Verification of Quantitative Precipitation Forecasts via Stochastic Downscaling

Brussolo, Elisa; Hardenberg, Jost von; Ferraris, Luca; Rebora, Nicola; Provenzale, A.

doi:10.1175/2008jhm994.1

Cited by 17 publications

(8 citation statements)

References 33 publications

(34 reference statements)

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“…Traditionally, there are three mechanisms for measuring or observing precipitation: rain gauges, weather radar, and satellite-based sensors [12][13][14]. Rain gauge networks remain the most common method for measuring precipitation [15][16][17] due to their higher accuracy in representing rainfall at their respective locations [18,19] and longer recording period for investigating long-term rainfall runoff processes [20]. Because a rain gauge is a point measurement for precipitation, representing rainfall spatial variability must be affected by the density and distribution of rain gauge networks.…”

Section: Introductionmentioning

confidence: 99%

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

et al. 2018

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Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.

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Section: Introductionmentioning

confidence: 99%

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

et al. 2018

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“…The critical role of high-resolution gridded rainfall data sets for hydrological simulations has led to the development of several rainfall disaggregation algorithms (e.g., Brussolo et al, 2008;Ferraris et al, 2003;Fowler et al, 2007;Frei et al, 2006;Maraun et al, 2010;Ning et al, 2011;Park, 2013;Rahman et al, 2009;Ramírez et al, 2006;Tao and Barros, 2010). The main assumption for some recently developed downscaling methods for satellite-based products is the relationship between spatial variability of rainfall and environmental factors such as topography and land surface conditions.…”

Section: Introductionmentioning

confidence: 99%

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

Seyyedi

Anagnostou

Beighley

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Deriving flood hazard maps for ungauged basins typically requires simulating a long record of annual maximum discharges. To improve this approach, precipitation from global reanalysis systems must be downscaled to a spatial and temporal resolution applicable for flood modeling. This study evaluates such downscaling and error correction approaches for improving hydrologic applications using a combination of NASA's Global Land Data Assimilation System (GLDAS) precipitation data set and a higher resolution multi-satellite precipitation product (TRMM). The study focuses on 437 flood-inducing storm events that occurred over a period of ten years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) in the Susquehanna River basin located in the northeastern United States. A validation strategy was devised for assessing error metrics in rainfall and simulated runoff as function of basin area, storm severity, and season. The WSR-88D gauge-adjusted radar-rainfall (stage IV) product was used as the reference rainfall data set, while runoff simulations forced with the stage IV precipitation data set were considered as the runoff reference. Results show that the generated rainfall ensembles from the downscaled reanalysis product encapsulate the reference rainfall. The statistical analysis consists of frequency and quantile plots plus mean relative error and root-mean-square error statistics. The results demonstrated improvements in the precipitation and runoff simulation error statistics of the satellite-driven downscaled reanalysis data set compared to the original reanalysis precipitation product. Results vary by season and less by basin scale. In the fall season specifically, the downscaled product has 3 times lower mean relative error than the original product; this ratio increases to 4 times for the simulated runoff values. The proposed downscaling scheme is modular in design and can be applied on any gridded satellite and reanalysis data set.

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“…to scalar values and has been used in many hydrological and meteorological applications (Hamill 2001;Candille and Talagrand 2005;Aligo et al 2007;Brussolo et al 2008;Clark et al 2009;Gaborit et al 2013;Duda et al 2014). In this technique a set of bins is defined by distributing the ensemble replicates on the real axis.…”

Section: B Rank Histogrammentioning

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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Verification of Quantitative Precipitation Forecasts via Stochastic Downscaling

Cited by 17 publications

References 33 publications

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

Satellite-driven downscaling of global reanalysis precipitation products for hydrological applications

Quantifying Precipitation Uncertainty for Land Data Assimilation Applications

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