ATRIBUTE VECTORS WERE USED AS INPUTS TO A NEURAL NETWORK CONSTITUTED OF 2 LAYERS, WITH 4 NEURONS IN THE INTERNAL LAYER AND 1 NEURON IN THE OUTPUT LAYER; LINE COMMAND WAS:

net2=newff(minmax(p),[4,1],{‘logsig’,’logsig’},’traingda’)

Newff = NETWORK WITH BACK-PROPAGATION

[4,1] = TWO LAYERS, 4 NEURONS IN THE HIDEN LAYER AND 1 NEURON IN THE OUTPUT LAYER; AND

logsig = TRANSFER FUNCTION OF EACH NEURON, DIFFERENTIABLE, WITH OUTPUT BETWEEN 0 AND 1.

RESULTS OF RUNS WITH DIFFERENT NEURAL NETWORKS ARE DEMONSTRATED FOR TWO OF THEM

TRAINNING WAS EFFECTED FOR OUTPUT VALUES BETWEEN O ANO 1, AS:IF OUTPUT ≥ 0.5 → RAIN, OUTPUT< 0.5 → N0-RAIN

“NOWCASTING WITH NEURAL NETWORK USING REFLECTIVITY IMAGES OF METEOROLOGICAL

RADAR”

R. Machado (1,2), C. A. Thompson (2) and R. V. Calheiros (1)

(1) Meteorological Research Institute (IPMet)/UNESP, Bauru, SP, 17033-360, Brazil(2) Polytechnic Institute (IPRJ)/UERJ, Nova Friburgo, RJ, 28601-970, Brazil 

GENERAL: SUPPORT TO OPERATIONAL NOWCASTING IN CENTRAL SÃO PAULO

SPECIFIC: IMPLEMENT A NEURAL NETWORK APPROACH TO IPMET’S OPERATIONAL FORECASTING PRACTICES

AS OF NOW: PRELIMINARY TESTS OF NETWORK PERFORMANCE RUN FOR DISTINCT COMPUTATION OF AVERAGES ON STATISTICAL TEXTURE DESCRIPTORS

PRODUCT: REFLECTIVITY CAPPIS AT 3,5 KM HEIGHT AGL, TO A 240 KM RANGE FROM THE BAURU RADAR (BRU)

PERIOD: SUMMERS OF 2002/2003 & 2003/2004

DATA SET: 300 IMAGES GATHERED IN INTERVALS OF 2 HOURS EACH, A COMPOSING TWO SUB SETS: CHARACTERIZED BY (1) RAIN AND (2) NORAIN SITUATION AT THE END OF THE TIME INTERNAL

TARGET AREA:

STATISTICAL TEXTURE DESCRIPTORS (ATTRIBUTES), I.E. MEAN, STD DEVIATION, SKEWNESS AND KURTOSIS WERE COMPUTED FOR EACH IMAGE

AVERAGES OF THE ATTRIBUTES WERE CALCULATED FOR ALL IMAGES WITHIN EACH 2 H INTERVAL

RESULTED TWO SETS OF ATRIBUTE VECTORS: ONE CORRESPONDING TO THE PROCESSING OF EACH IMAGE, AND THE OTHER FOR THE AVERAGE VALUES OF EACH ATRIBUTE (SEE TABLE 1)

TIME EVENT

ATTRIBUTE12:53 13:23 13:53 14:23 AVERAGES

MEAN 0.1529 0.2059 0.2629 0.2675 0.2223

STD DEVIATION -1.5224 1.8106 1.9487 2.0532 1.8337

SKEWNESS -0.1604 -1.3625 -1.9867 -1.8567 -1.3415

KURTOSIS 9.1118 8.1791 6.2289 5.2513 7.1927

TABLE 1: ATTRIBUTES FOR A SAMPLE EVENT 30-JAN-2004 FROM THE DATA BANK (ALL TIMES LT = UTC – 3)

OBS.: STATISTICS WERE COMPUTED ON THE IMAGE PIXEL IN mm.h-1 DERIVED WITH Z = 300R1,4

A)

B)

DATA SAMPLE:

OBJECTIVES & STATUS

DATA & AREA

PROCESSING

15 KM RADIUS CIRCLE AROUND THE RADAR

0

1i

m

iPa

(OBS. NA AREA PROBABILITY OF RAIN, (a) IF δI IS NA INDICATOR VARIABLE EQUAL TO 1 WHEN RAINS OCCURS AT A POINT 1, AND ZERO, IS FOR BRU, ECHO STATISTICS INDICATES A ~ 0.55 FOR SUMMER).

FIRST NETWORK

1.TRAINING: PERFORMANCE RESULT 2.SIMULATION: ATTRIBUTES FOR EACH IMAGE WERE USED. THE IS FIRST IMAGES IN WERE KNOWN TO RESULT IN RAIN, AND THE LAST 15 IMAGES IN NO-RAIN,

FIRST 15 IMAGES (VALUES SHOULD BE ≥ 0.5)0.8305 0.7978 0.789 0.7882 0.23810.2316 0.3901 0.421 0.897 0.74150.5778 0.6096 0.895 0.8603 0.8477

LAST 15 IMAGES (VALUES SHOULD BE < 0.5)0.1595 0.1132 0.24 0.322 0.27450.2205 0.2051 0.193 0.2726 0.32010.303 0.2559 0.3032 0.3114 0.2616

SECOND NETWORK (TRAINING NOT SHOWN)

SAME ATRIBUTES AND CRITERIA FOR RAIN (≥ 0.5) AND NO-RAIN (< 0.5 ) AS THE FIRST NETWORK, BUT USING AVERAGES OF THE ATRIBUTES TAKEN OVER EACH 2H INTERVAL.

FIRST 15 IMAGES (VALUES SHOULD BE ≥ 0.5)0.7375 0.6675 0.8093 0.8564 0.42180.6752 0.8411 0.9048 0.566 0.81660.972 0.8733 0.9503 0.9561 0.9485

LAST 15 IMAGES (VALUES SHOULD BE ≤ 0.5)0.3749 0.348 0.0304 0.166 0.02230.0519 0.2077 0.1444 0.054 0.02610.2231 0.108 0.081 0.2816 0.263

TABLE 3: RESULTS AT THE OUTPUT OF THE SECOND NETWORK ERRORS (VALUES IN RED) = 1/30 ≅≅ 3% 97% OF SUCCESS

TABLE 2: RESULTS AT THE OUTPUT OF THE FIRST NETWORK ERRORS (VALUE IN RED) = 4/30 ≅≅ 13% 87% OF SUCCESS

TWO SAMPLES (2 H INTERVALS) TAKEN. FOR THE FIRST x1=22, nI =30 AND FOR THE SECOND: x2=79, n2=90, WHERE xi=1,2 = RAIN, n i=1,2 = SAMPLE SIZE.

NULL HYPOTHESIS IS FORMULATED, I. E. RAIN/ NO - RAIN RELATIONS ARE TRUE. x i = 1,2 IS A RANDOM

VARIABLE. MODELING ITS BINOMIAL DISTRIBUTION BY A NORMAL DISTRIBUTION, THE

PROPORTION OF THE SAMPLE TAKEN BY x (θ = X/N) IF UNKNOWN, IS FOR THE

MODELED NORMAL DISTRIBUTION , IF THE RANDOM INDEPENDENT VARIABLES z1

AND z2 HAVE STANDARD NORMAL DISTRIBUTION, THEN (y= z2 + z2) HAS A CHI-SQUARE DISTRIBUTION

WITH m DEGREES OF FREEDOM.

• COMPUTING Y WITH THE ABOVE NUMBERS : θ = 0.84 y = 3.495

• FOR α = 0.05 AND υ(m) = 2 DEGREES OF FREEDOM = 5.991 (>3.495)

• NULL HYPOTHESIS IS SATISFIED TO 95% OF CONFIDENCE, I. E., FORECASTS ARE NOT BIASED.

A)IMPROVEMENT OF PERFORMANCE

A.1) ADD NEW TECHNIQUES, E. G.

• GABOR FILTERING AS A FIRST LAYER IN THE NETWORK SYSTEM TO EXTRACT TEXTURAL FEATURES,

WHICH WILL FEED THE INPUT LAYER OF THE FORECASTING NETWORK (GABOR FILTERING HAS SHOWN TO

IMPROVE THE PERFORMANCE OF NEURAL NETWORKS)• FUZZY LOGIC, DUE TO THE FACT THAT THERE IS NO CLEAR SEPARATION BETWEEN SEASONS, DAILY

INTERVALS, AND OTHER STRAFICATION FACTORS.• GENETIC ALGORITHMS, WHICH USE TECHNIQUES OF BIOLOGICAL DERIVATION THAT COULD BE APPLIED

TO RAINFALL CONFIGURATIONS SUCH AS : HERITAGE ( RAIN AT T0 IS RELATED TO T0 – 1), MUTATION

( RAIN PATTERNS CHANGE STRUCTURE IN TIME), NATURAL SELECTION (PREFERENTIAL DEVELOPMENT

CONDITIONS EXIST), AND RECOMBINATIONS (RAIN CELLS SPLIT AND MERGE IN TIME)

B) VERIFICATION/VALIDATION

CAMPARISONS WITH OTHER NOWCASTING TECHNIQUES EITHER IN TESTS OR OPERATIONAL, OR IN

CONSIDERATION FOR OPERATIONAL USE, AT IPMET FORECASTING SECTOR.

B.1) TITAN (THUNDERSTORM IDENTIFICATION, TRACKING, ANALYSIS AND NOWCASTING) PREDICTING

ECHO CENTROID POSITION EVOLUTION STATUS: UNDER OPERATIONAL EVALUATION

B.2) KAVVAS (ADAPTIVE EXPONENTIAL METHOD) PREDICTING SHORT-TERM EVOLUTION (15 MIN. TO 2 H) OF

CENTROID, BASED ON REFLECTIVITY AND VELOCITY (DOPPLER) STATUS: UNDER STUDY

B.3) VIL (VERTICALLY INTEGRATED LIQUID WATER CONTENT) PREDICTOR IS WATER COLUMN FROM

GROUND TO 12 KM AGL COMBINED WITH PRESENCE OF 45 dBZ ABOVE 3 KM. STATUS : OPERATIONAL

•NEURAL NETWORK APPROCH TO RADAR BASED NOWCASTING IN CENTRAL SÃO PAULO HAS

SHOWN CLEAR POTENTIAL.•STATISTICAL TEXTURE DESCRIPTORS HAVE PROVEN A VALID INPUT TO THE NOWCASTING WITH

NEURAL NETWORK IN CENTRAL SÃO PAULO.•IMPROVEMENTS RESULTING FROM AVERAGING DESCRIPTOR VALUES INDICATES THAT EVEN

RELATIVELY MINOR OPERATIONS ON IMAGE CHARACTERISTICS CAN SIGNIFICANTLY IMPACT

NETWORK PERFORMANCE.•FURTHER IMPROVEMENTS SHOULD BE PARTICULARLY EXPECTED FROM TEXTURE

CLASSIFICATION THROUGH GABOR FILTERING.

21

21ˆnn

xxθ

21

ˆ1ˆ

ˆ

n

nx

A.2) ADD NEW ATTRIBUTES, E. G.• NON-METEOROLOGICAL

–IMAGE ATTRIBUTES LIKE LAPLACE AND GRADIENT OPERATORS FOR EDGE DETECTION–PREDICTING ATTRIBUTES AS A NON-LINEAR TIME SERIES

• METEOROLOGICAL–DOPPLER RADAR WINDS–SATELLITE IMAGES (VIS, IR, WV & MW) INDIVIDUALLY OR IN COMBINATIONS TO INFER, E. G. RAIN/NO -

RAIN THRESHOLD.–VARIABLES, LIKE TEMPERATURE, PRESSURE, HUMIDITY.

CHI – SQUARE TEST

NEXT STEPS

CONCLUSIONS