References of "Guiot, Joël"
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See detailParameterization of a process-based tree growth model : comparison of optimization, MCMC and particle filtering algorithms
Gaucherel, C.; Campillo, F.; Misson, L. et al

in Environmental Modelling & Software (2008), 23(10-11), 1280-1288

Finely tuned process-based tree-growth models are of considerable help in understanding the variations of biomass increments measured in the dendrochronological series. Using site and species parameters ... [more ▼]

Finely tuned process-based tree-growth models are of considerable help in understanding the variations of biomass increments measured in the dendrochronological series. Using site and species parameters, as well as daily climate variables, the MAIDEN model computes the water balance at ecosystem level and the daily increment of carbon storage in the stem through photosynthesis processes to reproduce the structure of the tree-ring series. In this paper, we use three techniques to calibrate this model with Pinus halepensis data sampled in the Mediterranean part of France: a standard optimization (PEST), Monte Carlo Markov Chains (MCMC) and Particle Filtering (PF). Contrary to PEST, which tries to find an optimum fit (giving the lowest error between observations and simulations), the principle of MCMC and PF is to walk, from a priori distributions, in the parameter space according to particular statistical rules to compute each parameter distribution. The PEST and MCMC calibrations of our dendrochronological series lead to rather similar adjustments between simulations and observations. PF and MCMC calibrations give different parameter distributions, showing how complementary are these methods, with a better fit for MCMC. Yet, independent validations over 11 independent meteorological years show a higher efficiency of the recent PF method over the others. [less ▲]

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See detailData-model comparison using fuzzy logic in paleoclimatology.
Guiot, Joël; Boreux, Jean-Jacques ULg; Braconnot, P. et al

in Climate Dynamics (1999), 15(8), 569-581

Until now, most paleoclimate model-data comparisons have been limited to simple statistical evaluation and simple map comparisons. We have applied a new method, based on fuzzy logic, to the comparison of ... [more ▼]

Until now, most paleoclimate model-data comparisons have been limited to simple statistical evaluation and simple map comparisons. We have applied a new method, based on fuzzy logic, to the comparison of 17 model simulations of the mid-Holocene (6 ka BP) climate with reconstruction of three bioclimatic parameters (mean temperature of the coldest month, MTCO, growing degree-days above 5 °C, GDD5, precipitation minus evapotranspiration, P−E) from pollen and lake-status data over Europe. With this method, no assumption is made about the distribution of the signal and on its error, and both the error bars related to data and to model simulations are taken into account. Data are taken at the drilling sites (not using a gridded interpolation of proxy data) and a varying domain size of comparison enables us to make the best common resolution between observed and simulated maps. For each parameter and each model, we compute a Hagaman distance which gives an objective measure of the goodness of fit between model and data. The results show that there is no systematic order for the three climatic parameters between models. None of the models is able to satisfactorily reproduce the three pollen-derived data. There is larger dispersion in the results for MTCO and P−E than for GDD5. There is also no systematic relationship between model resolution and the Hagaman distance, except for P−E. The more local character of P−E has little chance to be reproduced by a low resolution model, which can explain the inverse relationship between model resolution and Hagaman distance. The results also reveal that most of the models are better at predicting 6 ka climate than the modern climate. [less ▲]

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See detailRadial tree-growth modelling with fuzzy regression.
Boreux, Jean-Jacques ULg; Gadbin-Henry, C.; Guiot, Joël et al

in Canadian Journal of Forest Research = Journal Canadien de la Recherche Forestière (1998), 28(8), 1249-1260

A so-called fuzzy linear regression is used in dendroecology to model empirically tree growth as a function of a bioclimatic index representing the water stress, i.e., the ratio of actual ... [more ▼]

A so-called fuzzy linear regression is used in dendroecology to model empirically tree growth as a function of a bioclimatic index representing the water stress, i.e., the ratio of actual evapotranspiration to potential evapotranspiration. The response function predicts tree growth as (fuzzy) intervals, narrow in the domain where the bioclimatic index is most limiting and becoming progressively larger elsewhere. The method is tested with a population of Pinus pineaL. from the Provence region in France. It is shown that fuzzy linear regression gives results comparable with those obtained using a linear response function. The interval of credibility given by the fuzzy regression suggests that more precise expected growth is obtained for high water stress, which is typical of Mediterranean climate. Fuzzy linear regression can be also a method to test different hypotheses on several potential predictors when any further experimental approach is quite impossible as it is for trees in their natural environment. To sum up, fuzzy regression could be a first step before the construction of a kind of growth simulator adapted to different environments of a given species. In environmental sciences, the fuzzy response function thus appears to be an approach between the mechanistic and the statistical descriptive approaches. [less ▲]

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See detailA method of comparison of two close batches data: Application to analysis of fog formation causes
Boreux, Jean-Jacques ULg; Guiot, Joël

in Geophysical Research Letters (1993), 20(12), 1179-1182

Given suitable conditions of air temperature and humidity, the density of a fog and its microphysical properties depend mainly on the availability of cloud condensation nuclei (CCN) and their nature. Fogs ... [more ▼]

Given suitable conditions of air temperature and humidity, the density of a fog and its microphysical properties depend mainly on the availability of cloud condensation nuclei (CCN) and their nature. Fogs become particularly dense near certain industrial plants because of high concentration of hygroscopic combustion particles in the air. Their role in dense fog formation is estimated by comparing the local climates and CCN concentrations at two similar sites, the first being more subject to air pollution and dense fogs than the second. Orthogonal regression is applied to three meteorological variables (air temperature, relative humidity, wind speed) and CCN concentration. As we compare very close variables, bootstrap provides precise confidence intervals independent of Gaussian assumptions. Two sites are compared: they are located in the Meuse valley (Belgium) at a distance of about 15 km. We found that the local climate of the polluted site is not only colder and wetter but also richer in CCN that the control site. These results suggest interactions of natural and anthropogenic causes in dense fog formation at industrial site. This method is useful in various domains of geophysics when correlated time series have to be compared. [less ▲]

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See detailA fog forecasting method in a deeply embanked valley.
Boreux, Jean-Jacques ULg; Guiot, Joël

in Atmospheric Environment, Part A : General Topics (1992), 26(5), 759-764

This paper presents a statistical model used to forecast fog in the Meuse Valley in Belgium. The method is a bootstrap discriminant analysis using eight predictors: river surface temperature, air pressure ... [more ▼]

This paper presents a statistical model used to forecast fog in the Meuse Valley in Belgium. The method is a bootstrap discriminant analysis using eight predictors: river surface temperature, air pressure, air temperature at two elevations, wind speed and relative humidity at the same two locations. These data are measured from November 1989 to April 1990. Tests are done to determine the number of resampling needed for this data set and the optimum projection delay for prediction from the meteorological data. The best results are obtained for the prediction at 0700h UT using meteorological data at 0400 h UT. [less ▲]

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