Uncertainty analysis of satellite rainfall algorithms over the tropical Pacific

Morrissey, Mark L.; Greene, J. Scott

doi:10.1029/98jd00309

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…These studies can be categorized into two groups: those that investigate and evaluate precipitation products (Morrissey and Greene 1998;McPhee and Margulis 2005;Tian and Peters-Lidard 2007;Habib et al 2009;Sapiano and Arkin 2009;Tian et al 2009;Tian and Peters-Lidard 2010;Shen et al 2010;Anagnostou et al 2010;Stampoulis and Anagnostou 2012;Habib et al 2012;Kirstetter et al 2013a;S. Chen et al 2013a,b;Joshi et al 2013;Alemohammad et al 2014;Tang et al 2014;Alemohammad et al 2015) and those that model the errors associated with these retrievals (Bellerby 2007;AghaKouchak et al 2009;Villarini et al 2009;AghaKouchak et al 2010b;Tian et al 2013;Kirstetter et al 2013b;Seyyedi et al 2014a,b;Maggioni et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Alemohammad

McLaughlin

Entekhabi

2015

Monthly Weather Review

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Ensemble-based data assimilation techniques are often applied to land surface models in order to estimate components of terrestrial water and energy balance. Precipitation forcing uncertainty is the principal source of spread among the ensembles that is required for utilizing information in observations to correct model priors. Precipitation fields may have both position and magnitude errors. However, current uncertainty characterizations of precipitation forcing in land data assimilation systems often do no more than applying multiplicative errors to precipitation fields. In this paper, an ensemble-based Bayesian method for characterization of uncertainties associated with precipitation retrievals from spaceborne instruments is introduced. This method is used to produce stochastic replicates of precipitation fields that are conditioned on precipitation observations. Unlike previous studies, the error likelihood is derived using an archive of historical measurements. The ensemble replicates are generated using a stochastic method, and they are intermittent in space and time. The replicates are first projected in a low-dimension subspace using a problem-specific set of attributes. The attributes are derived using a dimensionality reduction scheme that takes advantage of singular value decomposition. A nonparametric importance sampling technique is formulated in terms of the attribute vectors to solve the Bayesian sampling problem. Examples are presented using retrievals from operational passive microwave instruments, and performance of the method is assessed using ground validation measurements from a surface weather radar network. Results indicate that this ensemble characterization approach provides a useful description of precipitation uncertainties with a posterior ensemble that is narrower in distribution than its prior while containing both precipitation position and magnitude errors.

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Section: Introductionmentioning

confidence: 99%

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Alemohammad

McLaughlin

Entekhabi

2015

Monthly Weather Review

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“…However, satellite estimates are subject to significant uncertainties due to the indirect nature of satellite measurements [19][20][21][22][23][24]. The associated uncertainties arise from various factors such as estimation through cloud top reflectance, thermal radiance, retrieval algorithm and infrequent satellite overpasses [25][26][27]. The contribution of each error source in the overall uncertainty depends on the retrieval algorithm and the spatio-temporal scale.…”

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confidence: 99%

Accounting for Uncertainties of the TRMM Satellite Estimates

2009

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Recent advances in the field of remote sensing have led to an increase in available rainfall data on a regional and global scale. Several NASA sponsored satellite missions provide valuable precipitation data. However, the advantages of the data are limited by complications related to the indirect nature of satellite estimates. This study intends to develop a stochastic model for uncertainty analysis of satellite rainfall fields through simulating error fields and imposing them over satellite estimates. In order to examine reliability and performance of the presented model, ensembles of satellite estimates are simulated for a large area across the North and South Carolina. The generated ensembles are then compared with original satellite estimates with respect to statistical properties and spatial dependencies. The results show that the model can be used to describe the uncertainties associated to TRMM multi-satellite precipitation estimates. The presented model is validated using random sub-samples of the observations based on the bootstrap technique. The results indicate that the model performs reasonably well with different numbers of available rain gauges.

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A theoretical framework for the sampling error variance for three-dimensional climate averages of ICOADS monthly ship data

Morrissey

Greene

2008

Theor Appl Climatol

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Uncertainty analysis of satellite rainfall algorithms over the tropical Pacific

Cited by 7 publications

References 14 publications

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Accounting for Uncertainties of the TRMM Satellite Estimates

A theoretical framework for the sampling error variance for three-dimensional climate averages of ICOADS monthly ship data

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