Recent advances in (soil moisture) triple collocation analysis

Gruber, A.; Su, Chun‐Hsu; Zwieback, Simon; Crow, Wade T.; Dorigo, Wouter; Wagner, Wolfgang

doi:10.1016/j.jag.2015.09.002

Cited by 266 publications

(312 citation statements)

References 31 publications

Supporting

Mentioning

305

Contrasting

Unclassified

Order By: Relevance

“…Indeed, ρ 2 represents the unbiased signal to noise ratio, scaled between 0 and 1, which provides a measure of the relative similarity between two signals, independently from their phase differences. This was also underlined by Gruber et al (2016), who showed that ρ 2 is the complement of the f RMSE = σ 2 ε /σ 2 introduced by Draper et al (2013) (ρ 2 = 1−f RMSE), which was used previously to remove the dependency of the error variance pattern on the spatial climatology of the chosen ref-erence. Gruber et al (2016) also pointed out that the absolute error variance provides only limited information about the true data set quality because a certain amount of noise can be either acceptable or unacceptable depending on the strength of the underlying signal (i.e.…”

Section: Performance Scoresmentioning

confidence: 81%

“…When dealing with error variances, the products have to be rescaled to a common reference data space. However, such a rescaling imposes spatial patterns within the derived error metric which reflects the climatology of the chosen reference (Gruber et al, 2016). To this end, McColl et al (2014) noted that correlation coefficients can provide important new information about the performance of the measurement systems with respect to the absolute error variances obtained via Eq.…”

Section: Performance Scoresmentioning

confidence: 99%

“…The first applications of TC concerned geophysical variables such as ocean wind speed and wave height (Stoffelen, 1998). More recently, it has been used extensively to estimate errors in soil moisture (SM) products (Crow and Van Den Berg, 2010;Miralles et al, 2010;Dorigo et al, 2010;Draper et al, 2013;Su et al, 2014;Gruber et al, 2016). Given three estimates of the same variable, the main assumptions of the method are the (i) stationarity of the statistics, (ii) linearity between the three estimates (vs. the same target) across all timescales and (iii) existence of uncorrelated error between the three estimates.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An assessment of the performance of global rainfall estimates without ground-based observations

Massari

Crow

Brocca

2017

Hydrol. Earth Syst. Sci.

125

View full text Add to dashboard Cite

Abstract. Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets.In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth) of the aforementioned products without using a ground benchmark data set.The analysis is carried out during the period 2007-2012 using daily rainfall accumulation products obtained at 1 • ×1 • spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern Hemisphere.The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.

show abstract

Section: Performance Scoresmentioning

confidence: 81%

Section: Performance Scoresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An assessment of the performance of global rainfall estimates without ground-based observations

Massari

Crow

Brocca

2017

Hydrol. Earth Syst. Sci.

125

View full text Add to dashboard Cite

show abstract

“…TC is a method to estimate the RMSE (and, if desired, correlation coefficients) of three spatially and temporally collocated measurements by assuming a linear error model between the measurements (McColl et al, 2014;Stoffelen, 1998). This methodology has been widely used in error estimation of land and ocean parameters, such as wind speed, sea surface temperature, soil moisture, evaporation, precipitation, f APAR, and in the rescaling of measurement systems to reference system for data assimilation purposes (Alemohammad et al, 2015;D'Odorico et al, 2014;Gruber et al, 2016;Hain et al, 2011;Lei et al, 2015;Miralles et al, 2010Miralles et al, , 2011bParinussa et al, 2011), as well as in validating categorical variables such as the soil freeze-thaw state (McColl et al, 2016). The relationship between each measurement and the true value is assumed to follow a linear model:…”

Section: Target Dataset: a Bayesian Prior Using Triple Collocationmentioning

confidence: 99%

Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

et al. 2017

View full text Add to dashboard Cite

Abstract.A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H ), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solarinduced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H , and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H , and GPP from 2007 to 2015 at 1 • × 1 • spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events.Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events -such as the 2015 El Niño -on surface turbulent fluxes and GPP.

show abstract

“…For surface soil wetness, verification McColl et al (2014) have developed the Extended Triple Collocation (ETC) method to estimate the temporal correlation between a model or observation system and the unknown truth. The ETC method makes the same assumptions as the more widely known Triple Collocation (TC; Scipal et al 2008;Vogelzang and Stoffelen 2012;Zwieback et al 2012;Dorigo et al 2010;Yilmaz and Crow 2014;Gruber et al 2016;Draper et al 2013) method. ETC and TC require three mutually independent time-series estimates of the same quantity.…”

Section: Ozfluxmentioning

confidence: 99%