DYNAMICS OF HEAT FLUXES BY BOWEN AND MATMNXFLX AND NOAH FLDAS PRODUCTS IN THE PANTANAL OF MATO GROSSO.

This paper aimed to analyze the dynamics of the energy budget components: latent heat flux (LE), sensible heat flux (H) and soil heat flux (G), in the Mato Grosso Pantanal. The estimates of LE, H, and G were obtained by the Bowen ratio methods, using data from the micrometeorological tower located in the Baía das Pedras Park of SESC-Pantanal Ecological Resort, for the years 2011 to 2013. The normality of the variables Rn, LE, H and G, were tested by Kolmogorov-Smirnov test at 5% significance, and the seasonal differences of the fluxes were verified by the KruskalWallis test, α = 0.05. LE and H data from the remote sensing products MATMNXFLX and FLDAS_NOAH of the MERRA model was also acquired, and their comparison with the tower data was performed by the statistics of Spearman correlation (r), Mean Absolute Error (MAE), Root Mean Squared Erro (RMSE), bias, and Willmott's Concordance Index (d). It was observed that most of the available energy is used for evapotranspiration (latent heat), followed by sensible heat and soil heat flux. In the rainy season there is an increase in the partition of LE and G and reduction of H. Only the estimates of LE of MATMNXFLX and FLDAS_NOAH products correlate with the data observed in the meteorological tower. It is concluded that the energy partitions have a seasonal behavior and that the MATMNXFLX and FLDAS_NOAH products, after being calibrated, can be used to estimate LE in the Mato Grosso Pantanal

VARIABILIDADE INTERANUAL DAS PROPRIEDADES ÓTICAS DE AEROSSÓIS EM BIOMAS DISTINTOS NA AMAZÔNIA LEGAL

A análise das propriedades óticas dos aerossóis é extremamente importante para o entendimento dos impactos que o material particulado pode provocar nos fluxos radiativos. Nesse sentido, este estudo teve como principal objetivo utilizar as medidas da AERONET (do inglês, AErosol RObotic NETwork) para avaliar as propriedades absortivas e espalhadoras dos aerossóis atmosféricos nos biomas cerrado e floresta amazônica (ambos na Amazônia Legal), além de avaliar a aplicabilidade do novo algoritmo (V3) da AERONET. Neste trabalho foram recuperados os dados dos radiômetros CIMEL sun-sky para os sites de Alta Floresta e Cuiabá MIRANDA, com nível 2.0 de processamento (V3), para o período de 17 anos (2000-2016). As variáveis analisadas foram a AAOD (do inglês, Absorption Aerosol Optical Deph), profundidade ótica de absorção do aerossol o SSA (do inglês, Single Scattering Albedo), albedo de espalhamento simples e o EAA (do inglês, Expoent Angstrom Absorption) expoente Angstrom de Absorção (440 a 870 nm). Os resultados evidenciaram que ambos biomas são fortemente impactados pela queima de biomassa. Os maiores valores encontrados para SSA e AAOD ocorreram para os menores comprimentos de onda, indicando a predominância de carbono orgânico. Este resultado indica que estas partículas são compostas por elementos que apresentam forte dependência espectral. Apesar da queda anual no desmatamento na região amazônica após 2004, observa-se que AAOD não seguiu o mesmo padrão. Também foi verificado que o produto de inversão V3 se mostrou eficiente para avaliação das propriedades óticas dos aerossóis no período seco da série estudada

Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer

Advances in technical radiotherapy have resulted in significant sparing of organs at risk (OARs), reducing radiation-related toxicities for patients with cancer of the head and neck (HNC). Accurate delineation of target volumes (TVs) and OARs is critical for maximising tumour control and minimising radiation toxicities. When performed manually, variability in TV and OAR delineation has been shown to have significant dosimetric impacts for patients on treatment. Auto-segmentation (AS) techniques have shown promise in reducing both inter-practitioner variability and the time taken in TV and OAR delineation in HNC. Ultimately, this may reduce treatment planning and clinical waiting times for patients. Adaptation of radiation treatment for biological or anatomical changes during therapy will also require rapid re-planning; indeed, the time taken for manual delineation currently prevents adaptive radiotherapy from being implemented optimally. We are therefore standing on the threshold of a transformation of routine radiotherapy planning via the use of artificial intelligence. In this article, we outline the current state-of-the-art for AS for HNC radiotherapy in order to predict how this will rapidly change with the introduction of artificial intelligence. We specifically focus on delineation accuracy and time saving. We argue that, if such technologies are implemented correctly, AS should result in better standardisation of treatment for patients and significantly reduce the time taken to plan radiotherapy