Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane

The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Sacchetrum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index (LA!), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300). The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies.

Cloud streets and land-water interactions in the Amazon

Cloud streets are common feature in the Amazon Basin. They form from the combination of the vertical trade wind stress and moist convection. Here, satellite imagery, data collected during the COBRA-PARA (Caxiuan Observations in the Biosphere, River and Atmosphere of Para) field campaign, and high resolution modeling are used to understand the streets` formation and behavior. The observations show that the streets have an aspect ratio of about 3.5 and they reach their maximum activity around 15:00 UTC when the wind shear is weaker, and the convective boundary layer reaches its maximum height. The simulations reveal that the cloud streets onset is caused by the local circulations and convection produced at the interfaces between forest and rivers of the Amazon. The satellite data and modeling show that the large rivers anchor the cloud streets producing a quasi-stationary horizontal pattern. The streets are associated with horizontal roll vortices parallel to the mean flow that organizes the turbulence causing advection of latent heat flux towards the upward branches. The streets have multiple warm plumes that promote a connection between the rolls. These spatial patterns allow fundamental insights on the interpretation of the Amazon exchanges between surface and atmosphere with important consequences for the climate change understanding.

The role of convective parameterization in the simulation of a cyclone over the South Atlantic

Numerical simulations are carried out to examine the role of the Kuo and Kain-Fritsch (KF) cumulus parameterization schemes and dry dynamics on a cyclone development, in a weak baroclinic atmosphere, over subtropical South Atlantic Ocean. The initial phase of the cyclone development is investigated with a coarse horizontal mesh (75 km) and when the cyclone reaches the mature stage two different horizontal resolutions are used (75 and 25 km). The best performance simulation for the cyclone initial phase occurs when the Kuo convective scheme is applied, and this may be attributed to a greater diabatic warming in the troposphere. On the other hand, the dry simulation is not capable of simulating the correct location and intensity of the cyclone in its initial phase. During the mature phase, a cyclone over deepening occurs in the Kuo scheme experiment associated with larger latent heat release in a deep vertical column. The presence of downdraft currents in the KF scheme, which acts to cool and dry the lower levels, is essential to stabilize the atmosphere and to reproduce the nearest observation cyclone deepening rate. The largest cyclone deepening is found in the Kuo scheme high resolution experiment. This suggests that the KF convective scheme is less sensitive to the horizontal grid resolution. It was also revealed that the diabatic processes are crucial to simulate the observed features of this marine cyclone over subtropical region.

Probing the Large Magellanic Cloud age gap at intermediate cluster masses

The Large Magellanic Cloud (LMC) has a rich star cluster system spanning a wide range of ages and masses. One striking feature of the LMC cluster system is the existence of an age gap between 3 and 10 Gyr. But this feature is not clearly seen among field stars. Three LMC fields containing relatively poor and sparse clusters whose integrated colours are consistent with those of intermediate-age simple stellar populations have been imaged in BVI with the Optical Imager (SOI) at the Southern Telescope for Astrophysical Research (SOAR). A total of six clusters, five of them with estimated initial masses M < 104 M(circle dot), were studied in these fields. Photometry was performed and colour-magnitude diagrams (CMDs) were built using standard point spread function fitting methods. The faintest stars measured reach V similar to 23. The CMD was cleaned from field contamination by making use of the three-dimensional colour and magnitude space available in order to select stars in excess relative to the field. A statistical CMD comparison method was developed for this purpose. The subtraction method has proven to be successful, yielding cleaned CMDs consistent with a simple stellar population. The intermediate-age candidates were found to be the oldest in our sample, with ages between 1 and 2 Gyr. The remaining clusters found in the SOAR/SOI have ages ranging from 100 to 200 Myr. Our analysis has conclusively shown that none of the relatively low-mass clusters studied by us belongs to the LMC age gap.

Bayesian inference for a skew-normal IRT model under the centred parameterization

Item response theory (IRT) comprises a set of statistical models which are useful in many fields, especially when there is interest in studying latent variables. These latent variables are directly considered in the Item Response Models (IRM) and they are usually called latent traits. A usual assumption for parameter estimation of the IRM, considering one group of examinees, is to assume that the latent traits are random variables which follow a standard normal distribution. However, many works suggest that this assumption does not apply in many cases. Furthermore, when this assumption does not hold, the parameter estimates tend to be biased and misleading inference can be obtained. Therefore, it is important to model the distribution of the latent traits properly. In this paper we present an alternative latent traits modeling based on the so-called skew-normal distribution; see Genton (2004). We used the centred parameterization, which was proposed by Azzalini (1985). This approach ensures the model identifiability as pointed out by Azevedo et al. (2009b). Also, a Metropolis Hastings within Gibbs sampling (MHWGS) algorithm was built for parameter estimation by using an augmented data approach. A simulation study was performed in order to assess the parameter recovery in the proposed model and the estimation method, and the effect of the asymmetry level of the latent traits distribution on the parameter estimation. Also, a comparison of our approach with other estimation methods (which consider the assumption of symmetric normality for the latent traits distribution) was considered. The results indicated that our proposed algorithm recovers properly all parameters. Specifically, the greater the asymmetry level, the better the performance of our approach compared with other approaches, mainly in the presence of small sample sizes (number of examinees). Furthermore, we analyzed a real data set which presents indication of asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of strong negative asymmetry of the latent traits distribution. (C) 2010 Elsevier B.V. All rights reserved.

Bias correction in a multivariate normal regression model with general parameterization

This paper derives the second-order biases Of maximum likelihood estimates from a multivariate normal model where the mean vector and the covariance matrix have parameters in common. We show that the second order bias can always be obtained by means of ordinary weighted least-squares regressions. We conduct simulation studies which indicate that the bias correction scheme yields nearly unbiased estimators. (C) 2009 Elsevier B.V. All rights reserved.