Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

Abstract

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.

Characterization of precipitation product errors across the United States using multiplicative triple collocation

“…Several studies investigate and model the uncertainties in remotely-sensed precipitation estimates; however, they all depend on assuming the ground-based (gauge and/or radar) observations or models representing the zero-error precipitation (Krajewski et al (2000); McCollum et al (2002); Ebert et al (2007); Su et al (2008); Sapiano and Arkin (2009) ;Tian et al (2009) ;Vila et al (2009) ;Anagnostou et al (2010); Stampoulis and Anagnostou (2012); Habib et al (2012); Kirstetter et al (2012Kirstetter et al ( , 2013; Chen et al (2013); Alemohammad et al (2014); Maggioni et al (2014); Seyyedi (2014); Tang et al (2014); Salio et al (2015); Prat and Nelson (2015); Alemohammad et al (2015); Gebregiorgis and Hossain (2015); among others).…”

Performance Assessment of Multi-Source Weighted-Ensemble Precipitation (MSWEP) Product over India

“…For example, Alemohammad et al [27] used an ensemble-based Bayesian method for the characterization of uncertainties pertaining to satellite-based precipitation retrieval for land data assimilation over a part of central CONUS; Aghakouchak et al [28] studied the feasibility of using the copula-based method to model the uncertainty of multisensory-based rainfall estimates; Tian et al [29] studied the suitability of the additive error model and multiplicative error model for uncertainty representation of satellite precipitation products; Seyyedi [30] performed an uncertainty assessment of precipitation estimates for hydrological modeling; and Maggioni et al [31] proposed a framework for precipitation uncertainties from satellite precipitation to provide global estimates of errors at temporal resolution.…”

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

“…One possible reason is that the merged TRMM 3B42V6 precipitation flux over the HRB was underestimated; some evaluations of the TRMM precipitation over the HRB have proved the underestimation [55,56]. The accumulated volumes of 6 h remote sensing precipitation from 00:00 UTC on 21 June 2008, based on the TRMM and FY-2D satellite-retrieved are 0.75 km 3 and 0.86 km 3 , respectively, over the 1st domain (60 × 60 × 625 km 2 ). The accumulated volumes of the simulated 48 h precipitation from 00:00 UTC on 21 June 2008, based on the TRMM assimilation and FY-2D assimilations are 1.63 km 3 and 1.75 km 3 , respectively, over the 2nd domain (130 × 130 × 25 km 2 ).…”

“…The accumulated volumes of 6 h remote sensing precipitation from 00:00 UTC on 21 June 2008, based on the TRMM and FY-2D satellite-retrieved are 0.75 km 3 and 0.86 km 3 , respectively, over the 1st domain (60 × 60 × 625 km 2 ). The accumulated volumes of the simulated 48 h precipitation from 00:00 UTC on 21 June 2008, based on the TRMM assimilation and FY-2D assimilations are 1.63 km 3 and 1.75 km 3 , respectively, over the 2nd domain (130 × 130 × 25 km 2 ). Compared with the ratio of the remote sensing precipitation between the FY-2D and TRMM satellite-retrieved data, the ratio of the simulated precipitation between the FY-2D and TRMM assimilations is smaller; however, the accumulated precipitation flux difference cannot be ignored.…”

“…However, it is currently difficult to develop an accurate precipitation product with high spatiotemporal resolution at the watershed scale for complex terrains using solely the traditional ground-based observations, remote sensing products, or regional climate modeling [3,4].…”