Search Results

You are looking at 1 - 4 of 4 items for :

  • Author or Editor: Luis Gustavo G. de Goncalves x
  • Refine by Access: All Content x
Clear All Modify Search
Daniel A. Vila, Luis Gustavo G. de Goncalves, David L. Toll, and Jose Roberto Rozante

Abstract

This paper describes a comprehensive assessment of a new high-resolution, gauge–satellite-based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes to get the lowest bias when compared with the observed values (rain gauges). Intercomparisons and cross-validation tests have been carried out between independent rain gauges and different merging techniques. This validation process was done for the control algorithm [Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis real-time algorithm] and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite Precipitation Analysis, research version; and the combined scheme proposed in this paper. These methodologies were tested for different months belonging to different seasons and for different network densities. All compared, merging schemes produce better results than the control algorithm; however, when finer temporal (daily) and spatial scale (regional networks) gauge datasets are included in the analysis, the improvement is remarkable. The combined scheme consistently presents the best performance among the five techniques tested in this paper. This is also true when a degraded daily gauge network is used instead of a full dataset. This technique appears to be a suitable tool to produce real-time, high-resolution, gauge- and satellite-based analyses of daily precipitation over land in regional domains.

Full access
José Roberto Rozante, Demerval Soares Moreira, Luis Gustavo G. de Goncalves, and Daniel A. Vila

Abstract

The measure of atmospheric model performance is highly dependent on the quality of the observations used in the evaluation process. In the particular case of operational forecast centers, large-scale datasets must be made available in a timely manner for continuous assessment of model results. Numerical models and surface observations usually work at distinct spatial scales (i.e., areal average in a regular grid versus point measurements), making direct comparison difficult. Alternatively, interpolation methods are employed for mapping observational data to regular grids and vice versa. A new technique (hereafter called MERGE) to combine Tropical Rainfall Measuring Mission (TRMM) satellite precipitation estimates with surface observations over the South American continent is proposed and its performance is evaluated for the 2007 summer and winter seasons. Two different approaches for the evaluation of the performance of this product against observations were tested: a cross-validation subsampling of the entire continent and another subsampling of only areas with sparse observations. Results show that over areas with a high density of observations, the MERGE technique’s performance is equivalent to that of simply averaging the stations within the grid boxes. However, over areas with sparse observations, MERGE shows superior results.

Full access
Luis Gustavo G. de Goncalves, William J. Shuttleworth, Daniel Vila, Eliane Larroza, Marcus J. Bottino, Dirceu L. Herdies, Jose A. Aravequia, Joao G. Z. De Mattos, David L. Toll, Matthew Rodell, and Paul Houser

Abstract

The definition and derivation of a 5-yr, 0.125°, 3-hourly atmospheric forcing dataset that is appropriate for use in a Land Data Assimilation System operating across South America is described. Because surface observations are limited in this region, many of the variables were taken from the South American Regional Reanalysis; however, remotely sensed data were merged with surface observations to calculate the precipitation and downward shortwave radiation fields. The quality of this dataset was evaluated against the surface observations available. There are regional differences in the biases for all variables in the dataset, with volumetric biases in precipitation of the order 0–1 mm day−1 and RMSE of 5–15 mm day−1, biases in surface solar radiation of the order 10 W m−2 and RMSE of 20 W m−2, positive biases in temperature typically between 0 and 4 K depending on the region, and positive biases in specific humidity around 2–3 g kg−1 in tropical regions and negative biases around 1–2 g kg−1 farther south.

Full access
Andrea Ucker Timm, Débora R. Roberti, Nereu Augusto Streck, Luis Gustavo G. de Gonçalves, Otávio Costa Acevedo, Osvaldo L. L. Moraes, Virnei S. Moreira, Gervásio Annes Degrazia, Mitja Ferlan, and David L. Toll

Abstract

During approximately 80% of its growing season, lowland flooded irrigated rice ecosystems in southern Brazil are kept within a 5–10-cm water layer. These anaerobic conditions have an influence on the partitioning of the energy and water balance components. Furthermore, this cropping system differs substantially from any other upland nonirrigated or irrigated crop ecosystems. In this study, daily, seasonal, and annual dynamics of the energy and water balance components were analyzed over a paddy rice farm in a subtropical location in southern Brazil using eddy covariance measurements. In this region, rice is grown once a year in low wetlands while the ground is kept fallow during the remaining of the year. Results show that the energy budget residual corresponded to up to 20% of the net radiation during the rice-growing season and around 10% for the remainder of the year (fallow). The energy and water balance analysis also showed that because of the high water table in the region, soil was near saturation most of the time, and latent heat flux dominated over sensible heat flux by up to one order of magnitude in some cases. The estimate of evapotranspiration ET using the crop coefficient multiplied by the reference evapotranspiration K cETo and the Penman–Monteith equation ETPM, describing the canopy resistance through leaf area index (LAI) obtained by remote sensing, represent well the measured evapotranspiration, mainly in the fallow periods. Therefore, using a specific crop parameter like LAI and crop height can be an easy and interesting alternative to estimate ET in vegetated lowland areas.

Full access