Carlos F. Angelis

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...

Influência da precipitação na qualidade da água do Rio Purus

The impacts of precipitation on the water quality along the Purus River, located in the Brazilian State of the Amazonas, was investigated using data of precipitation estimated by satellite and information about water temperature, conductivity, pH, turbidity, dissolved oxygen and total suspended solids. The data were acquired in four different areas along the river. The results showed negative correlation between precipitation and turbidity. They also showed positive correlation between precipitation and temperature, conductivity, dissolved oxygen, total suspended solids and pH. The land use together with the rainfall regime seems to be the main sources of impact on the water quality around the sampling places.

A Comparison of Perturbed Initial Conditions and Multiphysics Ensembles in a Severe Weather Episode in Spain

AbstractEnsembles of numerical model forecasts are of interest to operational early warning forecasters as the spread of the ensemble provides an indication of the uncertainty of the alerts, and the mean value is deemed to outperform the forecasts of the individual models. This paper explores two ensembles on a severe weather episode in Spain, aiming to ascertain the relative usefulness of each one. One ensemble uses sensible choices of physical parameterizations (precipitation microphysics, land surface physics, and cumulus physics) while the other follows a perturbed initial conditions approach. The results show that, depending on the parameterizations, large differences can be expected in terms of storm location, spatial structure of the precipitation field, and rain intensity. It is also found that the spread of the perturbed initial conditions ensemble is smaller than the dispersion due to physical parameterizations. This confirms that in severe weather situations operational forecasts should address...

Constraining a 3DVAR Radar Data Assimilation System with Large-Scale Analysis to Improve Short-Range Precipitation Forecasts

AbstractIt is known from previous studies that radar data assimilation can improve short-range forecasts of precipitation, mainly when radial wind and reflectivity are available. However, from the authors’ experience radar data assimilation, when using the three-dimensional variational data assimilation (3DVAR) technique, can produce spurious precipitation results and large errors in the position and amount of precipitation. One possible reason for the problem is attributed to the lack of proper balance in the dynamical and microphysical fields. This work attempts to minimize this problem by adding a large-scale analysis constraint in the cost function. The large-scale analysis constraint is defined by the departure of the high-resolution 3DVAR analysis from a coarser-resolution large-scale analysis. It is found that this constraint is able to guide the assimilation process in such a way that the final result still maintains the large-scale pattern, while adding the convective characteristics where radar ...

Ground-based single-frequency microwave radiometric measurement of water vapour

The integrated water vapour content of the atmosphere is measured by a ground-based radiometer at 22.234 GHz at the Instituto Nacional de Pesquisas Espaciais (INPE), Brazil. The data set has been partitioned into two sets: one for no cloud and the other for cloudy conditions. The attenuation (dB) under the no cloud condition was always found to vary within 1.0–1.5 dB, except at around 1400 hrs (local time) through 1800 hrs (local time), and it reached a value of more than 1.5 dB with a maximum at 1700. This is in conformity with the theoretically calculated values of water vapour density over the same location. An effort has been made to get the regression relation between the measured and calculated water vapour content, for which the RMS error was found to be 0.49 kg m−2.

Basis for a Rainfall Estimation Technique Using IR-VIS Cloud Classification and Parameters over the Life Cycle of Mesoscale Convective Systems

Abstract This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared–visible (IR–VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs’ life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or...

Regionalization of the GOES-10 retrieval algorithm for tropical South America

Meteorological satellites provide a unique opportunity to obtain thermodynamic profiles in regions of the globe that do not have a dense meteorological upper-air station network, such as South America. Temperature and mixing ratio profiles were retrieved every hour with a spatial resolution of 10 km over South America from July 2007 to February 2009 using radiances from the Geostationary Operational Environmental Satellite 10 (GOES-10). The GOES-10 retrieval algorithm for thermodynamic profiles was developed by the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) in the USA, so some adjustments had to be made for its application on South America, such as the construction of a new covariance matrix. In this context, the scientific focus of this research was the construction of a new covariance matrix adapted to the meteorological conditions in South America. In addition, the algorithm results were validated by the use of the original and the new covariance matrices. The variables validated were air temperature, mixing ratio vertical profiles and total precipitable water (TPW). The data set used comprised a total of 1100 radiosonde observations recorded in the tropical region of South America at 00:00 and 12:00 Universal Time Clock (UTC), as well as thermodynamic profiles from 12 h forecasts from the Centre for Weather Forecast and Climate Studiess (CPTECs) Global Model, used as a first guess, and upwelling radiances of 18 infrared channels from GOES-10 for the period from July to November 2007. In general, the results indicated that with the regionalization of the covariance matrix, the algorithm performed better retrievals than with that of the original matrix. The greatest improvements were found in the mixing ratio profiles and in the values of the TPW. These results could be associated with the presence of the Amazon rainforest, which incorporated a greater amount of moisture into the new covariance matrix than the previous had the previous one.

A Severe Storm Warning System based in Radar and Satellite Data

This study describes an operation tool for severe storm nowcasting using GOES satellite images and cloud‐ground lightning data. A relationship was established between atmospheric discharges and penetrative convective clouds, combining Infrared and Water Vapor channels from the GOES‐10 geostationary satellite with cloud‐ground lightning data from the Brazilian Integrated Lightning Detection Network (RINDAT). The difference between water vapor and infrared brightness temperature is a tracer of penetrating clouds in the stratosphere. From this difference and the cloud‐ground electrical discharge, it was possible to adjust exponential curves that relate this brightness temperature difference to the probability of occurrence of cloud‐ground electrical discharges, with a very high coefficient of determination. These structures of penetrative clouds are followed in the time using an operational algorithm called ForTraCC (forecasting and tracking cloud cluster). This system allows extrapolating the penetrative cl...