Space-time models for moving fields with an application to significant wave height fields

Ailliot, Pierre; Baxevani, Anastassia; Cuzol, Anne; Monbet, Valérie; Raillard, Nicolas

doi:10.1002/env.1061

Cited by 22 publications

(20 citation statements)

References 25 publications

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“…This indicates that precipitation is represented very smoothly, meaning many days with precipitation values below 10 mm and only a limited number of days with heavier rainfall. This agrees with the findings of Ailliot et al (2011). Such precipitation estimate behavior might be explained by the spatial scale of the input data used by ERA-Interim.…”

Section: B Product Rankingsupporting

confidence: 91%

“…Such precipitation estimate behavior might be explained by the spatial scale of the input data used by ERA-Interim. The weather model uses information at a synoptic scale, which does not capture small-scale and rapidly moving variations (e.g., storm cells) and, thus, they might be smoothed out (Ailliot et al 2011). This feature is extremely important when daily hydrological applications, such as flood forecasting, are calculated as the effect may propagate in time.…”

Section: B Product Rankingmentioning

confidence: 99%

See 1 more Smart Citation

Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

Thiemig

Rojas

Zambrano‐Bigiarini

et al. 2012

Journal of Hydrometeorology

292

245

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Six satellite-based rainfall estimates (SRFE)-namely, Climate Prediction Center (CPC) morphing technique (CMORPH), the Rainfall Estimation Algorithm, version 2 (RFE2.0), Tropical Rainfall Measuring Mission (TRMM) 3B42, Goddard profiling algorithm, version 6 (GPROF 6.0), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Global Satellite Mapping of Precipitation moving vector with Kalman filter (GSMap MVK), and one reanalysis product [the interim ECMWF Re-Analysis (ERA-Interim)]-were validated against 205 rain gauge stations over four African river basins (Zambezi, Volta, Juba-Shabelle, and Baro-Akobo). Validation focused on rainfall characteristics relevant to hydrological applications, such as annual catchment totals, spatial distribution patterns, seasonality, number of rainy days per year, and timing and volume of heavy rainfall events. Validation was done at three spatially aggregated levels: point-topixel, subcatchment, and river basin for the period 2003-06. Performance of satellite-based rainfall estimation (SRFE) was assessed using standard statistical methods and visual inspection. SRFE showed 1) accuracy in reproducing precipitation on a monthly basis during the dry season, 2) an ability to replicate bimodal precipitation patterns, 3) superior performance over the tropical wet and dry zone than over semiarid or mountainous regions, 4) increasing uncertainty in the estimation of higher-end percentiles of daily precipitation, 5) low accuracy in detecting heavy rainfall events over semiarid areas, 6) general underestimation of heavy rainfall events, and 7) overestimation of number of rainy days in the tropics. In respect to SRFE performance, GPROF 6.0 and GSMaP-MKV were the least accurate, and RFE 2.0 and TRMM 3B42 were the most accurate. These results allow discrimination between the available products and the reduction of potential errors caused by selecting a product that is not suitable for particular morphoclimatic conditions. For hydrometeorological applications, results support the use of a performance-based merged product that combines the strength of multiple SRFEs.

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Section: B Product Rankingsupporting

confidence: 91%

Section: B Product Rankingmentioning

confidence: 99%

Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

Thiemig

Rojas

Zambrano‐Bigiarini

et al. 2012

Journal of Hydrometeorology

292

245

View full text Add to dashboard Cite

show abstract

“…Data from field measurements can be temporally extensive but are always spatially sparse. There have been many new developments [86] that try to interpolate sparse spatial data to obtain a more complete regional view of physical processes [85]. In particular, such space-time models have been an active research topic in the turbulence field [87][88][89].…”

Section: Field Measurementmentioning

confidence: 99%

Modeling ocean wave propagation under sea ice covers

2015

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Operational ocean wave models need to work globally, yet current ocean wave models can only treat icecovered regions crudely. The purpose of this paper is to provide a brief overview of ice effects on wave propagation and different research methodology used in studying these effects. Based on its proximity to land or sea, sea ice can be classified as: landfast ice zone, shear zone, and the marginal ice zone. All ice covers attenuate wave energy. Only long swells can penetrate deep into an ice cover. Being closest to open water, wave propagation in the marginal ice zone is the most complex to model. The physical appearance of sea ice in the marginal ice zone varies. Grease ice, pancake ice, brash ice, floe aggregates, and continuous ice sheet may be found in this zone at different times and locations. These types of ice are formed under different thermal-mechanical forcing. There are three classic models that describe wave propagation through an idealized ice cover: mass loading, thin elastic plate, and viscous layer models. From physical arguments we may conjecture that mass loading model is suitable for disjoint aggregates of ice floes much smaller than the wavelength, thin elastic plate model is suitable for a continuous ice sheet, and the viscous layer model is suitable for grease ice. For different sea ice types we may need different wave ice interaction models. A recently proposed viscoelastic model is able to synthesize all three classic models into one. Under suitable limiting conditions it converges to the three previous models. The complete theoretical framework for evaluating wave propagation through various ice covers need to be implemented in the operational ocean wave models. In this review, we introduce the sea ice types, previous wave ice interaction models, wave attenuation mechanisms, The project was supported by the US Office of Naval Research (N00014-13-1-0294). the methods to calculate wave reflection and transmission between different ice covers, and the effect of ice floe breaking on shaping the sea ice morphology. Laboratory experiments, field measurements and numerical simulations supporting the fundamental research in wave-ice interaction models are discussed. We conclude with some outlook of future research needs in this field.

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“…The work of Ziegel () might be very useful in this direction. Physical‐based Constructions. Constructions based on dynamical models or moving averages have been proposed by Ailliot et al () and Schlather (). We are not aware of such extensions to the case

S^{2} \times double-struckR

, but certainly it would be worth studying.…”

Section: Research Problemsmentioning

confidence: 99%

Modeling Temporally Evolving and Spatially Globally Dependent Data

Porcu

Alegría

Furrer

2018

Int Statistical Rev

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Summary The last decades have seen an unprecedented increase in the availability of data sets that are inherently global and temporally evolving, from remotely sensed networks to climate model ensembles. This paper provides an overview of statistical modeling techniques for space–time processes, where space is the sphere representing our planet. In particular, we make a distintion between (a) second order‐based approaches and (b) practical approaches to modeling temporally evolving global processes. The former approaches are based on the specification of a class of space–time covariance functions, with space being the two‐dimensional sphere. The latter are based on explicit description of the dynamics of the space–time process, that is, by specifying its evolution as a function of its past history with added spatially dependent noise. We focus primarily on approach (a), for which the literature has been sparse. We provide new models of space–time covariance functions for random fields defined on spheres cross time. Practical approaches (b) are also discussed, with special emphasis on models built directly on the sphere, without projecting spherical coordinates onto the plane. We present a case study focused on the analysis of air pollution from the 2015 wildfires in Equatorial Asia, an event that was classified as the year's worst environmental disaster. The paper finishes with a list of the main theoretical and applied research problems in the area, where we expect the statistical community to engage over the next decade.

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Space-time models for moving fields with an application to significant wave height fields

Cited by 22 publications

References 25 publications

Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

Validation of Satellite-Based Precipitation Products over Sparsely Gauged African River Basins

Modeling ocean wave propagation under sea ice covers

Modeling Temporally Evolving and Spatially Globally Dependent Data

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