SymposiumClousureNumbers

Registrations:192Participants166Frequent participants138

ARGENTINA 4 AUSTRALIA 1 BANGLADESH 2 BRAZIL 32 CANADA 7 CHINA 16 FINLAND 1 FRANCE 2 GERMANY 8 INDIA 1 JAPAN 8NETHERLANDS 1NEW ZEALAND 1SINGAPORE 2

SOUTH AFRICA 3 SPAIN 1 SWITZERLAND 1 TUNISIA 2 TURKEY 1 UNITED KINGDOM 12 UNITED STATES OF AMERICA 31

Evaluation Registration  …………………………………. 3,8Location  ……………….. ……………………. 4,5Grounds ………………………………….……. 4,3Coffee Breaks …………………………….…. 4,5Oral Session …………………………….……. 4,0Panel discussion …….. ……………………. 3,8Presentation …………………………………. 4,3Training ………………….……………….……. 4,5Staff ……………………….…………………….. 4,7Sound System Lights/Datashow …... 3,6Dinner ……………………………..……………

4,0

WSN12 Suggestion: Hong Kong and South Africa

Observations• network design & data communication• more/better data  & new/proxy observations• data quality & error characterization

Prediction• integration & synthesis of observations

(e.g., feature analysis, trends)

• conditioning of forecast system

(e.g., data assimilation, initialization)

• forecast generation

(heuristic, numerical, hybrid/blending)

• prediction error/uncertainty

(ensembles & probabilistic forecasting)

• predictability (do we understand limitations?)

Translation• understand impact of weather to user

(focus on avoidable weather impacts)

• tailoring to user application

(i.e., translation of weather to impact)

• sensitivity around critical thresholds

User Application• situational context (e.g., operational aspects,

compounding factors)

• risk management & mitigation strategies• decision making & actions

Automation Support• little time for decision & action• very complex situations (e.g., aviation)• repetitive & frequent updates• challenge to capture human expertise in algorithms

Human Involvement• in/over forecast process (quality control, identification

of features/objects, interpretation)

• automation to increase time focusing where it matters

& provide increased services (raise bar)

• user training & trust building• feedback for improvements

Performance Assessment• weather forecast accuracy/skill (situation, scale)• benefits to user (added value to decision making)• requirements definition

(requires end‐to‐end integration)

Some Departing Thoughts on Nowcasting –

MATHIAS STEINER

Dissemination• timely, easy understandable, & relevant to user• gridded, graphics, text & tabular products• dedicated computers, web display, mobile devices, etc.

Last mile is most difficult . . .• sophistication of user (meteorological background,

preparedness for response to alerts, etc.)

• lots of challenges (political, work culture & attitude,

technology, money, time, etc.)

WSN12 

WSN12‐

TRAINING

TRAINING CENTER

• HYDROMETEOROLOGY LAB – IAG USP• INFRASTRUCTURE –

UNIVERISTY OF SÃO PAULO

• EQUIPMENT (RADAR, AWS, DSD, EDDY, BASIN,…)• NOWCASTING (I‐CAST, CARDS, STEPS, SWIRLS,…)• SUMMER (JANUARY TO MARCH)• LATIM AMERICA AND  AFRICA• INVITED INSTRUCTORS (WGNR, EXPERTS)• 20 PARTICIPANTS• SHOWCASE – SÃO PAULO CITY

CONTINENTAL SCALE

RAINFALL OVER SOUTH AMERICARAINFALL OVER SOUTH AMERICA

Augusto J. Pereira FilhoAugusto J. Pereira Filho11

Camila G. M. RamosCamila G. M. Ramos11

John E. JanowiakJohn E. Janowiak22

Viviane B. S. SilvaViviane B. S. Silva33

1DAC/IAG, University of São Paulo, Brazil 2ESSIC, University of Maryland, College Park, USA

3Climate Prediction Center/NOAA, USA

Four Workshop of the International Precipitation Working Group 

October 13‐17, 2008 Beijing, China

<-La

Pla

ta

Amazon ->

S. F

ranc

isco

->

HOURLY ACCUMULATION2003 and 2004

(mm)

(Pereira et al., 2006)

TIME OF MAXIMUM RAINFALL

(mm)

(UTC)

2003 and 2004

(Pereira et al., 2006)

L

H

H

L

H

L

L

LL

H

HH

CONCLUSION• Rainfall Estimation Accumulation Time (worst -> best r2)

Daily (0.10) -> Monthly (0.58) -> Yearly (0.71);

• Rainfall Estimation Bias (worst -> best mm) SON (+80) -> JJA (-80) -> MAM (-20) -> DJF (0)Interior (+) -> Seashore (-)Convection (+) -> stratiform (-);

• CSI > 0.9 April -> September (>200 mm); • CSI < 0.8 January -> December (<150 mm);

• Water management of large watersheds of South America;

• Integration of CMORPH & raingauges;

MESOSCALE

Precipitation Climatology  in São Paulo

Prado, Pereira Filho and Hallak (2006)

PRECIPITAÇÃO ANNUAL SP (mm) 

FRAÇÃO DE DIAS CHUVOSOS  

y = -0,0549Ln(x) + 0,9756ρ2 = 0,669

0,5

0,6

0,7

0,8

0,9

1

0 50 100 150 200 250 300 350 400 450 500

Distância (km)

ρ

micro        meso                                macro

Evento

Anomalias El Niño La Niña Neutro

Positivas 19,6% 9,8% 23,5%

Negativas 11,8% 11,8% 23,5%

Freqüência de anomalias de precipitação média espacial para eventos de El Niño, La Niña e neutros entre 1947 e 1997.

CONCLUSION  •

ANNUAL CYCLE;

•

Circulation (LOCAL <25 km and meso‐escala < 160 km) summer;

•

Synoptic Circulation (> 160 km) – winter;

•

El Niño (+), La Niña (‐) e neutros (+/‐) –

anomalies; 

LOCAL SCALE

URBAM GROWTH IN MASP

2005

ESMPSP 11 e 12/08/2010

DAILY TEMPERATURE 1936 -2005

TEMPERATURE AND RU 1936 -2005

(Shein, 2006)

ANUAL GROUPING 1936-2005

DAILY GROUPING 1936-2005

IMPACTS

• Lightning• Hail• Wind Gusts• Floods• Tornados

Severe Weather in MASP

RMSP: dias 02 e 04 Fevereiro de 2004

Cidade de São Paulo 12/02/2004

GO

ES-

12 IR

ACQUA 20/06/ 2003

RSP - DAEE

(mm h-1)

ZONA SUL

ZONA LESTE

MORUMBI

02-02-2004

(mm)Fonte: OESP

Fonte: OESP

RIO ARICANDUVA

Fonte: OESP

Fonte: OESP

01/02 02/02 04/02

29/01 30/01 31/01

(mm)

PRECIPITAÇÃO DIÁRIA ESTIMADA COM RADAR METEOROLÓGICO

- 2004

SEA BREEZE 2002 -

2004

HEAT ISLAND –

MASP

(Pereira Filho et at., 2002)

# FLOODS

• MICRO CLIMATE CHANGES: -LESS VEGETATION+HORIZONTAL E VERTICAL URBANIZATION+AIR POLLUTION-NOT GLOBAL;

• TAR

+ 2,1O

C;• RAIN + 395 MM;• E + 0,5 M S-1;• S -

1,0 M S-1;

• RH -

7%.

CONCLUSION

MXPOL

Radar Meteorológico Móvel MXPOL – LabHidro.

MXPOL SYSTEM DESCRIPTION

REFLECTOR

Parabolic

Diameter 2.44 m

Antenna Gain 44 dB

HPBW @ 3dB < 1.0o

PEDESTAL

Azimuth scan o to 360o

Elevation scan 0 to 90o

Maximum scan 36º s-1

Pointing imprecision < 0.1o

TRANSMITTER

Magnetron

Frequency 9.3 to 9.5 GHz

Peak power 80 KW

Pulse modulation

PRF 500Hz to 5000Hz

Pulse width 0.2 μs to 2μs

Linear polarization (H,V) simultaneous

Solid state modulator

Duty cycle 0.001

RECEPTION

Two digital channels (H,V)

Radar Noise Figure < 2.5 dB

Dynamic range (H,V) > 80 dB

ADC 14 bits

Local oscillator DAFC

MDS (H,V) -113 dBm @ 2μs

PPI @ 0.6o of Kdp on 1831 UTC on 09 Feb 2007. Brown box with colored dots shows the location of lightning strikes every 5 min. from 1830 UTC to 1850 UTC (red, blue, pink and green).

X‐section of Kdp X‐section of Vr

MXPOL 

PPI 

of 

Zh

@ 

1.0o

at 

1608 

UTC 

on 

16 

February 

2007 

in 

São 

José

dos 

Campos. site (23o11.7’; 45o57.9’)

X‐section of Zh

X‐section of Vr

PPI ‐

Zh PPI ‐

Vr

PPI ‐

ZdrPPI ‐

W

MXPOL PPIs at 0.6o

at 1528 UTC on 26 April 2007 in Barueri City, 

São Paulo, Brazil (23o32.2’S; 46o52.8’

W).

ACQUA/MODIS ‐

Eastern São Paulo 

State on 20 July 2003.

PMSP ‐CADES SP 20/01/2010

RURAL  

URBANO 

20 km