420 research outputs found
Variational Downscaling, Fusion and Assimilation of Hydrometeorological States via Regularized Estimation
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
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 proper
regularization, which are constraints consistent with the degree of smoothness
and probabilistic structure of the underlying state. We review relevant
regularization methods in derivative space and extend classic formulations of
the aforementioned problems with particular emphasis on hydrologic and
atmospheric applications. Informed by the statistical characteristics of the
state variable of interest, the central results of the paper suggest that
proper regularization can lead to a more accurate and stable recovery of the
true state and hence more skillful forecasts. In particular, using the Tikhonov
and Huber regularization in the derivative space, the promise of the proposed
framework is demonstrated in static downscaling and fusion of synthetic
multi-sensor precipitation data, while a data assimilation numerical experiment
is presented using the heat equation in a variational setting
Transport on river networks: A dynamical approach
This study is motivated by problems related to environmental transport on
river networks. We establish statistical properties of a flow along a directed
branching network and suggest its compact parameterization. The downstream
network transport is treated as a particular case of nearest-neighbor
hierarchical aggregation with respect to the metric induced by the branching
structure of the river network. We describe the static geometric structure of a
drainage network by a tree, referred to as the static tree, and introduce an
associated dynamic tree that describes the transport along the static tree. It
is well known that the static branching structure of river networks can be
described by self-similar trees (SSTs); we demonstrate that the corresponding
dynamic trees are also self-similar. We report an unexpected phase transition
in the dynamics of three river networks, one from California and two from
Italy, demonstrate the universal features of this transition, and seek to
interpret it in hydrological terms.Comment: 38 pages, 15 figure
Shrunken Locally Linear Embedding for Passive Microwave Retrieval of Precipitation
This paper introduces a new Bayesian approach to the inverse problem of
passive microwave rainfall retrieval. The proposed methodology relies on a
regularization technique and makes use of two joint dictionaries of
coincidental rainfall profiles and their corresponding upwelling spectral
radiative fluxes. A sequential detection-estimation strategy is adopted, which
basically assumes that similar rainfall intensity values and their spectral
radiances live close to some sufficiently smooth manifolds with analogous local
geometry. The detection step employs a nearest neighborhood classification
rule, while the estimation scheme is equipped with a constrained shrinkage
estimator to ensure stability of retrieval and some physical consistency. The
algorithm is examined using coincidental observations of the active
precipitation radar (PR) and passive microwave imager (TMI) on board the
Tropical Rainfall Measuring Mission (TRMM) satellite. We present promising
results of instantaneous rainfall retrieval for some tropical storms and
mesoscale convective systems over ocean, land, and coastal zones. We provide
evidence that the algorithm is capable of properly capturing different storm
morphologies including high intensity rain-cells and trailing light rainfall,
especially over land and coastal areas. The algorithm is also validated at an
annual scale for calendar year 2013 versus the standard (version 7) radar
(2A25) and radiometer (2A12) rainfall products of the TRMM satellite
Rotated Spectral Principal Component Analysis (rsPCA) for Identifying Dynamical Modes of Variability in Climate Systems.
Spectral PCA (sPCA), in contrast to classical PCA, offers the advantage of identifying organized spatiotemporal patterns within specific frequency bands and extracting dynamical modes. However, the unavoidable trade-off between frequency resolution and robustness of the PCs leads to high sensitivity to noise and overfitting, which limits the interpretation of the sPCA results. We propose herein a simple nonparametric implementation of sPCA using the continuous analytic Morlet wavelet as a robust estimator of the cross-spectral matrices with good frequency resolution. To improve the interpretability of the results, especially when several modes of similar amplitude exist within the same frequency band, we propose a rotation of the complex-valued eigenvectors to optimize their spatial regularity (smoothness). The developed method, called rotated spectral PCA (rsPCA), is tested on synthetic data simulating propagating waves and shows impressive performance even with high levels of noise in the data. Applied to global historical geopotential height (GPH) and sea surface temperature (SST) daily time series, the method accurately captures patterns of atmospheric Rossby waves at high frequencies (3-60-day periods) in both GPH and SST and El Niño-Southern Oscillation (ENSO) at low frequencies (2-7-yr periodicity) in SST. At high frequencies the rsPCA successfully unmixes the identified waves, revealing spatially coherent patterns with robust propagation dynamics
Variational Data Assimilation via Sparse Regularization
This paper studies the role of sparse regularization in a properly chosen
basis for variational data assimilation (VDA) problems. Specifically, it
focuses on data assimilation of noisy and down-sampled observations while the
state variable of interest exhibits sparsity in the real or transformed domain.
We show that in the presence of sparsity, the -norm regularization
produces more accurate and stable solutions than the classic data assimilation
methods. To motivate further developments of the proposed methodology,
assimilation experiments are conducted in the wavelet and spectral domain using
the linear advection-diffusion equation
Network robustness assessed within a dual connectivity perspective
Network robustness against attacks has been widely studied in fields as
diverse as the Internet, power grids and human societies. Typically, in these
studies, robustness is assessed only in terms of the connectivity of the nodes
unaffected by the attack. Here we put forward the idea that the connectivity of
the affected nodes can play a crucial role in properly evaluating the overall
network robustness and its future recovery from the attack. Specifically, we
propose a dual perspective approach wherein at any instant in the network
evolution under attack, two distinct networks are defined: (i) the Active
Network (AN) composed of the unaffected nodes and (ii) the Idle Network (IN)
composed of the affected nodes. The proposed robustness metric considers both
the efficiency of destroying the AN and the efficiency of building-up the IN.
We show, via analysis of both prototype networks and real world data, that
trade-offs between the efficiency of Active and Idle network dynamics give rise
to surprising crossovers and re-ranking of different attack strategies,
pointing to significant implications for decision making
The influence of migrating bed forms on the velocity-intermittency structure of turbulent flow over a gravel bed.
Modeling turbulent flows at high Reynolds number requires solving simplified variants of the Navier-Stokes equations. The methods used to close the resulting Reynolds-averaged, or eddy simulation equations usually follow classical theory and, at small enough scales, postulate universal scaling for turbulence that is independent of the velocity itself. This may not be the best way to conceptualize geophysical turbulence. Turbulent intermittency may be defined in terms of the local “roughness” of the velocity signal as measured by pointwise Hölder exponents. This study investigates the joint velocity-intermittency structure of flow over a gravel-bed surface with migrating bed forms. We report clear velocity-intermittency dependence and quantify its nature above the moving bed form profile. Our results imply differences in energy transfer close to bed forms at shorter wavelengths than those forced directly. Hence, progress in modeling flows of geophysical relevance may require a reconsideration of the principles on which turbulence closures are based
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