CHUVA
Cloud processes of tHe main precipitation systems in Brazil:
A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement)
Portuguese Title
Processos de Nuvens Associados aos principais Sistemas Precipitantes no Brasil: Uma contribuição a Modelagem da Escala
de Nuvens e ao GPM (Medida Global de Precipitação)
Thematic Project Submitted to FAPESP September 2009
CONTENTS
RESUMO – PORTUGUESE 3
SUMMARY 4 INTRODUCTION 5 SCIENTIFIC ASPECTS AND OBJECTIVES 7
THE PROJECT FRAMEWORK 9
DESCRIPTION OF THE FIELD CAMPAIGN 11 WORKING GROUP – 1
CHARACTERISTICS OF THE PRECIPITATING SYSTEMS AS FUNCTION OF THE REGION AND LIFE STAGE
17
WORKING GROUP – 2
PRECIPITATION ESTIMATION – DEVELOPMENT AND VALIDATION ALGORITHM
20
WORKING GROUP – 3
ELETRIFICATION PROCESS: MOVING FROM CLOUDS TO THUNDERSTORMS
25
WORKING GROUP – 4
CHARACTERISTICS OF THE BOUNDARY LAYER FOR DIFFERENT CLOUD PROCESSES AND PRECIPITATION REGIMES
28
WORKING GROUP – 5
MODEL IMPROVEMENTS AND VALIDATION, WITH FOCUS IN CLOUD MICROPHYSICS AND AEROSOL INTERACTIONS, FOR SATELLITE PRECIPITATION ESTIMATES IN BRAZIL
31
INTERACTION AMONG SUBGROUPS 34
PROJECT OUTPUT 37
TIME TABLE 37
REFERENCES 38
LIST OF PARTICIPANTS 45
Resumo Português
O processo físico no interior das nuvens é um dos componentes mais desconhecidos do sistema climático. A descrição desses processos através de parâmetros meteorológicos convencionais ainda precisa ser bastante aprofundada de forma que modelos de previsão de tempo e clima consigam descrever, com precisão, o tipo e as características dos hidrometeoros, os perfis de liberação de calor latente, o balanço radiativo, o entranhamento de ar na nuvem e as correntes ascendentes e descentes. Os modelos uenuméricos estão se aprimorando e rodando em resoluções espaciais nas quais esses processos precisam ser explicitamente descritos. Por exemplo, a análise dos efeitos do aquecimento global em uma dada região necessita de simulações descrevendo todos esses processos. Outra aplicação importante que necessita conhecer os processos das nuvens é a estimativa de precipitação por satélite. O programa espacial brasileiro tem planejado o lançamento em 2014 de um satélite para inferência da precipitação - esse satélite fará parte da constelação do GPM (Global precipitation Measurement). Nuvens quentes, responsáveis por grande parte da precipitação nos trópicos, principalmente nas regiões costeiras, são pouco estudadas e não são consideradas nas estimativas de precipitação por satélite. Este Projeto realizará experimentos de campo em sete sítios com diferentes padrões climáticos, para estudar os regimes de precipitação no Brasil. Esses experimentos utilizarão radar polarimétrico, lidar polarizado, radiômetro de microonda, disdrômetros, radiosondas e vários outros instrumentos. As análises serão realizadas considerando as características microfísicas e a evolução com o ciclo de vida, os modelos de estimativa de precipitação, o desenvolviemnto da tempestade e a formação de descargas elétricas, os processos na camada limite e a modelagem da microfísica. Este projeto tem o objetivo de reduzir as incertezas na estimativa da precipitação e progredir no conhecimento dos processos das nuvens, principalmente das nuvens quentes. A pesquisa a ser realizada abrangerá estudos de clima e os processos físicos por meio de observações convencionais e especiais para criar um banco de dados descrevendo os processos de nuvens dos principais sistemas de precipitação no Brasil. O Projeto pretende criar e explorar essa base de dados para melhorar a estimativa de precipitação por satélites e validar e estudar as parametrizações da microfísica das nuvens.
Summary
The physical process inside the clouds is one of the most unknown components of the weather and climate system. A description of the cloud processes through the use of standard meteorological parameters in numerical models has to be strongly improved to accurately describe the characteristics of hydrometeors, the latent heating profiles, the radiative balance, the air entrainment and the cloud updrafts and downdrafts.
Numerical models have been improved to run at higher spatial resolutions where it is necessary to describe explicit these cloud processes. For instance, to analyze the effects of global warming in a given region is necessary to perform simulations considering all these cloud processes described above. Another important application that needs to have this knowledge is the precipitation estimation by satellite. The Brazilian space program is planning to launch, in 2014 a satellite to measure precipitation, which will be part of the GPM (Global Precipitation Measurement) constellation program. Warm clouds are responsible for a large amount of the precipitation in the tropics, especially in coastal regions. This cloud type is little studied and is not considered in satellite rainfall retrievals. This project will carry out field experiments at seven sites to investigate the different precipitation regimes in Brazil. To study these precipitation regimes, the field campaigns will make use of dual polarization radar, lidar, microwave radiometers, disdrometer, Radiosonde and various other instruments. The analysis will be performed considering the microphysical evolution and the cloud life cycle, the different precipitation estimation algorithms, the development of thunderstorms and lightning formation, the processes in the boundary layer and cloud microphysics modeling. This project intends to progress in the knowledge of the cloud processes to reduce the uncertainties in the precipitation estimation, mainly from warm clouds and consequently improving the knowledge of the water and energy budget and the cloud microphysics. This research project will carry studies on climate and physical processes by the means of conventional and special observations in order to create a database that can describe the cloud processes of the main precipitating system in Brazil. Accordingly, this proposal aims the development of a database that can be carried out to improve the remote sensing precipitation estimation thus validating and improving the cloud microphysical parameterization in the cloud models. This project will especially focus on the warm cloud precipitation produced by different types of convection.
1. Introduction
Precipitation estimation has been noticeably improved by the TRMM satellite and consequently the development of new algorithms (Adler et al., 2001; Huffman et al., 2007). However, precipitation estimation over land still has several deficiencies because it is indirectly estimated (Berg et al., 2006) and precipitation from warm clouds is barely retrieved (Short and Nakamura, 2000; Liu and Zipser, 2009). Warm clouds can significantly contribute to the total rainfall in a tropical region mainly near the coast. For instance, during November last year 600 mm was precipitated in two days in Southeast Brazil from warm clouds, but most of the precipitation estimation algorithms showed only a very small amount of precipitation. It is very important to quantify the amount of precipitation from warm clouds that is not being considered in satellite based climate datasets and it is likely possible that for some regions it corresponds to the majority of the precipitation amount. The description of the cloud processes and precipitation from warm clouds will be one of the main focus of this project.
The best way to estimate precipitation from satellite is by the physical approach, i.e, knowing the cloud processes like: the tridimensional structure and time evolution, the hydrometeor associated processes and the cloud life stage. Precipitation estimation techniques can also take into account the cloud life cycle (Delgado et al., 2008). Preliminary studies have shown that the relationship between the column-integrated ice water content and surface rainfall is also function of life cycle of the convective system. The knowledge of the cloud life cycle can give a considerable improvement in the precipitation estimation over land for ice clouds and will also be focus in this project.
Brazil is participating in the GPM (Global Precipitation Measurement Mission), an international Project led by NASA and JAXA, in two components: the ground validation and in the satellite constellation. Brazil is working in cooperation with CNES and NASA to have a passive microwave radiometer flying in the Brazilian multi-mission platform in equatorial orbit in 2014. The ground validation component intends to validate the precipitation estimation from the GPM constellation and develop new algorithms for improving the performance and precision of the remote precipitation estimation.
Stephens and Kummerow (2007) stated that assumptions on the vertical cloud and precipitation structures as well as the details of ice particle properties and size distributions are a dominant source of uncertainties in the estimation of precipitation. They consider that a better definition of the atmospheric state and the vertical structure of clouds and precipitation are needed to improve the information extracted from satellite observations. The data to be collected in this project combined with cloud modeling can form a very solid basis for an improved database and ultimately improved precipitation algorithms. Chandrasekar et al.
(2008) discussed how ground based dual-polarization radars can provide physical insight into the development and interpretation of spaceborne precipitation measurements. The new space-based lidar and active radar techniques has improved the knowledge of the cloud processes. However, the time evolution can not be captured by these devices; only ground weather radar can give a clear description of the cloud processes evolution. A ground dual polarimetric Radar-Lidar dataset describing different precipitation regimes is fundamental to improve the understanding of the cloud processes.
Climate system includes a variety of physical processes, such as cloud processes, radiative processes and boundary-layer processes, which interact with each other on many temporal and spatial scales. These are the components proposed to be studied in this project. Due to the limited resolutions of the models, many of these processes are not resolved adequately by the model grid and must therefore be parameterized. The differences between parameterizations are an important reason why climate model results differ (IPCC report A4 - Randall, 2007). Same result was found by Chen and Del Genio (2009), errors in cloud feedback estimation in general circulation models are associated with both incorrect occurrences of different cloud regimes and errors in the cloud properties within these regimes. Cloud processes affect the climate system by regulating the flow of radiation at the top of the atmosphere, by producing precipitation, by accomplishing rapid and sometimes deep redistributions of atmospheric mass and through additional mechanisms too numerous to be listed here (Arakawa and Schubert 1974; Arakawa, 2004). Microphysical parameterizations are used to predict the distributions of liquid and ice clouds, a realist description of the cloud processes improve climate
simulation and reduce the uncertainty in climate sensitivity (Iacobellis et al., 2003). Realistic parameterizations of cloud processes are a prerequisite for reliable current and future climate simulations (Randall et al. 2007) and can help the understanding of processes acting to form and maintain cloud systems and leading to improvements in the representation of cloud in numerical models (Korolev et al, 2007).
Clouds play a critical role in Earth's weather and climate, but the lack of understanding of clouds has since long limited scientists' ability of making accurate predictions about weather and climate change. Climate simulations are sensitive to parameterizations of deep convective processes in the atmosphere. The weather and climate modeling is improving space and time resolution and to accomplish this task is necessary to move from cloud parameterization to explicit microphysical description inside the cloud. Therefore, to improve space- time resolution reducing climate model uncertainties is necessary to better understand the cloud processes. A 3-D microphysical processes description of the main precipitating system in Brazil can strongly contributes to this matter and thus will be one of the tasks of the project.
Another important issue to be attempted in this project is related to the interaction between clouds and aerosol. A large error bar is associated to the radiative forcing from the indirect effect of aerosol. To improve the knowledge of this feedback and reduce uncertainty, a description of the water-ice profile including the depth of each layer and the mixed water-ice layer is necessary. A dataset containing this information combining with information about aerosol amount can give a very important contribution to understand the indirect interaction between clouds and aerosol.
This project intends to progress in the knowledge of the cloud processes to reduce uncertainty in the precipitation estimation, mainly from warm clouds and consequently improving the knowledge of the water and energy budget and the cloud microphysics.
This Project also has an educational component. Most of the selected sites chosen to describe the precipitation systems in Brazil are close to the campus of the Universities having Bachelor Degrees in Meteorology or in Environmental Sciences. It is also important to note that there is a lack in Brazil on atmospheric remote sensing and microphysics specialists. The project will contribute to the student learning about the instrumentation and science in these areas. The idea is to stimulate the participation of the students and local faculty in the campaigns and teach short courses on remote sensing and the instrumentation employed.
2. SCIENTIFIC ASPECTS AND OBJECTIVES
This research project covers climate and physical processes studies using conventional and special observations (like polarimetric radar, radiometer, LIDAR, and several others instrumentations) to create a database describing the cloud processes of the main precipitating system in Brazil. It intends to create and exploit this database to improve remote sensing precipitation estimation, rainfall ground validation and microphysical parameterizations of the tri-dimensional characteristics of the precipitating clouds. This project will especially focus on the warm cloud precipitation produced by different types of convection (orography, cumulus congestus, CB initiation, stratus cumulus, life phase of the MCS). The Project proposes the collection of data regarding cloud processes, as being performed at different sites, ranging from middle latitude to tropical humid and semi arid regions.
Answer or improve the knowledge about the following basic questions and statement problems will be the foci of this project:
• How to estimate rainfall from warm clouds?
• What is the contribution of rain from warm clouds to the total precipitation in different regions of Brazil?
• How to improve both space and time precipitation estimation of rainfall over the continent for the GPM constellation?
• What are the average characteristics (3D - cloud processes) of the main regimes of precipitation in Brazil?
• What is the contribution of the aerosol in the process of formation of precipitation?
• What are the main surface and boundary layer processes in the formation and maintenance of clouds?
• How cloud microphysics and electrification processes evolves during the cloud life cycle?
• How to improve precipitation estimation and cloud microphysics description by using conventional and polarimetric radar?
Considering these statement problems the specific objectives are:
1. Gather data on the tri-dimensional structure of clouds in different regions of Brazil.
2. Build a data base of cloud properties including associated electrification features.
3. Build a data base of modeled structure of clouds, validated by observations in Brazil, for use on improvements in of algorithms for precipitation estimation.
4. Improve convective parameterizations of warm clouds and of cloud-aerosol interactions specifically for their impact on rainfall.
5. Assessment of the contribution of rain from warm clouds to the total precipitation in different regions of Brazil;
6. Study the mean characteristics of the main precipitation regimes in Brazil in terms of the cloud processes.
7. Study different methodologies to estimate precipitation from warm clouds.
8. Determine the thresholds of the onset of convection for liquid water and ice content;
9. Establish relationships between integrated ice content and precipitation as function of the cloud life stage;
10. Examine the differences between clouds processes for storms formed by different concentrations of aerosols.
11. Study the cloud life cycle from the microphysics point of view.
12. Compute the satellites rainfall estimation errors by different methodologies.
13. Study the minimum acceptable area for the integration of precipitation estimated by satellite?
14. Describe the temporal evolution of the electrical field during the thunderstorm development in conjunction with polarimetric variables, lightning discharges and their effects in the upper atmosphere signaled by Sprites and other Transient Luminous Events.
15. .
16. Depict the temporal evolution of the lightning activity, cloud area, surface rainfall and rainfall vertical structure.
17. Characterize the thunderstorms season over Brazil.
18. Characterize the atmosphere boundary layer for situations of the development of shallow and deeper clouds at different vegetation-climate regions.
19. Describe the typical atmosphere boundary layer for different cloud life cycles and cloud hydrometeor for different precipitation regimes.
20. Investigate the oscillation patterns of the integrated water vapor values in the periods that precede the events of strong storm.
21. Analyze how cloud processes occurs as function of distribution of turbulent fluxes, turbulent kinetic energy by using atmospheric modeling of heat and moisture transfer inside the Atmospheric Boundary Layer (ABL).
3. PROJECT FRAMEWORK
3.1 GLOBAL PRECIPITATION MEASUREMENT – GPM
Water is essential for life on Earth. Its distribution drives mankind way of life and its behavior determines weather, climate and environmental conditions. Nowadays water quality and transformations in the environment and future fresh water resources are permanent societal and scientific concerns in every nation on Earth. In the atmosphere the condensation/sublimation of water vapor into precipitation is responsible for the release of a large amount of heat which drives the general circulation of the atmosphere, thus redistributing the energy and water around the globe. In this sense, precipitation is the centerpiece of our planet’s hydrological cycle, and understanding it is crucial to unraveling many of the uncertainties about the Earth’s climate.
One of the most challenging research problems in the Earth Sciences is related to the lack of understanding about the physics involved in the measurement of the various precipitation processes (volume, distribution, rates and associated heat release). Furthermore, this difficulty is augmented by the small number of observations, especially over the Tropics and the oceans.
Besides, decision makers and local agencies need accurate and real-time rainfall measurements to improve weather prediction models and water resources management, or even the detection of extreme events like: hurricanes, thunderstorms, floods, droughts and landslides that affect daily life.
As a consequence precipitation remote sensing estimate is the only available tool to monitor the precipitation at different temporal and spatial scales. Nonetheless, our best current technology has some limitations on satellite rainfall estimation: estimates at 0.25 x 0.25 degree and 3 hours interval (merged IR and microwave). Therefore, to improve the temporal and spatial resolution new algorithms and measurements should be implemented.
Considering the efficiency of TRMM in retrieving rainfall measurements from space (according Kummerow et al., 2000, the inter-comparisons between rainfall algorithms performance before and after TRMM launching showed an agreement improvement to 24% for global tropical monthly averages), the concept of the “Global Precipitation Measurement – GPM” was conceived as presented by Shepherd and Starr (2001), and may be the perfect program to provide global accurate rainfall measurements in the next decades.
By using a constellation with 8 satellites, the program will allow precipitation measurements over entire globe within a spatial resolution of 25 x 25 km every 3 hours.
Note that GPM Program is an extension of TRMM Mission in the sense that the GPM core satellite will carry on board similarly as TRMM, the Precipitation Radar and the Microwave Radiometer (Shepherd and Starr, 2001). Other GPM’s constellation satellites will carry on board similar microwave radiometers. This satellite constellation will improve significantly the spatial accuracy and temporal frequency of the measurements.
The program also includes actions related to atmosphere and surface observations for satellite data calibration and validation. These activities will be done through the establishment of a data collection network and field campaigns as proposed by Petersen and Hou, 2008.
Concerning Brazilian participation in GPM Program, it was established in June of 2004 GPM Brazil (GPM-Br) Program through the Brazilian Space Agency (AEB) after been invited by NASA/GPM Coordination Group. A Coordination Committee for GPM-Br was designated to coordinate actions in the Program. Members of this committee are participating in all GPM meetings presenting the progresses and status of GPM-Br.
Consolidating the participation in GPM Program, last year, Brazil hosted the Third GPM International GV Research Planning Workshop and a project had been accepted by NASA PMM Group: The Brazilian proposal to the NASA PMM Science Team: Contribution of the GPM-Br Science Team. Nowadays GPM- Br is signing Cooperation Agreement Terms with several Brazilian institutions involved in precipitation measurement and interested in data applications for the establishment of GPM-Br observation and validation network. AEB and the National Institute for Space Research (INPE) are in negotiations with NASA and CNES
for supplying the payload for GPM-Br satellite. INPE is in charge to supply the spacecraft – Multi Mission Platform (PMM) for GPM-Br satellite. Besides the measurement of microwave radiation, is proposed that the GPM-Br satellite will be equipped with a lighting detector.
Scientific groups around the globe participating in the GPM Program will be working in research and development of algorithms and techniques for improving rainfall estimation by using microwave satellite data.
3.2 YEAR OF TROPICAL CONVECTION
The World Climate Research Program (WCRP) and the World Weather Research Program (WWRP)/THORPEX are proposing a Year of coordinated observing, modeling and forecasting of organized tropical convection and its influences on predictability as a contribution to the United Nations Year of Planet Earth. Year of Tropical Convection is a year of coordinated observing, modeling, and forecasting with a focus on organized tropical convection, its prediction, and predictability. The realistic representation of tropical convection in global models is a long-standing, grand challenge for both numerical weather prediction and climate prediction. Incomplete knowledge and practical issues in this area disadvantage the modeling and prediction of prominent phenomena of the tropical atmosphere across a wide range of scales. CHUVA is a campaign in view to study this issue and can be associated to this effort.
3.3 EDUCATIONAL EFFORT
The CMCH (Hydrology, Meteorology and Climatology Commission) is an interministerial commission of the federal government in Brazil in charge of coordinating activities in these areas. The working group responsible for education identified the necessity to improve formation in remote sensing and cloud microphysics. This Project has a secondary goal that is to disseminate knowledge in these areas trough the participation of faculty and students in the field campaigns and giving lectures in three specific topics: radar, satellite and cloud microphysics
3.4 INCT- MUDANÇAS CLIMÁTICAS
This Project is associated to the Climate Change Program more specific in the Sub-Project – Reduce uncertainties in modeling and climate change scenarios. Several researchers of this Project are participants (or coordinator) of this sub-project. The uncertainties can be reduced in two ways through out the more precise estimation of the precipitation field. As already suggested, warm cloud precipitation is not correctly included in any global rainfall dataset and for the Brazilian region, due to its geographical position, this it is much more significant. The other way is by the improvement in the knowledge of the cloud processes and in the validation methodologies that will allow improving cloud-resolving model performance and accuracy. Also, the development of the Brazilian unified model for weather and climate change simulations will be helped by this dataset base for model validation and test.
3.5 CONNECTION WITH FAPESP THEMATIC PROJECTS
This Project can interact with the Global Climate Change Program starting this year, by offering a dataset for validation studies and knowledge about the most important precipitating systems in Brazil. This feature is still more important for downscaling studies. This Project also will have a close collaboration with the Project – Aeroclima - direct and indirect effects of aerosols on climate in Amazonia and Pantanal, coordinated by Dr. Paulo Artaxo. In spite of both Projects having different objectives, during the field campaign in Amazonia; the data collected will be complementary.
4. DESCRIPTION OF THE FIELD CAMPAIGN
The objective of the field campaign is to collect information about the cloud processes of the main precipitating systems over Brazil (MPSB) to evaluate and improve algorithms related to data retrieval and quantification of rainfall and cloud microphysical description in cloud resolving models The main focus of this study are the warm clouds and to create and validate a 3-D cloud processes data base of the MPSB. Cold fronts, tropical and middle latitudes squall lines, mesoscale convective systems, the intertropical convergence zone (ITCZ), the easterly waves, the South Atlantic Convergence Zone (SACZ), and local convection will be measured, as well the interaction of these systems with different landscapes and topography. Figure 1 shows an illustration of the geographical location of these systems.
Figure 1 – Schematic view of the main precipitating system over Brazil
These synoptic and mesoscale systems have geographical and seasonal components that will be taken into account in an experiment designed to travel around Brazil. The field campaign equipment will be able to move, deploy and measure easily in short periods of time. These field campaigns have essentially the characteristics of a mobile experiment. Figure 2 shows the seasonal variability of the precipitation and lightning
(thunderstorms) in Brazil that was used to define the regions and seasons of each field campaign. As pointed in Figure 1, for each region it is possible to target a specific systems type, for instance, cold fronts and Mesoscale Convective Complexes (MCC) can be the foci in Southern Brazil, while warm clouds and easterly waves related convection will be addressed in Northeast Brazil as well as in the Amazon region where tropical squall lines will also be monitored.
To accomplish this task the strategy will be to make campaigns using the facilities of the Meteorology University Campus, which cover most of the regions of Brazil. This strategy will help on adding local expertise to the analysis of the weather system and, additionally, undergraduate and graduate students will have an opportunity to be involved in the measurements, as well as to be trained in a competitive research environment. This project will seek to add specialists in remote sensing and cloud microphysical processes through the use of the instrumentation, advising students, giving lectures and preparing joint studies about the characteristics of the local systems.
In order to capture the main precipitating systems around Brazil, the mobile field campaign will last around 15-30 days of continuous measurements in each region which will hopefully allow the measurements of about 3 to 5 meteorological systems per site.
4.1 FIELD CAMPAIGN SCHEDULE
The tentative schedule for the field campaign is shown in Table I.
Table I : Field Campaign Schedule
Jan FEB Mar Apr May Jun Jul Aug Sep Oct Nov Dez
2010 Centro de
Lançamento de Alcântara
São Luiz
Paraitinga 2011 São Luiz
Paraitinga Fortaleza Fortaleza Belém Belém Manaus Manaus
2012 Londrina Londrina Santa
Maria Santa
Maria Brasília Brasília
The first experiment in the Centro de Lançamento de Alcântara is considered to be a “pilot” and it is being planning using AEB and INCT funds. All the participants of this project are involving in this preliminary field campaign.
Each place is associated to at least one University or Research Institute. Table II presents the local partners that will facilitate the operation of the field campaign, as well as their involvement in the scientific analysis, data collection and education outreach. Figure 3 shows the location of each scientific organization that will host the experiment.
Table II: Organizations Hosting the Experiment
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dez
2010 Centro de
Lançamento de Alcântara CTA and Several Institutions
Universidade
Federal do Rio de Janeiro – UFRJ, Universidade de São Paulo – USP Universidade de Taubaté - UNITAU 2011 Universidade
Federal do Rio de Janeiro – UFRJ, Universidade de São Paulo – USP Universidade de Taubaté – UNITAU
Universidade
Estadual de Ceará (UECE) FUNCEME Universidade Federal de Campina Grande ( UFCG) Universidade Federal de Alagoas (UFAL)
Universidade Estadual de Ceará (UECE) FUNCEME Universidade Federal de Campina Grande ( UFCG) Universidade Federal de Alagoas (UFAL)
Universidade Federal do Pará (UFPA) SIPAM Museu Goeldi
Universidade Federal do Pará (UFPA) SIPAM Museu Goeldi
Universidade
Estadual do Amazonas (UEA)
INPA SIPAM
Universidade Estadual do Amazonas (UEA)
INPA SIPAM
2012 CIRAM (SC)
Universidade Tecnológica Federal do Paraná SIMEPAR Universidade Estadual do Paraná (UEPR)
CIRAM (SC) Universidade Tecnológica Federal do Paraná SIMEPAR Universidade Estadual do Paraná (UEPR)
Universidade Federal de Pelotas – UFPEL Universidade Federal de Santa Maria - UFMS
Universidade Federal de Pelotas – UFPEL Universidade Federal de Santa Maria - UFMS
INMET Universidade De Brasília Universidade Federal do Mato Grosso do Sul – UFMS
INMET Universidade De Brasília Universidade Federal do Mato Grosso do Sul – UFMS
PRECIPITATION ANNUAL CYCLE LIGHTNING ANNUAL CYCLE
Figure 2: Seasonal Cycle of precipitation and lighting from TRMM data (Morales personal comunication)
Figure 3 – Proposed sites for the Field Campaign
Table III shows the main precipitating system target for each field campaign.
Table III – Main precipitation systems for each region
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dez
2010 Warm Clouds,
Tropical Squall Lines, Easterly Waves
SACZ, local convection
and orographic enhancement of precipitation Warm Clouds 2011 SACZ, local convection
and orographic enhancement of precipitation Warm Clouds
Warm Clouds,
Easterly Waves, ITCZ
Warm Clouds, Easterly Waves, ITCZ
Tropical Squall Lines, Warm Clouds, Easterly Waves
Tropical Squall Lines, Warm Clouds, Easterly Waves
Easterly and
Westerly Convection type, local convection SACZ Warm clouds
Easterly and Westerly Convection type, local convection SACZ Warm clouds
2012 Cold Front,
squall lines, MCC General convective system
Cold Front, squall lines, MCC General convective system
Cold Front, squall lines, MCC General convective system
Cold Front, squall lines, MCC General convective system
Continental convective system Warm clouds
Continental convective system Warm clouds
A campaign will normally be scheduled for 45-60 days in a specific place, corresponding to 15-30 days for measurements and the rest of days of the period to deploy the instrumentation, to unpack and to move to another region.
4.2 INSTRUMENTATION PACK
The following instruments will compose the typical instrumentation pack to be used in the field campaign:
1. Dual Polarization Doppler X band radar (see figure 4) 2. EZ Lidar ALS450 (see Figure 4)
3. Microwave Radiometer MP3000 (see Figure 4) 4. 6 Laser Disdrometer (Figure 5)
5. 3 Meteorological Surface Stations 6. 2 Field Mill - Campbell
7. Radiosonde RS-92
8. Turbulent fluxes of heat and moisture 9. GPS Station
10. Soil Moisture
11. Airplane equipped with cloud microphysical instrumentation (only some campaigns) 12. Rain gauges
13. CCD cameras
14. Vertical pointing radar – micro rain radar (Figure 6) 15. Low-light Level CCD cameras
4.3 EVENTUAL LOCAL DATA
At some sites the basic pack information will be complemented by others instruments available like X Band dual polarization and Doppler radar (Alcântara)
S-Band Doppler radar (Belém, Manaus, Curitiba, São Roque, Pelotas and Fortaleza) Micrometeorological Tower (Maranhão- Amazonas – Santa Maria)
4.4 ADDITIONAL DATA
Satellites images: NOAA, TRMM, Megha-Tropique, AQUA, GOES and MSG CPTEC analysis
Radiosondes from Brazilian network Surface Station (raingauge)
Brasildat network - Brazilian lightning detection network): http://www.rindat.com.br STARNET (South American lightning network) - http://www.zeus.iag.usp.br
Figure 4 – Radar, microwave radiometer and Lidar.
Figure 5: Two pairs of Parsivels disdrometers
Figure 6 : Vertical pointing micro rain radar.
Each instrument is able to measure/retrieval several important information about the cloud processes.
Table IV presents a description of the parameters measured by each instrument.
Table IV – Instrumentation for observing cloud processes to be used in the field Campaign
Instrument Specification Measurement Retrieval Parameters Data Collected Radar Doppler X band dual
polarization - METEOR 50DX - Selex
Doppler spectrum, radar reflectivity (H and V), differential reflectivity velocity and width,
Reflectivity, precipitation, droplet size distribution parameters.
100 km radius Volume scans of several elevations each 10 minut
Microwave Radiometrics MP 3000 Brightness temperature from 35 channels (22-30 and 51-59 Ghz)
Temperature, water vapor
and liquid water profile Profile from surface to 10 km eve 10s
Lidar EZ Lidar ALS450 Laser 355 nm backscattering and extinction profile - cross polarized
Cloud base and top, water/ice determination, radius of droplets and shape ratio for ice crystals
Up to 15 km, vertical and time resolution of 5 meters and 10s
Disdrometer Rain drop distribution Rain Droplet Size
Distribution (DSD) DSD every 20s for 6 disdrometer inside a radius of 50 km from the radar.
Radiosonde Vaisala RS-92 Profile of temperature,
humidity and wind Profile of temperature,
humidity and wind Two per/day and one prior, during and after the passage of precipita system
Surface Station Automatic weather station Weather surface data Temperature, humidity, winds, pressure, rain, solar and infrared radiation
3 stations separated by around 5 km, data every 1 minute
Field Mill Campbell Electric field Variation of the electric
field Every 1 min in fair weather and 1 Hz during thunderstorm activity Turbulent fluxes 3D Sonic Anemometer
(Campbel Scientific) and LI-COR IRGA
Fast response measurements of temperature, winds (3D), humidity
Momentum, sensible and
latent heat fluxes Time series of the fluxes
Dual frequency GPS
receiver Trimble NetR8 GNSS reference receiver with 76 Channels
GPS: L1 C/A Code, L2C, L1/L2/L5
GLONASS: L1 C/A and P codes, L2 P code, L1/L2
Zenithal Tropospheric Delay
Integrated water vapor
High resolution IWV each 1 min
Soil Moisture Sensor ML2x ThetaProbe and
WET Sensor type WET-2 Soil parameters Soil Volumetric Moisture, condutivicty and temperature
Measurements at 2, 5, 10 e 20 40 60, 80 e 100 cm deep UECE – FUNCEME
Bandeirante Airplane Warm cloud microphysics
package CCN counter, Drop size distribution, liquid water, pressure, temperature and humidity
Concentration of CCN, drop size distribution (0.5 um to 5 mm),, liquid water and temperature (dry and wet) and pressure.
30 hours in the Fortaleza and 30 hours in São Luiz do Paraitinga f Campaign
Rain gauge Tipping bucket Rainfall Rainfall Rainfall each time precipitation is larger than 0.254 mm CCD cameras Cloud images sky images View of the weather
conditions around 360 degrees
Every 1 second.
Micro rain radar Vertical pointing micro Doppler rain radar Keplel – 24.1 Ghz
Instantaneous or average droplet spectral profile, reflectivity and rain rate
Vertical pointing Every 10 seconds
Low-light level CCD
cameras Visible and near-IR Sprites and TLEs
ocurrence Morphology, and location
(lat, lon estimate) images at 30 frames per second
4.5 EDUCATION PACK AND CAPACITY BUILDING
A set of 6 (six) seminars covering satellite, radar, electromagnetic wave propagation, microphysics, atmosphere electrification and boundary layer meteorology will be prepared and presented in each site as lectures.
Also a basic pack of research for graduate and undergraduate students will be available for the local partner. This research will focus on the local weather conditions. Case studies combining Radar, Lidar, radiometer and satellite, evolution of the boundary layer during the storm case, evolution of electrification for the case study and BRAMS simulation of the storm case will be some of the studies to be prepared for the regional studies.
This project also has a strong component on the Graduate School. A list of Master and PhD Students are presented in the lat section of this project. All the working groups have several graduate students basically from INPE and USP University. However, for each site we intend to have close collaboration with the local advisers and graduate school.
5. WORKING GROUPS
This Proposal is subdivided into five working groups that will have specific tasks although a strong collaboration is expected and will be described further on.
5.1 Working Group – 1: Characteristics of the precipitating systems as function of the region and life stage. Coordinator: Luiz Augusto Toledo Machado
5.1.1 Introduction
Among all atmospheric parameters precipitation is one of the more difficult to measure due to its high variability in time and space (Habets et al., 2004). The only way to have information about the rainfall on global basis is through the estimation by using remote sensing. There are different techniques for precipitation estimation using microwave frequencies (Ferraro et al., 2000; Staelin and Chen, 2000; Kummerow and Giglio, 1994, Mech et al., 2007). For example, the technique used by Ferraro et al. (2000), use the channels of 89 GHz and 150 GHz to estimate rainfall based on the indirect relationship between the ice scattering and precipitation. Staelin and Chen (2000) applied neural networks to estimate the rainfall rate from channels around 183 GHz. Another technique, proposed by Kummerow and Giglio (1994), is based on an inversion algorithm using a database of hidrometeor profiles and the radiances observed by the satellites. This database was built by the simulation of a radiative transfer model of the brightness temperatures of each microwave channel, using tridimensional description of clouds from numerical models. The best rainfall estimation is achieved if we have a preliminary knowledge about the microphysical properties of clouds such as vertical structures of water vapor content, liquid water content, distribution of ice and snow as discussed and modeled by Lima et al. (2007).
Passive microwave algorithms over land have evolved much more slowly than their oceanic counterparts due to difficulties related to radiometrically warm and variable surfaces. The only algorithms that have received sustained attention are scattering type algorithms that exploit the relationship between ice aloft and rainfall at the surface. The physical base of these algorithms considers that the cloud integrated ice path is related to changes in the precipitation rain rate. They are, of course, highly empirical and the perceived correlation between ice and rainfall is actually rather weak except in a climatological sense. These algorithms do not consider a specific cloud process and the life cycle.
These are only some considerations about ice clouds over land, but the problem becomes much more complicated when warm clouds are considered. Clouds without ice phase are very common, especially in the tropical region. Warm clouds can be associated to orographic clouds, early stages of cumulus nimbus, stratus, stratus cumulus, or even cumulus congestus that have their entire life cycle within the warm phase. It often occurs in the Northeast of Brazil and preliminary analyses using the PR and TMI (active and passive microwave sensor from TRMM satellite), indicate that warm clouds (classified as scattering index <10) account for 21% of the precipitating area during the rainy season (when we have large probability of having cold-ice clouds). These clouds do not lead to a significant brightness temperature depression and are often classified as “no rain” by the screening procedure.
Another example of the importance of warm clouds can be seen in Figure 7. It shows the case of intense rainfall observed in November 2008 in Santa Catarina State, affecting around 60 cities, with 133 deaths and more than 78,000 people forced to leave their homes. This severe event which produced 600 mm of rain in two days had rainfall almost entirely from warm clouds. In this picture it can be seen that all the traditional algorithms for precipitation estimation, either using microwave or infrared, did not detect the huge amount of rainfall as recorded by surface stations. The estimations neglect the large amount of rain from warm clouds.
We can also see in Figure 7 two snapshots from this event where no rain is observed by the MSPPS algorithm (NOAA algorithm) and around 20 mm/hour is measured by the surface station. Still in this figure one can note the CAPPI (Constant Altitude Plan Position Indicator) for 3 and 5 km, showing that the rain is restrained to the lower levels.
Warm clouds are described as clouds whose temperatures at all levels are above 0°C or/and haven’t developed the ice phase. Prupparcher and Klett (1978), Rogers and Yau (1989) and Glickman (2000) and
several others studies describe the microphysics characteristics of the warm cloud. Most of the tropical rainfall comes from warm clouds that may or may not evolve into cold clouds (Beard and Ochs, 1993). Rain from warm clouds is formed from an efficient mechanism that transforms cloud droplets in rain droplet. According to Rogers and Yau (1989) cloud droplet formed by condensation (size of some μm) grows by coalecence- collision processes, which are accelerated by the terminal velocity, forming rain droplet (size of some mm).
The cloud droplet growing is extensively discussed by Braham (1968), Ochs (1978) and Johnson (1982).
Small and Chuang (2008) listed several mechanisms that could be responsible for the formation of rain in warm clouds, as the effect of ultra-giant aerosols (Johnson, Lash-Trapp et al., 2002 and Blyth et al., 2003), the entrainment of air in the cloud (Latham and Reed, 1977, Lash-Trapp et. al., 2005 and Telford et al., 1984) and small-scale turbulence (Jonas, 1996, Shaw, 2003 and Tisler et al., 2005)
Figure 7: Precipitation on the state of Santa Catarina for the period of 20 to 24 November 2008 by the Hidroestimador, CMORPH, 3B-42 and recorded by raingauge (Left side). On the right side, the instantaneous precipitation estimation using the MSPPS (Ferraro et al. 2000) for November 23, 2008 at 17 UTC and 24 UTC to 04, the precipitation observed over the surface by the raingauge at the same time and the CAPPI radar for 3 and 5 km for the day 23.
In a recent article Susuki and Stephens (2008) describe the droplet size and the average reflectivity of the warm clouds using the Cloudsat satellite. They found different patterns of size distribution for different intervals of radar reflectivity. The A-Train satellites are a great tool to study the clouds processes; however the data is confined to a narrow scanning. The basic microphysical processes of warm clouds are described in detail in a review of Cotton (1982).
5.1.2 Objectives
The aim of this working group is to improve space-time remote sensing rainfall estimation over the Brazil based on the GPM constellation satellites. Particularly, focusing on the rainfall from warm clouds and on the cloud processes description of the main precipitation regime in Brazil.
The idea is to develop a new class of physical algorithm for estimation of precipitation using the knowledge of the cloud processes of the main precipitation regime and the advances in models of emissivity of the surface to determine if water from clouds or precipitation is present above the surface. The research proposal aims to try to answer the following questions: What is the capacity of these methods to identify water in the atmosphere? Is it possible to separate areas covered by clouds with and without rainfall? How to determine the transition of cloud to precipitation? Is there a threshold of liquid water content at which the cloud starts to precipitate? How to estimate rainfall over the continent from warmer clouds? What is the contribution of precipitation from warm clouds in the cumulative total of rain? Can we improve precipitation estimation adding the information from life stage?
Especially the following studies will be performed:
a) Assessment of the contribution of rain from warm clouds in total precipitation in different regions of Brazil;
b) Study the mean characteristics of the main precipitation regime in Brazil in terms of the cloud processes (microphysics and macrophysics);
c) Study different methodologies to estimate precipitation from warm clouds.
d) Prepare a database of radiance and profiles of cloud microphysics;
e) Determine the thresholds of the onset of convection for liquid water and ice content;
f) Establish relationships between integrated ice content and precipitation as function of the cloud life stage;
g) Examine the differences between clouds processes for storms formed by different concentrations of aerosols.
h) Study the cloud life cycle from the microphysics point of view.
5.1.3 Planned Activities and Specific Scientific Studies:
These proposed studies will be based on measures of coincident radar, radiometer, disdrometer, ground GPS, Lidar and airplane measurements as a little as a single FOV of the TRMM satellite, METOP, AQUA and NOAA. The Lagrangian structures of the clouds (from GOES satellite image) and precipitation (from radar CAPPI) will be followed by using the Fortracc algorithm (Machado and Laurent, 2004, Vila et al. 2008). Finally the radiossonde information combined with the output from numerical model simulations (BRAMS) and the radiative transfer code (RTTOVS) will allow studying the evolution of the microphysics with the life cycle of the clouds.
The following specific studies will be performed:
A) Warm Clouds.
Precipitation from warm clouds, as already mentioned, is hardly retrievable by conventional algorithms. This raises a secondary question which can also be related to oceans: what do we know about the transition from clouds to precipitation? Radiometer algorithms tend to set the transition as a function of liquid water path only.
We know very little about how the real world works. We therefore wish to establish whether, within similar synoptic conditions, there is indeed liquid water path thresholds at which clouds begin to precipitate.
From the use of continuous measurements of temperature, humidity (soundings as well as GPS from IWV retrievals) and liquid water acquired by surface microwave radiometer (MP300), scanning radar, rain gauges and soil moisture measurements under a given area covered by ground radar and satellite will allow to answer the questions discussed above.
Changes in surface emissivity are related to changes in the soil moisture or the change on the vegetation characteristics. The surface emissivity varies linearly for all channels. Such nearly linear relationship has high correlation coefficients for clear sky situations. However, in the presence of large cloud droplets this correlation decreases drastically which could be the indicative of the presence of precipitation.
Also the determination of microphysical properties of the clouds and its variation with the life cycle can are essential information to simulate the microwave signal and consequently help to detect and estimate the rainfall rate.
B) Typical cloud properties of the main precipitation regime in Brazil and the variation with the life cycle.
Each microwave channel response depends on a specific physical property of the cloud, for example, frequencies above 60 GHz are more affected by ice and below this value by liquid water absorption. The simulated satellite microwave signal for typical Brazilian synoptic regimes will permit the assessment of synthetic radiances from radiative transfer model and mesoscale model simulations to compare with satellite and ground measurements. Considering this property of the interaction between radiation and clouds, it is possible to create a database and validate the three dimensional clouds microphysics simulated by the numerical model (Defer et al., 2007). This database developed in working group 5 will be used to improve rainfall estimation algorithms. Typical cases of the most common cloud organization in Brazil, like MCC, cold fronts, ZCAS, Easterly Waves and Tropical Squall will be simulated producing hydrometeor and thermodynamic profiles as function of the life cycle. This new radiance-convection database will be tested and compared against the currently databank provided by GPROF (Kummerow et al., 2001). These profiles will be used to improve the GPROF rain estimation algorithms. Through these results we expect 1) to improve algorithms for precipitation retrieval over land and 2) to better understand the cloud processes.
C) The impact of MCS life cycle information on the precipitation estimation
Figure 8 shows the average reflectivity profile as a function of the life cycle of cells, for precipitating cells life cycle greater than 160 minutes (Machado and Martins, 2008). One can note that during the precipitation cell initiation phase there is nearly the same amount of precipitation as in the mature stage. However, the cloud integrated ice path is often much smaller during the mature stage. The knowledge about the MCS type, life stage, and physical processes will certainly help to estimate cloud properties and consequently precipitation.
We will explore how ice-scattering and precipitation relationship varies with cloud morphology (e.g. cloud lifecycle stage, system classification, location of pixel within the cloud). Cloud morphology, merge/split situation, life stage, lifespan, and meteorological fields will be derived from geostationary satellite and radar data using the Fortracc algorithm (Machado and Laurent, 2004 and Vila et. Al., 2009), to try to answer the following question: The relationship between Integrated Ice Path and precipitation depends on the life cycle and cloud morphology?
Figure 8 – The mean vertical reflectivity profile as a function of the long lived rain cell life cycle (>160 minutes). Rain cell was defined as a cluster of pixel having reflectivity f> 20 dBz. (Machado and Martins, 2008)
5.2 WORKING GROUP 2 – Evaluation of Rainfall Estimation over South America. Coordinator:
Carlos Frederico Angelis
5.2.1 Introduction
This subproject focus on the evaluation of a series of algorithms that assess hydrometeorological parameters like rainfall, drop size distribution (DSD), liquid water content, water vapor content and soil moisture. All of those parameters are involved with the rainfall occurrence whose spatial and temporal variability are far to be quantified. The complexity of the rainfall estimation increases when the environment where rain occurs comprehends tropical and extra-tropical areas. This is the case of Brazil where in its large area different rainfall regimes are observed. To monitor the rain events in this area it is necessary the use of all methods available to estimate rainfall including satellite, radar and pluviometers.
The following paragraphs briefly describe the topics involved in the rainfall retrieval and parameterization. The different approaches to estimate rainfall must be evaluated so that the use of algorithms that convert remote sensing measurements into meteorological variables could be optimized.
5.2.2 Satellite Rainfall Estimation
Satellite observations provide outstanding cover of wide areas in different temporal and spatial resolutions.
Based on these observations it is possible to estimate rainfall, using algorithms that turn cloud or raindrop radiance (emitted or reflected) observed by the satellites into precipitation rates (Levizzani, 2002).
Regional differences in the structure and microphysical properties of precipitating systems are the result of the difference between meteorological regimes. Changes in these meteorological regimes can result in systematic errors in quantifying precipitation as the algorithms universalize the regimes for which they were
developed. That is, the algorithms provide more uncertainties when used in locations different from those they were developed for (Ebert, 2007).
In order to minimize uncertainties in the measurement of precipitation, the algorithms have to be calibrated and checked based on rainfall values observed over the surface of different climate regimes and in different seasons. This helps to increase the efficiency of the algorithms in recovering rainfall values; yet other uncertainties can cause final measurement errors due to changes in the morphology of the clouds associated with changes in the regime and / or type of precipitation, such as a transition from convective to stratiform rain.
Thus, to assess the precipitation in a certain location with the minimum uncertainty possible, it is necessary to first know the characteristics and conditions for which the algorithms were developed, and also to know which application each algorithm best applies to. The range of existing algorithms, as well as the potential different applications, makes it difficult to have the “the best” algorithm.
5.2.3 Rainfall Parameterization using Disdrometers Measurements
Along the history of meteorological instrumentation, a series of instruments have been developed to measure rainfall at the surface. Apart from the traditional pluviometers, a new generation of equipment allows one to acquire direct and indirect information about rainfall. In this project disdrometers and a ground radiometer will be used together with satellite, weather radars and pluviometers to support the hard task focused on the rainfall estimation.
Disdrometers measure the drop size distribution (DSD) and terminal velocity of raindrop and snowflakes.
This information is instrumental for calibrating the Z-R radar relationship and to improve our understanding on rain microphysics. Several recent papers have highlighted the usefulness of precise estimates of the DSD to analyze severe rain episodes (Smith, et al. 2009, Tokay et al. 2008). Also, DSD estimates have been found useful to analyze spatial climate variability effects such as those induced by the ENSO (Ushiyama et al. 2009).
Disdrometric estimates are also used to calibrate and validate satellite-borne radars and radiometers. The use of a network of disdrometers may help to improve not only satellite estimates, but also our modelling of systems such as squall lines (Morrison et al. 2009). In spite of its importance, the spatial variability of the DSD (Harikumar et al. 2009) makes its estimation a complex task. Further complexity arises in the post-processing from the fact that depending on the modelling method used, large errors may appear (Cao 2009).
5.2.4 Radiometric Observation of Different Phases of Atmospheric Water
Studies of meteorological parameters, particularly water vapor and cloud liquid water play an important role in controlling the atmospheric energy budget. Ground based remote sensing in microwave bands has been recognized as a feasible mean for the measurement of water vapor and cloud liquid water (Karmakar, 2001).
Such measurements are important to understand physical process involved in the rainfall genesis on both local and wide- scale. Ground based microwave observations provide advantages over the conventional radiosonde observation since it may be used continuously over long periods of time. However, the radiosonde observations will be of paramount importance for comparison with the radiometric observations although radiosonde observation is a discrete, not continuous process (Karmakar and Chattopadhyay, 2004). Also the radiosonde observations by balloon flight may not provide the actual vertical path due to horizontal displacement by the wind field. Moreover, using a microwave ground radiometer in South America may provide a unique opportunity to measure and compare the results of water vapor and cloud liquid water over a tropical location.
Studies of atmospheric water vapor and cloud liquid content have been a subject of continued interest during last two decades. For this purpose, a number of techniques are employed and they are: 1)Ground based and space borne radiometric technique 2)Use of Radiosonde data 3)Ground based Radar 4) Differential GPS and 5)LIDAR. Amongst these, the ground based method has been demonstrated as the most common and provides continuous measurement at a low maintenance cost without much error. So far , It has been found that the ground based method is the most accurate one to measure the integrated vapor and liquid prevalent in the atmosphere. The multi frequency or at least two selective frequencies namely at 20.6 and 30 GHz radiometric studies may provide the continuous monitoring of integrated water vapour (IVW) and cloud
liquid (LWP). We have chosen 20.6 GHz as this frequency is found to be independent of pressure and hence from collision broadening at the particular place of choice.
5.2.5 Soil Moisture Estimation
Soil moisture is highly correlated to the rainfall and plays an important role in the interactions between land surface and atmosphere; as well it is a key variable to many aspects of agricultural, hydrological, and meteorological research. During the seventies, some scientists discovered the promising possibilities of microwave instruments onboard of satellites in estimating soil moisture. Since then, soil moisture together with rainfall estimation has been used not only to understand the hydrological cycle, but also to improve the predictions of numerical weather forecast models.
The low frequency microwave signals can penetrate clouds and are able to provide physical information of the land surface (Njoku and Li, 1999). Some parameters like emissivity can be retrieved and used to calibrate the sensors on board of the satellites and can also be used to estimate soil moisture content (Wigneron et al., 2003). In Brazil, there are different geographic and climatic conditions that must be studied to increase our understanding about the relationship between rainfall and soil moisture using passive microwave response for different vegetation types and land cover across the country.
Different meteorological systems are found over Brazil and the rainfall observed in each area is caused by different precipitating regimes. The spatial and temporal variability of the rainfall causes impacts on many variables linked to the water cycle, and considering that soil moisture is closely linked to the rainfall
occurrence, it is necessary to assure that different places over Brazil can be used as validating sites for both soil moisture and precipitation estimated by satellite. In short time scales variation in the surface emissivity is related to variations in the soil moisture. Understanding this variation can help estimate precipitation from warm clouds, because it depends from the knowledge of the surface emissivity.
5.2.6 Objectives
The large number of existing algorithms that estimate rainfall raises questions that focus on their efficiency and application. For South America many of these questions have to be answered accurately, as follows:
1. Which uncertainty is associated with the diurnal cycle of rainfall over South America retrieved by infrared and/or hybrid algorithms?
2. Can the rainfall fields calculated from algorithms be validated and adjusted by their pairs calculated by ground radars?
3. Can the rainfall estimated by satellites be used in the study of regional or synoptic meteorological phenomena in South America?
4. What is the minimum acceptable area for the integration of precipitation estimated by satellite?
5. Can the algorithms be used to evaluate possible impacts of the land cover change over Brazil on the local and regional rainfall regime?
6. Is it possible to quantify accurately the amount and intensity of rainfall produced by deep convection and warm clouds?
The answers to the questions above will certainly help many areas and studies to use the precipitation estimating algorithms. In addition, the activities that focus on the development of new algorithms will also benefit from them.
In order to answer the questions listed in previous item the following specific objectives will be achieved:
1. To evaluate, to validate and to calibrate the performance of satellite rainfall algorithms in estimating rainfall produced by different rainfall regimes and precipitating system;
2. To generate a database of DSD estimates for the experimental sites using a disdrometer array;
3. To use the rainfall estimates for case studies of the physical processes of precipitation in deep convections and warm clouds;
