Daniel Vila

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimates over Continental South America

Thispaperdescribesacomprehensiveassessmentofanewhigh-resolution,gauge‐satellite-basedanalysisof daily precipitation over continental South America during 2004. This methodology is based on a combination ofadditiveandmultiplicativebiascorrectionschemestogetthelowestbiaswhencomparedwiththeobserved 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 [TropicalRainfallMeasuringMission(TRMM)MultisatellitePrecipitationAnalysisreal-timealgorithm]and five different merging schemes: additive bias correction; ratio bias correction; TRMM Multisatellite PrecipitationAnalysis,researchversion;andthecombinedschemeproposedinthispaper.Thesemethodologieswere 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 appearstobe a suitabletoolto producereal-time, high-resolution,gauge-and satellite-based analysesofdaily precipitation over land in regional domains.

Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) Using Satellite Infrared Imagery: Methodology and Validation

Abstract The purpose of this study is to develop and validate an algorithm for tracking and forecasting radiative and morphological characteristics of mesoscale convective systems (MCSs) through their entire life cycles using geostationary satellite thermal channel information (10.8 μm). The main features of this system are the following: 1) a cloud cluster detection method based on a threshold temperature (235 K), 2) a tracking technique based on MCS overlapping areas in successive images, and 3) a forecast module based on the evolution of each particular MCS in previous steps. This feature is based on the MCS’s possible displacement (considering the center of the mass position of the cloud cluster in previous time steps) and its size evolution. Statistical information about MCS evolution during the Wet Season Atmospheric Mesoscale Campaign (WETAMC) of the Large-Scale Biosphere–Atmosphere Experiment in Amazonia (LBA) was used to obtain area expansion mean rates for different MCSs according to their lifet...

Combining TRMM and Surface Observations of Precipitation: Technique and Validation over South America

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.

The Chuva Project: How Does Convection Vary across Brazil?

CHUVA, meaning “rain” in Portuguese, is the acronym for the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (GPM). The CHUVA project has conducted five field campaigns; the sixth and last campaign will be held in Manaus in 2014. The primary scientific objective of CHUVA is to contribute to the understanding of cloud processes, which represent one of the least understood components of the weather and climate system. The five CHUVA campaigns were designed to investigate specific tropical weather regimes. The first two experiments, in Alcantara and Fortaleza in northeastern Brazil, focused on warm clouds. The third campaign, which was conducted in Belem, was dedicated to tropical squall lines that often form along the sea-breeze front. The fourth campaign was in the Vale do Paraiba of southeastern Brazil, which is a region with intense lightning activity. In addition to contributing to the understanding of clo...

ACRIDICON–CHUVA Campaign: Studying Tropical Deep Convective Clouds and Precipitation over Amazonia Using the New German Research Aircraft HALO

AbstractBetween 1 September and 4 October 2014, a combined airborne and ground-based measurement campaign was conducted to study tropical deep convective clouds over the Brazilian Amazon rain forest. The new German research aircraft, High Altitude and Long Range Research Aircraft (HALO), a modified Gulfstream G550, and extensive ground-based instrumentation were deployed in and near Manaus (State of Amazonas). The campaign was part of the German–Brazilian Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (Global Precipitation Measurement) (ACRIDICON– CHUVA) venture to quantify aerosol–cloud–precipitation interactions and their thermodynamic, dynamic, and radiative effects by in situ and remote sensing measurements over Amazonia. The ACRIDICON–CHUVA field observations were carried out in cooperation with the second intensive operating period...

Improved Global Rainfall Retrieval Using the Special Sensor Microwave Imager (SSM/I)

Abstract Global monthly rainfall estimates have been produced from more than 20 years of measurements from the Defense Meteorological Satellite Program series of Special Sensor Microwave Imager (SSM/I). This is the longest passive microwave dataset available to analyze the seasonal, annual, and interannual rainfall variability on a global scale. The primary algorithm used in this study is an 85-GHz scattering-based algorithm over land, while a combined 85-GHz scattering and 19/37-GHz emission is used over ocean. The land portion of this algorithm is one of the components of the blended Global Precipitation Climatology Project rainfall climatology. Because previous SSM/I processing was performed in real time, only a basic quality control (QC) procedure had been employed to avoid unrealistic values in the input data. A more sophisticated, statistical-based QC procedure on the daily data grids (antenna temperature) was developed to remove unrealistic values not detected in the original database and was emplo...

The South American Land Data Assimilation System (SALDAS) 5-Yr Retrospective Atmospheric Forcing Datasets

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 farth...

Shape and radiative properties of convective systems observed from infrared satellite images

The main purpose of this study is to identify a series of parameters to characterize the shape and internal structure of a convective system (CS). One year of brightness temperatures derived from Meteosat 3 (July 1992-June 1993) (ISCCP-B3 data) was used to develop this work. The identification of convective systems was performed by pixels whose brightness infrared temperature values (T ir) were below 245 K. The main results obtained are: (a) the shape of a given system which can be categorized in three classes: (i) linear systems, (ii) circular and elliptical systems and (iii) fragmented systems; (b) the restitution of a given CS using only statistical information; (c) the identification of the distribution of the number of coldest tops inside a given system; and (d) the evaluation of the stage of the life cycle through a statistical study of internal structure and radiative parameters.

The Performance of Hydrological Monthly Products Using SSM/I–SSMI/S Sensors

AbstractGlobal 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, star...

Characteristics and Diurnal Cycle of GPM Rainfall Estimates over the Central Amazon Region

Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the characteristics of Global Precipitation Measurement (GPM) satellite-based precipitation estimates in representing the diurnal cycle over the Brazilian Amazon. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and the Goddard Profiling Algorithm—Version 2014 (GPROF2014) algorithms are evaluated against ground-based radar observations. Specifically, the S-band weather radar from the Amazon Protection National System (SIPAM), is first validated against the X-band CHUVA radar and then used as a reference to evaluate GPM precipitation. Results showed satisfactory agreement between S-band SIPAM radar and both IMERG and GPROF2014 algorithms. However, during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes around 00:00–04:00 UTC and 15:00–18:00 UTC. This overestimation is particularly evident over the Negro, Solimoes and Amazon rivers due to the poorly-calibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm.