. The new product, called Linear Optimal Runoff Aggregate (LORA), is a valuable synthesis of existing runoff products and will be freely available for download on https://geonetwork.nci.org.au/geonetwork/srv/eng/catalog.search#/metadata/f9617_9854_8096_5291 (last access: 31 January 2019).

scholarly journals Combining different ECG derived respiration tracking methods to create an optimal reconstruction of the breathing pattern

2015 ◽  
Vol 1 (1) ◽  
pp. 54-57 ◽  
Author(s):  
Gustavo Lenis ◽  
Felix Conz ◽  
Olaf Dössel
Keyword(s):  
Linear Combination ◽  
Breathing Pattern ◽  
Accurate Estimation ◽  
The Past ◽  
Optimal Linear Combination ◽  
Fixed Set ◽  
Optimal Linear ◽  
Respiration Signal ◽  
Electrocardiogram Ecg ◽  
Ecg Derived Respiration

AbstractECG derived respiration (EDR) is a technique applied to estimate the respiration signal using only the electrocardiogram (ECG). Different approaches have been proposed in the past on how respiration could be gained from the ECG. However, in many applications only one of them is used while the others are not considered at all. In this paper, we propose a new algorithm for the optimal linear combination of different EDR methods in order to create a more accurate estimation. Using two well known databases, it was statistically shown that an optimally chosen fixed set of coefficients for the linear combination delivers a better estimation than each of the methods used solely.

scholarly journals Calm Multi-Baryon Operators

2018 ◽  
Vol 175 ◽  
pp. 05029
Author(s):  
Evan Berkowitz ◽  
Amy Nicholson ◽  
Chia Cheng Chang ◽  
Enrico Rinaldi ◽  
M.A. Clark ◽  
...  
Keyword(s):  
Correlation Function ◽  
Excited State ◽  
Variational Method ◽  
Linear Combination ◽  
Correlation Functions ◽  
Nuclear Physics ◽  
Physical Point ◽  
Euclidean Time ◽  
Optimal Linear Combination ◽  
Optimal Linear

There are many outstanding problems in nuclear physics which require input and guidance from lattice QCD calculations of few baryons systems. However, these calculations suffer from an exponentially bad signal-to-noise problem which has prevented a controlled extrapolation to the physical point. The variational method has been applied very successfully to two-meson systems, allowing for the extraction of the two-meson states very early in Euclidean time through the use of improved single hadron operators. The sheer numerical cost of using the same techniques in two-baryon systems has so far been prohibitive. We present an alternate strategy which offers some of the same advantages as the variational method while being significantly less numerically expensive. We first use the Matrix Prony method to form an optimal linear combination of single baryon interpolating fields generated from the same source and different sink interpolating fields. Very early in Euclidean time this optimal linear combination is numerically free of excited state contamination, so we coin it a calm baryon. This calm baryon operator is then used in the construction of the two-baryon correlation functions.To test this method, we perform calculations on the WM/JLab iso-clover gauge configurations at the SU(3) flavor symmetric point with mπ~ 800 MeV — the same configurations we have previously used for the calculation of two-nucleon correlation functions. We observe the calm baryon significantly removes the excited state contamination from the two-nucleon correlation function to as early a time as the single-nucleon is improved, provided non-local (displaced nucleon) sources are used. For the local two-nucleon correlation function (where both nucleons are created from the same space-time location) there is still improvement, but there is significant excited state contamination in the region the single calm baryon displays no excited state contamination.

scholarly journals Robust historical evapotranspiration trends across climate regimes

2020 ◽  
Author(s):  
Sanaa Hobeichi ◽  
Gab Abramowitz ◽  
Jason Evans
Keyword(s):  
Land Surface ◽  
Climate Extremes ◽  
Historical Period ◽  
Hybrid Techniques ◽  
Time Period ◽  
Optimal Linear Combination ◽  
Uncertainty Estimates ◽  
Out Of Sample ◽  
Physically Based ◽  
Optimal Linear

Abstract. Evapotranspiration (ET) links the hydrological, energy, and carbon cycle on the land surface. Quantifying ET and its spatiotemporal changes is also key to understanding climate extremes such as droughts, heatwaves and flooding. Regional ET estimates require reliable observationally-based gridded ET datasets, and while many have been developed using physically-based, empirically-based and hybrid techniques, their efficacy, and particularly the efficacy of their uncertainty estimates, is difficult to verify. In this work, we extend the methodology used in Hobeichi et al. (2018) to derive a new version of the Derived Optimal Linear Combination Evapotranspiration (DOLCE) product, with observationally constrained spatiotemporally varying uncertainty estimates, higher spatial resolution, more constituent products and extended temporal reach (1980–2018). After successful evaluation of the efficacy of these uncertainty estimates out-of-sample, we derive novel ET climatology clusters for the land surface, based on the magnitude and variability of ET at each location. The verified uncertainty estimates and extended time period then allow us to examine the robustness of historical trends spatially and in each of these six ET climatology clusters. We find that despite robust decreasing ET trends in some regions, these do not correlate with behavioural ET clusters. Each cluster, and the vast majority of the Earth's surface, show clear robust increases in ET over the recent historical period.

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