An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.

A radar reflectivity-runoff model for use in flood warning

This model connects directly the radar reflectivity data and hydrological variable runoff. The catchment is discretized in pixels (4 Km × 4 Km) with the same resolution of the CAPPI. Careful discretization is made so that every grid catchment pixel corresponds precisely to CAPPI grid cell. The basin is assumed a linear system and also time invariant. The forecast technique takes advantage of spatial and temporal resolutions obtained by the radar. The method uses only the measurements of the factor reflectivity distribution observed over the catchment area without using the reflectivity - rainfall rate transformation by the conventional Z-R relationships. The reflectivity values in each catchment pixel are translated to a gauging station by using a transfer function. This transfer function represents the travel time of the superficial water flowing through pixels in the drainage direction ending at the gauging station. The parameters used to compute the transfer function are concentration time and the physiographic catchment characteristics. -from Authors

Análise de superfície de tendência aplicada à chuva, medida por radar meteorológico, nas regiões de Assis e Piracicaba, SP

This paper presents the results from lineal trend surface analysis technique application. The purpose was to detect positive and negative anomalies in the rain measure distribution obtained by the meteorological radar Doppler, band S, located in Bauru, during the period of 21 of October/2004 to 29 of April/2005 in the areas of Assis and Piracicaba. Using three Z-R radar relations for rain quantification was chosen the specific equation Z = 32R1,65, as the best one. The results showed that the applied methodology was able to indicate the space distribution of the rain accumulated, identifying and locating the regions where there was rainy excess and rainy lack during each analyzed period. Such results indicate areas with larger pluvial impact and consequently more favorable for environmental damages.

Impacto do balanço hídrico em diferentes tipos de solos: comparação entre dados de radar-pluviômetro e análise de tendência da chuva em área agrícola

Pós-graduação em Geociências e Meio Ambiente - IGCE

Impact of Z-R relationship on flow estimates in central São Paulo

Mean areal radar rainfall over catchments in the State of São Paulo is an operational product under development by the Meteorological Research Institute - IPMet. A pilot project is being carried out which focuses on the important Corumbatai River basin, under surveillance by the IPMet-operated Bauru radar. Previous work on the project explored the relative impact of factors like time resolution of radar data and reflectivity to rain-rate conversion relationships, when the relevance of the latter was verified. This paper deals with the stratification of those relationships by daily intervals and its impact on flow estimates. Daily values of radar mean rainfall using gauges and different conversion relationships are plotted against the corresponding flow at the basin outlet. Flow estimates derived by applying the rainfall from the different relationships to a previously obtained rainfall-runoff curve for the basin is compared to the historical hydrograph. Preliminary results suggest stratification has hydrological significance.

Análise da queda de granizo em Guarulhos-SP e seus impactos socioeconômicos

The climate and weather have direct implications on society, are factors that can to regulate or restrict human occupation in a determined space. In this way, studies of climate and weather are justified to be extremely necessary for urban planning and economic activities, especially agriculture. The precipitation of hail, which enters in the classification of severe storm, causes large, direct and indirect impacts on society, mainly when it occurs in urban areas. This work aims to study the precipitation of hail, explain what is a severe storms and how hail is formed in clouds, making use of a literature review in geography and also in weather sciences. It is also an aim of this work analyze the genesis, evolution and dissipation of a specific case of precipitation of hail occurred in the metropolitan region of São Paulo, especially in Guarulhos, on 21st of September 21 of 2010, through the use of GOES-12’ satellite’s images and the use of São Roque’s weather radar. And in this way, show the potential impact of hail storms in society and contribute to a greater preparedness of the population, urban planners associations and emergency management, such as municipalities, the civil defense and fireman

Sistema baseado em regras fuzzy do tipo Takagi-Sugeno aplicado a ecos de radares meteorológicos

Pós-graduação em Ciências Ambientais - Sorocaba

Avaliação da chuva produzida pelo modelo eta de previsão do tempo para o Estado de São Paulo com uso de radar meteorológico para aplicações agrícolas

Pós-graduação em Agronomia (Irrigação e Drenagem) - FCA

Raman Lidar monitors emissions from sugar cane fires in the State of São Paulo: a pilot-project integrating radar, sodar, aerosol and gas observations

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Atributos polarimétricos de imagem radar na inferência de parâmetros morfológicos de macrófitas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Monitoramento da atenuação natural de pluma de contaminação pelo método de radar de penetração no solo (GPR)

O monitoramento da atenuação natural em áreas contaminadas tem se mostrado uma técnica alternativa e de baixo custo para a remediação de áreas contaminadas. A degradação por microorganismos é um dos processos mais importantes na atenuação natural de contaminantes, especialmente compostos de fase líquida não aquosa (NAPL). em muitos casos, a ação efetiva deste processo resulta na geração de ácidos orgânicos, que sob elevadas concentrações ocasionam a dissolução de minerais presentes em subsuperfície onde se encontra a contaminação, com conseqüente liberação de íons. O aumento na quantidade de íons colabora para o aumento da condutividade elétrica do meio. O princípio físico da técnica de Radar de Penetração no Solo (GPR) é a emissão de ondas eletromagnéticas de alta freqüência. A propagação da onda eletromagnética é condicionada à freqüência de sinal emitido e as propriedades elétricas do meio. O aumento da condutividade elétrica do meio resulta na atenuação do sinal e, por conseqüência, na diminuição da profundidade de penetração da onda eletromagnética. Este fator permite o monitoramento de áreas contaminadas sob atenuação natural a partir de análises temporais com o GPR. Este trabalho apresenta um estudo comparativo entre perfis de GPR adquiridos em 1998 e 2003 em uma área contaminada por compostos de fase líquida leve não aquosa (LNAPL), sob atenuação natural. Os resultados indicam um aumento da condutividade elétrica do meio, a partir da atenuação acentuada do sinal GPR observada nas seções de 2003. Este aumento pode estar associado à liberação de íons por dissolução de minerais, pelo ataque de ácidos orgânicos resultantes do processo de biodegradação.

Weather-based prediction of anthracnose severity using artificial neural network models

Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21.9% for the Australian and 22.1% for the South American model. of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development.

Convective storm initiation in a moist tropical environment

Radar and satellite data from the Tropical Rainfall Measuring Mission-Large-Scale Biosphere-Atmosphere (TRMM-LBA) project have been examined to determine causes for convective storm initiation in the southwest Amazon region. The locations and times of storm initiation were based on the National Center for Atmospheric Research (NCAR) S-band dual-polarization Doppler radar (S-Pol). Both the radar and the Geostationary Operational Environmental Satellite-8 (GOES-8) visible data were used to identify cold pools produced by convective precipitation. These data along with high-resolution topographic data were used to determine possible convective storm triggering mechanisms. The terrain elevation varied from 100 to 600 m. Tropical forests cover the area with numerous clear-cut areas used for cattle grazing and farming. This paper presents the results from 5 February 1999. A total of 315 storms were initiated within 130 km of the S-Pol radar. This day was classified as a weak monsoon regime where convection developed in response to the diurnal cycle of solar heating. Scattered shallow cumulus during the morning developed into deep convection by early afternoon. Storm initiation began about 1100 LST and peaked around 1500-1600 LST. The causes of storm initiation were classified into four categories. The most common initiation mechanism was caused by forced lifting by a gust front (GF; 36%). Forcing by terrain (>300 m) without any other triggering mechanism accounted for 21% of the initiations and colliding GFs accounted for 16%. For the remaining 27% a triggering mechanism was not identified. Examination of all days during TRMM-LBA showed that this one detailed study day was representative of many days. A conceptual model of storm initiation and evolution is presented. The results of this study should have implications for other locations when synoptic-scale forcing mechanisms are at a minimum. These results should also have implications for very short-period forecasting techniques in any location where terrain, GFs, and colliding boundaries influence storm evolution.