Use of Scale Recursive Estimation for assimilation of precipitation data from TRMM (PR and TMI) and NEXRAD

Bocchiola, Daniele

doi:10.1016/j.advwatres.2007.05.012

Cited by 23 publications

(54 citation statements)

References 72 publications

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“…(1) above. The details of SSRC are also reported elsewhere (Bocchiola, 2007; and are not reported here. We investigated scaling of the seasonal cumulated precipitation against altitude for all the investigated rain gauges here (reported in ), but we found no significant dependence against altitude in any season.…”

Section: Downscaling Approachmentioning

confidence: 99%

“…The spatial downscaling approach (e.g. Bocchiola, 2007) is instead specific for the catchment and does not depend upon GCM, because it depicts the spatial variability of precipitation within the catchment area.…”

Section: Downscaling Approachmentioning

confidence: 99%

“…Bocchiola, 2007). Each layer in the tree represents a lattice, where the size of the cells (or nodes) is coincident with the resolution (or scale) associated to the samples of the rainfall field obtained with some measurement device(s), or otherwise estimated.…”

Section: Downscaling Approachmentioning

confidence: 99%

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Evaluation of future hydrological cycle under climate change scenarios in a mesoscale Alpine watershed of Italy

Groppelli

Soncini

Bocchiola

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. We investigate future (2045)(2046)(2047)(2048)(2049)(2050)(2051)(2052)(2053)(2054) hydrological cycle of the snow fed Oglio (≈1800 km 2 ) Alpine watershed in Northern Italy. A Stochastic Space Random Cascade (SSRC) approach is used to downscale future precipitation from three general circulation models, GCMs (PCM, CCSM3, and HadCM3) available within the IPCC's data base and chosen for this purpose based upon previous studies. We then downscale temperature output from the GCMs to obtain temperature fields for the area. We also consider a projected scenario based upon trends locally observed in former studies, LOC scenario. Then, we feed the downscaled fields to a minimal hydrological model to build future hydrological scenarios. We provide projected flow duration curves and selected flow descriptors, giving indication of expected modified (against control run for 1990-1999) regime of low flows and droughts and flood hazard, and thus evaluate modified peak floods regime through indexed flood. We then assess the degree of uncertainty, or spread, of the projected water resources scenarios by feeding the hydrological model with ensembles projections consistent with our deterministic (GCMs + LOC) scenarios, and we evaluate the significance of the projected flow variables against those observed in the control run. The climate scenarios from the adopted GCMs differ greatly from one another with respect to projected precipitation amount and temperature regimes, and so do the projected hydrological scenarios. A relatively good agreement is found upon prospective shrinkage and shorter duration of the seasonal snow cover due to increased temperature patterns, and upon prospective increase of hydrological losses, i.e. evapotranspiration, for the same reason. However, precipitation patterns are less consistent, because HadCM3 and PCM models project noticeably increased precipitation for 2045-2054, whereas CCSM3 provides decreased precipCorrespondence to: D. Bocchiola (daniele.bocchiola@polimi.it) itation patterns therein. The LOC scenario instead displays unchanged precipitation. The ensemble simulations indicate that several projected flow variables under the considered scenarios are significantly different from their control run counterparts, and also that snow cover seems to significantly decrease in duration and depth. The proposed hydrological scenarios eventually provide a what-if analysis, giving a broad view of the possible expected impacts of climate change within the Italian Alps, necessary to trigger the discussion about future adaptation strategies.

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Section: Downscaling Approachmentioning

confidence: 99%

Section: Downscaling Approachmentioning

confidence: 99%

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Evaluation of future hydrological cycle under climate change scenarios in a mesoscale Alpine watershed of Italy

Groppelli

Soncini

Bocchiola

et al. 2011

Nat. Hazards Earth Syst. Sci.

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“…The strong variability and intermittence of convective tropical rainfall constitute an adequate setting to study the scaling characteristics of rainfall in a wide range of spatio-temporal scales [19][20][21][22][23][24][25][26][27][28][29][30][31]. In particular, Ref.…”

Section: Statistical Scaling and Multiplicative Random Cascadesmentioning

confidence: 99%

Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy

Salas¹,

Poveda²,

Sánchez³

2017

Entropy

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Abstract:We study spatial scaling and complexity properties of Amazonian radar rainfall fields using the Beta-Lognormal Model (BL-Model) with the aim to characterize and model the process at a broad range of spatial scales. The Generalized Space q-Entropy Function (GSEF), an entropic measure defined as a continuous set of power laws covering a broad range of spatial scales, S q (λ) ∼ λ Ω(q) , is used as a tool to check the ability of the BL-Model to represent observed 2-D radar rainfall fields. In addition, we evaluate the effect of the amount of zeros, the variability of rainfall intensity, the number of bins used to estimate the probability mass function, and the record length on the GSFE estimation. Our results show that: (i) the BL-Model adequately represents the scaling properties of the q-entropy, S q , for Amazonian rainfall fields across a range of spatial scales λ from 2 km to 64 km; (ii) the q-entropy in rainfall fields can be characterized by a non-additivity value, q sat , at which rainfall reaches a maximum scaling exponent, Ω sat ; (iii) the maximum scaling exponent Ω sat is directly related to the amount of zeros in rainfall fields and is not sensitive to either the number of bins to estimate the probability mass function or the variability of rainfall intensity; and (iv) for small-samples, the GSEF of rainfall fields may incur in considerable bias. Finally, for synthetic 2-D rainfall fields from the BL-Model, we look for a connection between intermittency using a metric based on generalized Hurst exponents, M(q 1 , q 2 ), and the non-extensive order (q-order) of a system, Θ q , which relates to the GSEF. Our results do not exhibit evidence of such relationship.

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“…Furthermore, a few studies have addressed methodologies to optimally combine the products of the TRMM precipitation radar (PR) with the TRMM microwave imager (TMI) using Bayesian inversion and weighted least squares (WLS) approaches [e.g., Masunaga and Kummerow, 2005;Kummerow et al, 2010]. From another direction, Gaussian filtering methods on Markovian tree-like structures, the socalled scale recursive estimation (SRE), have been proposed to merge spaceborne and ground-based rainfall observations at multiple scales [e.g., Gorenburg et al, 2001;Tustison et al, 2003;Bocchiola, 2007;Van de Vyver and Roulin, 2009;Wang et al, 2011], see also Kumar [1999] for soil moisture applications. Recently, using the Gaussian-scale mixture probability model and an adaptive filtering approach, Ebtehaj and FoufoulaGeorgiou [2011a] proposed a fusion methodology in the wavelet domain to merge TRMM-PR and ground-based NEXRAD measurements, aiming to preserve the nonGaussian structure and local extremes of precipitation fields.…”

Section: Introductionmentioning

confidence: 99%

On variational downscaling, fusion, and assimilation of hydrometeorological states: A unified framework via regularization

Ebtehaj

Foufoula‐Georgiou

2013

Water Resour. Res.

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[1] Improved estimation of hydrometeorological states from down-sampled observations and background model forecasts in a noisy environment has been a subject of growing research in the past decades. Here we introduce a unified variational framework that ties together the problems of downscaling, data fusion, and data assimilation as ill-posed inverse problems. This framework seeks solutions beyond the classic least squares estimation paradigms by imposing a proper regularization, expressed as a constraint consistent with the degree of smoothness and/or probabilistic structure of the underlying state. We review relevant smoothing norm regularization methods in derivative space and extend classic formulations of the aforementioned problems with particular emphasis on land surface hydrometeorological applications. Our results demonstrate that proper regularization of downscaling, data fusion, and data assimilation problems can lead to more accurate and stable recovery of the underlying non-Gaussian state of interest with improved performance in capturing isolated and jump singularities. In particular, we show that the Huber regularization in the derivative space offers advantages, compared to the classic solution and the Tikhonov regularization, for spatial downscaling and fusion of non-Gaussian multisensor precipitation data. Furthermore, we explore the use of Huber regularization in a variational data assimilation experiment while the initial state of interest exhibits jump discontinuities and non-Gaussian probabilistic structure. To this end, we focus on the heat equation motivated by its fundamental application in the study of land surface heat and mass fluxes.Citation : Ebtehaj, A. M., and E. Foufoula-Georgiou (2013), On variational downscaling, fusion, and assimilation of hydrometeorological states: A unified framework via regularization, Water Resour.

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Use of Scale Recursive Estimation for assimilation of precipitation data from TRMM (PR and TMI) and NEXRAD

Cited by 23 publications

References 72 publications

Evaluation of future hydrological cycle under climate change scenarios in a mesoscale Alpine watershed of Italy

Evaluation of future hydrological cycle under climate change scenarios in a mesoscale Alpine watershed of Italy

Testing the Beta-Lognormal Model in Amazonian Rainfall Fields Using the Generalized Space q-Entropy

On variational downscaling, fusion, and assimilation of hydrometeorological states: A unified framework via regularization

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