Aspectos da variabilidade espacial e arrasto em sítios experimentais da Floresta Amazônica

INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA - INPA UNIVERSIDADE DO ESTADO DO AMAZONAS - UEA CURSO DE PÓS-GRADUAÇÃO EM CLIMA E AMBIENTE

ASPECTOS DA VARIABILIDADE ESPACIAL E ARRASTO EM

SÍTIOS EXPERIMENTAIS DA FLORESTA AMAZÔNICA

CLEDENILSON MENDONÇA DE SOUZA

Manaus, Amazonas Setembro, 2014

CLEDENILSON MENDONÇA DE SOUZA

ASPECTOS DA VARIABILIDADE ESPACIAL E ARRASTO EM

SÍTIOS EXPERIMENTAIS DA FLORESTA AMAZÔNICA

PROF. DR. LEONARDO DEANE DE ABREU SÁ - orientador

PROF A. DRA. MARGARETE OLIVEIRA DOMINGUES - co-orientadora

Tese apresentada ao Instituto Nacional de Pesquisas da Amazônia e Universidade do Estado do Amazonas, como parte dos requisitos para obtenção do título de Doutor em Clima e Ambiente.

Manaus, Amazonas Setembro, 2014

INSTITUTO NACIONAL DE PESQUISAS DA AMAZÔNIA - INPA UNIVERSIDADE DO ESTADO DO AMAZONAS - UEA CURSO DE PÓS-GRADUAÇÃO EM CLIMA E AMBIENTE

Tese apresentada ao Instituto Nacional de Pesquisas da Amazônia e Universidade do Estado do Amazonas, como parte dos requisitos para obtenção do título de Doutor em Clima e Ambiente.

Avaliado por:

Manaus, Amazonas Setembro, 2014

Manaus, Amazonas Setembro, 2014

Sinopse:

Foram estudados aspectos do arrasto superficial e consequências de sua existência em escoamentos acima de florestas densas, a detecção de ondas de gravidade, a existência de diferentes regimes turbulência noturnos e a caracterização de diferentes feições superficiais com o auxilio da Transformada Wavelet Contínua 2-D.

Palavras-chave: Floresta Amazônica, ATTO, Perfis de vento, Ondas de gravidade,

Para minha esposa, Geise

Lopes dos Reis e meus

passarinhos: Felipe Reis de

Souza e Yasmin Reis de Souza.

Ao Dr. Leonardo Deane de Abreu Sá pela orientação, ensinamentos e discussões que serviram de base e direcionamento para a realização deste trabalho.

A Dra. Margarete Oliveira Domingues pelo incentivo, ensinamentos, paciente auxílio nas técnicas computacionais e companheirismo em horas difíceis.

Ao Instituto Nacional de Pesquisas da Amazônia (INPA) e Universidade do Estado do Amazonas (UEA) através do Programa de Pós-Graduação em Clima e Ambiente (CLIAMB) coordenado pela Profa. Dra. Rita Valéria Andreoli de Souza.

Agradeço a CAPES e à FAPEAM pela bolsa. Ao IBAMA/Ji-Paraná e ao Projeto LBA e a todos que contribuíram, direta ou indiretamente, na realização deste trabalho.

Aos amigos Francisco Otávio Miranda, Claudomiro Batista Sales e a todos os demais alunos do curso de Mestrado e Doutorado em Clima e Ambiente pelo grande apoio concedido.

A minha família pelo apoio, incentivo e paciência.

“O único homem que está isento de erros,

é aquele que não arrisca acertar.” (Albert Einstein)

S719 Souza, Cledenilson Mendonça de

Aspectos da Variabilidade Espacial e Arrasto em Sítios Experimentais da Floresta Amazônica / Cledenilson Mendonça de Souza. --- Manaus: [s.n.], 2014.

104 p. : il. color.

Tese (Doutorado) --- INPA/UEA, Manaus, 2014. Orientadores: Leonardo Deane de Abreu Sá. Coorientador: Margarete Oliveira Domingues. Área de concentração : Clima e ambiente.

1. Perfis de vento. 2. Ondas de gravidade. I. Título.

RESUMO

Nesta tese de doutorado caracteriza-se e estuda-se a interação floresta-atmosfera por meio de ferramentas não-lineares utilizando dados de torres micrometeorológicas e de imagens de alta resolução de satélites ambientais.

Foram analisadas a interação floresta-atmosfera e características da heterogeneidade horizontal referentes aos sítios experimentais amazônicos de Reserva de Desenvolvimento Sustentável de Uatumã (1º 56' S 2º 21' S e 59º 16' W 57º 57' W) e da Reserva Biológica do Jarú em Rondônia (10º 05' S 10º 19' S e 61º 35' W 61º 57' W), os quais contêm floresta primária. Foram analisados aspectos do perfil vertical do vento médio acima e no interior do dossel florestal, tais como expressões analíticas para ele, como, por exemplo, a função tangente hiperbólica e variante suas, considerando-se inclusive a variabilidade da altura do ponto de inflexão do perfil e sua relação com a escala de ocorrência de estruturas coerentes.

Foram estudados aspectos do arrasto superficial e consequências de sua existência em escoamentos acima de florestas densas. Procurou-se investigar a ocorrência de ondas de gravidade (OGs) acima de sítio experimental na Amazônia central, geradas pela ondulação do terreno na camada limite noturna (CLN). Utilizou-se o número de Scorer para classificar ondas internas de gravidade forçadas ou não pela orografia. Para isso foram determinadas escalas características das ondulações do terreno para várias direções do vento compreendidas entre vento de norte-leste e de leste-sul, que são predominantes na região. Foram utilizadas imagens altimétricas com resolução de 30 m, as quais forneceram informações para a detecção de escalas de comprimento características das ondulações do terreno em direções específicas, via análise multi-escala, utilizando-se a Transformada Wavelet Contínua, com uma wavelet analisadora de Morlet. Também foram utilizados dados meteorológicos medidos em uma torre de 80 m de altura (2º 08' 40,0" S 59° 00' 10,0" W) que se encontra aproximadamente a 130 m acima do nível do mar, como parte integrante do projeto científico teuto-brasileiro “ATTO” (Amazonian Tall Tower Observatorium), no sitio experimental da Reserva de Desenvolvimento Sustentável de Uatumã, a nordeste do Amazonas. Os resultados mostraram que a maior parte das OGs detectadas satisfizeram o critério de Steeneveld et al. (2009) para caracterização de ondas induzidas orograficamente. Aquelas que não satisfizeram tal critério foram as

primeiro, em que a direção do vento esteve entre 0º a 45º, e o segundo, com a direção do vento entre 120º e 180º. Os eventos de OGs não forçadas pela orografia estão associados ao “Regime Noturno de Turbulência 3” proposto por Sun et al. (2012), em que os efeitos locais não predominam na geração de turbulência, mas efeitos associados a fortes movimentos descendentes de ar bem localizados no tempo (fenômenos do tipo top-down).

Como parte da caracterização da heterogeneidade horizontal do terreno, utilizou-se imagem de satélite de alta resolução espacial (IKONOS, 1-4 m) para investigar aspectos da variabilidade em escala da textura de regiões cobertas pela por floresta e desmatadas em área da Rebio-Jarú. Procurou-se determinar a existência de possíveis padrões de variabilidade na textura da cobertura vegetal por escala em regiões tanto cobertas por floresta, pastagem, floresta-pastagem, área cultivada e corpos de água. A detecção de escalas características da textura foi desenvolvida por meio de estudo direcional baseado na aplicação da Transformada Wavelet Contínua Bidimensional, com a utilização da função wavelet analisadora de Morlet.

A Análise de Recorrência e de seus quantificadores foi um método de análise de séries temporais não lineares utilizados para discriminar regiões cobertas por florestas de outras com distintos usos da terra em área situada a oeste da Amazônia brasileira a partir de uma imagem de satélite de alta resolução. Isso foi efetuado como parte dos esforços para identificar e caracterizar padrões de heterogeneidade da floresta tropical e de áreas com outras coberturas na região amazônica. Os resultados sugerem a existência de possíveis eixos preferenciais de padrões de textura em áreas cobertas por floresta e de padrões específicos para áreas com diferentes coberturas do solo e com corpos de água.

Além desses resultados e metodologias inovadoras, a conclusão deste estudo destaca a importância de caracterizar adequadamente a variabilidade horizontal do terreno e de investigar aspectos da relação entre esta variabilidade e a manifestação de fenômenos físicos específicos na camada limite atmosférica. Tal tipo de investigação certamente apontará caminhos inovadores na busca de parametrizações mais adequadas dos processos de troca floresta-atmosfera em terrenos complexos.

ABSTRACT

In this doctoral thesis is characterized and studies the forest-atmosphere interaction through nonlinear tools using data from micrometeorological towers and high-resolution environmental satellites images.

We analyzed the forest-atmosphere interaction and characteristics of horizontal heterogeneity regarding the Amazonian experimental sites Sustainable Development Reserve Uatumã (1 56 'S 2 21' S and 59 16 'W 57 57' W) and the Biological Reserve in Jarú Rondônia (10 05 'S 10 19' S and 61 35 'W 61 57' W), which contain primary forest. We analyzed the average aspect vertical wind profile above and inside the canopy structure, such as analytical expressions for it, for example, a hyperbolic tangent function and its variant, considering also the variability of the inflection point of the height of the profile and its relation to the scale of the occurrence of coherent structures.

We studied aspects of surface drag and consequences of its existence drains above dense forests. We have investigated the occurrence of gravity waves (GWs) above experimental site in central Amazonia, generated by the ground swell in the nocturnal boundary layer (NBL). We used the number of internal waves to classify Scorer forced by gravity or no topography. To this were certain characteristic scales of rolling terrain for various wind directions between wind north-east and south-east, which are prevalent in the region. Altimetric data were used with a resolution of 30 m, which provided information to detect the characteristic length scales of the terrain undulations in specific directions via multi-scale analysis using the Continuous Wavelet Transform with a Morlet wavelet analyzer. Were also used meteorological data measured in a tower 80 meters high (2 08 '40.0 "S 59 ° 00' 10.0" W) which lies approximately 130 meters above sea level, as part of German-Brazilian scientific project "ATTO" (Amazonian Tall Tower Observatorium) in experimental site of Sustainable Development Reserve Uatumã, northeast of Amazonas. The results showed that most of the GWs detected satisfied the criterion Steeneveld et al. (2009) to characterize waves induced Orografically. Those who did not meet this criterion were detected preferably in two arcs centered on meteorological tower, the first in which the wind direction was between 0° to 45°, and the second, with the wind direction between 120° and 180°. WGs events not forced by the topography are associated with the "Night Regime

in the generation of turbulence, but effects associated with strong descending air movements and located in time (the top-down phenomena).

As part of the characterization of horizontal heterogeneity of the land, we used high spatial resolution satellite imagery (IKONOS, 1-4 m) to investigate aspects of variability in scale texture regions covered by the forest and deforested area in the Rebio- Jarú. We sought to determine the existence of possible variability patterns in the texture of vegetation cover for scale in both regions covered by forest, pasture, forest-pasture, cultivated area and water bodies. The detection of texture characteristic scales was developed through directional study based on the application of Continuous Wavelet Transform Two-dimensional, using the Morlet wavelet function of analyzer.

The Recurrence Analysis and its quantifiers was a method of analysis of nonlinear time series used to discriminate regions covered by forests of others with different land uses in the area located west of the Brazilian Amazon from a satellite image of high resolution . This was done as part of efforts to identify and characterize heterogeneity patterns of tropical forest and other areas with coverage in the Amazon region. The results suggest the existence of possible preferential axes of texture patterns in forested areas and specific standards for areas with different land cover and water bodies.

In addition to these results and innovative methodologies, the conclusion of this study highlights the importance of adequately characterize the horizontal variability of the land and to investigate aspects of the relationship between this variability and the occurrence of specific physical phenomena in the atmospheric boundary layer. This type of research will point certainly innovative ways to identify the most appropriate parameterization of forest-atmosphere exchange processes in complex terrain.

SUMÁRIO

INTRODUÇÃO

CAPÍTULO 1 – An empirical-analytic model to describe the vertical wind speed profile above and within amazon forest.

CAPÍTULO 2 – Coherent structures detected in the unstable atmospheric surface layer

above the Amazon forest.

CAPÍTULO 3 - Variabilidade em Escala da Textura da Floresta Amazônica e de

Região Desmatada utilizando imagem IKONOS da Reserva Rebio Jarú-Rondônia.

CAPÍTULO 4 – Orographically Induced Gravity Waves in the Stable Boundary Layer

Above the Amazon Forest.

CAPÍTULO 5 - Spatial pattern identification for rain forest heterogeneity and land use

regions by recurrence quantification analysis of IKONOS image dataset.

RESULTADOS

CONCLUSÃO GEARAL REFERÊNCIAIS

LISTA DE FIGURAS POR CAPÍTULO CAPÍTULO 1

Fig.1. Hyperbolic model vertical mean wind profile compared with observed profile (first version).

Fig.2. Modeled wind profiles for different values of the (α) parameter. Fig.3. Modeled wind profiles for different values of the (β) parameter. Fig.4. Modeled wind profiles for different values of the (γ) parameter. Fig.5. Modeled wind profiles for different values of the (μ) parameter. Fig.6. Modeled wind profiles for different values of the (ω) parameter. Fig.7. Modeled wind profiles for different values of the (LAI) parameter.

Fig.8. Observed mean wind profile and associated standard deviation compared with hyperbolic tangent function fitted data (second version).

Fig.9. Modeled and observed wind profiles for values above 1.5 m/s. (class I). Fig.10. Modeled and observed wind profiles for values below 1.5 m/s. (class II).

Fig.11. Hourly mean squared error values of observed wind profiles as compared with hyperbolic tangent modeled ones.

CAPÍTULO 2

Fig.1. Meteorological tower erected at Rebio-Jaru Forest reserve.

Fig.2. Fast response instruments in the meteorological tower at Rebio-Jaru.

Fig.3. Cup anemometers ranged at different eights in the meteorological tower at Rebio-Jaru.

Fig.4. Histogram of frequency of wind direction for observations of the intervals: (a) from11:00h to 16:00h local-time and (b) from 20:00h up to 9:00h local-time.

Fig.6. Scale variance of the wavelets coefficients as a function of the time scale, for virtual temperature time series obtained from 09:00h to 09:30h, local time, 10 February, 1999.

Fig.7. Third degree polynomial best fitting for the vertical wind speed profile (low response wind speed data measured from15:30h to 16:00h, on 19 February, 1999. Fig.8. Vertical profile of dimensionless wind velocity for a partially overcast sky. Fig.9. Evolution of the coherent structures time-scale hourly-mean values (a) and the vertical wind profile inflection point height (b), along with their standard deviations, obtained above Rebio-Jaru forest reserve, for 144 available half-hourly data sets.

Fig.10. Comparison of the evolution of the inflection point height and the coherent structures time-scale above Rebio-Jaru forest reserve.

Fig.11. Comparison between the inflection point height and the coherent structure time-scale for 144 run data for day-time conditions.

Fig.12. Comparison of two time-scales: coherent structure time scale (y-axis) and inflection point time-scale (x-axis) for144 half-hourly data sets.

CAPÍTULO 3

Fig.1: Imagem pancromática do satélite IKONOS com resoluço espacial 1m. Área da Rebio Jarú coberta com floresta primária com algumas áreas desmatadas. O retângulo (a) indica o recorte da área com floresta e o retângulo (b) a área desmatada utilizada neste estudo.

Fig.2: Efeito da variação do ângulo de rotação da CWT2D (θ) para um parâmetro escala da CWT2D fixo a = 2. As setas tracejadas indicam a direção ou ângulo θ em que a CWT2D está analisado a imagem.

Fig.3: Efeito da variação do parâmetro escala da CWT2D (a) para um ângulo de rotação

θ = 0.

Fig.4: Efeito da variação do ângulo de rotação da CWT2D (θ) para um parâmetro escala da CWT2D fixo a = 5. Imagem sintética em que os objetos da imagem estão dispostos em eixos preferenciais nas direções horizontal e vertical. As setas indicam a direção ou ângulo θ em que a CWT2D está sendo aplicada na imagem.

Fig.5: Efeito da variação do parâmetro escala da CWT2D (a) para um ângulo de rotação

θ = π/4. As Figuras 5a, b, c e d correspondem às escalas 1, 5, 10 e 20 respectivamente.

para um ângulo de rotação θ = π/4. As Figuras 6b, c e d mostram os escalogramas para as escalas 1, 10 e 20 respectivamente resultantes da aplicação da CWT2D sobre a imagem da Figura 6a. Esta sendo visualizado o módulo dos coeficientes nos escalogramas.

Fig.7: Recorte (a) indicado na Figura 1 como área de floresta. A linha pontilhada indica o eixo preferencial tomado a 30 graus no sentido anti-horário.

Fig.8: Recorte (b) indicado na Figura 1 como área desmatada. A linha pontilhada indica o eixo preferencial tomado a 30 graus no sentido anti-horário.

Fig.9: Mostra a variância por escala da parte real dos coeficientes da CWT2D de Morlet para θ = π/4. A linha contínua mostra os resultados obtidos para a área de floresta e a linha pontilhada para área desmatada.

Fig.10: Cálculo da curtose por escala das séries espaciais obtidos das imagens mostrada nas Figuras 7 e 8. A reta horizontal tracejada com ordenada igual a 3 indica a região de validade da condição de Gaussianidade.

CAPÍTULO 4

Fig.1. Uatumã Sustainable Development Reserve, Manaus city, Amazonas State, Brazil. The black rectangle area corresponds to the experimental site area. Source: Instituto de Desenvolvimento Sustentável do Estado do Amazonas (IDESAM) – Institute of Sustainable Development of the State of Amazonas, Brazil.

Fig.2. Area of approximately 900 km2 of the experimental site inside Uatumã Sustainable Development Reserve meteorological tower. The axes represent directions (0o, 5o, 10o,15o, ..., 175º, 180º). The colors variations of the legend that follow from blue to red represent terrain elevation in meters relative to sea level.

Fig.3. Length scales associated with maximum variance of terrain height for each one of the thirteen investigated wind directions.

Fig.4. Example of the variability of terrain height for the 30° direction. The tower is located on the “zero” abscissa.

Fig.5. w (vertical velocity) and T (temperature) time series with wavelike characteristics of a GW occurrence obtained during the Julian day 153, at 04:00 LT.

Fig.6 (a). Topographic map of the experimental Uatumã site around the meteorological tower; (b) Scheme presenting the main axes of GWs occurrence, in which the black dots on the axes represent surface-induced GWs occurrences and the points in grayscale indicate GWs events which were not induced by the surface.

Fig.8. Local times of occurrence of the detected gravity waves events at the Uatumã site.

Fig.9. Histogram of ks / Ls, where class I is in “grey” and class II is in “black”, and ks is

the terrain wave number and Ls is the Scorer parameter.

Fig.10. Scheme of the VTKE x U plot presenting the three turbulence regimes applied to the Uatumã site data (each regime is associated with a color indicated on the right down panel). The dashed line on the left represents the Regime 1, the continuous line on the right indicates the Regime 2 and the points inside the ellipse are associated with the Regime 3. The black dots represent the CLASS II GWs events and the grayscale rectangles represent the CLASS I ones.

CAPÍTULO 5

Fig.1. Space series representing forest, pasture, mixed forest-pasture, tillage and river areas extracted from Fig. 2, considering Table 5.

Fig.2. Study area and dataset. (a) IKONOS panchromatic image from Rebio Jaru with one meter of space resolution: forest (green), pasture (orange), forest-pasture (red), tillage (yellow), and river (blue). (b-e) A closer view of the selected areas.

Fig.3. Recurrent plots for space series from forest, pasture, and mixed forest-pasture area related to the space series plotted in Fig.1.

Fig.4. Recurrent plots for space series from tillage and river area related to the space series plotted in Fig.1.

Fig.5. RQA measures series using a running window for the river surface serie in Fig. 1(e).

Fig.6. Normalized RQA measures for the forest, forest-pasture, pasture, tillage, and river areas presented in Fig.1.

Fig.7. Space series representing predominantly forest, pasture, mixed forest-pasture and river areas.

Fig.8. Normalized RQA measures for the forest, forest-pasture, pasture, tillage, and river areas presented in Fig.7.

Neste estudo buscou-se aprofundar a pesquisa de aspectos do arrasto superficial e consequências de sua existência em escoamentos acima de florestas densas, os quais já foram investigados pelo autor com a utilização de dados da Reserva Biológica do Jarú (Rebio-Jarú) em Rondônia (Souza, 2009; Dias Júnior et al., 2013; Souza et al., 2013), estendendo a pesquisa de tal forma a utilizar também informações proporcionados pelo Projeto teuto-brasileiro ATTO (Amazon Tall Tower Observatory), referentes à Reserva de Desenvolvimento Sustentável de Uatumã, a Nordeste do Amazonas.

O dispositivo experimental do Projeto ATTO, parcialmente implantado, prevê a possibilidade de estudar processos de convergência e/ou divergência do escoamento através da disposição de torres instrumentadas de 80m em vértices de retângulos com a torre de 320m no centro (Tollefson, 2010). A possibilidade de obtenção de perfis verticais das grandezas meteorológicas em vários níveis até a altura de 320 m certamente permitirá também uma melhor compreensão da termodinâmica da atmosfera tropical acima da floresta e certamente oferecerá informações importantes para o aperfeiçoamento da simulação do escoamento atmosférico acima da floresta tropical em modelos numéricos. Contudo, os atrasos na implantação do Projeto ATTO, bem como acidentes de percurso, com a perda total da instrumentação colocada em uma das torres, reduziram bastante a disponibilidade de dados particularmente no que se refere aos perfis verticais de grandezas meteorológicas, restringindo severamente a possibilidade de utilização dos dados existentes para a obtenção de gradientes verticais precisos de grandezas tais como velocidade do vento e temperatura potencial vertical, o que certamente diminuiu o alcance das pesquisas previamente previstas para a presente tese, com a utilização desses dados.

Dadas tais dificuldades experimentais encontradas, foi reforçada a pesquisa sobre variabilidade espacial da topografia e de outras grandezas ambientais pertinentes com base na informação disponibilizada via sensoriamento remoto em bandas referentes ao espectro visível, infravermelho próximo e microondas, a partir de imagens do satélite IKONOS e LANDSAT, além de variáveis geomorfométricas derivadas da missão SRTM (Shuttle Radar Topographic Mission), disponíveis no portal http//www.dsr.jnpe.br/topodata/dados.php (Valeriano, 2008). Apesar de já haver considerável acervo de publicações sobre variabilidade espacial de grandezas referentes

à floresta amazônica a partir de imagens de satélites (Asner et al., 2002; Li et al., 2008; Malhi e Román-Cuesta, 2008; Palace et al., 2008; Fyllas et al., 2009), poucas são aquelas nas quais foram aplicadas técnicas específicas para a obtenção de informação sobre escalas de variabilidade espacial, inclusive com a utilização da Transformada Wavelet Bidimensional (Daubechies, 1992; Farge, 1992; Antoine et al., 2004), como é o caso de Souza et al. (2013).

No que se refere ao estudo do escoamento acima do sítio experimental de Uatumã, sua complexidade associa-se a vários problemas ainda não suficientemente compreendidos, tais como:

a) O fato de o terreno ser muito irregular, heterogêneo e apresentar ondulações em certas direções preferenciais. Efetivamente há resultados que mostram que o padrão de distribuição e variabilidade espacial da vegetação em determinada área pode exercer grande influência no arrasto superficial (Carpenter, 1997; Sirovich e Karlsson, 1997). Ademais, a própria complexidade da distribuição da vegetação pode conter no seu bojo expressões não tão óbvias de simetria, inclusive auto-similaridade, dimensão fractal (La Scala Jr. et al., 2009), ou mesmo anisotropia (Malhado et al., 2009), o que enriquece o potencial de pesquisa sobre a variabilidade espacial (Malhi e Román-Cuesta, 2008; Fyllas et al., 2009).

b) A existência de uma cobertura vegetal complexa, na qual há percolação de momentum do escoamento acima, à qual se associam padrões de instabilidades peculiares do escoamento atmosférico, tais como instabilidades do ponto de inflexão, instabilidade gravitacional, instabilidade de Kelvin-Helmholtz (Arya, 1988; Robinson, 1991; Raupach et al., 1996; Brunet e Irvine, 2000; Finnigan, 2000; Cheng et al., 2005; Py, 2005; Starkenburg et al., 2013) e a formas de auto-organização do escoamento turbulento ainda não completamente conhecidos, como as encontradas nas estruturas coerentes (Thomas e Foken, 2005; 2007; Dias Júnior et al., 2013);

c) O fato de o sítio experimental se localizar no trópico úmido, com características de evolução da camada limite atmosférica peculiares, particularmente nos períodos de ocorrência da Zona de Convergência Intertropical acima da região com a influência de complexos de nuvens convectivas (Garstang et al., 1998; Garstang e Fitzjarrald, 1999; Machado et al., 2002; Silva Dias et al., 2002; Strong et al., 2005).

complexidade dos problemas se acentua, o que ficou patente a partir da divulgação dos resultados do experimento de campo intitulado “Cooperative Atmosphere-Surface Exchange Study” CASES-99 (Poulos et al., 2002), realizado na região central dos Estados Unidos no final da década de 90 do século passado, que utilizou todos os meios disponíveis para estudar uma grande quantidade de fenômenos existentes na CLN e sua potencial capacidade de interação entre si, além de efeitos de retroalimentação (feedback) associados a fenômenos tais como ondas de gravidade, correntes de densidade, jatos de baixos níveis, ondas isoladas, intermitência, ventos catabáticos, dentre muitos outros (Blumen et al., 2001; Poulos et al., 2002; Sun et al., 2002; Newsom e Banta, 2003; Sun et al., 2004; Cheng et al., 2005; Steeneveld et al., 2009; Sun et al., 2012).

Uma questão importante nos estudos da CLN é o da caracterização dos regimes de estabilidade noturnos e problemas correlatos (Mahrt, 1998; Mahrt et al., 1998; Mahrt, 1999; Acevedo e Fitzjarrald, 2003; Cava et al., 2004; Acevedo et al., 2009; Sun et al., 2012). Estudos recentes na Amazônia mostraram diferenças significativas entre os sítios experimentais de Uatumã e da Rebio-Jarú no que concerne: a) à ocorrência de ondas de gravidade, à influência de nuvens na formação de estruturas coerentes nas grandezas escalares do escoamento e à existência de diferentes regimes de turbulência noturnos (Sales, 2014), o que teve por base os artigos de Cava et al. (2004) e o de Sun et al. (2012); b) à variabilidade sazonal dos fluxos e concentrações de CO2 acima da

floresta de Uatumã, em função do regime de turbulência existente (Mafra, 2014). Um resultado interessante desta última publicação foi o de sugerir que haveria variações sazonais no arrasto superficial, o que, com base nos estudos de Yi (2008), poderia estar associado à variação do índice de área de folha (IAF) da floresta amazônica. Ressalte-se que as características de variabilidade sazonal do IAF na floresta amazônica já foram comprovadas por Doughty e Goulden (2008) com base em medidas realizadas in loco e também de informações providas por sensoriamento remoto via índice de diferença normalizada de vegetação (NDVI – normalized difference vegetation index) proporcionado pelo MODIS LAI (MOD15A2 V004), sendo o MODIS (Moderate Resolution Imaging Spectroradiometer), um espectroradiômetro embarcado em satélite de observação da Terra.

A presente tese procura avançar nas temáticas abordadas acima, voltadas principalmente para dados referentes às reservas florestais amazônicas da Rebio-Jarú e de Uatumã.

Um primeiro artigo (Artigo 01), intitulado “An empirical-analytic model to describe the vertical wind speed profile above and within amazon forest”, a ser submetido à revista Acta Amazonica (ISSN: 0044-5967), refere-se às questões do arrasto superficial na interface floresta-atmosfera e à possibilidade de obtenção de relação geral para o perfil da velocidade do vento médio acima e dentro da copa florestal, que leva em conta o valor do índice de área de folha e a altura do ponto de inflexão no perfil da velocidade média do vento.

Um segundo artigo (Artigo 02), intitulado “Coherent structures detected in the unstable atmospheric surface layer above the Amazon forest”, já publicado pela revista Journal of Wind Engineering & Industrial Aerodynamics (ISSN: 0167-6105), v.115: 1-8, doi: 10.1016/j.jweia.2012.12.019, 2013 (da Editora Elsevier), refere-se às questões da variabilidade da escala temporal das estruturas coerentes encontradas nas séries temporais de temperatura acima da Rebio-Jarú e à sua relação com a altura do ponto de inflexão no perfil da velocidade média do vento.

Um terceiro artigo (Artigo 03), intitulado “Variabilidade em Escala da Textura da Floresta Amazônica e de Região Desmatada utilizando imagem IKONOS da Reserva Rebio Jarú-Rondônia”, já publicado pela revista Tema – Tendências em Matemática Aplicada e Computacional (ISSN: 1677-1966), v.14, No 3: 415-428, doi: 10.5540/tema.2013.014.03.0415, 2013 (da Sociedade Brasileira de Matemática Aplicada e Computacional), o qual utiliza imagem de satélite de alta resolução espacial (IKONOS, 1-4m) para investigar características da textura de regiões cobertas pela floresta amazônica na Rebio-Jarú. Para isso foi utilizada metodologia baseada na aplicação da Transformada Wavelet Contínua Bidimensional, com emprego da wavelet de Morlet.

Um quarto artigo (Artigo 04), intitulado “Orographicaly Induced Gravity Waves in the Stable Boundary Layer above the Amazon Forest”, a ser submetido à revista Agricultural and Forest Meteorology (ISSN: 0168-1923), da Editora Elsevier, refere-se às questões do arrasto superficial na interface floresta-atmosfera e sua capacidade de gerar ondas de gravidade. Para isso são utilizados dados do Projeto ATTO obtidos na

(Shuttle Radar Topographic Mission).

Um quinto artigo (Artigo 05), intitulado “Spatial pattern identification for rain forest heterogeneity and land use regions by recurrence quantification analysis of IKONOS image dataset” (a ser submetido à revista International Journal of Remote Sensing, (ISSN: 2319-3484, (http://www.ijrsg.com/)), no qual se utiliza análise de recorrência (para identificar e caracterizar diferentes padrões de variabilidade espacial da superfície), a qual é aplicada a imagem pan-cromática de satélite de alta resolução espacial IKONOS (espectro do visível, resolução espacial de 1-4m) de região da Reserva Biológica do Jarú, Rondônia, coberta por floresta, pastagem, floresta-pastagem e corpos de água. A metodologia é aplicada a diferentes séries espaciais das diferentes coberturas superficiais contidas na imagem supramencionada, cada uma apresentando características distintas em função da sua textura.

OBJETIVO

Caracterizar adequadamente a variabilidade horizontal nos sítios experimentais a partir da utilização de imagem topográfica e de imagem de alta resolução espacial no espectro do visível e investigar aspectos da relação entre esta variabilidade e a manifestação de fenômenos físicos específicos da Camada Limite Noturnos.

CAPÍTULO 1

Souza, C. M.; Tóta, J.; Sá L. D. A. Sá end Dias Junior C. Q., 2014. “An empirical-analytic model to describe the vertical wind speed profile above and within amazon forest”. (a ser submetido à revista Acta Amazonica).

An empirical-analytic model to describe the vertical wind speed profile

above and within amazon forest

Cledenilson Mendonça de SOUZA1, Júlio TÓTA2, Leonardo Deane de Abreu SÁ3, Cléo Quaresma DIAS JUNIOR1

ABSTRACT

We study mean wind velocity profiles measured on a 60 m height tower built in the forest reserve Jarú (10004.70'S, 61056.02'W), located in the Brazilian north-western state of Rondonia. The data was collected during LBA (Large Scale Biosphere-Atmosphere Experiment in Amazonia) wet season intensive campaign. Nine cup anemometers whose measurements were used in this work were vertically placed in a way to provide good calculation of the mean velocity wind profile inflectional point value. This allowed us to investigate an formulation for the mean vertical wind speed profile, u(z), based on key parameters such as the inflectional point height of the profile, as well as the leaf area index and with the introduction of a modified hyperbolic tangent function in order to provide a more flexible fit for the available experimental data. We also add an exponential term into the u(z) function to allow it to assume a suitable “S” shape near the ground. Thus, some parameters have been incorporated into the analytical profile function to enable more flexibility into this “S” shape region. The results provide good fit for experimental data measured above within Amazon forest canopy, since the wind speed values are above an empirical threshold.

KEYWORDS: Wind profile; Roughness sub-layer; Hyperbolic tangent function; Inflectional point;

Amazon forest; Turbulence.

___________________________

1 _{Instituto Nacional de Pesquisas da Amazônia, Programa de Pós-Graduação em Clima e Ambiente – PPG_CLIAMB ,}

Av. André Araújo, 2936, Aleixo, Manaus, AM, Brazil. CEP 69060-001. e-mail: [email protected]

2 _{Universidade do Estado do Amazonas, Centro de Estudos Superiores do Trópico Úmido - CESTU, Av. Djalma Batista,}

3579, sala 111, Flores, Manaus, AM, Brazil. CEP 69059 – 010.

3 _{Instituto Nacional de Pesquisas Espaciais, Centro Regional da Amazônia - CRA, Parque da Ciência e Tecnologia do}

2

INTRODUCTION

The first investigations regarding atmospheric flow similarity relationships above forest canopies have pointed out some specific aspects of turbulent exchange above and below tall vegetation (Thom et al., 1975) and raised interesting questions concerning the existence of a roughness sub-layer (RSL) associated with anomalous turbulent flow above very complex surfaces as forests (Cellier and Brunet, 1992; Raupach and Thom, 1981). Under such conditions, similarity conditions observed above smooth and horizontally homogeneous surfaces do not hold (Högström and Bergström, 1996), turbulent fluxes estimation becomes a difficult task (Mahrt, 2010; von Randow et al., 2002). The existence of an inflectional point in the mean wind profile introduces new kind of turbulent instabilities in the flow because of the strong wind shear in the forest-atmosphere interface, which creates regions of peculiar coherent “roll” vortices (Raupach et al., 1996; Robinson, 1991) and may generate spectral short-circuiting phenomena and turbulent wake fluctuations within the canopy trunk space (Cava and Katul, 2008; Finnigan, 2000). These complex flow characteristics make accurate estimation of turbulent variables very difficult in the RSL above tall vegetation, as is the case of Amazonian forest. Thus, one important question associated with the role of Amazonian forest in biosphere-atmosphere exchanges, is the momentum transfer from the atmosphere to the surface. Problems related to the coupling between above canopy flow and below Amazonian forest canopy flow have been studied by authors as Fitzjarrald et al. (1990), Kruijt et al. (2000), Sá and Pachêco (2006), Viswanadham et al. (1990) among others. In spite of developing much work on this subject, there are few systematic studies on universal wind profile relationships for both, above and inside canopy flow, from the understory zone up to the RSL top. Such issues have to be considered in surface-atmosphere exchange schemes for modeling purposes.

2. Material and methods 2.1 Experimental site

The Jaru Forest Reserve is located in south-western Amazon, where there is a 268,000 hectares area typical tropical rain forest, between 10005'S and 10019'S, and between 61035'W and 61057'W, at approximately 100-150 m above sea level height. A 60 m height micrometeorological tower was built in the Jaru Reserve, which presents horizontally homogeneous conditions from north-west to south-east which is the dominant wind direction (in a clockwise sense). At the remaining directions, around 1km homogeneous fetch conditions hold. Some tree species as Cedrella adorata, Inga sp., Diodea cf bicolor Bth., Strychnos amazonicus Krukoff, Protium polybotrium and Glacicarpa Ruiz, might reach up to 45 m height (McWilliam et al., 1996). Wright et al. (1996) reported a 4.6 value for the Jaru Reserve Leaf Area Index (LAI). This is lesser than 6.1, the LAI value reported for the central Amazonia "Ducke" Reserve in Manaus and lesser than 5.4, the LAI value reported for the south-eastern Amazonia "Vale do Rio Doce" Reserve in Marabá (Roberts et al., 1996). McWilliam et al. (1996) present information concerning the tree species found in Jaru Reserve. Andreae et al. (2002) and Culf et al. (1996) present geographical and climate information concerning this experimental site.

2.2 Micrometeorological instruments and data The Jaru Reserve is one of the several sites in which the Large Scale Biosphere-Atmosphere Experiment in Amazonia was carried out. The LBA intensive experimental campaigns have been performed in two steps: The wet season campaign from 25th January to 5th March 1999, and the dry-to-wet season campaign from 15th September to 10th November 2002 (Silva Dias et al., 2002; Zeri and Sá, 2010). As a part of the Jaru Reserve scientific activities, energy budget components, wind velocity, temperature, humidity data was measured at several heights on a micrometeorological tower. To perform the wind profile analysis we used data provided by nine cup anemometers (Low Power A100L2, Vector Instruments Inc.) ranged in previously defined levels to give accurate information about the inflectional point height on the vertical wind profile and also to provide useful information

about the inside and above canopy flows. Thus, the instruments were vertically ranged at the heights of 55.00 m, 50.55 m, 47.70 m, 42.90 m, 40.25 m, 37.80 m, 32.85 m, 26.65 m, and 14.30 m in order to provide the needed experimental data by performing 60 min mean calculations for each hour of the day.

2.3. Theoretical elements and methodology

As it has been investigated (Cellier and Brunet, 1992; Lalic et al., 2003; Raupach et al., 1996; Thom et al., 1975) there is experimental evidence that mean wind speed vertical profile relationships provided by the Monin-Obukhov Similarity Theory (MOST) do not hold in RSL above tall vegetation. Many attempts have been done to obtain empirical expressions for the wind velocity profile in which some very known roughness parameters such as the zero-plane displacement height, d, the roughness length, zo,

and other scaling parameters are no more suitable for fitting experimental data and are to be replaced by other parameters in order to better represent the actual physical processes regarding biosphere-atmosphere transfers (Lalic et al., 2003; Marshall et al., 2002; Raupach et al., 1996; Sá and Pachêco, 2006; Yi, 2008).

To solve such a problem we have to determine adequate characteristic scales for the flow variables in the RSL. For example, the choice of a length scale associated with the wind shear at the canopy top, Lh, which has been

proposed earlier by Marshall et al. (2002), and Sá and Pachêco (2006) is:

Lh = < uh >/[(d< u >/dz)|h ] (1)

where < uh > is the mean wind velocity at the

level h, which corresponds to the mean top-canopy height and (d< u >/dz)|h is the mean

velocity gradient at h. Indeed, Raupach et al. (1996) successfully used this length-scale to obtain universal relationships based on Hyperbolic Tangent Function (HTF) to describe the above and inside-canopy mean flow characteristics. Fitzjarrald et al. (1990) have also indicated that there is some evidence that the mean canopy height, h, might be an important characteristic length-scale for the flow next to the canopy.

Marshall et al. (2002), using wind tunnel data, and Sá and Pachêco (2006), using experimental data from Amazon forest, have investigated a profile formulation that incorporates both, the physical information contained in the wind profile inflectional point, and the one provided by the wind shear stress measured at the canopy top mean height. As scaling parameters they have used: i) as a characteristic velocity-scale, ui, the mean wind velocity at the inflectional

point height, zi; ii) a characteristic length scale, Lh,

which is defined by Eq. 1. Their analyses have been extended to both above and inside canopy data. With these two characteristic scales, a general scaling relationship is obtained for the vertical profile which holds both, above and inside the forest canopy:

< u >/ui = F[(z – zi)/Lh] (2)

where < u > is the mean wind velocity at a height

z above the ground and F is a function whose

mathematical form is to be determined empirically from experimental data. It fits very well experimental data measured at heights far from the ground surface zone, but fail in fitting the ones measured near surface. To overcome such a drawback, we propose an improved formulation for the vertical profile by introducing a modified HTF, which is more flexible for fitting experimental data near ground surface. This has been performed through a suitable mathematical device in order to obtain a better curve fit near the ground, based on improvements proposed on earlier models for RSL vertical profiles (Lalic et al., 2003; Marshall et al., 2002; Raupach et al., 1996; Sá and Pachêco, 2006; Yi, 2008), since a wind speed value threshold has been achieved. It is capable of provide: 1) a good analytical model for the wind speed profile for the whole region located from the ground surface up to the above canopy flow zone, which preserves the property of having an inflectional point and fits to experimental data obtained in any specific RSL flow conditions; 2) a good compromise relationship between a modified hyperbolic tangent function and an exponential function and taking into account specific parameters associated with the crop vertical structure and aerodynamic properties of the coupling of above and within

4

canopy flow. 3. Results

As first step we have proposed an analytical model for the vertical wind speed incorporating only modifications in the Yi (2008) HTF (Eq. 3):

 

















u     (3)

where H is the height of the highest measuring level (55 m, for the Rebio Jaru experimental site); uH is the mean wind speed at the H height;

β and γ are fitting parameters; LAItotal is the leaf

area index; zi is the inflectional height of the

vertical wind profile; z is a measuring height; and u(z) is the mean wind speed at the z height. The curve generated by this model are presented in Fig. 1.

Fig. 1. Hyperbolic model vertical mean wind profile compared with observed profile (first version). As it is possible to observe, they depict shapes which are not more symmetric with respect to the inflectional point but hold, yet, some stiffness regarding the below inflectional point profiles.

In order to improve the profile fit, and to allow it to incorporate a “S” shape near the ground, we add an exponential term into the u(z) analytical form, which multiplies all Eq. 3 right hand, as is presented in Eq. 4:

 



















where µ, α, β, γ and ω are fit parameters and we use LAItotal = 6, as the leaf area index value for

the Rebio-Jaru forest (Moura, 2001).

The parameters µ and ω have been added to the analytical function < Eq.4 > to enable more flexibility in the “S” shape profile near the ground (below 20 m height) and does not

interfere on the inflectional point height nor on the superior part of the profile. Thus, these parameters might incorporate some of the mechanical effects due to the vegetation roughness, as well as to buoyancy effects. In order to account for the physical role of each one of the fit parameters which have mentioned above, we present figures with wind speed profile plots in which only one of these parameters is allowed to vary. Thus, Fig. 2 shows distinct wind profiles for different values of the parameter α. It amplifies the profile wind speed values as a whole.

Fig. 2. Modeled wind profiles for different values of the (α) parameter.

Fig. 3 depicts wind profiles for different values of β. As we can observe, these profiles present the same wind speed value at the highest measuring height and the differences between the two groups of wind profiles are more pronounced in the region corresponding to the inside canopy zone.

Fig. 3. Modeled wind profiles for different values of the (β) parameter.

Fig. 4 shows wind profiles for distinct values of γ. As in the Fig. 3, we observe the same wind speed values for the highest measuring height. But, unlike the observed in the previous figure, it is in the above-canopy region that the differences between Fig. 3 and 4 are sharper.

Fig. 4. Modeled wind profiles for different values of the (γ) parameter.

Fig. 5 presents wind profiles for several values of µ. As we can observe, the wind profile shapes of the Fig. 2 and 5 are quite similar, except for the fact that in the region immediately above the surface the profiles in the Fig. 5 are converging gently for one and same curve, unlike what happens in the Fig. 2.

The Wind profile is very sensitive to changes in the value of the parameter ω. Small variations of this can generate significant distortions in the wind profile, as shown in Fig. 6.

Fig. 5. Modeled wind profiles for different values of the (μ) parameter.

Fig. 6. Modeled wind profiles for different values of the (ω) parameter.

The LAI parameter has a marked influence on the wind profile shape. It can be observed that when the LAI value is relative small, the wind profile loses its S-shapes, as shown in the Fig. 7.

Fig. 7. Modeled wind profiles for different values of the (LAI) parameter.

On the other hand, the β and γ parameters, which are inside the HTF argument, are linked to physical effects which takes place in the inflectional point zone and above it. These parameters can be associated the existence of SRT, it structures of the vegetation and of the atmospheric conditions.

As may be observed in the Fig. 8, the mean wind speed profile fits very well with the modeled ones, in spite of the strong observed scatter. In order to understand the origin of such data dispersion, we have split the available data set I two classes, according with a wind velocity Threshold Value (TV): class I with wind speeds higher than the TV, and class II otherwise. This threshold value is around 1.5 m/s, as shown in Fig. 9 and Fig. 10.

Fig. 8. Observed mean wind profile and associated standard deviation compared with hyperbolic tangent function fitted data (second version).

In the Fig. 9 is presented a vertical profile which has wind speed values above 1.5 m/s. in such a situation, there is a better fit between the data provided by the model and those observed experimentally.

Fig. 9. Modeled and observed wind profiles for values above 1.5 m/s. (class I).

In the Fig. 10 is presented a vertical profile which has wind speed values below 1.5 m/s. In situation, it appears that the data generated by the model do not fit well with those

observed experimentally.

Fig. 10. Modeled and observed wind profiles for values below 1.5 m/s. (class II).

In the Fig. 11 is presented an hourly mean squared error of the observed mean wind speed profile as compared with the HTF model profile, for 24 hour available data sampled at 10 Hz rate. The squared error is relatively low along the day with some more important alterations in the end of the afternoon.

Fig. 11. Hourly mean squared error values of observed wind profiles as compared with hyperbolic tangent modeled ones.

4. Conclusions

We have proposed an empirical-analytic model to describe the vertical wind speed profile above and within a rainy Amazon forest has been proposed. To obtain a general non-dimensional relationship for the wind profile, we have taken into account the importance of modifying the well known HTF in order to obtain a better fit for experimental data collected above the Amazonian forest. In a first step, we have investigated only the tangent hyperbolic formulation. Such function has provided good fits for many experimental situations, but failed in fitting data associated to wind profiles with relative maxima near the ground. In order to obtain a very general relationship, which is able to incorporate also, wind profiles with such relative maxima, we propose an improved analytic function which is able to represent the “S” shape profile near the ground. This formulation provides a better fit for experimental data measured both, above and within the Amazonian forest canopy. We have considered two classes of experimental data, according with a wind speed threshold value. We show that the better wind speed fits have been obtained for the class of higher wind speed data.

5. Acknowledgements

This work is part of The Large Scale Biosphere-Atmosphere Experiment in Amazônia

6

(LBA) and was supported by the Fundação do Amparo à Pesquisa do Estado de São Paulo (FAPESP)/Brazil (process No 1997/9926-9) and the Fundação do Amparo à Pesquisa do Estado do Amazonas (FAPEAM)/Brazil. The authors are grateful to INCRA/Ji-Paraná and to IBAMA/Ji-Paraná by the experimental facilities and to Mr. Augusto César Oliveira Freire by his help in English translation. Leonardo Sá thanks the Conselho Nacional de Pesquisas e Desenvolvimento Tecnológico (CNPq) by his research grant (process No 303.728/2010-8). 6. References

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CAPÍTULO 2

Dias Júnior, C. Q.; Sá, L. D. A.; Pachêco, V. B. and Souza, C. M., 2013 "Coherent structures detected in the unstable atmospheric surface layer above the Amazon forest ",

Journal of Wind Engineering & Industrial Aerodynamics (ISSN: 0167-6105), v. 115: 1-8,

Coherent structures detected in the unstable atmospheric

surface layer above the Amazon forest

C.Q. Dias Ju´niora,n

, L.D.A. Sa´b, V.B. Pachˆecoc, C.M. de Souzaa

a

Instituto Nacional de Pesquisas da Amazˆonia (INPA), Av. Andre´ Arau´jo, no. 2936, Aleixo, Manaus, Amazonas, Brazil

b_{Centro Regional da Amazˆonia (CRA), Instituto Nacional de Pesquisas Espaciais (INPE), Parque de Ciˆencia e Tecnologia do Guama´, Av. Perimetral, no. 2651, Bele´m, Para´, Brazil} c

Universidade Federal do Amazonas, Av. General Rodrigo Octa´vio, no. 6200, Coroado I, Manaus, Amazonas, Brazil

a r t i c l e i n f o

Article history: Received 15 May 2012 Received in revised form 27 December 2012 Accepted 29 December 2012 Keywords: Turbulence Coherent structures Roughness sub-layer Wind profile Shear instability Time-scales Wavelets a b s t r a c t

Some characteristics of the turbulence structure above primary forest localized in the south-western Amazon are analyzed. The data was collected in 60 m height meteorological tower erected in Rebio-Jaru´ Reserve, Brazil. The Morlet’s wavelet is used to detect coherent structures (CS) ‘‘ramp’’ time scales from turbulent virtual temperature data measured above forest, under day-time conditions. It is shown that there is a close relationship between time scale of the coherent structure (TCS) and the height to the inflection point in the mean wind speed profile (IP). A time scale associated with the IP is used to provide useful information on inside canopy penetration flow in order to be compared with the CS time-scale. The results show a very robust correlation between these two time scales (for 144 half-hourly data sets, a correlation coefficient value of 0.9 have been obtained). Such results provide new insights regarding shear instability and turbulent eddy characteristics above tall vegetation, in the surface roughness sub-layer (SRS).

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1. Introduction

The first investigations regarding atmospheric flow similarity relationships above forest canopies have pointed out turbulent exchange above and below tall vegetation as scalar diffusivity anomalies (Thom, 1975) and the existence of a roughness transi-tion sub-layer (RTS) associated with turbulent flow above very complex surfaces as forests and the failure of the familiar Monin– Obukhov similarity theory (MOST) which is suitable for smooth and horizontally homogeneous surfaces (H ¨ogstr ¨om and Bergstr ¨om, 1996;Raupach and Thom, 1981). In the RTS, turbulent exchange processes are strongly influenced by coherent struc-tures generated by shear instability mechanisms associated with the existence of the inflection point in the mean wind profile. Actually strong wind shear in the forest-atmosphere interface creates regions of peculiar coherent ‘‘roll’’ eddies (Gerz et al., 1994; Boldes et al. 2003,2007; Raupach et al., 1996; Robinson, 1991) and generates spectral short-circuiting and wake produc-tion within the canopy trunk space (Cava and Katul, 2008;

Finnigan, 2000). Such features introduce new kind of problems in applying footprint and fetch concepts for measuring

meteorological variables (Horst and Weil, 1992; Lee, 2003), and in estimating vertical fluxes (Horst and Weil, 1994;Mahrt, 2010;

Sakai et al., 2001).

One important issue associated with the role of Amazonian forest in biosphere-atmosphere exchanges, is the momentum transfer from the atmosphere to the surface. Problems related to the coupling between above canopy flow and below Amazo-nian forest canopy flow have been discussed by authors as

Fitzjarrald et al. (1990), Kruijt et al. (2000), Silva Dias et al. (2002),Viswanadham et al. (1990), among others. Despite much work being developed on this subject, there are few studies relating wind speed profile inflection point occurrence and scalar ramp (CS) characteristics, as time or frequency scales (Raupach et al., 1996; Thomas and Foken, 2005) above tall vegetation. CS are ubiquitous in turbulent flows (Antonia et al., 1979; Boldes et al., 2007;Gilliam et al., 2000;Jordan et al., 1997;Thomas and Foken, 2007). They are distinct large-scale fluctuation patterns regularly observed in turbulent flows (Wilczak, 1984). Above tall forests CS are linked with inside canopy gust penetration, and present specific frequency or time-scale of occurrence. Two time (or frequency) scales are compared: the time of occurrence of CS based onGao and Li (1993)procedure of detecting TCS in a TV

(virtual temperature) time-series using wavelet transform, and a time scale related with a gust penetration scale, calculated using inflection point in the mean wind speed profile information, as proposed by Marshall et al. (2002). The gust penetration time Contents lists available atSciVerse ScienceDirect

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Corresponding author. Tel.:þ 55 92 3643 3377. E-mail addresses: [email protected], [email protected] (C.Q. Dias Ju´nior).

scale proposition is based onRobinson (1991)discussion about the consequences of the existence of an inflection point in the wind speed profile above tall vegetation which is capable to generate slow-dissipation rolls-like CS, which are ranged trans-versally to the mean stream flow. According to Raupach et al. (1996), and their ‘‘mixing layer analogy’’, such CS are related to the creation of an oscillation mode and vorticity generation at the interface between above and inside canopy flows. It is just this kind of oscillation which is to be detected. To this end analyses are carried out using the proposed time scale.

2. Experimental and methods 2.1. Site and data

The Rebio-Jaru forest reserve is a 268,000 ha located in south-western Amazon, (Andreae et al., 2002). It is an area of typical tropical rain forest, between 101050_{S and 10119}0_{S and between}

611350_{W and 61157}0_{W, at approximately 145 m above sea level}

height. A 60 m height micrometeorological tower which the following coordinates 10140_42.3600_{S; 61156}0_1.6200_{W (Zeri and Sa´,}

2010) was built in the Rebio-Jaru reserve (Fig. 1), which presents homogeneous fetch conditions from north-west to south-east sides of the tower (in a clockwise sense). At the remaining directions, around 1 km homogeneous fetch conditions hold (Sa´ and Pachˆeco, 2006). River Machado is approximately 800 m at southern side.Culf et al. (1996)present geographical and clima-tological information concerning this experimental site in which a 60 m height meteorological tower has been erected. As men-tioned by von Randow et al. (2002), at the top of the tower, a vertical beam was placed in which a 3-D sonic anemometer (CSAT3, Campbell Scientific Inc.), whose specifications are described in the documenthttp://s.campbellsci.com/documents/ us/manuals/csat3.pdf(Appendix C), was installed at the height of 67 m above the forest floor, at a sampling rate of 16 Hz. The sonic anemometer has measured the three wind components (u, v, w) and the sonic virtual air temperature (Tv), and it is shown inFig. 3.

According toAndreae et al. (2002) forest canopy height ranges from 30 m to 35 m.Wright et al. (1996)reported a 4.6 value for leaf area index andMcWilliam et al. (1996)also present informa-tion concerning the tree species found in the Rebio-Jaru reserve. The Rebio-Jaru reserve is one of the several sites in which the large scale biosphere-atmosphere experiment in Amazonia (LBA) was carried out (Silva Dias et al., 2002). In the present study 144 fast response virtual temperature half-hourly data sets sampled at 16 Hz have been used, and low response wind profile data sampled at 0.1 Hz, corresponding to day-time periods of the 1999

Julian days 41, 42, 43, 45, 46, 50. As a part of the Rebio-Jaru reserve scientific activities energy budget components, wind velocity, temperature, humidity data was measured at several heights on a micrometeorological tower. To investigate the relationship between TCS and the IP, data provided by nine cup anemometers (Low Power A100L2, Vector Instruments Inc.) ranged in previously defined heights to give accurate resolution information regarding the inflection point in the mean wind speed profile have been used, as shown in Fig. 2. Thus, 1 h averaged wind speed values, which have been measured at heights of 55.00 m, 50.55 m, 47.70 m, 42.90 m, 40.25 m, 37.80 m, 32.8 m, 26.65 m, and 14.30 m above the ground, are available for calculations.

Virtual temperature time series has been obtained with sonic anemometer-thermometer measurements. As the speed of sound varies with temperature and humidity, but is approximately stable with pressure change, sonic anemometers are also used as thermo-meters. Their basic principle is based upon the measurement of the traveling time of an ultrasound pulse between two transducers. As is presented in the Manufacturer’s Manual (CSAT3 Three Dimensional Sonic Anemometer—Instruction Manual, Campbell Scientific, Inc., 1998–2012, Appendix C), (http://s.campbellsci.com/documents/us/ manuals/csat3.pdf), the sonic virtual temperature, in degrees Cel-sius, is given by: Ts¼(c2/gdRd) 273. 15, wheregd¼1.4 is the ratio of

Fig. 1. Meteorological tower erected at Rebio-Jaru forest reserve.

Fig. 2. Fast response instruments in the meteorological tower at Rebio-Jaru.

Fig. 3. Cup anemometers ranged at different heights in the meteorological tower at Rebio-Jaru.

C.Q. Dias Jr. et al. / J. Wind Eng. Ind. Aerodyn. 115 (2013) 1–8 2