Journal of Aerospace Technology and Management ISSN: 1984-9648 [email protected] Instituto de Aeronáutica e Espaço Brasil
Souza Corrêa, Cleber Statistical Analysis of Spatial and Temporal Variability of Maximum Precipitation Events on the Rio Grande do Sul Journal of Aerospace Technology and Management, vol. 4, núm. 2, abril-agosto, 2012, pp. 227-235 Instituto de Aeronáutica e Espaço São Paulo, Brasil
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Statistical Analysis of Spatial and Temporal Variability of Maximum Precipitation Events on the Rio Grande do Sul
Cleber Souza Corrêa* ,QVWLWXWRGH$HURQiXWLFDH(VSDoR6mR-RVpGRV&DPSRV63±%UD]LO
Abstract: A statistical analysis of precipitation at Rio Grande do Sul State was presented in this article. The aim of this worN was to identify spatial and temporal patterns of maximum precipitation, which was achieved Ey ¿ttinJ a theoretical varioJram in maximum annual rainfalls and its times of occurrence. ,n the literature, it was found that this pattern occurs accordinJ to phenomena typical from middle latitude, such as low and hiJh level Met, and interactions Eetween them. Some years aJo, the relationship Eetween maximum annual rainfalls and synoptic predominant con¿Jurations was found. Therefore, this worN souJht on understandinJ the climatic characteristics that are important in Aerospace and Aeronautics, as extreme weather can cause numerous consequences in these activities. The use and validation of this proposed method would maNe possiEle its application in other reJions of interest in the %ra]ilian Aerospace. 8nderstandinJ these climatoloJical features of the atmospheric circulation dynamics, and analy]inJ maximum annual rainfall would allow a more ef¿cient and appropriate climate trend forecast and its application in aerospace activities. Keywords: Geostatistics, Maximum Annual Rainfall, Low Level Jet.
INTRODUCTION
In Rio Grande do Sul State, the formation of mesoscale States of America (Whiteman et al 7KHH[DPLQD- convective systems (MCS) may be associated with different tion of radiosonde data in cases with and without jet has a spatial and temporal scales, both in the planetary boundary GLIIHUHQWVSHFL¿FKXPLGLW\DYHUDJHRIDERXWJNJ7KH layer (PBL) and mesoscale, with a wide range of mechanisms transport performed of the LLJ overnight associated with responsible for its formation. PDJQLWXGHVRIWKHVSHFL¿FKXPLGLW\RIDERXWJNJFDQ Helfand and Schubert (1995) argue that low-level jets JHQHUDWHDYHU\LQWHQVHÀX[DQGVHYHUHSUHFLSLWDWLRQ&KHQ (LLJ) are the key processes in the transportation balance of et al. (1991) studied cyclones in the lee of the mountains moisture entering the mainland United States overnight. (shortwave) in East Asia, in the Eastern Tibetan Plateau, Higgins et al. (1997) featured a daily cycle of precipita- especially in the summer rainy season over Southeast China tion using hourly observations from 1963 to 1993, revealing and Taiwan area, from May to June. D ZHOOGH¿QHG PD[LPXP QLJKW RQ WKH *UHDW 3ODLQV LQ This period is characterized by severe rainfall and is spring and in summer. DFFRPSDQLHGE\WKHGHYHORSPHQWDQGLQWHQVL¿FDWLRQRI//- During the summer, there was 25% more precipitation 1RUWKZDUG LQ WKH ORZHU WURSRVSKHUH &KHQ DQG J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 227 Corrêa, C.S. /DFNPDQQ GLVFXVVHVWKHFRQWULEXWLRQWKDWWKHmass and energy even with lower magnitude speeds than DGLDEDWLFSRWHQWLDOYRUWLFLW\SURGXFHGLQWKHPD[LPXPLQWHQ- a LLJ acting on synoptic scales typical of mesoalpha (Į) sity of LLJ. This potential link between the bands of front and mesobeta (ȕ), corresponding to spatial scales between precipitation and adiabatic redistribution of potential vortic- DQGNP ity in the horizontal structure of the winds that accompany )ORZVDQG//-LQWKHYHUWLFDOZLQGSUR¿OHLQORZOHYHOV WKH//-DFWLQJGLUHFWO\RQWKHLQWHQVLW\RIWKHVH0DGGR[ in the troposphere have different spatial and temporal and Doswell III (1992) showed that the PBL may produce scales, featuring a wealth of combinations of structures heat by convection and by condensation, and heat advection in PBL and the lower atmosphere, connecting with meso in the lower troposphere. to continental scale. This dynamic structure at low atmo- This process is associated with the circulations of the type sphere levels results in a layered structure, in which there 6WUHDPV//-ZKLFKSHUIRUPWKDWWUDQVSRUWRIVHQVLEOHDQG LVERWKLWVLQÀXHQFHDWDQLQWHUPHGLDWHOHYHO K3D DV latent heat in the lower atmosphere, dominating the differ- WKHORZHVWOHYHORIDURXQG K3DIRUFRQYHFWLYHV\VWHPV ential vorticity advection in the middle troposphere, forcing over Rio Grande do Sul. An important effect of LLJ is the upward vertically, resulting in the organization of convective daily cycle of moisture as water vapor, and the convergence events and generating MCS. of transport within the PBL implies a kind of moisture Uccellini and Johnson (1979) show that the high level jet storage over large areas and basins. These can generate (HLJ) and LLJ can often be coupled by the adjustments of the meteorological phenomena, such as restricted visibility by vertical mass, which occurs with the spread of HLJ. haze and fog and cloud layers and type as low stratus and This transport in the lower troposphere helps creating a stratucumulus. In cases where there is a positive balance, favorable environment for the development of severe thun- this moisture storage becomes a source of water vapor for derstorms, especially when the interaction of the jets occurs future convection occurrence. Therefore, this structure WKURXJKWKHLQWHUVHFWLRQRIWKHD[HVSHUSHQGLFXODURFFXUULQJ GDLO\WUDQVSRUWSOD\VDQH[WUHPHO\LPSRUWDQWUROHLQZDWHU within the region's output of HLJ. balance of a watershed in middle latitudes. In South America, the work of Abdoulaev et al. (1996) 7KHUHDUHIHZVFLHQWL¿FVWXGLHVOLQNLQJWKH//-HYHQWVZLWK VKRZHGWKDWWKH0&6PD\EHUHVSRQVLEOHIRUDVLJQL¿FDQW high rainfall, and the majority of studies on this topic are case portion of precipitation that occurs in Southern Brazil, occur- ones with reanalysis data of global models, which according ring in at least 13 events per year, with intense rainfall and WR%HUEHU\DQG&ROOLQL OLPLWVWKHH[SORLWDWLRQRIUHVXOWV may be associated with LLJ intense. due to low resolution of the data. Abdoulaev et al VWXG\LQJQRQOLQHDUV\VWHPV 'H %DUURV DQG 2\DPD DQDO\]HG WKH ZHDWKHU with mesoscale severe convection, observed the occur- systems associated with the occurrence of precipitation in UHQFHRIDKLJKUDLQIDOOSUREDELOLW\H[FHHGLQJPPKDW WKH$OFkQWDUDODXQFKEDVHEHWZHHQDQG,WDLPHG about 6 oclock in the morning, with a seasonal variability at characterizing the systems associated with precipitation between the beginning and end of the convective cells. In HYHQWVLQWKH$OFkQWDUD/DXQFK&HQWHU &/$ EHWZHHQ summer, on average, the precipitations have a cycle of 6 DQGXVLQJWRWDOKRXUVRISUHFLSLWDWLRQ1&(3UHDQDO\VLV WRKRXUVDQGPRVW0&6HQGOLIHEHWZHHQDQG GDWDRI1&(31&$5EULJKWQHVVWHPSHUDWXUHRIWKHVDWHOOLWH hours. This led Abdoulaev et al WRFRQFOXGHWKDW *2(6DQGORQJZDYHUDGLDWLRQ7RWKLVHQGZHGH¿QHG the cycle of night and early morning storms are amplified criteria to identify the weather systems of large, meso and and modulated by the convergence of water vapor, which local scale precipitation associated with the CLA, the results is mainly characterized by the LLJ. ZHUHWKDWRIWKHSUHFLSLWDWLRQSURFHVVHVZHUHVWUDWLIRUPHG ,Q5LR*UDQGHGR6XO&RUUrD REVHUYHGLQUDGLR- GHULYHGDQGFRQYHFWLYH sonde data held in Porto Alegre and Uruguaiana the dynamic This paper attempts to test the methodology of Geosta- VWUXFWXUHRI6WUHDPV//-LQWKHWUDQVSRUWSURFHVVDWORZOHYHOV WLVWLFVFDOFXODWLQJWKHVHPLYDULRJUDPRIPD[LPXPUDLQIDOO Flows in the vertical wind profile can be defined as and using it in future researches to the region of Alcântara, streams that have varying speeds of the order of 5 ms-1 in Maranhão. The use of data observed in this study, by WR//-RQHV DERXWPV-1), and they do not also present FDOFXODWLQJWKHVHPLYDULRJUDPRIPD[LPXPSUHFLSLWDWLRQ the vertical structure of the jet. The flows are significant is an attempt to identify spatial and temporal patterns in because they may be associated with the convergence of SUHFLSLWDWLRQPD[LPXP 228 J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 6WDWLVWLFDO$QDO\VLVRI6SDWLDODQG7HPSRUDO9DULDELOLW\RI0D[LPXP3UHFLSLWDWLRQ(YHQWVRQWKH5LR*UDQGHGR6XO DATA AND METHODOLOGY a function of m that is used to estimate the variogram. It is assumed the hypothesis "Matheron" and also that the process is )ORZVRIZDWHUYDSRUDUHLQWKHYHUWLFDOSUR¿OHRIZLQGDQG second order stationary, in which the average random process LLJ direction and they have variable frequency, are associated LVJHQHUDOO\FRQVWDQWWKDWLVWKHH[SHFWHGYDOXHE[Z(x)] ȝ. It with weather systems, and may be responsible for generating is also assumed that the variance is constant (var[Z(x)] ı2), part of the convection and, hence, rainfall. These precipita- WKHFRYDULDQFHH[LVWVDQGLWLVRQO\GHSHQGHQWRQWKHGLIIHU- WLRQVYDU\LQWLPHDQGVSDFHDVZHOODVWKHLUPD[LPXP ence between Z(x) and Z(x h), represented by Eq. 5 and 6: 7RVWXG\WKHUHODWLRQVKLSEHWZHHQWKH//-DQGWKHPD[L- mum precipitation, precipitation data at 52 rainfall stations C(h) = E[{Z(x) - ȝ}{Z(xh) - ȝ}], (5) of the Agência Nacional de Águas (ANA) in the state of Rio Grande do Sul were analyzed. We attempted to use only and observation stations with complete series. It was calculated, using the statistical software GENSTAT© C YDU>Z(x)] = E[{Z(x) - ȝ}2] (6) PD[LPXPUDLQIDOOLQRQHWZRWKUHHIRXUDQG¿YHGD\VIRU HDFKVWDWLRQFRPSULVLQJWKH\HDUVIURP WR$OVRWKH The covariance is correlated by the semi-variogram by Eq. 7: GD\ZKHQWKHHYHQWRIPD[LPXPSUHFLSLWDWLRQKDSSHQHGZDV determined, followed by a procedure similar to that used for the Ȗ(h) = C C(h). (7) HYHQWVIRURQHWZRWKUHHIRXUDQG¿YHGD\VIRUHDFKVWDWLRQ This statistical analysis used a model implemented in To calculate the variogram, the "FVARIOGRAM" func- GENSTAT© that has the method of the semi-variogram, allow- WLRQLVXVHGZKLFKFUHDWHVDVHPLYDULRJUDPH[SHULPHQWDOVHW ing a quantitative representation within the range of a localized of variable values Z(x) distributed into one or two dimensions, SKHQRPHQRQ +XLMEUHJWV ZKLFKFDQEHH[SUHVVHGDV(T XVLQJWKHIRUPXODGH¿QHGE\(T Z(x) ȝ İ(x), (1) 1 m (h ) Ȗ^ = {Z (xi)-Z(xi +h)}2 2m (h) i =1 where, Z(x) is the value of the random variable, ȝ is the mean of the variable Z(x), where: and the term İ(x) is a self-correlated random function with Z(xi) and Z(xi h) are the values of the position xi h. The ]HURPHDQDQGYDULDQFHGH¿QHGE\(T term m(h) is the number of pairs for comparison, which contributes to the estimate. As the data show scattering irregu- var[İ(x) - İ(xh)] = E[{İ(x) - İ(xh)}2], (2) lar spatial precipitation, h is discretized in such a way that its value is constant, the variation in the distance increase and and h is the distance between the point x and x h. It is the same change of direction are considered to be isotropic. assumed that, on average, Z is constant in space such as Eq. 3: 7KH)9$5,2*5$0IXQFWLRQFDOFXODWHVWKHH[SHULPHQWDO semi-variogram. With the estimated semi-variogram already E[Z(x) - Z(xh @ GRQHZHKDYHWULHGWR¿WDPDWKHPDWLFDOPRGHOIRUWKHVHPL variogram obtained. The degree of dependence between points x and x h is In this analysis, we used a semi-linear variogram, by represented by the variogram Ȗ(h ZKLFKLVGH¿QHGDVWKH using the "MVARIOGRAM" in GENSTAT©. Table 1 shows PDWKHPDWLFDO H[SHFWDWLRQ RI WKH VTXDUH RI WKH GLIIHUHQFH DQH[DPSOHRIWKHHVWLPDWHRIWKHFDOFXODWLRQRIWKHVHPL between the values given by Eq. 4: YDULRJUDPLQDSHULRGRID\HDUEDVHGRQREVHUYDWLRQSRVWV DQGDPD[LPXPVSDFLQJRIPDQGDOHQJWKRIZDV var[Z(x) - Z(xh)] = E[{z(x) - Z(xh)}]2 = Ȗ(h) (4) considered when calculating the semi-variogram, homogene- ity in all directions. Where N is the number of observations, The variance depends not only on the distance h and the h is the length and V is the estimated variance of the semi- position x. The amount is Ȗ semi-variance, which in turn is variogram (Table 1). J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 229 Corrêa, C.S. Table 1. Estimations of computing the semi-variogram. WKHPRGHOH[SODLQVGDWDYDULDQFH7KXVDYDULDQFHRIDERXW N h V ZRXOGEHLQWHUSUHWHGDVUHVXOWLQJIURPDVWURQJOLQHDU relationship, which can be interpreted as absence of spatial or 27 6715.9259 temporal consistency of precipitation. 54 0RUHRYHUFORVHWR]HUR ZRXOGEHW\SLFDORID 56 low-linear relationship, which could be interpreted as the 1.135 absence of spatially and temporal uniform precipitation. As 66 1.363 the theoretical model has a linear adjustment, it is observed in 69 1.623 the calculation of linear regression model that the best adjust- 64 PHQWH[SODLQVPRVWRIWKHYDULDQFHRIWKHVSDWLDORUWHPSRUDO 62 2.121 7393.1452 PD[LPXPSUHFLSLWDWLRQV 53 The method was used to perform a linear adjustment of 61 2.624 WKHVHPLYDULRJUDP,QWKLVW\SHRIDQDO\VLVPRUHFRPSOH[ nonlinear models as model "boundedlinear" and Gaussian 3.142 could be used. However, it was used the linear model, which 93 although simple has good results. This analysis also tried to 3.626 consider the spatial homogeneity of variance, however this 71 15791.6127 could not be considered, which would bring the calculation of 73 12657.9932 the semi-variogram with estimates of predominant directions. 7KHDQQXDOUHVXOWVRIWKHYDULDQFHH[SODLQHGE\WKHOLQHDU 4.629 PRGHOIRUHDFK\HDUEHWZHHQ DQGDUHFRPSDUHG 25 ZLWK WKH VLJQDO RI (O 1LxR6RXWKHUQ 2VFLOODWLRQ (162 obtained on the website www.cpc.ncep.noaa.gov, with the aim After this calculation is performed, an adjustment of a of correlating changes in scale with the spatial and temporal linear estimate of the semi-variogram, obtaining an estimate WUHQGVRIWKHPD[LPXPSUHFLSLWDWLRQ RIWKHSHUFHQWDJHRIYDULDQFHH[SODLQHGE\WKHPRGHOFDQEH Figure 2 shows the spatial distribution of jobs and Table 2 VHHQLQWKHOLQHDU¿WRIWKHPRGHOLQ)LJ shows the 52 rain gauge stations of the National Water Agency Linear (ANA) on the state of Rio Grande do Sul. × 16000 × × × × 14000 × -28 × 12000 × 10000 × -29 × × × × Variance 8000 × × × × × × -30 6000 4000 -31 Latitude (Degrees) 2000 0 -32 01234 5 Log Distance -33 Figure 1. Adjustment of the linear model to the semi-variogram. -57 -56 -55 -54 -53 -52 -51 This analysis uses a statistical hypothesis that performs Figure 2. Spatial distribution map of rainfall and weather stations the calculation of the ratio of the variance, showing how in Rio Grande do Sul. 230 J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 6WDWLVWLFDO$QDO\VLVRI6SDWLDODQG7HPSRUDO9DULDELOLW\RI0D[LPXP3UHFLSLWDWLRQ(YHQWVRQWKH5LR*UDQGHGR6XO Table 2. Rainfall stations of ANA in Rio Grande do Sul. Code Locality Latitude Longitude Network Passo Tainhas ANA Antonio Prado ANA Passo do Prata -51.45 ANA Passo Migliavaca ANA Prata -51.62 ANA Encantado -29.23 ANA Nova Palmira -29.33 -51.19 ANA Porto Garibaldi ANA São Vendelino -59.19 -51.37 ANA Sapucaia do Sul ANA Guaiba Country Club -51.65 ANA Quiteria ANA São Lourenço do Sul -31.37 -51.99 ANA Bouqueirão ANA Passo do Mendonça ANA Santa Clara do Ingai -53.19 ANA Botucarai -29.72 ANA Dona Francisca -29.63 -53.35 ANA Canguçu -31.39 ANA Granja São Pedro -31.67 ANA Ponte Cordeiro de Farias -31.57 -52.46 ANA Pedras Altas -31.74 -53.59 ANA Pinheiro Machado ANA Granja Coronel Pedro Osório -52.65 ANA Granja Cerrito -32.35 -52.54 ANA Granja Santa Maria -52.56 ANA Arroio Grande -32.24 ANA Granja Osório -32.95 -53.12 ANA Herval ANA Paim Filho -51.77 ANA Sananduva -27.95 ANA Erebango ANA Linha Cescon ANA Alto Uruguai -54.13 ANA Porto Lucena ANA Carazinho -52.79 ANA Colonia Xadrez -52.75 ANA Giruá -54.34 ANA Conceição -53.97 ANA Passo Major Zeferino -54.65 ANA Passo Viola ANA Garruchos -55.64 ANA Passo do Sarmento -55.32 ANA Fazenda S. Cecília de Butui ANA Itaqui -29.12 -56.56 ANA Cacequi ANA Ernesto Alves -29.37 -54.73 ANA Furnas do Segredo -29.36 ANA Jaguarí -29.49 -54.69 ANA Cachoeira Santa Cecília -55.47 ANA Passo Mariano Pinto -29.31 ANA Plano Alto -29.77 -56.52 ANA J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 231 Corrêa, C.S. RESULTS 7DEOHVKRZVWKHUHVXOWVRIWKHOLQHDU¿WRIWKHYDULRJUDPFRKHUHQFHVSDWLDODQGWHPSRUDO DQG models, calculated by presenting their respective percentages 7KHVH\HDUVKDGDQH[SODLQHGYDULDQFHZLWKPRUHPHDQLQJIXO RIYDULDQFHH[SODLQHGE\WKHOLQHDUPRGHO7KHUHVXOWVVKRZ values for intensities and scales of two, three, and four days. WKHH[LVWHQFHRIVSHFL¿FVSDWLDODQGWHPSRUDOFRKHUHQFHRIWKH Such characteristics show weather systems, which cycles have PD[LPXPUDLQIDOOLQVRPH\HDUVLQWKHSHULRGEHWZHHQ a tendency of being timescale of the order of one week. DQGRQWKHVWDWHRI5LR*UDQGHGR6XO During the formation of a weather system with these 1RWHWKDWLQWKLVSHULRGWKHUHZHUHIRXU\HDUVZLWKVLJQL¿FDQW features, in the timescale of a week, there is a combination of 7DEOH3HUFHQWDJHRIYDULDQFHH[SODLQHGE\WKHDGMXVWPHQWRIWKHOLQHDUPRGHOEHWZHHQ WRDERXW5LR*UDQGHGR6XO6WDWH Number of Year Percentage (%) Observations 0D[,B 0D[,B 0D[,B 0D[,B 0D[,B 'LD0B 'LD0B 'LD0B 'LD0B 'LD0B 1971 33 24 75 71 59 1972 52 59 57 57 63 16 1973 42 53 47 57 32 36 32 25 1974 51 55 43 47 46 45 72 56 52 34 1975 35 39 39 43 49 71 26 33 35 1976 51 15 32 32 7 3 33 1977 49 16 19 14 11 52 42 65 74 79 76 67 1979 15 13 33 7 52 4 2 51 14 25 56 15 49 63 67 63 66 26 37 74 53 5 21 43 49 2 23 15 49 57 75 62 69 45 42 45 79 17 49 41 43 7 49 67 79 79 33 3 49 37 41 46 42 3 4 29 52 27 1 22 4 4 46 15 12 1991 42 13 26 33 33 32 19 5 9 1992 47 1 1993 45 2 7 16 1994 46 9 14 1 9 1995 44 27 46 66 62 3 1996 42 5 9 44 33 31 7 1997 32 36 25 61 15 52 46 47 2 12 64 56 1999 45 35 53 15 57 46 11 12 35 17 41 71 56 55 12 Average 26 32 34 36 13 16 21 21 19 SD 4 24 29 26 19 22 23 24 26 232 J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 6WDWLVWLFDO$QDO\VLVRI6SDWLDODQG7HPSRUDO9DULDELOLW\RI0D[LPXP3UHFLSLWDWLRQ(YHQWVRQWKH5LR*UDQGHGR6XO GLIIHUHQWW\SHVRIXSULJKWÀRZRU//-ZKLFKVKRZVPD[LPD KDYHGLIIHUHQWVL]HVDQGOLIHF\FOHV7KHH[LVWHQFHRIFOLPDWH and minima patterns of behavior. They, when associated with variability in the state of Rio Grande do Sul implies that rain- WKHYDOXHVRIDFHUWDLQPDJQLWXGHRI//-DQGPD[LPXPSUHFLSL- fall varies in the four seasons, both in intensity and duration tation, are connected to a particular group of weather systems DQGLQDUHDRIFRYHUDJH5DRDQG+DGD GHPRQVWUDWHG (atmospheric waves, and locking systems MCS), which are part WKHH[LVWHQFHRIVLJQL¿FDQWFRUUHODWLRQEHWZHHQSUHFLSLWDWLRQ of a cascade structure comprising the variable climate in this in the spring in Rio Grande do Sul and the ENSO signal respect, when this occurs, these structures can generate possible during the same season or earlier, with the possibility of in-homogeneities, both spatial and temporal precipitations. predicting seasonal rainfall. $FFRUGLQJWR7DEOHIRU DQGWKHUHLVVSDWLDOFRKHUHQFHLQ 7DEOH &RPSDULVRQRIWKHSHUFHQWDJHRIYDULDQFHH[SODLQHGE\ WKHPD[LPXPUDLQIDOOEXWWHPSRUDOFRKHUHQFHZDVQRWREVHUYHG the adjustment of the linear model with the ENSO signal, Table 4 shows that El Niño had two predominant situations, EHWZHHQ WR WKHSUHGRPLQDQFHRIVSDWLDOFRKHUHQFH\HDUV ¿YH\HDUV DQG Year El Niño La Niña No Sign of ENSO \HDUVZLWKRXWVSDWLDODQGWHPSRUDOFRKHUHQFHV ¿YH\HDUV ,Q 1971 2 La Niña years, there were three situations, namely: three years 1972 3 with temporal coherence, three years without spatial coher- 1973 3 ence, and temporal and spatial coherence in four years. 1974 4 7KH\HDUV DQG VKRZHGVSDWLDODQG 1975 2 temporal coherence and they are also from El Niño, La Niña, 1976 1 and ENSO unsigned. 1977 1 The spatial coherence years were divided into years with 4 El Niño and La Niña, but the El Niño years have been charac- 1979 3 terized by two predominant situations, with a spatial coherence 1 and the other without spatial and temporal coherences. The 1 weather systems of these periods had a dynamic, in which 3 WKHUHZDVWKHSUHGRPLQDQFHRIFHUWDLQVSHFL¿FPHWHRURORJLFDO 3 scales generating spatial coherence. 1 ,Q(O1LxR\HDUVWKH+/-RQ6RXWK$PHULFDH[KLELWJUHDWHU 4 intensity and persistence than in other phases of ENSO, and, 3 consequently, a greater intensity of LLJ in these periods. The 3 passage of frontal systems, such as short-waves, mesoscale 1 convective and other weather systems with organized convec- 1 WLYHVWUXFWXUHVWHQGWREHFKDUDFWHUL]HGZLWKPD[LPXPVSDWLDO 1 coherence, but these weather systems were not the same as 1991 1 the standard for consistency over time, which can be seen in 1992 1 (O1LxR\HDUVLQ7DEOHVDQG1LFROLQLDQG6DXOR 1993 1 Liebmann et al DQG6DXORet al 1994 1 The physical processes that are involved in the generation 1995 3 of precipitations as in MCS have different spatial dimensions, 1996 1 and their formation is associated with different meteorological 1997 4 mechanisms, instabilities forming isolating or forming groups, 2 as in cells Cumulunimbus (Cb) (a few miles) to clusters Cb in a 1999 3 PHVRVFDOHFRQYHFWLYHFRPSOH[ 0&& ZLWKWHQVWRKXQGUHGV 3 RINLORPHWHUV7KHUHIRUHWKHPD[LPXPUDLQIDOOLQVSDFHPD\ vary from a few to hundreds of kilometers. Data from ENSO. Source: www.cpc.ncep.noaa.gov. This is an important feature of the precipitation, since the QRFRQVLVWHQWVSDWLDOWHPSRUDO ZLWKWHPSRUDOFRKHUHQFH generation mechanisms of MCS not always repeat in time, ZLWKVSDWLDOFRKHUHQFH ZLWKFRQVLVWHQWVSDWLDOWHPSRUDO J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 233 Corrêa, C.S. 7KHPD[LPXPSUHFLSLWDWLRQLQ DQG is very important in the maintenance and generation of baroclinic showed no spatial coherence, but showed consistency over DQGFRQYHFWLYHSURFHVVHV7KLVUDQJHLVFKDUDFWHUL]HGE\ÀRZV time. This fact is interesting because it was La Niña years WKDWRFFXULQWKH3%/ZKLFKLVWKHPD[LPXPWUDQVSRUWLQWKH DQG ±DQG PRGHUDWHWRVWURQJ WKHFOLPDWLF YHUWLFDOZLQGSUR¿OHRIDVWUXFWXUHZLWK//-7KHUROHRI//- situation characterizes periods of drought, causing a tendency within this turbulent structure is to be a link between the different to decrease frequency and intensity of weather phenomena VSDWLDOVFDOHVRIPHVRPHVRDQGPHVRFRQWLQHQWDOVFDOHV with different scales on Rio Grande do Sul. ,QWKLVVLWXDWLRQLQHDFKVSDWLDOVFDOHPD[LPXPUDLQIDOOV Another important point illustrated in Table 3 shows the situa- are associated. Therefore, in heavy rainfall periods, they are tion in which the lack of spatial and temporal coherence, namely, associated with large-scale meteorological vertical develop- ment in the atmosphere, whether as locks, associated with the 1993, 1994, 1996, is distributed in all stages of ENOS signal. FRQ¿JXUDWLRQRI+/-ZKHWKHUDVYRUWLFHVFROGDWDOWLWXGHLQ These periods have not showed characteristics of spatial and the atmosphere and the years of ENSO (El Niño), with strong temporal coherences, which can result in characterizing the physi- LLJ. This structure of continental scale and meso, with strong FDOSURFHVVLQWKHJHQHUDWLRQRIPD[LPXPSUHFLSLWDWLRQRI\HDUV //-FDQJHQHUDWHPD[LPXPSUHFLSLWDWLRQZLWKUHJXODUGLVWUL- ,WVKRZVWKHH[LVWHQFHRIDZLGHYDULHW\RIZHDWKHUDQGVZLWFKLQJ bution in space and time, the size of the spatial and temporal scales of different types of weather structures in space and time. scale involved, as the year of 1997. The different types of weather systems had relations in space, In other situations, the LLJ may be weaker and predomi- and time variations can occur only at mesoscale, or act in concert nant in the order and mesoscale, which can generate and with other scales (meso to meso, meso and macro, macro to maintain structures and baroclinic instabilities that can cause macro scale), resulting in changes in the characteristics of weather irregular or regular precipitations in space and time. In this systems, and may intensify them or diminish them in intensity. FRQWH[WZLWKLQWKLVG\QDPLFVWUXFWXUHGHVFULEHGWKHÀRZRU The combinations of these structures show a trend of stabil- LLJ always occurs, independent of whether there is spatial ity and uniformity in growth, development, and persistence homogeneity or temporal variability of rainfall. of weather systems, hence, an increase of spatial coherence, Therefore, the same weather scale may act differently in but these same relationships may or may not have temporal WKHJHQHUDWLRQRIPD[LPXPUDLQIDOOIHDWXULQJDYHU\FRPSOH[ FRKHUHQFH$QH[DPSOHRIWKLVLVZKHQDUHDVDUHIRUPHGE\ structure that comprises a set of dynamic weather systems, divergent instabilities associated with HLJ, they may have DQGZKLFKWRJHWKHUIRUPWKH¿HOGRISUHFLSLWDWLRQDQGLV irregularities on a spatial region of the order of mesoscale. LQÀXHQFHGE\ORFDOIDFWRUVZLWKUHJLRQDODQGODUJHVFDOHV ,WLVGH¿QHG0D[,B0D[,B0D[,B0D[,B DQG0D[,BZLWKDPD[LPXPRIRQHGD\RIUDLQDQGWKH PD[LPXPWKDWRFFXUUHGLQWZRWKUHHIRXUDQG¿YHGD\V CONCLUSIONS 7KH'LD0B'LD0B'LD0B'LD0B'LD0BDUH GH¿QHGDVWKHGD\VRIWKH\HDULQZKLFKWKHVHHYHQWVPD[LPXP In this study, it was observed that in La Niña years, SUHFLSLWDWLRQRFFXUUHGWKXVH[DPLQHGWKHWHPSRUDOYDULDELOLW\ there is no predominant feature that characterizes an ENSO :KHQWKLVGLYHUJHQFHDVVRFLDWHGZLWK+/-LVLQWHQVL¿HGDQG VLJQDOGH¿QHGVSDWLDORUWHPSRUDOFRKHUHQFHFKDUDFWHUL]LQJ deepened, generating a wave structure in the atmosphere (tropi- WKH DEVHQFH RI WUHQG IRU WKH PD[LPXP DQQXDO UDLQIDOO LQ cal cyclones) with a higher spatial and temporal scales, the level those years. However, during the El Niño, there was well- of continental scale leads to a dynamic structure and a possibly characterized evidence for the spatial coherence tendency. PRUHKRPRJHQHRXV¿HOGRISUHFLSLWDWLRQ,Q(O1LxR\HDUVWKH Therefore, the use of Geostatistical analysis calculating the HLJ are more intense and persistent and they are associated with VHPLYDULRJUDPDQG¿WWLQJDOLQHDUPRGHOSURYLGHGLQIRUPD- //-FRQ¿JXUDWLRQVWKDWKDYHDWHQGHQF\WRVSDWLDOKRPRJHQHLW\ tion of the spatial and temporal coherences of rainfall on the The physical mechanisms of weather systems are strongly state of Rio Grande do Sul. These results could obtain char- baroclinic (turbulent), however, through their relationships DFWHULVWLFVRISUHGRPLQDQFHRIWKHPD[LPXPDQQXDOUDLQIDOO between the different meteorological scales involved, their and their application to forecast climate trends. Consequently, physical properties in space and time change, both intensify and it enabled its use in planning activities in aviation airports in weaken these systems. There is, within this dynamic system, a Southern Brazil, and the estimation of the most likely year for range of interaction between different synoptic structures, which the Aerospace activities. 234 J. Aerosp. Technol. Manag., São José dos Campos, Vol.4, No 2, pp. 227-235, Apr.-Jun., 2012 6WDWLVWLFDO$QDO\VLVRI6SDWLDODQG7HPSRUDO9DULDELOLW\RI0D[LPXP3UHFLSLWDWLRQ(YHQWVRQWKH5LR*UDQGHGR6XO REFERENCES Abdoulaev, S. et al ³6LVWHPDV GH 0HVRHVFDOD GH +XLMEUHJWV&- ³5HJLRQDOL]HGYDULDEOHVDQGTXDQWLWD- Precipitações no Rio Grande do Sul. Parte 2: Tempestades tive analysis of spatial data, In: Davis, J. C., McCullagh, M. em Sistemas não lineares de convecção severa, Revista J. (ed.), Display and analysis of spatial data, New York, John %UDVLOHLUDGH0HWHRURORJLD9RO1RSS :LOH\SS Abdoulaev, S. et al ³,QWHUQDO6WUXFWXUHRI1RQ/LQH /DFNPDQQ * 0 ³&ROGIURQWDO SRWHQWLDO YRUWLFLW\ Mesoscale Convective System in Southern Brazil, In: PD[LPDWKHORZOHYHOMHWDQGPRLVWXUHWUDQVSRUWLQH[WUDWURSL- Congresso Brasileiro De Meteorologia, Campos do Jordão, FDOF\FORQHV´0RQWKO\:HDWKHU5HYLHZ9ROSS $QDLVYSS Liebmann, B. et al³6XEVHDVRQDO9DULDWLRQVRI5DLQIDOO 'H%DUURV662\DPD0'³6LVWHPDV0HWHR- in South America in the vicinity of the Low-level Jet East of the rológicos Associados à Ocorrência de Precipitação no Centro Andes and Comparison to those in the South Atlantic Conver- de Lançamento de Alcântara, Revista Brasileira de Meteoro- JHQFH=RQH´-RXUQDORI&OLPDWH9ROSS logia, Vol. 25, No. 3, pp. 333-344. 0DGGR[5$'RVZHOO,,,&$ ³$QH[DPLQDWLRQRIMHW %HUEHU\(+&ROOLQL($³6SULQJWLPHSUHFLSLWDWLRQ VWUHDPFRQ¿JXUDWLRQVPEYRUWLFLW\DGYHFWLRQDQGORZOHYHO DQGZDWHUYDSRUÀX[FRQYHUJHQFHRYHUVRXWKHDVWHUQ6RXWK WKHUPDODGYHFWLRQSDWWHUQVGXULQJH[WHQGHGSHULRGVRILQWHQVH $PHULFD´0RQWKO\:HDWKHU5HYLHZ9ROSS FRQYHFWLRQ´0RQWKO\:HDWKHU5HYLHZ9ROSS &RUUrD&6³(VWXGRHVWDWtVWLFRGDRFRUUrQFLDGHMDWRVQR 1LFROLQL0DQG6DXOR$&³(WDFKDUDFWHUL]DWLRQRI SHU¿OYHUWLFDOGRYHQWRQDEDL[DDWPRVIHUDHDVXDUHODomRFRP WKH ZDUPVHDVRQ&KDFRMHWFDVHV´3UHSULQWVRI eventos de intensa precipitação pluvial no Rio Grande do Sul the 6th International Conference on Southern Hemisphere (In Portuguese), PhD Thesis, Instituto de Pesquisas Hidráulicas, 0HWHRURORJ\DQG2FHDQRJUDSK\&KLOHSS Universidade Federal do Rio Grande do Sul, Porto Alegre. 5DR9%+DGD. ³&KDUDFWHULVWLFVRI5DLQIDOORYHU &KHQ *7- Chen, C. et al ³$6WXG\RID0RXQWDLQJHQHUDWHG Saulo, A. 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