International audienceThe 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 lifetime durations. This nowcasting tool was applied to evaluate the MCS displacement and size evolution over the Del Plata basin in South America up to 120 min with 30-min intervals. The Forecast and Tracking the Evolution of Cloud Clusters (ForTraCC) technique's performance was evaluated based on the difference between the forecasted and observed images. This evaluation shows good agreement between the observed and forecast size and minimum temperature for shorter forecast lead times, but tends to underestimate MCS size (and overestimate the minimum temperature) for larger forecast lead times

Between 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 of Green Ocean Amazon 2014/15 (GoAmazon2014/5). In this paper we focus on the airborne data measured on HALO, which was equipped with about 30 in situ and remote sensing instruments for meteorological, trace gas, aerosol, cloud, precipitation, and spectral solar radiation measurements. Fourteen research flights with a total duration of 96 flight hours were performed. Five scientific topics were pursued: 1) cloud vertical evolution and life cycle (cloud profiling), 2) cloud processing of aerosol particles and trace gases (inflow and outflow), 3) satellite and radar validation (cloud products), 4) vertical transport and mixing (tracer experiment), and 5) cloud formation over forested/deforested areas. Data were collected in near-pristine atmospheric conditions and in environments polluted by biomass burning and urban emissions. The paper presents a general introduction of the ACRIDICON– CHUVA campaign (motivation and addressed research topics) and of HALO with its extensive instrument package, as well as a presentation of a few selected measurement results acquired during the flights for some selected scientific topics.

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.

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.