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  • Author or Editor: Daniel Vila x
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Ramon Campos Braga and Daniel Alejandro Vila

Abstract

This study focuses on the possible relationship between ice water path (IWP) retrievals using high-frequency channels (89 and 150 GHz) from the Advanced Microwave Sounding Unit-B and Moisture Humidity Sounder sensors (NOAA-16NOAA-19) and the life cycle stage of convective clouds. In the first part of this study, the relationship between IWP and the cloud area expansion rate is analyzed using the 235-K isotherm from Geostationary Operational Environmental Satellite-12 (GOES-12) thermal infrared images (10.7 μm). Next, the relationships between cloud convective fraction, rain rates (from ground radar), and cloud life cycle are analyzed. The selected area and time period coincide with the research activities of the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the Global Precipitation Measurement (CHUVA)–Geostationary Lightning Mapper (GLM) project at São José dos Campos, Brazil. The results show that 84% of precipitating clouds contain ice, according to the Microwave Surface and Precipitation Products System (MSPPS) algorithm. Convective systems in the intensifying stage (when the area is expanding and the minimum temperature is decreasing) tend to have larger IWPs than systems in the dissipating stage. Larger rain rates and convective fractions are also observed from radar retrievals in the early stage of convection compared with mature systems. Hydrometeor retrieval data from polarimetric X-band radar suggest that particle effective diameter D e and IWP patterns inferred with the MSPPS algorithm could be used to determine the life cycle stage of a given convective system. Using this information, a new set of equations is evaluated for rainfall retrievals using D e and IWP from the current retrieval algorithm. This new approach outperforms the current algorithm in the studied region.

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Daniel Vila, Cecilia Hernandez, Ralph Ferraro, and Hilawe Semunegus

Abstract

Global monthly rainfall estimates and other hydrological products have been produced from 1987 to the present using measurements from the Defense Meteorological Satellite Program (DMSP) series of the Special Sensor Microwave Imager (SSM/I). The aim of this paper is twofold: to present the recent efforts to improve the quality control (QC) of historical antenna temperature of the SSM/I sensor (1987–2008) and how this improvement impacts the different hydrological products that are generated at NOAA/National Environmental Satellite, Data, and Information Service (NESDIS). Beginning in 2005, the DMSP Special Sensor Microwave Imager/Sounder (SSMI/S) has been successfully operating on the F-16, F-17, and F-18 satellites. The second objective of this paper is focused on the application of SSMI/S channels to evaluate the performance of several hydrological products using the heritage of existing SSM/I algorithms and to develop an improved strategy to extend the SSM/I time series into the SSMI/S era, starting with data in 2009 for F-17. The continuity of hydrological products from SSM/I to SSMI/S has shown to be a valuable contribution for the precipitation and climate monitoring community but several sensor issues must be accounted for to meet this objective.

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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.

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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.

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