ERAD THE 7TH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

Similar documents
Impact of ATOVS geopotential heights retrievals on analyses generated by RPSAS

Weather Radar Basics

Note taker: Katherine Willingham (NSSL)

MULTIRESOLUTION WAVELET TRANSFORM AND NEURAL NETWORKS METHODS FOR RAINFALL ESTIMATION FROM METEOROLOGICAL SATELLITE AND RADAR DATA

ANALYSIS OF THUNDERSTORM CLIMATOLOGY AND CONVECTIVE SYSTEMS, PERIODS WITH LARGE PRECIPITATION IN HUNGARY. Theses of the PhD dissertation

Basics of weather interpretation

T.A. Tarasova, and C.A.Nobre

FLOODALERT: A SIMPLIFIED RADAR-BASED EWS FOR URBAN FLOOD WARNING

Guy Carpenter Asia-Pacific Climate Impact Centre, School of energy and Environment, City University of Hong Kong

Weather Help - NEXRAD Radar Maps. Base Reflectivity

Flash Flood Science. Chapter 2. What Is in This Chapter? Flash Flood Processes

Monsoon Variability and Extreme Weather Events

One approach to the use of the practices of CMMI- DEV V1.3 level 2 in a process of development of Embedded Systems

IBM Big Green Innovations Environmental R&D and Services

Improved Warnings for Natural Hazards: A Prototype System for Southern California

CRS 610 Ventura County Flood Warning System Website

FORENSIC WEATHER CONSULTANTS, LLC

In a majority of ice-crystal icing engine events, convective weather occurs in a very warm, moist, tropical-like environment. aero quarterly qtr_01 10

Empirical study of the temporal variation of a tropical surface temperature on hourly time integration

Hurricanes. Characteristics of a Hurricane

Storms Short Study Guide

WxFUSION. A. Tafferner. Folie 1. iport Meeting DLR OP

NowCastMIX. A fuzzy logic based tool for providing automatic integrated short-term warnings from continuously monitored nowcasting systems

Development of an Integrated Data Product for Hawaii Climate

Armenian State Hydrometeorological and Monitoring Service

Estação Meteorológica sem fio VEC-STA-003

Long-term Observations of the Convective Boundary Layer (CBL) and Shallow cumulus Clouds using Cloud Radar at the SGP ARM Climate Research Facility

Nowcasting: analysis and up to 6 hours forecast

Roelof Bruintjes, Sarah Tessendorf, Jim Wilson, Rita Roberts, Courtney Weeks and Duncan Axisa WMA Annual meeting 26 April 2012

European Conference on Severe Storms (ECSS 2007) September 2007

Nowcasting of significant convection by application of cloud tracking algorithm to satellite and radar images

P8.27 TWO RECORD BREAKING AUSTRALIAN HAILSTORMS: STORM ENVIRONMENTS, DAMAGE CHARACTERISTICS AND RARITY

Outline. Case Study over Vale do Paraiba 11 February Comparison of different rain rate retrievals for heavy. Future Work

Project Title: Quantifying Uncertainties of High-Resolution WRF Modeling on Downslope Wind Forecasts in the Las Vegas Valley

MEDIA RELEASE No.4 SEVERE TROPICAL CYCLONE ULA AND TROPICAL DEPRESSION 07F A TROPICAL CYCLONE ALERT IS NOW INFORCE FOR THE LAU GROUP.

BECAUSE WEATHER MATTERS UBIMET

Regional Forecast Center Timişoara 15. Gh. Adam St., Timişoara, Romania,

A.4 SEVERE WEATHER PLAN

Disaster Risk Reduction through people centered National Multi-hazard Early Warning System in the context of Maldives

WIND RESOURCE OF MICROREGIONS IN SOUTH AND NOTHEAST OF BRAZIL: AN EVALUATION OF METEROLOGICAL DATA AND COMPUTACIONAL TOOL

1. Specific Differential Phase (KDP)

Pre-LBA Amazonian Region Micrometeorological Experiment (ARME) Data

Tropical Cyclogenesis Monitoring at RSMC Tokyo Mikio, Ueno Forecaster, Tokyo Typhoon Center Japan Meteorological Agency (JMA)

Networking Break 3:00 pm 3:30 pm

Environmental Data Services for Delaware:

BRAZILIAN CONTRIBUTION TO THE LISN PROJECT

MIAMI-SOUTH FLORIDA National Weather Service Forecast Office

Catastrophe Bond Risk Modelling

CHUVA. by CHUVA Science Team. 4 th CHUVA Planning Meeting 13 December 2010 San Francisco, CA. Rachel I. Albrecht rachel.albrecht@cptec.inpe.

Authors and Affiliations Kristopher Bedka 1, Cecilia Wang 1, Ryan Rogers 2, Larry Carey 2, Wayne Feltz 3, and Jan Kanak 4

UNIT 6a TEST REVIEW. 1. A weather instrument is shown below.

EMERGENCY MANAGEMENT DECISION-MAKING DURING SEVERE WEATHER

Financing Community Wind

RFP GP Clarifications to Bidders III

Standard Operating Procedures for Flood Preparation and Response

The relationship between biomass burning aerosols, cloud condensation nuclei and cloud stucture in Amazonia

2.8 Objective Integration of Satellite, Rain Gauge, and Radar Precipitation Estimates in the Multisensor Precipitation Estimator Algorithm

[ Climate Data Collection and Forecasting Element ] An Advanced Monitoring Network In Support of the FloodER Program

Suman Goyal Sc E /In-charge SYNOPTIC Application Unit Satellite Meteorology Davison

Precipitation forms from water droplets or ice crystals.

CORRELATIONS BETWEEN RAINFALL DATA AND INSURANCE DAMAGE DATA ON PLUVIAL FLOODING IN THE NETHERLANDS

Introduction to the forecasting world Jukka Julkunen FMI, Aviation and military WS

P2.7 Online Weather Studies in a 2-year program in Applied Meteorology at West Virginia State University

Tropical Cloud Population

Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations

A Microwave Retrieval Algorithm of Above-Cloud Electric Fields

Cloud Grid Information Objective Dvorak Analysis (CLOUD) at the RSMC Tokyo - Typhoon Center

Big Data Assimilation Revolutionizing Weather Prediction

ICAO Developments (Hazardous Weather Information)

Activity 8 Drawing Isobars Level 2

MM5/COSMO-DE Model Inter-Comparison and Model Validation

Weather Briefing for Southeast Texas October 24 th, 2015

ANALYSIS OF A ANTROPIC INFLUENCES IN A FLOODPLAN

A Romanian Forecast and Training Experience, August 2002

meteorolodists to access, manipulate and visualise meteorological data on UNIX workstations. Fields can

When the fluid velocity is zero, called the hydrostatic condition, the pressure variation is due only to the weight of the fluid.

WEATHER AND CLIMATE practice test

SIXTH GRADE WEATHER 1 WEEK LESSON PLANS AND ACTIVITIES

Climate Extremes Research: Recent Findings and New Direc8ons

Real Time Flood Alert System (RTFAS) for Puerto Rico

Havnepromenade 9, DK-9000 Aalborg, Denmark. Denmark. Sohngaardsholmsvej 57, DK-9000 Aalborg, Denmark

Evalua&ng Downdra/ Parameteriza&ons with High Resolu&on CRM Data

4.3. David E. Rudack*, Meteorological Development Laboratory Office of Science and Technology National Weather Service, NOAA 1.

Finnish Meteorological Institute s Services for Insurance Sector

Flash Flood Guidance Systems

SITE SPECIFIC WEATHER ANALYSIS REPORT

Snow Measurement Guidelines for National Weather Service Snow Spotters

The spatial and temporal variability of the vertical dimension of rainstorms and their relation with precipitation intensity

The Anatomy of a Forecast

Thomas Fiolleau Rémy Roca Frederico Carlos Angelis Nicolas Viltard.

RBMC: The main geodetic infrastructure contributing for land reform and weather researches in Brazil

2013 Annual Climate Summary for the Southeast United States

JASPERS Networking Platform

Scholar: Elaina R. Barta. NOAA Mission Goal: Climate Adaptation and Mitigation

Tropical Cyclone Report Hurricane Fausto (EP072008) July John L. Beven II National Hurricane Center 19 November 2008

Basic Climatological Station Metadata Current status. Metadata compiled: 30 JAN Synoptic Network, Reference Climate Stations

Comparative Evaluation of High Resolution Numerical Weather Prediction Models COSMO-WRF

CITIES AND CLIMATE CHANGE PROGRAMME

National Weather Service Flash Flood Modeling and Warning Services

Risk and vulnerability assessment of the build environment in a dynamic changing society

Transcription:

Severe Storm in the metropolitan region of Belém: A Case Study Jorge Luís Machado Lopes, Jaci M. B. Saraiva, Carlos Simões Pereira; Sistema de Proteção da Amazônia SIPAM Belém-PA Brasil jorge.lopes@sipam.gov.br, jaci.saraiva@sipam.gov.br, carlos.pereira@sipam.gov.br 1 INTRODUCTION The advent of weather radar came to filling in a gap opened by information with better spatial and temporal resolutions, which are essential in the detection and monitoring of severe storms. Thus, the nowcasting is essential for generating weather alerts in a timely manner for decision making and propose mitigation actions. With 11 weather radar installed in the legal Amazon, SIPAM through the divisions of meteorology (DivMet) of the three RC Regional Centers (Belém, Manaus and Porto Velho) invests in training and improving your body's operational, aimed at forecasting and warning of severe events time, posing potential threat to any human activity with a significant socioeconomic impact. The object of study is the storm which occurred on January 15, 21 that generated a maximum rainfall of 72 mm showing the first 2 hours standard convective rain in the remaining three hours that followed the rain decreased from stratiform to submit feature. This severe event left nearly submerged the city of Belém on several points. In addition to the points of flooding, some trees did not survive the high winds that prevailed heavy rains. 2 - MATERIALS AND METHODS Were used for the production of case study satellite images (GOES 1) and products of the S-band weather radar located in the town of Belém (lat 1º24 'S - long 48º 27' W). The software used to analyze the data collected by weather radar was the TITAN (Thunderstorm Identification Tracking Analysis and Nowcasting). The TITAN has routines for identifying and qualifying storms among them; VIL (Vertically Integrated Liquid) - Calculation of the contents of vertically integrated liquid water; VIL_overhang - VIL calculated from a threshold up and down, subtracting the top of the bottom. This parameter identifies areas of upward flow. As the TITAN also estimates the height of the top, this allows the derivation of the parameter DVIL, VIL density, another important parameter for quantifying the degree of severity. DVIL density (VIL) - Estimates the severity of storms. DVIL The parameter is calculated by the equation: DVIL = [VIL(kg/m 2 )/TOP(m)]x1 Finally parameter SSS (Storm Severity Structure) which is a parameter calculated according to the reflectivity (dbz max ) at the bottom, middle and top of the cell, and a value is distributed throughout the cell is not a point value (Visser 21).

3 - RESULTS AND DISCUSSION 1.3 Analysis of the environment that the storm surge The satellite images (Figure 1) show very cloudy over the region, and in time of 19 UTC (Fig. 1-a) the north shore has a small active cell, one hour later (Fig. 1-b) this cloudy already has line format and intensifies. The growth of this line Cumulonimbus (CB) is coming very fast, its top temperature of-8c in the cells near Belém (Fig.1-c and 1-d). a) b) c) d) Figure 1 Satellite images GOES-12-15/1/21; a) 19 UTC, b) 2 UTC; c) 23 UTC; e d) 21 UTC. The green arrow indicates the location of Belém. Source: CPTEC/INPE (Centro de Previsão de Tempo e Estudos Climáticos Instituto Nacional de Pesquisas espaciais) 3.2 - The storm weather radar with a view of Belém The weather radar of Belém located in the 1º 24 'S 48º 27' W within the metropolitan area. The Figure 2 shows the reflectivity data (dbz), the entire sequence of the storm, which includes the time 19:21 to 21:9, when it begins to dissipate; The 19:21 UTC the storm begins to intensify while the 4km east of the radar (Fig. 2a), 2:9 from the line arrives in Belém where to start precipitation and winds gust at the surface, the cell east of the radar has around 6 DBZ height reaching 13.4 km, the torrential rains are (Fig. 2b). At the time of 2:33 UTC, the gust front moves to the west of the radar, but the city is still under the influence of convective cell but shows lowering winds at the surface, however following intense rain (Fig. 2c). The following image (Fig. 2d) shows a continuation of the storm rainfall without significant surface winds, radar already shows a large area west of the capital convective and stratiform extensive area over Belém.

Figure 2 Representation of the Maximum reflectivity a) 19:21 UTC; b) 2:9 UTC; c) 2:33 UTC e d) 21:9 UTC. Source: SIPAM/CR-BE The Figure 3 shows, for the hours of 19:45 UTC, some parameters that TITAN software provides. Was made a cross-sectional line storm (shown in phantom). This line was 4km east of the radar. In the central part are shown the values of the top of various cells with values between 12.6 and 14.9 km. In the above right side of the figure is a cross section about the line 7 km and below two histograms, one with the parameters VII, mass flow area and volume of precipitation and the last rising the parameters for the probability of occurrence of hail. When calculating the parameter settings of the storm area, volume and flow of precipitation showed the same trend intensified as the histogram shown in figure 3. The storm vertical development cannot be well represented because the storm has occurred very close to the radar, still the right of the graph Figure 5 shows the vertical Development also a few cells. It is noteworthy that the red line that represents the parameter estimation of cell movement (tracking) showed the direction of the storm south of the radar in the region of the neighborhoods most affected (Marco, São Braz and Terra Firme) by storm.

Figure 3 View of the TITAN. Source: SIPAM/CR-BE 3.2.1 - Analysis of the severity indexes calculated by TITAN The severity of the storm can be seen with the data of Table 1, where are recorded the values of VIL, TOP, DVIL, maximum reflectivity index and SSS severity, throughout its development. Given these data, it appears that the parameter SSS was the best qualified the severity of storms in their various stages of development. VIL values range from 5.9 to 66.2 moving to 19:33 UTC 2:9 UTC when the storm reached its peak decreasing to 26.8 to 21 UTC. The findings corroborate Greene and Clark (1972) who concluded that the rapid development of values of VIL is a strong indicator of "explosive development". DVIL values calculated for this storm (Table 1), ranging from 4.94 at the peak of the storm to 2.68 in its decay. Paxton and Shepherd (1993) proposed the use of this parameter to estimate the severity of storms. The same parameter was used by Gomes and Held (24) in the central part of the state of Sao Paulo. DVIL These values are above the values found by Gomes & Held (24) for the central area of São Paulo, where DVIL between 2.3 and 3.3 were considered indication of high winds, deserving a severe storm warning and also above those found by Queiroz (29) who studied severe storms using radar data from Bauru and the São Roque DVIL found values between 1.8 and 3.8. Another relevant parameter is the SSS (Storm Severity Structure), which represented the best variations in several volumes considered, as shown in Table 1.

Table 1: Evolution of Parameters: VIL, TOP, DVIL, Maximum reflectivity e Storm Severity Structure (SSS). Tempo (UTC) 19:33:6 2:9:5 2:33:5 21:9:9 VIL (Kg/m2) 5,9 66,2 47,7 26,8 TOP (Km) 11,9 13,4 1,4 1,1 DVIL (g/m3) 4,2 4,94 4,55 2,68 dbz 64 65 65 56 SSS 2-5 7-8 4-5 2 Table 2 shows the comparison of the values of rainfall in 24 h in which the event occurred, with values recorded for two seasons INMET and the values estimated by TITAN. The automatic station located at Almirante Barroso Avenue recorded 5.4 mm while the conventional station located southeast recorded rainfall of 76.2 mm. The precipitation rate calculated by the radar showed values close to those observed in the two stations of INMET. Table 2: Values of rainfall mm INMET automatic station (Almirante Barroso -48,3 x -1,41) 5,4 TITAN 48,8 INMET conventional station (CEASA -48,4 x -1,43) 76,2 TITAN 63,14 4 - CONCLUSIONS The storm occurred on January 15, 21 had a fast development and reached a wide area allowed to be viewed by GOES satellite image 1. The weather radar located in the city of Belem was used to analyze the same. The TITAN software allowed the calculation of various parameters including parameters indicative of severe storm such as VIL and DVIL, both showed higher values than those found for other regions of the country. The Titan was a good predictor to track and predict the progress of the storm cells to neighborhoods south of the radar where the damage was greatest. The parameter SSS, this storm proved to be very sensitive in monitoring the severity of the storm. 5-REFERENCES GOMES, A M.; HELD, G., 24. Determinação e avaliação do parâmetro Densidade VIL para alerta de tempestade. In: Congresso Brasileiro de Meteorologia, 13, Fortaleza. Anais SBMET, 12 pp. PAXTON, C. H. And SHEPHEAD J. M. S., 1993. Radar Diagnostics parameters as indicator of severe weather in central Florida. NOAA Tech. Memo. NWS SR- 149, 12 pp. QUEIROZ, A P., 29. Monitoramento e Previsão Imediata de Tempestades Severas Usando Dados de Radar. Dissertação de Mestrado, INPE, são José dos Campos, SP. VISSER P. J. M., 21. The Storm -Structure -Severity Method for the identification of convective storm characteristics with conventional weather radar. Meteorological Aplication, 8:1:1-1 Cambridge University Press.