Academic literature on the topic 'GPM'

Bakalauro baigiamąjame darbe atskleidžiama tiesioginių mokesčių vieta Lietuvos mokesčių sistemoje, išskiriant mokesčių rūšis priskiriamas prie tiesioginių mokesčių grupės, nagrinėjami GPM, pelno mokesčio ir turto mokesčių (nekilnojamojo turto mokesčio, žemės mokesčio, paveldimo turto mokesčio) apmokestinimo instrumentarijai. Nagrinėjama nacionalinio biudžeto mokestinių pajamų struktūrą, dinamika 2006-2010 m., atskleidžiama pajamų iš tiesioginių mokesčių lyginamieji svoriai nacionalinio biudžeto mokestinių pajamų struktūroje. Atliktas pajamų iš GPM, pelno mokesčio, turto mokesčių tyrimas, kurio metu atskleidžiama pajamų iš GPM, pelno mokesčio, turto mokesčių dinamika 2006-2010 m. pajamų iš GPM, pelno mokesčio, turto mokesčių surinkimo plano vykdymas, įdentifikuoti veiksniai įtakojantys pajamų iš GPM, pelno mokesčio, turto mokesčių kitimo tendencijos, atskleistos pajamų iš GPM pagal apmokestinimo objektus, pajamų iš pelno mokesčio pagal įmonių rūšis ir pajamų iš turto mokesčių pagal mokesčių rūšis kitimo tendencijos. Taip pat įdentifikuotos pajamų iš tiesioginių mokesčių didinimo galimybės.<br>Bachelor of baigiamąjame disclosure of direct taxes in the work of the tax system, excluding tax type assigned to direct tax group, deal with gpm, corporation tax and wealth tax (real estate tax, tax, inheritance tax) instrumentarijai taxation. The national budget tax income structure, dynamics 2006-2010, disclosed the revenue from direct taxes weighting national budget of tax revenue. A revenue from gpm, income tax, wealth tax trial, disclosed the revenue from gpm, income tax, wealth tax dynamics 2006-2010. revenue from gpm of income tax, wealth tax collection plan execution, įdentifikuoti factors contributing to income from gpm, income tax, wealth tax trends, disclosed the taxation of income from gpm objects, revenue from income tax by the undertakings and the types of property income tax by tax type trends. Also įdentifikuotos revenue from direct tax increases.

Availability of precipitation data is very important in every aspect related to hydrology. Readings from the ground stations are reliable and are used in hydrological models to do various analysis. However, the predictions are always associated with uncertainties due to the limited number of ground stations, which requires interpolation of the data. Meanwhile, groundbreaking approach in capturing precipitation events from vantage point through satellites in space has created a platform to not only merge ground data with satellite estimates to produce more accurate result, but also to find the data where ground stations are not available or scarcely available. Nevertheless, the data obtained through these satellite missions needs to be verified on its temporal and spatial resolution as well as the uncertainties associated before we make any decisions on its basis. This study focuses on finding and evaluating data obtained from two multi-satellite precipitation measurements missions: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) ii) Global Precipitation Measurement (GPM) mission. GPM is the latest mission launched on Feb 28, 2014 after the successful completion of TRMM mission which collected valuable data for 17 years since its launch in November 1997. Both near real time and final version precipitation products for TMPA and GPM are considered for this study. Two study areas representing eastern and western parts of the United States of America (USA) are considered: i) Charlotte (CLT) in North Carolina ii) San Francisco (SF) in California. Evaluation is carried out for daily accumulated rainfall estimates and single rainfall events. Statistical analysis and error categorization of daily accumulated rainfall estimates were analyzed in two parts: i) Ten yeas data available for TMPA products were considered for historical analysis ii) Both TMPA and GPM data available for a ten-month common period was considered for GPM Era analysis. To study how well the satellite estimates with their finest temporal and spatial resolution capture single rainfall event and to explore their engineering application potential, an existing model of SF watershed prepared in Infoworks Integrated Catchment Model (ICM) was considered for hydrological simulation. Infoworks ICM is developed and maintained by Wallingford Software in the UK and SF watershed model is owned by San Francisco Public Works (SFPW). The historical analysis of TMPA products suggested overestimation of rainfall in CLT region while underestimation in SF region. This underestimation was largely associated with missed-rainfall events and negative hit events in SF. This inconsistency in estimation was evident in GPM products as well. However, in the study of single rainfall events with higher magnitude of rainfall depth in SF, the total rainfall volume and runoff volume generated in the watershed were over-estimated. Hence, satellite estimates in general tends to miss rainfall events of lower magnitude and over-estimate rainfall events of higher magnitude. From statistical analysis of GPM Era data, it was evident that GPM has been able to correct this inconsistency to some extent where it minimized overestimation in CLT region and minimized negative error due to underestimation in SF. GPM products fairly captured the hydrograph shape of outflow in SF watershed in comparison to TMPA. From this study, it can be concluded that even though GPM precipitation estimates could not quiet completely replace ground rain gage measurements as of now, with the perpetual updating of algorithms to correct its associated error, it holds realistic engineering application potential in the near future.

Studies that investigate and evaluate the quality, limitations and uncertainties of satellite rainfall estimates are fundamental to assure the correct and successful use of these products in applications, such as climate studies, hydrological modeling and natural hazard monitoring. Over regions of the globe that lack in situ observations, such studies are only possible through intensive field measurement campaigns, which provide a range of high quality ground measurements, e.g., CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GlobAl Precipitation Measurement) and GoAmazon (Observations and Modeling of the Green Ocean Amazon) over the Brazilian Amazon during 2014/2015. This study aims to assess the uncertainty of the Global Precipitation Measurement (GPM) satellite constellation in representing the main characteristics of precipitation over different regions of Brazil. The Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) and the Goddard Profiling Algorithm (GPROF) (level-2) algorithms are evaluated against ground-based radar observations, specifically, the S-band weather radar from the Amazon Protection National System (SIPAM) and the X-band dual polarization weather radar (X-band CHUVA radar) as references. The space-based rainfall estimates, based on active microwave (e.g., TRMM-PR and GPM-DPR [at Ku-band] radars) are also used as references. The results for the CHUVA-Vale campaign suggest that GPROF has relatively good agreement (spatial distribution and accumulated rainfall), especially for convective rain cases, due the significant presence of ice scattering. However, the intensity and volume of light/moderate rains is overestimated and performance related to light/heavy rains (underestimated) are intrinsically linked to convectivestratiform rainfall occurrences over the study region. For the study over the Central Amazon Region (CHUVA-GoAmazon), results showed that during the wet season, IMERG, which uses the GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) sensor, significantly overestimates the frequency of heavy rainfall volumes at around 00:0004:00 UTC and 15:0018:00 UTC. This overestimation is particularly evident over the Negro, Solimões and Amazon rivers due to the poorlycalibrated algorithm over water surfaces. On the other hand, during the dry season, the IMERG product underestimates mean precipitation in comparison to the S-band SIPAM radar, mainly due to the fact that isolated convective rain cells in the afternoon are not detected by the satellite precipitation algorithm. The study based on verification of GPM level 2 by traditional and object-based analysis shows that volume and occurrence of heavy rainfall are underestimated, a good agreement of GPROF2014 for TMI and GMI versus TRMM PR and GPM DPR (Ku band) rainfall retrievals, respectively, was noted. Such most evident good performances were found through continuous and categorical analyses, especially during the wet season, where the number of objects and larger areas were observed. The larger object area seen by GPROF2014(GMI) compared to DPR (Ku band) was directly linked to the structure of vertical profiles of the precipitanting systems and the presence of bright band was the main source of uncertainty on the estimation of precipitation area and intensity. The results via error modeling, through the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework, demonstrated that the PUSH model was suitable for characterizing the error from the IMERG algorithm when applied to S-band SIPAM radar estimates. PUSH could efficiently predict the error distribution in terms of spatial and intensity distributions. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over the river. Although the estimated error showed a lower standard deviation than the observed error, they exhibited good correlations to other, especially in capturing the systematic error along the Negro, Solimões and Amazon rivers, especially during the wet season.<br>Estudos que investigam e avaliam a qualidade, limitações e incertezas das estimativas de precipitação de satélites são fundamentais para assegurar o uso correto e bem-sucedido desses produtos em aplicações, como estudos climáticos, modelagem hidrológica e monitoramento de desastres naturais. Em regiões do globo que não possuem observações in situ, esses estudos apenas são possíveis através de campanhas intensivas de medição de campo, que oferecem uma gama de medições de superfície de alta qualidade, por exemplo, CHUVA (Cloudprocesses of tHe main precipitation systems in Brazil: A contribUtion to cloud re-solVing modeling and to the GlobAl Precipitation Measurement) e GoAmazon (Observations and Modeling of the Green Ocean Amazon) sobre a Amazônia Brasileira durante 2014/2015. Este estudo tem como objetivo avaliar as incertezas provenientes da constelação de satélites do Global Precipitation Measurement (GPM) em representar as principais características da precipitação em diferentes regiões do Brasil. Os algoritmos Integrated Multi-satellitE Retrievals for GPM (IMERG) (level-3) e Goddard Profiling Algorithm (GPROF) (level-2) são avaliados em contraste as observações de radares meteorológicos, especificamente, do Sistema Nacional de Proteção da Amazônia (SIPAM) e o radar meteorológico banda X de dupla polarização (X-band CHUVA radar) como referência. As estimativas de precipitação, baseadas em radares de microondas ativos (por exemplo, radares TRMM-PR e GPM-DPR [na banda Ku]) também são utilizadas como referência. Os resultados da campanha CHUVA-Vale sugerem que o GPROF possui uma boa concordância (distribuição espacial e precipitação acumulada), especialmente para casos de chuva convectiva, devido à presença significativa de espalhamento por gelo. No entanto, a intensidade e volume de chuvas leves/moderadas é superestimada e um desempenho (subestimado) relacionado às chuvas fracas/intensas diretamente ligado às ocorrências de chuvas convectivasestratiformes na região do estudo. Para o estudo da região da Amazônia Central (CHUVA-GoAmazon), os resultados mostraram que, durante a estação chuvosa, o IMERG, que utiliza as estimativas de precipitação do GPROF2014 a partir do sensor GPM Microwave Imager (GMI), superestima significativamente a freqüência de chuvas intensas em torno de 00:00-04:00 UTC e 15:00-18:00 UTC. Essa superestimativa é particularmente evidente nos rios Negro, Solimões e Amazonas devido ao algoritmo apresentasse erroneamente calibrado sobre as superfícies de água. Por outro lado, durante a estação seca, o produto IMERG subestima a precipitação média em comparação com o radar banda-s do SIPAM, principalmente devido ao fato de que células convectivas isoladas à tarde não são detectadas por tal algoritmo. O estudo baseado na verificação das estimativas do GPM Level 2 por abordagens tradicional e baseada em objeto mostra que, embora a subestimiativa do volume e ocorrência de chuvas intensas, foi observada uma boa concordância do GPROF2014 (TMI e GMI) versus TRMM PR e GPM DPR (Ku band), Respectivamente. Tais evidentes melhores desempenhos foram encontrados através de análises contínua e categórica, especialmente durante a estação chuvosa, onde o maior número e maiores áreas de objetos foram observados. As maiores áreas, observadas pelo GPROF2014 (GMI) comparada ao DPR (banda Ku) esteve diretamente ligada à estrutura de perfis verticais dos sistemas de precipitantes e a presença de banda brilhante foi a principal fonte de incerteza na estimativa da área e intensidade de precipitação. Os resultados referentes à modelagem do erro, através da ferramenta Precipitation Uncertainties for Satellite Hydrology (PUSH), as análises demonstraram que o modelo PUSH foi adequado para caracterizar o erro do algoritmo IMERG quando aplicado às estimativas de radar banda S do SIPAM. O modelo PUSH pôde prever eficientemente a distribuição de erro em termos espaciais e de intensidade. No entanto, observou-se uma subestimativa (superestimativa) das taxas de chuva fracas do satélite durante o período seco (chuvoso), especialmente ao longo do rio. Embora o erro estimado tenha apresentado menor desvio padrão do que o erro observado, eles apresentaram boas correlações entre si, especialmente na captura do erro sistemático ao longo dos rios Negro, Solimões e Amazonas, especialmente durante a estação chuvosa.

La thèse s’inscrit dans le contexte de sûreté nucléaire relatif à l'hydruration des gainages combustibles en Zircaloy, en modélisant, à l’échelle atomique, les phénomènes d’ordre chimique hydrogène - lacune atomique, sur le sous-réseau interstitiel tétraédrique des hydrures de zirconium CFC. Une telle démarche s’est déclinée en deux étapes : en premier lieu, le développement d’un modèle énergétique atomistique à la fois précis et peu coûteux numériquement, puis l’implémentation d’approches thermostatistiques de type Monte-Carlo à l’aide de ce modèle. En prenant pour point de départ un Hamiltonien de Liaisons Fortes (TB), la construction du modèle énergétique a reposé sur la dérivation d’interactions multiatomiques entre atomes d’hydrogène, à l’aide de la méthode des perturbations généralisée (GPM) basée sur une représentation de l’état de désordre interstitiel dans l’Approximation du Potentiel Cohérent (CPA). La démarche a permis de réduire l'énergie d'ordre à un modèle d'Ising effectif dérivé des liaisons fortes (TBIM), basé sur les interactions de paires effectives entre atomes d’hydrogène. Le TBIM a ensuite été validé, en comparant les énergies de structures ordonnées d’une part reconstruites en TBIM, et d’autre part obtenues par des calculs directs d’énergie totale effectués soit en Liaisons Fortes, soit par des méthodes ab initio (DFT). L'implémentation d'une approche Monte-Carlo canonique par le TBIM a permis de caractériser les différentes transitions ordre-désordre, et d'établir un diagramme de phase de l’ordre chimique hydrogène - lacune atomique, sur le sous-réseau interstitiel tétraédrique des hydrures de zirconium CFC<br>The thesis addresses the nuclear safety issue of Zircaloy fuel cladding hydruration, by modelling, at atomistic scale, chemical ordering processes between hydrogen and atomic vacancies on tetrahedral interstitial sublattice of CFC zirconium hydrides. This has been achieved into two steps : first the development of an atomistic energetic model sufficiently precise and not too much CPU time consuming, and secondly its implementation in Monte-Carlo thermostatistical simulations. Starting from a Tight-Binding (TB) Hamiltonian, the energetic model has been derived from the calculation of multiatomic interactions between hydrogen atoms, using the Generalized Perturbation Method (GPM) applied to an interstitial disorder described within the Coherent Potential Approximation (CPA). The path allows us to reduce the ordering energy to a Tight-Binding Ising Model (TBIM), based on effective pairwise interactions between hydrogen atoms. The TBIM has been validated by comparing ordering energies of ordered structures either reconstructed using TBIM, or directly obtained from total energy calculations perfor- med both within TB and ab initio (DFT) methods.By implementing a canonical Monte-Carlo with TBIM, we obtain different order-disorder phase transitions, and a phase diagram of H-vacancy chemical ordering, on the tetrahedral interstitial sublattice of CFC zirconium hydrides

Motivation: Next generation sequencing technologies have revolutionized our ability to rapidly and affordably generate vast quantities of sequence data. Once generated, raw sequences are assembled into contigs or scaffolds. However, these assemblies are mostly fragmented and inaccurate at the whole genome scale, largely due to the inability to integrate additional informative datasets (e.g. physical, optical and genetic maps). To address this problem, we developed a semi-automated software tool-Genome Puzzle Master (GPM)-that enables the integration of additional genomic signposts to edit and build 'new-gen-assemblies' that result in high-quality 'annotation-ready' pseudomolecules. Results: With GPM, loaded datasets can be connected to each other via their logical relationships which accomplishes tasks to 'group,' 'merge,' 'order and orient' sequences in a draft assembly. Manual editing can also be performed with a user-friendly graphical interface. Final pseudomolecules reflect a user's total data package and are available for long-term project management. GPM is a web-based pipeline and an important part of a Laboratory Information Management System (LIMS) which can be easily deployed on local servers for any genome research laboratory.

This project served to analyze the effects that energy storage can have on energy production.  The study was aimed at Johannes CHP bio fuel. Johannes produces electricity for the SE3 region and heat for the district heating in Gävle. The electricity market is the main driving factor for energy production. It is ideal for Johannes to produce as much electricity as possible during high Elspot prices. Two accumulator tanks are already installed at Johannes so the surplus of heat can be stored from high electrical production. This study served to utilize the planning horizons for the future Elspot prices. The two forecasting methods presented are the 12 hour prices presented by Nord pool and the four day forecast predicted by ARIMA modeling. Several different energy storage technologies were theoretically discussed after which Gravity Potential Module, latent heat storage using phase change materials and open accumulator tanks were analyzed. The ideal system proved to be utilizing the four day ARIMA modeled forecast with a storage system consisting of Gravity Potential Module and latent heat storage. The system resulted in a gross profit of 1.4 million SEK and an increased average electrical production efficiency of .02% during 2011.

It is evident that reliable hydrologic prediction and water resource management are still a challenge for water resource engineers and planners, due to the unavailability of good network of hydro-meteorological stations. Especially, the situation of observation stations is not improving in developing countries. Ubiquity of satellite-based precipitation products has availed hydrologic studies in ungaged regions; however, assessment of satellite estimates is consequential before using it for hydrological utilities. Moreover, assessment of satellite precipitation products is important for the betterment of quality of precipitation estimates. The assessment requires comparison of gridded satellite precipitation products with point gage observations. Thus, it is also necessary to identify better methods of comparison among the point-based gage data and gridded satellite products. In this study, rainfall estimates from Integrated Multi-satellite Retrievals for Global Precipitation Measurement mission (IMERG) at monthly temporal scale were evaluated over Sugar Creek Basin from May 2014 to April 2018. Initially, monthly IMERG at was evaluated using three different methods of comparision: a) Ordinary Kriging (OK) b) Inverse Distance Weighting (IDW) c) Point-to-Pixel. Secondly, IMERG data were downscaled to finer resolution (1km) using the Random Forest (RF) algorithm and evaluated against gage observation using point-to-pixel method. Results showed, IMERG at monthly temporal resolution showed better results when evaluated using OK and IDW, compared to point-to-pixel method. Monthly IMERG performed well in capturing temporal variability of rainfall and estimated the quantity of rainfall efficiently, however, it slightly overestimated the precipitation. Besides, RF algorithm showed promising performance in spatial downscaling of IMERG at monthly scale. Downscaled results at finer resolution successfully captured the spatial distribution and temporal variability of rainfall with acceptable accuracy.

Les précipitations résultent d'un phénomène atmosphérique caractérisé par une variabilité spatiale et temporelle forte. Cette variabilité dans la distribution des pluies et des évènements intenses a des impacts en hydrologie de surface (e.g. inondations) variés selon les régions du monde. Toute modification du climat tropical est associée à une modification du cycle de l'eau et de l'énergie dans ces régions. Dans un contexte de changement climatique, il est donc important de développer des outils permettant d'estimer quantitativement les précipitations, à l'échelle du globe, à la fois sur les surfaces continentales et les surfaces océaniques. Les travaux présentés dans cette thèse s'intéressent à l'observation des précipitations depuis l'espace. En effet, la mesure des pluies nécessite une densité d'observations élevée qui, sur l'ensemble des Tropiques, n'est accessible qu'à partir d'observations spatiales. Depuis plusieurs décades, les moyens satellitaires à disposition ont beaucoup évolué et offrent aujourd'hui une densité d'observations de plus en plus fortes. Grâce aux nouvelles missions déployées telles que Megha-Tropiques au sein de la future constellation GPM (Global Precipitation Measurement), on a accès à un ensemble de systèmes d'observations qui amène à une densité accrue d'observations spatiales. L'estimation quantitative des précipitations n'était possible qu'à l'échelle mensuelle, il est maintenant envisageable d'estimer la pluie par satellite à des échelles de temps de plus en plus fines. Cette thèse s'intéresse aux échelles 1°/1-jour, échelle clé pour les études météorologiques et hydrologiques. Il existe un large spectre de méthodes d'estimations de précipitations par satellite, de qualité inégale. Dans un premier temps, une analyse des produits issus des développements les plus récents montre que leur qualité a atteint un degré suffisant pour être utilisé de manière quantitative aux échelles de temps pertinentes en météorologie. Il apparaît également qu'à ces échelles de temps, il est nécessaire d'utiliser les estimations de cumul de précipitations conjointement avec leurs barres d'erreurs. Une nouvelle méthode d'estimations de précipitations sur l'ensemble de la ceinture tropicale, appelé TAPEER (Tropical Amount of Precipitation with an Estimate of ERrors), est donc développée dans le but d'estimer des cumuls de pluie et leurs erreurs associées à l'échelle 1°/1-jour. Cette approche est fondée sur une méthode de fusion de données de l'imagerie Infrarouge d'une constellation de satellites géostationnaires et d'estimations de taux de pluie issues de radiomètres Micro-ondes d'une constellation de satellites défilant. Des techniques modélisations sont mises en oeuvre afin d'associer une erreur aux cumuls de pluie produits. Une investigation détaillée du bilan d'erreur de la méthode TAPEER montre que les sources principales d'incertitudes sont liées à l'échantillonnage et aux biais systématiques sur les taux de pluie d'intensité moyenne. Une étude sur l'été 2009 révèle l'importance de l'utilisation de la barre d'erreur dans l'analyse de la distribution des pluies, en particulier pour les plus forts cumuls sur la ceinture tropicale