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Filipe Aires

Abstract

The analysis of the affect of weather and climate on human activities requires the construction of impact models that are able to describe the complex links between weather and socioeconomic data. In practice, one of the biggest challenges is the lack of data, because it is generally difficult to obtain time series that are long enough. As a consequence, derived impact models predict well the historical record but are unable to perform well on real forecasts. To avoid this data-limitation problem, it is possible to train the impact model over a large spatial domain by “pooling” data from multiple locations. This general impact model needs to be spatially corrected to take local conditions into account, however. This is particularly true, for example, in agriculture: it is not efficient to pool all of the spatial data into a single very general impact model, but it is also not efficient to develop one impact model for each spatial location. To solve these aggregation problems, mixed-effects (ME) models have been developed. They are based on the idea that each datum belongs to a particular group, and the ME model takes into account the particularities of each group. In this paper, ME models and, in particular, random-effects (RE) models are tested and are compared with more-traditional methods using a real-world application: the sales of salt for winter road deicing by public service vehicles. It is shown that the performance of RE models is higher than that of more-traditional regression models. The development of impact models should strongly benefit from the use of RE and ME models.

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Filipe Aires

Abstract

This study addresses in general terms the problem of the optimal combination of multiple observation datasets. Only satellite-retrieved geophysical parameter datasets are considered here (not the raw satellite observations). This study focuses on the terrestrial water cycle and presents methodologies to obtain a coherent dataset of four water cycle key components: precipitation, evapotranspiration, runoff, and terrestrial water storage. Various innovative “integration” methodologies are introduced: simple weighting (SW), constrained linear (CL), optimal interpolation (OI), and neural networks (NN). The term “integration” will be used here, not “assimilation,” as no model will be included in the data fusion process. A simple postprocessing filtering (PF) step can be used to impose the water cycle budget closure after the integration method. It is shown that this constraint actually improves the estimation of the water cycle components. The integration techniques are tested using real observation data over the Mississippi and Niger basins from satellite and in situ measurements. A Monte Carlo experiment with a synthetic uncertainty perturbation model is used to measure the ability of the SW, OI, and NN, with or without the PF step, to retrieve the four water cycle components. Once the PF closure constraint is added, the methodologies have equivalent accuracies. The need for these types of methodologies should increase in the future since multiple observation datasets are now available and the climate community needs to combine them into a unique, optimal, and coherent dataset of multiple parameters. A companion paper will test these methodologies on satellite observation datasets at the basin and global scales.

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Frédérique Chéruy and Filipe Aires

Abstract

This paper demonstrates how satellite observations of the cloudiness over a complex area such as the European Mediterranean area can be classified into distinct cloud regimes by application of a K-means clustering algorithm to pixel-level cloud properties. The study contrasts with previous approaches in the fact that the clustering is done on the cloud physical properties at the pixel level and not on statistics of these properties over a coarser grid. A method to choose the number of clusters is described. “Shallow cumulus,” “stratocumulus,” and “frontal” clusters are robustly identified, and associated environmental properties are described. The approach helps to refine the diagnosis of errors in model simulations. In addition to isolated classical errors of climate models (lack of midlevel clouds, overestimation of the cloud optical thickness, and underestimation of the stratocumulus) and a dramatic underestimation of the shallow cumulus clouds over land, an underestimation of the boundary layer depth is detected for some regimes as well as an incorrect stratification for the shallow cumulus. The clustering on the individual cloud properties classifies them in a much more homogeneous way than the clustering on the statistics of the cloud properties; the use of individual cloud properties at the pixel level produced by climate models activating simulators combined with subgrid-scale sampling procedures may be considered as an alternative to the use of the statistical products for the evaluation of models. The approach can also be applied to a high-resolution regional climate model.

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Jordane A. Mathieu and Filipe Aires

Abstract

Statistical meteorological impact models are intended to represent the impact of weather on socioeconomic activities, using a statistical approach. The calibration of such models is difficult because relationships are complex and historical records are limited. Often, such models succeed in reproducing past data but perform poorly on unseen new data (a problem known as overfitting). This difficulty emphasizes the need for regularization techniques and reliable assessment of the model quality. This study illustrates, in a general way, how to extract pertinent information from weather data and exploit it in impact models that are designed to help decision-making. For a given socioeconomic activity, this type of impact model can be used to 1) study its sensitivity to weather anomalies (e.g., corn sensitivity to water stress), 2) perform seasonal forecasting (yield forecasting) for it, and 3) quantify the longer-term (several decades) impact of weather on it. The size of the training database can be increased by pooling data from various locations, but this requires statistical models that are able to use the localization information—for example, mixed-effect (ME) models. Linear, neural-network, and ME models are compared, using a real-world application: corn-yield forecasting over the United States. Many challenges faced in this paper may be encountered in many weather-impact analyses: these results show that much care is required when using space–time data because they are often highly spatially correlated. In addition, the forecast quality is strongly influenced by the training spatial scale. For the application that is described herein, learning at the state scale is a good trade-off: it is specific to local conditions while keeping enough data for the calibration.

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Filipe Aires, William B. Rossow, and Alain Chédin

Abstract

The Independent Component Analysis (ICA) is a recently developed technique for component extraction. This new method requires the statistical independence of the extracted components—a stronger constraint that uses higher-order statistics—instead of the classical decorrelation (in the sense of “no correlation”), which is a weaker constraint that uses only second-order statistics. This technique has been used recently for the analysis of geophysical time series with the goal of investigating the causes of variability in observed data (i.e., exploratory approach). The authors demonstrate with a data simulation experiment that, if initialized with a Principal Component Analysis (PCA), the ICA performs a rotation of the classical PCA (or EOF) solution. This experiment is conducted using a synthetic dataset, where the correct answer is known, to more clearly illustrate and understand the behavior of the more familiar PCA and less familiar ICA. This rotation uses no localization criterion like other rotation techniques; only the generalization of decorrelation into full statistical independence is used. This rotation of the PCA solution seems to be able to avoid the tendency of PCA to mix several components, even when the signal is just their linear sum.

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Dinh Thi Lan Anh and Filipe Aires

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River discharge (RD) estimates are necessary for many applications, including water management, flood risk, and water cycle studies. Satellite-derived long-term GIEMS-D3 surface water extent (SWE) maps and HydroSHEDS data, at 90-m resolution, are here used to estimate several hydrological quantities at a monthly time scale over a few selected locations within the Amazon basin. Two methods are first presented to derive the water level (WL): the “hypsometric curve” and the “histogram cutoff” approaches at an 18 km × 18 km resolution. The obtained WL values are interpolated over the whole water mask using a bilinear interpolation. The two methods give similar results and validation with altimetry is satisfactory, with a correlation ranging from 0.72 to 0.89 in the seven considered stations over three rivers (i.e., Wingu, Negro, and Solimoes Rivers). River width (RW) and water volume change (WVC) are also estimated. WVC is evaluated with GRACE total water storage change, and correlations range from 0.77 to 0.88. A neural network (NN) statistical model is then used to estimate the RD based on four predictors (SWE, WL, WVC, and RW) and on in situ RD measurements. Results compare well to in situ measurements with a correlation of about 0.97 for the raw data (and 0.84 for the anomalies). The presented methodologies show the potential of historical satellite data (the combination of SWE with topography) to help estimate RD. Our study focuses here on a large river in the Amazon basin at a monthly scale; additional analyses would be required for other rivers, including smaller ones, in different environments, and at higher temporal scale.

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Filipe Aires, Fabrice Papa, and Catherine Prigent

Abstract

A climatology of wetlands has been derived at a low spatial resolution (0.25° × 0.25° equal-area grid) over a 15-yr period by combining visible and near-infrared satellite observations and passive and active microwaves. The objective of this study is to develop a downscaling technique able to retrieve wetland estimations at a higher spatial resolution (about 500 m). The proposed method uses an image-processing technique applied to synthetic aperture radar (SAR) information about the low and high wetland season. This method is tested over the densely vegetated basin of the Amazon. The downscaling results are satisfactory since they respect the spatial hydrological features of the SAR data and the temporal evolution of the low-resolution wetland estimates. A new long-term and high-resolution wetland dataset has been generated for 1993–2007 for the Amazon basin. This dataset represents a new and unprecedented source of information for climate and land surface modeling of the Amazon and for the definition of future hydrology-oriented satellite missions such as Surface Water and Ocean Topography (SWOT).

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Catherine Prigent, Filipe Aires, and William B. Rossow

Microwave land surface emissivities have been calculated over the globe for ~10 yr between 19 and 85 GHz at 53° incidence angle for both orthogonal polarizations, using satellite observations from the Special Sensor Microwave Imager (SSM/I). Ancillary data (IR satellite observations and meteorological reanalysis) help remove the contribution from the atmosphere, clouds, and rain from the measured satellite signal and separate surface temperature from emissivity variations. The method to calculate the emissivity is general and can be applied to other sensors. The monthly mean emissivities are available for the community, with a 0.25° × 0.25° spatial resolution.

The emissivities are sensitive to variations of the vegetation density, the soil moisture, the presence of standing water at the surface, or the snow behavior, and can help characterize the land surface properties.

These emissivities (not illustrated in this paper) also allow for improved atmospheric retrieval over land and can help evaluate land surface emissivity models at global scales.

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Yonghua Chen, Filipe Aires, Jennifer A. Francis, and James R. Miller

Abstract

A neural network technique is used to quantify relationships involved in cloud–radiation feedbacks based on observations from the Surface Heat Budget of the Arctic (SHEBA) project. Sensitivities of longwave cloud forcing (CFL) to cloud parameters indicate that a bimodal distribution pattern dominates the histogram of each sensitivity. Although the mean states of the relationships agree well with those derived in a previous study, they do not often exist in reality. The sensitivity of CFL to cloud cover increases as the cloudiness increases with a range of 0.1–0.9 W m−2 %−1. There is a saturation effect of liquid water path (LWP) on CFL. The highest sensitivity of CFL to LWP corresponds to clouds with low LWP, and sensitivity decreases as LWP increases. The sensitivity of CFL to cloud-base height (CBH) depends on whether the clouds are below or above an inversion layer. The relationship is negative for clouds higher than 0.8 km at the SHEBA site. The strongest positive relationship corresponds to clouds with low CBH. The dominant mode of the sensitivity of CFL to cloud-base temperature (CBT) is near zero and corresponds to warm clouds with base temperatures higher than −9°C. The low and high sensitivity regimes correspond to the summer and winter seasons, respectively, especially for LWP and CBT. Overall, the neural network technique is able to separate two distinct regimes of clouds that correspond to different sensitivities; that is, it captures the nonlinear behavior in the relationships. This study demonstrates a new method for evaluating nonlinear relationships between climate variables. It could also be used as an effective tool for evaluating feedback processes in climate models.

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Filipe Aires, Francis Marquisseau, Catherine Prigent, and Geneviève Sèze

Abstract

A statistical cloud classification and cloud mask algorithm is developed based on Advanced Microwave Sounding Unit (AMSU-A and -B) microwave (MW) observations. The visible and infrared data from the Meteosat Third Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI) are used to train the microwave classifier. The goal of the MW algorithms is not to fully reproduce this MSG-SEVIRI cloud classification, as the MW observations do not have enough information on clouds to reach this level of precision. The objective is instead to obtain a stand-alone MW cloud mask and classification algorithm that can be used efficiently in forthcoming retrieval schemes of surface or atmospheric parameters from microwave satellite observations. This is an important tool over both ocean and land since the assimilation of the MW observations in the operational centers is independent from the other satellite observations.

Clear sky and low, medium, and opaque–high clouds can be retrieved over ocean and land at a confidence level of more than 80%. An information content analysis shows that AMSU-B provides significant information over both land and ocean, especially for the classification of medium and high clouds, whereas AMSU-A is more efficient over ocean when discriminating clear situations and low clouds.

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