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Enbäck, Henrik, and Charlotta Eriksson. "Hybrid Rainfall Estimates from Satellite, Lightning and Ground Station Data in West Africa." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-254757.
Testo completoAbstract (sommario):
Most of the working population in Ghana are farmers. It is of importance for them to know where and when precipitation will occur to prevent crop losses due to droughts and floodings. In order to have a sustainable agriculture, improved rainfall forecasts are needed. One way to do that is to enhance the initial conditions for the rainfall models. In the mid-latitudes, in-situ rainfall observations and radar data are used to monitor weather and measure rainfall. However, due to the lack of station data and the present absence of a radar network in West Africa, other rainfall estimates are needed as substitutes. The rainfall amount in convective systems, dominating in West Africa, is coupled to their vertical structure. Therefore, satellite measurements of cloud top temperatures and microwave scatter, as well as the number of lightning, can be used to estimate the amount of rainfall. In this report, derived rainfall estimates from satellites and the use of lightning data are analysed to see how well they estimate the actual rainfall amount. The satellite datasets used in this report are NOAA RFE2.0, NOAA ARC2, and the EUMETSAT MPE. The datasets were compared to in-situ measurements from GTS- and NGO collaborating observation stations in order to verify which satellite dataset that best estimates the rainfall or, alternatively, if a combination between two or all the datasets is a better approach. Lightning data from Vaisala GLD360 have been compared to GTS-station data and RFE2.0 to see if a relation between the number of lightning and rainfall amount could be found. It was also tested whether a combination between the satellite- and lightning data could be a better estimate than the two approaches separately. Rainfall estimates from RFE2.0 alone showed the best correlation to GTS- and the NGO collaborating station data. However, a difference in how well RFE2.0 estimated rainfall at GTS-stations compared to reference stations was seen. Comparing RFE2.0 to GTS-stations showed a better correlation, probably due to the use of these observations in the build up of RFE2.0. Even though RFE2.0 showed the best correlation compared to other datasets, satellite estimates showed in general poor skill in catching the actual rainfall amount, strongly underestimating heavy rainfall and somewhat overestimating lighter rainfall. This is probably due to the rather basic assumptions that the cloud top temperature is directly coupled to rain rate and also the poor temporal resolution of the polar orbiting satellites (carrying microwave sensors). Better instruments and algorithms need to be developed to be able to use satellite datasets as an alternative to rainfall measurements in West Africa. Furthermore, due to the lack of station data, only tentative results between GLD360 and GTS-stations could be made, showing a regime dependence. When further analysed to RFE2.0, a stronger temporal dependence, i.e. seasonal variation, rather than a spatial one was seen, especially during the build up of the monsoon. However, due to poor rainfall estimates from RFE2.0, no accurate rainfall-lightning relation could be made but trends regarding the relation were seen. The use of GLD360 showed to be an effective way to erase false precipitation from satellite estimates as well as locating the trajectory of convective cells. To be able to further analyse rainfall/lightning relation, more measurements of the true rainfall is needed from e.g. a radar.<br>Majoriteten av Ghanas befolkning arbetar inom jordbrukssektorn. Det är viktigt för jordbrukarna att veta när och var nederbörd kommer att falla för att deras skörd inte ska bli förstörd av till exempel torka eller översvämningar. Det behövs därför bättre nederbördsprognoser för ett hållbart jordbruk. Ett sätt att få mer noggranna prognoser är att förbättra initialvärden till nederbördsmodellerna. Vid de mellersta breddgraderna på norra halvklotet används nederbördsmätningar från in-situ stationer samt data från radarsystem som initialvärden, men på grund av få mätstationer och inget radarsystem i västra Afrika behövs alternativa nederbördsestimater. Nederbörden i västra Afrika domineras av konvektiva system, vars regnmängd är kopplad till dess vertikala struktur. Satellitmätningar av molntoppstemperaturen och mikrovågornas spridning och absorption, liksom antalet blixtar är också relaterat till molnets struktur och kan därför användas för att estimera nederbördsmängden. I den här rapporten analyserades nederbördsestimater från satellitdata samt användning av blixtdata för att undersöka hur bra metoderna är på att estimera den verkliga nederbördsmängden. Satellitdataseten som analyserades var NOAA RFE2.0, NOAA ARC2 och EUMETSAT MPE. Dataseten jämfördes med in-situ mätningar från GTS-stationer samt observationerfrån NGO-samarbetande jordbrukare för att verifiera vilket satellitdataset som ger det bästa nederbördsestimatet, alternativt att en kombination mellan två eller alla dataset ger det bästa estimatet. Vidare har blixtdata från Vaisala GLD360 jämförts med GTS-stationer och RFE2.0 för att se om antalet blixtar är relaterat till nederbördsmängden. Slutligen har det också undersökts om en kombination mellan satellit- och blixtdata är ett bättre än de två metoderna separat. Nederbördsestimater från RFE2.0 visade på bäst korrelation med både GTS- och NGO-stationer. En tydlig skillnad noterades dock i RFE2.0:s förmåga att estimera nederbörd vid jämförelse mellan de två stationsdataseten. En bättre korrelation mellan RFE2.0 och GTS-stationerna påvisades, troligen för att RFE2.0 använder dessa observationer i uppbyggnaden av datasetet. Även om RFE2.0 visade på bäst korrelation i jämförelse med ARC2 och MPE var samtliga satellitdataset dåliga på att estimera den verkliga nederbördsmängden. De underestimerar starkt stora mängder nederbörd samtidigt som de överestimerar små mängder. Anledningen är troligen det relativt enkla antagandet att molntoppstemperaturen är direkt kopplad till molnets regnmängd samt den dåliga tidsupplösningen på de polära satelliterna som är utrustade med mikrovågssensorer. För att satellitdataseten ska kunna användas som ett alternativt nederbördsestimat i Västafrika behövs bättre mätinstrument och algoritmer. Vid analysen mellan GLD360 och GTS-stationer kunde, på grund av för få stationsdata, endast övergripande resultat erhållas. Ett områdesberoende gick dock att urskilja som vid en ytterligare analys mellan GLD360 och RFE2.0 visade på ett större säsongsberoende, särskilt under uppbyggnaden av monsunperioden i april och maj. Eftersom RFE2.0 visade sig ha dåliga nederbördsestimat kunde ingen noggrann koppling hittas, utan resultatet visade på trender samt möjligheter att kunna använda blixtdata som ett alternativt nederbördsestimat. Till exempel visade det sig att GLD360 kunde användas som ett verktyg för att sålla bort falsk nederbörd från satellitestimat samt identifiera trajektorien för ett konvektivt system. För en djupare analys i att relatera blixtar och nederbörd i Västafrika krävs bättre tekniker för att estimera nederbörd eller fler in-situ observationer.
2
Mote, Shekhar Raj. "EVALUATION OF STATE-OF-THE-ART PRECIPITATION ESTIMATES: AN APPROACH TO VALIDATE MULTI-SATELLITE PRECIPITATION ESTIMATES." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2364.
Testo completoAbstract (sommario):
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.
3
Smolinski, Steven P. "Marine boundary layer depth and relative humidity estimates using multispectral satellite measurements." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23069.
Testo completoAbstract (sommario):
A technique is presented to estimate surface relative humidity and boundary layer depth from multispectral satellite measurements using the AVHRR sensor on TIROS-N generation satellites. A sensitivity study quantifies the effect of a combination of input measurement errors of sea-surface temperature, optical depth and total water vapor used in the technique to produce outputs of surface relative humidity and boundary layer depth under simulated conditions and model atmospheres. Technique verification is then accomplished with satellite data compared to ship and aircraft vertical soundings and sea-surface temperature measurements. The root mean square differences between the surface relative humidity/boundary layer depth satellite-measured estimates and verified measurements are 6% and 75 m respectively. Finally, synoptic-scale mapping of the surface relative humidity and boundary layer depth fields based on the satellite derived estimates is accomplished with monochromatic and color enhanced satellite images. Horizontal variability of surface relative humidity and boundary layer depth on the order of kilometers can be visually detected from these images. Keywords: Remote sensing; MABL(Marine Boundary Layer); Marine atmospheres; Air ocean interface; Advanced very high resolution radiometers; Theses. (edc)
4
Teo, Chee-Kiat. "Application of satellite-based rainfall estimates to crop yield forecasting in Africa." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.434333.
Testo completo
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Margulis, Steven A. (Steven Adam) 1973. "Temporal disaggregation of satellite-derived monthly precipitation estimates for use in hydrologic applications." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/17453.
Testo completo
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Horvath, Akos. "Differences between satellite measurements and theoretical estimates of global cloud liquid water amounts." Diss., The University of Arizona, 2004. http://hdl.handle.net/10150/280553.
Testo completoAbstract (sommario):
This dissertation investigated the estimation of global cloud water amounts. The study was prompted by the large discrepancy in published global mean values of cloud liquid water path. Microwave and optical satellite measurements of this quantity range from 25 g/m² to 80 g/m². Theoretical estimates are significantly larger with a current best guess value of 380 g/m². The major limitations of microwave measurements were found to be the inadequate separation of the cloud- and rainwater components, and the lack of retrievals over land. Optical observations were found to be constrained by the truncation of retrieved optical thickness due to saturation effects, the limited knowledge of drop effective radius as a function of optical thickness and rain rate, and plane-parallel retrieval errors due to 3D effects. An analysis of the potential uncertainties concluded that the current theoretical estimate of the global mean cloud liquid water path of 380 g/m² was reasonable with an uncertainty of ±80 g/m². Errors in the optical retrievals due to 3D effects were estimated using a multiangle data set. A microwave-optical comparison revealed that a drop effective radius significantly larger than the common assumption of 8-10 μm was required to remove the low bias of optical retrievals of cloud liquid water in precipitating systems. The low bias due to saturation effects was accounted for by sigmoidal extrapolation of the cumulative distribution of cloud optical thickness. Overall it was found that the optical measurement of the global mean cloud liquid water path could be increased to a maximum of 150 g/m² over the oceans. The failure to close the gap between satellite measurements and theoretical estimates can partly be attributed to, but cannot be completely explained by, the lack of the most intense continental clouds in the ocean-only data set used in this study. It is unlikely that optical measurements can be corrected to accurately retrieve the largest liquid water amounts. New techniques are required to handle the wettest precipitating clouds.
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Athey, Ashley Taylor. "Verification of Satellite Derived Precipitation Estimates Over Complex Terrain: A Ground Truth Analysis for Nepal." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/52917.
Testo completoAbstract (sommario):
Precipitation estimates from the satellite-based Tropical Rainfall Measuring Mission (TRMM) instrumentation play a key role in flood analysis and water resource management across many regions of the world where rain gauge data are sparsely available. Previous studies have produced conflicting results regarding the accuracy of satellite-derived precipitation products, and several authors have called for further examination of their utility, specifically across the Himalaya Mountains region of southern Asia. In this study, daily precipitation estimates generated by TRMM were compared to daily precipitation measurements from a rain gauge network across the country of Nepal. TRMM data were statistically analyzed to quantify their representation of the gauge data during the four precipitation-defined seasons of Nepal. A detailed case study was assembled for the TRMM grid cell characterized by the greatest precipitation gauge density to develop a deeper understanding of local precipitation variability that the coarse resolution TRMM product cannot capture. The results illustrate that TRMM performs relatively well across all seasons, though the performance of TRMM during frozen precipitation events is not clear. In general, TRMM underestimates daily precipitation during the monsoon and pre-monsoon seasons, and overestimates during the winter and post-monsoon season. The case study analysis revealed a threshold for TRMM bias of 10-20mm of daily precipitation, overestimating lighter precipitation events while underestimating heavier precipitation events. Still, TRMM data compare favorably to gauge data, which contributes to the confidence with which they and other satellite-derived data products are used.<br>Master of Science
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Hyzer, Garrett. "Effects of GPS Error on Animal Home Range Estimates." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4338.
Testo completoAbstract (sommario):
This study examined how variables related to habitat cover types can affect the positional accuracy of Global Positioning System (GPS) data and, subsequently, how wildlife home range analysis can be influenced when utilizing this inaccurate data. This study focused on measuring GPS accuracy relative to five habitat variables: open canopy, sparse canopy, dense canopy, open water, and building proximity. The study took place in Hillsborough County, in residential areas that contain all of these habitat types. Five GPS devices, designed for wildlife tracking purposes, were used to collect the data needed for this study. GPS data was collected under the aforementioned scenarios in order to induce error into the data sets. Each data set was defined as a 1-hour data collecting period, with a fix rate of 60 seconds, which resulted in 60 points per sample. The samples were analyzed to determine the magnitude of effect the five variables have on the positional accuracy of the data. Thirty samples were collected for each of the following scenarios: (1) open grassland with uninhibited canopy closure, (2) sparse vegetation canopy closure, (3) dense vegetation canopy closure, (4) close proximity to buildings (<2 m), and (5) open water with uninhibited canopy closure. Then, GPS errors (in terms of mean and maximum distance from the mean center of each sample) were calculated for each sample using a geographic information system (GIS). Confidence intervals were calculated for each scenario in order to evaluate and compare the levels of error. Finally, this data was used to assess the effect of positional uncertainty on home range estimation through the use of a minimum convex polygon home range estimation technique. Open grassland and open water cover types were found to introduce the least amount of positional uncertainty into the data sets. The sparse coverage cover type introduces a higher degree of error into data sets, while the dense coverage and building proximity cover types introduce the greatest amount of positional uncertainty into the data sets. When used to create minimum convex polygon home range estimates, these data sets show that the home range estimates are significantly larger when the positional error is unaccounted for as opposed to when it is factored into the home range estimate.
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Robertson, Noel Arthur. "Model-based and satellite estimates of snow hydrology and carbon fluxes at high latitudes." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555106.
Testo completoAbstract (sommario):
This thesis is a study of how satellite data can be used to improve our understanding of the snow hydrology of boreal regions and its effects on their hydrological and carbon balance. The key parameter is the snow water equivalent (SWE). The thesis addresses two themes; (1) snow parameter retrieval from satellite data, and (2) the dependence on SWE of the hydrological and carbon balance of boreal regions. Using radiative transfer models, linked to a climate-driven snow model, it was found that the brightness temperature is most sensitive to the snow grain size, with SWE the second most significant parameter. Comparison of model predictions with SSMII satellite data showed a significant difference in spatial patterns in central Siberia. The most likely reasons are thought to be either an inaccurate estimate of snow grain size, or errors in the land cover description. For SWE or snow depth retrieval from passive microwave data to be successful, the evolution of the grain size needs to be better quantified. The combination of dynamic algorithm in early winter and static algorithm in middle to late winter produces the best overall results, particularly in Eurasia. The final part of the thesis uses a Dynamic Vegetation Model, SDGVM, to model the hydrological and carbon balance of the major boreal catchments, using a simple climate-driven model of SWE. It was found that there. was broad agreement in the annual water balance between the SDGVM and measured data of river discharge. There is a significant difference in seasonal timing due to the absence of some hydrological processes in SDGVM. Analysis of 21 SI century climate scenarios for the Ob basin indicates a slight reduction in annual runoff, but a significant increase in Net Biome Productivity.
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Oliveira, Rômulo Augusto Jucá. "Characteristics and error modeling of GPM satellite rainfall estimates during CHUVA campaign in Brazil." Instituto Nacional de Pesquisas Espaciais (INPE), 2017. http://urlib.net/sid.inpe.br/mtc-m21b/2017/05.22.17.16.
Testo completoAbstract (sommario):
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.
11
Quaas, Johannes, Olivier Boucher, Nicolas Bellouin, and Stefan Kinne. "Which of satellite- or model-based estimates is closer to reality for aerosol indirect forcing?" Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-189545.
Testo completoAbstract (sommario):
In their contribution to PNAS, Penner et al. (1) used a climate model to estimate the radiative forcing by the aerosol first indirect effect (cloud albedo effect) in two different ways: first, by deriving a statistical relationship between the logarithm of cloud droplet number concentration, ln Nc, and the logarithm of aerosol optical depth, ln AOD (or the logarithm of the aerosol index, ln AI) for present-day and preindustrial aerosol fields,
a method that was applied earlier to satellite data (2), and, second, by computing the radiative flux perturbation between two simulations with and without anthropogenic aerosol sources. They find a radiative forcing that is a factor of 3 lower in the former approach than in the latter [as Penner et al. (1) correctly noted, only their “inline” results are useful for the comparison].
12
Quaas, Johannes, Olivier Boucher, Nicolas Bellouin, and Stefan Kinne. "Which of satellite- or model-based estimates is closer to reality for aerosol indirect forcing?" National Acadamy of Sciences, 2011. https://ul.qucosa.de/id/qucosa%3A13972.
Testo completoAbstract (sommario):
In their contribution to PNAS, Penner et al. (1) used a climate model to estimate the radiative forcing by the aerosol first indirect effect (cloud albedo effect) in two different ways: first, by deriving a statistical relationship between the logarithm of cloud droplet number concentration, ln Nc, and the logarithm of aerosol optical depth, ln AOD (or the logarithm of the aerosol index, ln AI) for present-day and preindustrial aerosol fields,
a method that was applied earlier to satellite data (2), and, second, by computing the radiative flux perturbation between two simulations with and without anthropogenic aerosol sources. They find a radiative forcing that is a factor of 3 lower in the former approach than in the latter [as Penner et al. (1) correctly noted, only their “inline” results are useful for the comparison].
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Blinn, Christine Elizabeth. "Increasing the Precision of Forest Area Estimates through Improved Sampling for Nearest Neighbor Satellite Image Classification." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/28694.
Testo completoAbstract (sommario):
The impacts of training data sample size and sampling method on the accuracy of forest/nonforest classifications of three mosaicked Landsat ETM+ images with the nearest neighbor decision rule were explored. Large training data pools of single pixels were used in simulations to create samples with three sampling methods (random, stratified random, and systematic) and eight sample sizes (25, 50, 75, 100, 200, 300, 400, and 500). Two forest area estimation techniques were used to estimate the proportion of forest in each image and to calculate forest area precision estimates. Training data editing was explored to remove problem pixels from the training data pools. All possible band combinations of the six non-thermal ETM+ bands were evaluated for every sample draw. Comparisons were made between classification accuracies to determine if all six bands were needed. The utility of separability indices, minimum and average Euclidian distances, and cross-validation accuracies for the selection of band combinations, prediction of classification accuracies, and assessment of sample quality were determined.
Larger training data sample sizes produced classifications with higher average accuracies and lower variability. All three sampling methods had similar performance. Training data editing improved the average classification accuracies by a minimum of 5.45%, 5.31%, and 3.47%, respectively, for the three images. Band combinations with fewer than all six bands almost always produced the maximum classification accuracy for a single sample draw. The number of bands and combination of bands, which maximized classification accuracy, was dependent on the characteristics of the individual training data sample draw, the image, sample size, and, to a lesser extent, the sampling method. All three band selection measures were unable to select band combinations that produced higher accuracies on average than all six bands. Cross-validation accuracies with sample size 500 had high correlations with classification accuracies, and provided an indication of sample quality.
Collection of a high quality training data sample is key to the performance of the nearest neighbor classifier. Larger samples are necessary to guarantee classifier performance and the utility of cross-validation accuracies. Further research is needed to identify the characteristics of "good" training data samples.<br>Ph. D.
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Roy, Tirthankar, Hoshin V. Gupta, Aleix Serrat-Capdevila, and Juan B. Valdes. "Using satellite-based evapotranspiration estimates to improve the structure of a simple conceptual rainfall–runoff model." COPERNICUS GESELLSCHAFT MBH, 2017. http://hdl.handle.net/10150/623239.
Testo completoAbstract (sommario):
Daily, quasi-global (50° N–S and 180° W–E), satellite-based estimates of actual evapotranspiration at 0.25° spatial resolution have recently become available, generated by the Global Land Evaporation Amsterdam Model (GLEAM). We investigate the use of these data to improve the performance of a simple lumped catchment-scale hydrologic model driven by satellite-based precipitation estimates to generate streamflow simulations for a poorly gauged basin in Africa. In one approach, we use GLEAM to constrain the evapotranspiration estimates generated by the model, thereby modifying daily water balance and improving model performance. In an alternative approach, we instead change the structure of the model to improve its ability to simulate actual evapotranspiration (as estimated by GLEAM). Finally, we test whether the GLEAM product is able to further improve the performance of the structurally modified model. Results indicate that while both approaches can provide improved simulations of streamflow, the second approach also improves the simulation of actual evapotranspiration significantly, which substantiates the importance of making <q>diagnostic structural improvements</q> to hydrologic models whenever possible.
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SHRESTHA, Mandira Singh. "Bias-Adjustment of Satellite-Based Rainfall Estimates over the Central Himalayas of Nepal for Flood Prediction." 京都大学 (Kyoto University), 2011. http://hdl.handle.net/2433/142254.
Testo completo
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Claggett, Seton Paul. "Evaluation of the Utility of Satellite Rainfall Estimates for Water Resource Applications using Sub-Basin Areal Averages and Pixel-to-Pixel Comparisons." Thesis, The University of Arizona, 2001. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_etd_hy0020_m_sip1_w.pdf&type=application/pdf.
Testo completo
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Greatrex, Helen. "The application of seasonal rainfall forecasts and satellite rainfall estimates to seasonal crop yield forcasting for Africa." Thesis, University of Reading, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578012.
Testo completoAbstract (sommario):
Rain-fed agriculture is extremely important in sub-Saharan Africa, thus the ability to forecast and monitor regional crop yields throughout the growing season would be of enormous benefit to decision makers. Of equal importance to be able to assign a measure of uncertainty to the forecast, especially considering that many predictions are made in the context of a complex climate and sparse meteorological and agricultural observations. This work investigates these issues in the context of an operational updating regional crop yield forecast, concentrating in particular on a case study forecasting Ethiopian maize. Part 1 of the work presented a detailed discussion of Ethiopia' s climate and agricultural systems. As real-time ground based weather observations are sparse in Africa, Part 2 contains an investigation into remotely sensed satellite rainfall estimates. A daily TAMSAT calibration and the geostatistical process of sequential simulation were used to create a spatially correlated ensemble of Meteosat-derived rainfall estimates. The ensemble mean was evaluated as a daily deterministic rainfall product and was found to be as good as or better than other products applied in the same region. A validation of the full ensemble showed that they realistically estimated Ethiopian rainfall fields that agreed both with observed spatial correlations and input pixel level statistics. Part 3 of the work includes a discussion on regional crop simulation modelling and presents a new parameterisation of the GLAM crop simulation model for tropical maize. GLAMMAIZE was then driven using individual members of the satellite ensemble; this was shown to exhibit the correct sensitivities to climate inputs and performed reasonably against yield observations. Finally, Part 4 presented a new method of creating stochastic spatially and temporally correlated rainfall fields. This 'regional weather generator' was tested using a case study on Ethiopian April rainfall and a detailed discussion was included about future development plans.
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Yilmaz, Koray Kamil. "Towards Improved Modeling for Hydrologic Predictions in Poorly Gauged Basins." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195252.
Testo completoAbstract (sommario):
In most regions of the world, and particularly in developing countries, the possibility and reliability of hydrologic predictions is severely limited, because conventional measurement networks (e.g. rain and stream gauges) are either nonexistent or sparsely located. This study, therefore, investigates various systems methods and newly available data acquisition techniques to evaluate their potential for improving hydrologic predictions in poorly gaged and ungaged watersheds.Part One of this study explores the utility of satellite-remote-sensing-based rainfall estimates for watershed-scale hydrologic modeling at watersheds in the Southeastern U.S. The results indicate that satellite-based rainfall estimates may contain significant bias which varies with watershed size and location. This bias, of course, then propagates into the hydrologic model simulations. However, model performance in large basins can be significantly improved if short-term streamflow observations are available for model calibration.Part Two of this study deals with the fact that hydrologic predictions in poorly gauged/ungauged watersheds rely strongly on a priori estimates of the model parameters derived from observable watershed characteristics. Two different investigations of the reliability of a priori parameter estimates for the distributed HL-DHMS model were conducted. First, a multi-criteria penalty function framework was formulated to assess the degree of agreement between the information content (about model parameters) contained in the precipitation-streamflow observational data set and that given by the a priori parameter estimates. The calibration includes a novel approach to handling spatially distributed parameters and streamflow measurement errors. The results indicated the existence of a significant trade-off between the ability to maintain reasonable model performance while maintaining the parameters close to their a priori values. The analysis indicates those parameters responsible for this discrepancy so that corrective measures can be devised. Second, a diagnostic approach to model performance assessment was developed based on a hierarchical conceptualization of the major functions of any watershed system. "Signature measures" are proposed that effectively extract the information about various watershed functions contained in the streamflow observations. Manual and automated approaches to the diagnostic model evaluation were explored and were found to be valuable in constraining the range of parameter sets while maintaining conceptual consistency of the model.
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Potgieter, Andries B. "Determining broadacre crop area estimates through the use of multi-temporal modis satellite imagery for major Australian winter crops." University of Southern Queensland, Faculty of Engineering and Surveying, 2009. http://eprints.usq.edu.au/archive/00006613/.
Testo completoAbstract (sommario):
[Abstract]: Since early settlement, agriculture has been one of the main industries contributing to the livelihoods of most rural communities in Australia. The wheat grain industry is Australia’s second largest agricultural export commodity, with an average value of $3.5 billion per annum. Climate variability and change, higher input costs, and world commodity markets have put increased pressure on the sustainability of the grain industry. This has lead to an increasing demand for accurate, objective and near real-time crop production information by industry. To generate such production estimates, it is essential to determine crop area planted at the desired spatial and temporal scales. However, such information at regional scale is currently not available in Australia.The aim of this study was to determine broadacre crop area estimates through the use of multi-temporal satellite imagery for major Australian winter crops. Specifically, the objectives were to: (i) assess the ability of a range of approaches to using multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) imagery to estimate total end-of-season winter crop area; (ii) determine the discriminative ability of such remote sensing approaches in estimating planted area for wheat, barley and chickpea within a specific cropping season; (iii) develop and evaluate the methodology for determining the predictability of crop area estimates well before harvest; and (iv) validate the ability of multi-temporal MODIS approaches to determine the pre-harvest and end-of-season winter crop area estimates for different seasons and regions.MODIS enhanced vegetation index (EVI) was used as a surrogate measure for crop canopy health and architecture, for two contiguous shires in the Darling Downs region of Queensland, Australia. Multi-temporal approaches comprising principal component analysis (PCA), harmonic analysis of time series (HANTS), multi-date MODIS EVI during the crop growth period (MEVI), and two curve fitting procedures (CF1, CF2) were derived and applied. These approaches were validated against the traditional single-date approach. Early-season crop area estimates were derived through the development and application of a metric, i.e. accumulation of consecutive 16-day EVI values greater than or equal to 500, at different periods before flowering. Using ground truth data, image classification was conducted by applying supervised (maximum likelihood) and unsupervised (K-means) classification algorithms. The percent correctly classified and kappa coefficient statistics from the error matrix were used to assess pixel-scale accuracy, while shire-scale accuracy was determined using the percent error (PE) statistic. A simple linear regression of actual shire-scale data against predicted data was used to assess accuracy across regions and seasons. Actual shire-scale data was acquired from government statistical reports for the period 2000, 2001, 2003 and 2004 for the Darling Downs, and 2005 and 2006 for the entire Queensland cropping region.Results for 2003 and 2004 showed that multi-temporal HANTS, MEVI, CF1, CF2 and PCA methods achieved high overall accuracies ranging from 85% to 97% to discriminate between crops and non-crops. The accuracies for discriminating between specific crops at pixel scale were less, but still moderate, especially for wheat and barley (lowest at 57%). The HANTS approach had the smallest mean absolute percent error of 27% at shire-scale compared to other multi-temporal approaches. For early-season prediction, the 16-day EVI values greater than or equal to 500 metric showed high accuracy (94% to 98%) at a pixel scale and high R2 (0.96) for predicting total winter crop area planted.The rigour of the HANTS and the 16-day EVI values greater than or equal to 500 approaches was assessed when extrapolating over the entire Queensland cropping region for the 2005 and 2006 season. The combined early-season estimate of July and August produced high accuracy at pixel and regional scales with percent error of 8.6% and 26% below the industry estimates for 2005 and 2006 season, respectively. These satellite-derived crop area estimates were available at least four months before harvest, and deemed that such information will be highly sought after by industry in managing their risk. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. Specific area estimates for wheat, barley and chickpea were, respectively, 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006). Closer investigation suggested that the higher error in 2006 area estimates for barley and chickpea has emanated from the industry figures collected by the government.Area estimates of total winter crop, wheat, barley and chickpea resulted in R2 values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high R2 (0.87) was achieved for total winter crop area estimates in Augusts across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps.This thesis concluded that time-series MODIS EVI imagery can be applied successfully to firstly, determine end-of-season crop area estimates at shire scale. Secondly, capturing canopy green-up through a novel metric (i.e. 16-day EVI values greater than or equal to 500) can be utilised effectively to determine early-season crop area estimates well before harvest. Finally, the extrapolability of these approaches to determine total and specific winter crop area estimates showed good utility across larger areas and seasons. Hence, it is envisaged that this technology is transferable to different regions across Australia. The utility of the remote sensing techniques developed in this study will depend on the risk agri-industry operates at within their decision and operating regimes. Trade-off between risk and value will depend on the accuracy and timing of the disseminated crop production forecast.
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Bisht, Gautam. "Satellite-based estimates of net radiation and modeling the role of topography and vegetation on inter-annual hydro-climatology." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60706.
Testo completoAbstract (sommario):
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 229-260).<br>The Fourth Assessment Report of the Intergovernmental Panel on Climate Change acknowledged that the lack of relevant observations in various regions of the world is a crucial gap in understanding and modeling impacts of climate change related to hydrologic cycle. The Surface Radiation Budget (SRB) is an important component in the study of land surface processes. Existing SRB retrieval algorithms generally suffer from two major shortcomings: difficulty in dealing with cloudy sky conditions and reliance on study-site specific ancillary ground data. In this work, a framework of estimating net radiation from the MODerateresolution Imaging Spectroradiometer (MODIS) data is presented that is applicable under all-sky conditions, while solely relying on satellite data. The results from the proposed methodology are compared against several ground measurements within the United States for the entire 2006. Finally, monthly radiation maps for the Continental United States are produced. Modeling, similar to observations, is critical to the Earth Sciences and the second part of this work focuses on the impact of incorporating vegetation dynamics and topography in modeling hydro-climatology over large river basins. Land and atmosphere are coupled with each other through the exchange of heat, momentum and water at the boundary. This work involves coupling of a physically-based, fully distributed ecohydrology model with a numerical atmospheric model, using high performance computing. The ability of the ecohydrology model (in an offline mode) to accurately resolve hydro-climatic signatures and vegetation dynamics is first examined. The ecohydrology model is applied in a highly instrumented catchment, Walnut Gulch Experimental Watershed (WGEW) in Arizona, for a period of 11-years (1997-2007). The ecohydrology model is able to capture the behavior of several key hydrologic variables and vegetation dynamics within the WGEW. A series of three synthetic experiments are conducted with a coupled land-atmosphere model. The anomalies of various simulated quantities between the synthetic experiments are examined within the rainfall-soil moisture feedback hypothesis proposed by Elathir [1998]. The results from the experiments highlight the need to explicitly account for vegetation dynamics and topography within a numerical atmospheric model. The thesis concludes with a discussion of contributions, and future directions for this work.<br>by Gautam Bisht.<br>Ph.D.
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Kandasamy, Sivasathivel. "Leaf Area Index (LAI) monitoring at global scale : improved definition, continuity and consistency of LAI estimates from kilometric satellite observations." Phd thesis, Université d'Avignon, 2013. http://tel.archives-ouvertes.fr/tel-00967319.
Testo completoAbstract (sommario):
Monitoring biophysical variables at a global scale over long time periods is vital to address the climatechange and food security challenges. Leaf Area Index (LAI) is a structure variable giving a measure of the canopysurface for radiation interception and canopy-atmosphere interactions. LAI is an important variable in manyecosystem models and it has been recognized as an Essential Climate Variable. This thesis aims to provide globaland continuous estimates of LAI from satellite observations in near-real time according to user requirements to beused for diagnostic and prognostic evaluations of vegetation state and functioning. There are already someavailable LAI products which show however some important discrepancies in terms of magnitude and somelimitations in terms of continuity and consistency. This thesis addresses these important issues. First, the nature ofthe LAI estimated from these satellite observations was investigated to address the existing differences in thedefinition of products. Then, different temporal smoothing and gap filling methods were analyzed to reduce noiseand discontinuities in the time series mainly due to cloud cover. Finally, different methods for near real timeestimation of LAI were evaluated. Such comparison assessment as a function of the level of noise and gaps werelacking for LAI.Results achieved within the first part of the thesis show that the effective LAI is more accurately retrievedfrom satellite data than the actual LAI due to leaf clumping in the canopies. Further, the study has demonstratedthat multi-view observations provide only marginal improvements on LAI retrieval. The study also found that foroptimal retrievals the size of the uncertainty envelope over a set of possible solutions to be approximately equal tothat in the reflectance measurements. The results achieved in the second part of the thesis found the method withlocally adaptive temporal window, depending on amount of available observations and Climatology as backgroundestimation to be more robust to noise and missing data for smoothing, gap-filling and near real time estimationswith satellite time series.
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Willis, Josh K. "Combining satellite and in situ data to make improved estimates of upper-ocean thermal variability on eddy to global scales /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2004. http://wwwlib.umi.com/cr/ucsd/fullcit?p3137208.
Testo completo
23
Patel, Ekta, Gurtina Besla, and Kaisey Mandel. "Orbits of massive satellite galaxies - II. Bayesian estimates of the Milky Way and Andromeda masses using high-precision astrometry and cosmological simulations." OXFORD UNIV PRESS, 2017. http://hdl.handle.net/10150/624428.
Testo completoAbstract (sommario):
In the era of high-precision astrometry, space observatories like the Hubble Space Telescope (HST) and Gaia are providing unprecedented 6D phase-space information of satellite galaxies. Such measurements can shed light on the structure and assembly history of the Local Group, but improved statistical methods are needed to use them efficiently. Here we illustrate such a method using analogues of the Local Group's two most massive satellite galaxies, the Large Magellanic Cloud (LMC) and Triangulum (M33), from the Illustris dark-matter-only cosmological simulation. We use a Bayesian inference scheme combining measurements of positions, velocities and specific orbital angular momenta (j) of the LMC/M33 with importance sampling of their simulated analogues to compute posterior estimates of the Milky Way (MW) and Andromeda's (M31) halo masses. We conclude that the resulting host halo mass is more susceptible to bias when using measurements of the current position and velocity of satellites, especially when satellites are at short-lived phases of their orbits (i.e. at pericentre). Instead, the j value of a satellite is well conserved over time and provides a more reliable constraint on host mass. The inferred virial mass of the MW(M31) using j of the LMC (M33) is M-vir,M- MW = 1.02(-0.55)(+0.77) x 10(12) M-circle dot (M-vir,M- M31 = 1.37(-0.75)(+1.39) x 10(12) M-circle dot). Choosing simulated analogues whose j values are consistent with the conventional picture of a previous (<3 Gyr ago), close encounter (<100 kpc) of M33 about M31 results in a very low virial mass for M31 (similar to 10(12) M-circle dot). This supports the new scenario put forth in Patel, Besla & Sohn, wherein M33 is on its first passage about M31 or on a long-period orbit. We conclude that this Bayesian inference scheme, utilizing satellite j, is a promising method to reduce the current factor of 2 spread in the mass range of the MW and M31. This method is easily adaptable to include additional satellites as new 6D phase-space information becomes available from HST, Gaia and the James Webb Space Telescope.
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Vishwakarma, Bramha Dutt [Verfasser], and Nico [Akademischer Betreuer] Sneeuw. "Understanding and repairing the signal damage due to filtering of mass change estimates from the GRACE satellite mission / Bramha Dutt Vishwakarma ; Betreuer: Nico Sneeuw." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2017. http://d-nb.info/1138234877/34.
Testo completo
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Ye, Yufang [Verfasser], Justus [Akademischer Betreuer] [Gutachter] Notholt, and Thomas [Gutachter] Jung. "Correcting Multiyear Sea Ice Concentration Estimates from Microwave Satellite Observations with Air Temperature, Sea Ice Drift and Dynamic Tie Points / Yufang Ye. Betreuer: Justus Notholt. Gutachter: Justus Notholt ; Thomas Jung." Bremen : Staats- und Universitätsbibliothek Bremen, 2016. http://d-nb.info/1107458366/34.
Testo completo
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Cassé, Claire. "Impact du forçage pluviométrique sur les inondations du fleuve Niger à Niamey : Etude à partir de données satellitaires et in-situ." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30236/document.
Testo completoAbstract (sommario):
Depuis le développement des mesures satellites de nombreuses missions spatiales sont dédiées au suivi de l'atmosphère et de la surface terrestre. Ces travaux de thèse s'inscrivent dans le cadre de la mission Megha-Tropiques dédiée au cycle de l'eau et de l'énergie en zone tropicale. L'objectif est d'évaluer le potentiel des estimations de précipitation par satellite pour des applications hydrologiques en zone tropicale. Les Tropiques réunissent les plus grands fleuves du globe, mais ne bénéficient pas de réseaux d'observation in-situ denses et continus permettant une gestion intégrée efficace de la ressource et des systèmes d'alertes. Les estimations des précipitations issues des systèmes d'observation satellite offrent une alternative pour ces bassins peu ou pas instrumentés et souvent exposés aux extrêmes climatiques. C'est le cas du fleuve Niger, qui a subi une grande variabilité climatique depuis les années 1950, mais aussi d'importants changements environnementaux et hydrologiques. Depuis les années 2000, le Niger moyen connaît une recrudescence des inondations pendant la période de crue Rouge (engendrée par ses affluents sahéliens pendant la mousson). A Niamey, des niveaux record de hauteur d'eau et de période d'inondation ont été enregistrés en 2003, 2010, 2012 et 2013, engendrant de nombreuses pertes humaines et matérielles. Ces travaux analysent l'influence du forçage pluviométrique sur les inondations liées à la crue Rouge à Niamey. Une gamme de produits pluviométriques (in situ et satellite) et la modélisation hydrologique (ISBA-TRIP) sont combinés pour étudier : (i) l'apport des produits satellite pour diagnostiquer la crue Rouge récente, (ii) l'impact des caractéristiques des produits et de leurs incertitudes sur les simulations et enfin (iii) l'évaluation du rôle des précipitations, face aux changements de conditions de surface, dans l'évolution de la crue Rouge à Niamey depuis les années 1950. L'étude a mis en évidence l'impact des caractéristiques des estimations des précipitations (cumul, intensité et distribution spatio-temporelle) sur la modélisation hydrologique et le potentiel des produits satellites pour le suivi des inondations. Les caractéristiques des précipitations se propageant dans la modélisation, la détection des inondations est plus efficace avec une approche relative à chaque produit plutôt qu'avec un seuil absolu. Ainsi des produits présentant des biais peuvent être envisagés pour la simulation hydrologique et la détection des inondations. Le nouveau produit TAPEER de la mission MT présente un fort potentiel hydrologique, en 2012 et pour la zone d'étude. D'autre part, l'étude de la propagation de l'erreur associée à ces précipitations a mis en évidence, la nécessité de déterminer la structure du champ d'erreur pour l'utilisation d'une telle information en hydrologie. Enfin la modélisation a été utilisée comme levier pour décomposer les sensibilités de la crue Rouge aux variations des précipitations et des conditions de surface. Pour simuler les changements hydrologiques entre les périodes 1953-1982 et 1983-2012, les changements d'occupation du sol et d'aire de drainage doivent être pris en compte. Puis les variations des précipitations peuvent expliquer les changements majeurs décennaux et annuels entre les années 1983 et 2012<br>Since the development of satellite based remote sensing in the 1970s, many missions have been dedicated to monitoring the terrestrial atmosphere and surfaces. Some of these satellites are dedicated to the Tropics with specific orbits. Megha-Tropiques (MT) is devoted to the water and energy cycle in the tropical atmosphere and provides an enhanced sampling for rainfall estimation in the tropical region. This PhD work was initiated within MT hydro-meteorological activities, with the objective of assessing the hydrological potential of satellite rainfall products in the Tropics. The world most important rivers lay in tropical areas where the in situ observation networks are deficient. Alternative information is therefore needed for water resource management and alert systems. The present work focuses on the Niger River a basin which has undergone drastic climatic variations leading to disasters such as droughts and floods. Since 1950, the Niger has been through 3 main climatic periods: a wet period (1950-1960), a long and intense drought period (1970-1980) and since 1990 a partial recovery of the rainfall. These climatic variations and the anthropic pressure, have modified the hydrological behaviour of the basin. Since 2000, the middle Niger River has been hit by an increase of floods hazards during the so-called Red flood period. In Niamey city, the highest river levels and the longest flooded period were recorded in 2003, 2010, 2012 and 2013, leading to heavy casualties and property damage. This study combines hydrological modelling and a variety of rainfall estimation products (satellite and in-situ) to meet several objectives: (i) the simulation of the Niamey Red flood and the detection of floods (during the recent period 2000-2013) (ii) the study of the propagation of satellite rainfall errors in hydrological modelling (iii) the evaluation of the role of rainfall variability, and surface conditions, in the changes of the Red flood in Niamey since the 50s. The global model ISBA-TRIP, is run with a resolution of 0.5° and 3h, and several rainfall products were used as forcing. Products derived from gauges (KRIG, CPC), pure satellite products (TAPEER, 3B42RT, CMORPH, PERSIANN) and mixed satellite products adjusted by rain gauges (3B42v7, RFE2, PERSIANN-CDR). This work confirms the hydrological potential of satellite rainfall products and proposes an original approach to overcome their biases. It highlights the need for documenting the errors associated with the rainfall products and the error structure. Finally, the hydrological modelling results since the 1950s have given a new understanding of the relative role of rainfall and surface conditions in the drastic increase of flood risk in Niamey
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Ouhechou, Amine. "Analyse de la variabilité multi-échelles du rayonnement solaire incident sur la façade atlantique de l'Afrique Centrale : observations in-situ, estimations satellitaires, et simulations climatiques CMIP6." Electronic Thesis or Diss., Université Grenoble Alpes, 2024. http://www.theses.fr/2024GRALU007.
Testo completoAbstract (sommario):
L'Afrique centrale occidentale abrite les forêts les plus denses du bassin du Congo, deuxième plus grand massif forestier tropical après l’Amazonie. Elle se caractérise par un climat équatorial avec des températures élevées, un régime pluviométrique bimodal et surtout une longue saison sèche nuageuse, de juin à septembre. Malgré son importance écologique, la variabilité climatique de cette région a été peu étudiée par rapport à d'autres régions du continent africain, principalement en raison de la rareté des observations in-situ.Reconnaissant ces défis posés par le manque de données in situ, cette étude explore la variabilité climatique en Afrique centrale occidentale sous l’angle du rayonnement solaire en surface, paramètre clef pour le fonctionnement des forêts tropicales. Dans ce contexte, cette thèse s’attache à établir une première climatologie du rayonnement solaire en surface pour la région, à en documenter la variabilité, en particulier durant la saison sèche nuageuse de juin à septembre, et à évaluer la performance des produits satellitaires, des réanalyses et des simulations des modèles climatiques CMIP6.Dans une première partie, une évaluation de huit produits satellitaires d’estimation du rayonnement solaire (CERES-EBAF, CERES-SYN1deg, TPDC, CMSAF SARAH-2, CMSAF CLARA-A2, CAMS-JADE, WorldClim 2 et les réanalyses ERA5), révèle des différences dans les champs spatiotemporels. Tout en capturant avec succès les cycles annuels moyens de rayonnement solaire, les produits présentent des variations régionales, soulignant l'impact des paramètres atmosphériques sur l'estimation précise du rayonnement solaire. En outre, tous les produits à l’exception de WorldClim 2 s’accordent sur le fait que la façade atlantique reçoit moins de rayonnement solaire que les autres régions d’Afrique Centrale. La performance de ces produits est également évaluée par rapport aux observations in-situ sur la base de quatre types de cycle diurne de rayonnement solaire - les jours Obscurs, Obscurs AM (matin), Obscurs PM (après-midi) et Lumineux. Les produits représentent correctement la forme de ces quatre types, mais avec une amplitude plus grande.La deuxième partie se concentre sur l’étude de la variabilité interannuelle et les tendances du rayonnement solaire pendant la saison sèche nuageuse juin-septembre, mettant en évidence des différences marquées entre le produit satellitaire CMSAF SARAH-2 et la réanalyse climatique ERA5. Dans cette partie, l’étude a également permis d’identifier les dates de début et de fin de la saison sèche à partir du rayonnement solaire, et en établissant une relation significative entre les températures de surface de l'océan Atlantique équatorial et le début de la saison sèche.Dans la dernière partie, la capacité des modèles climatiques globaux CMIP6 à reproduire les niveaux moyens de rayonnement solaire dans la région a été évaluée. Les résultats soulignent des disparités sous-régionales dans la performance des modèles. Les modèles utilisés dans cette étude sous-estiment le rayonnement solaire au sud-ouest du Gabon-Congo tandis qu’ils le surestiment au nord-est, principalement d’avril à décembre. Les différences les plus importantes étant observées pendant la saison des pluies octobre-novembre. Ces disparités semblent provenir de la nébulosité, en particulier des nuages de basse et moyenne altitude, qui influencent de manière significative le rayonnement solaire mais avec des relations variables selon les modèles. Cette partie met également en évidence la téléconnexion entre la température de surface de l’océan Atlantique équatorial et le rayonnement solaire dans les modèles, mais qui varie entre le littoral et l’intérieur du Gabon, soulignant la nécessité de travailler avec des modèles climatiques régionaux mieux résolus<br>Western Central Africa, home to the densest forests of the Congo Basin - the second largest tropical forest massif after Amazonia - is characterized by an equatorial climate with high temperatures, a bimodal rainfall pattern and, a long and cloudy dry season from June to September. Despite its ecological importance, the climate variability of this region has been less studied compared with other parts of the African continent, mainly because of the scarcity of in-situ observations.Recognizing these challenges posed by the lack of in-situ data, this study explores the climate variability in Western Central Africa through the lens of surface solar radiation, a key parameter for the functioning of tropical forests. In this context, this thesis aims to establish an initial climatology of surface solar radiation for the region, to document its variability, particularly during the cloudy dry season from June to September, and to assess the performance of satellite products, reanalyses and CMIP6 climate model simulations.In the first part, an evaluation of eight satellite products for estimating solar radiation (CERES-EBAF, CERES-SYN1deg, TPDC, CMSAF SARAH-2, CMSAF CLARA-A2, CAMS-JADE, WorldClim 2 and ERA5 reanalysis) reveals differences in the spatiotemporal fields. While successfully capturing mean annual solar radiation cycles, the products show regional variations, highlighting the impact of atmospheric parameters on the accurate estimation of solar radiation. In addition, all the products except WorldClim 2 agree that the Atlantic coast receives less solar radiation than the other regions of Central Africa. The performance of these products is also assessed against in-situ measurements based on four types of solar radiation diurnal cycle - Obscure, Obscure AM (morning), Obscure PM (afternoon) and Bright days. The products correctly represent the shape of these four types, but with a larger amplitude.The second part focuses on studying the interannual variability and trends in solar radiation during the June-September cloudy dry season, highlighting notable differences between CMSAF SARAH-2 satellite product and ERA5 reanalysis. The study also made it possible to identify the onset and cessation dates of the dry season based on solar radiation, and establishing a significant relationship between surface temperatures of the equatorial Atlantic ocean and the onset of the dry season.In the final part, the capacity of CMIP6 global climate models to reproduce average levels of solar radiation in the region was assessed. The results highlight sub-regional disparities in model performance. The models used in this study underestimate solar radiation in the south-west of Gabon-Congo, while they overestimate it in the north-east, mainly from april to december. The largest differences were observed during the october-november rainy season. These disparities seem to be caused by cloud cover, in particular low- and medium-level clouds, which have a significant influence on solar radiation, although the relationship varies according to the models. This section also highlights the teleconnection between the surface temperature of the equatorial Atlantic ocean and solar radiation in the models, which varies between the coastal and inland areas of Gabon, underlining the need to use regional climate models
28
Stubblefield, Cedrick L. "Microwave estimates of the extratropical transitions process." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Mar%5FStubblefield.pdf.
Testo completo
29
Frost, Edmond M. "Global scale estimates of aerosol particle characteristics." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23245.
Testo completoAbstract (sommario):
Approved for public release; distribution is unlimited<br>NOAA-7 AVHRR data from April 1982 and 1983 were used to perform a global scale analysis of aerosol particle characteristics. Several improvements were incorporated into an AVHRR multichannel satellite data technique developed by Pfeil (1986). This included better cloud and sunglint discrimination, removal of Rayleigh radiance and accounting for ozone absorption. The characteristics analyzed were optical depth and Aerosol Particle Size Index (S₁₂). S₁₂ provides the slope of the aerosol particle size distribution curve. Both of these parameters were evaluated during several naturally occurring events, foremost of which were the 1982 El Chicon eruption and the 1982-1983 El Nino-Southern Oscillation event. The results provided evidence that a significant amount of aerosol particles over marine regions are from land-derived sources. However, the results also provided evidence that some marine aerosol particles may be of biogenic origins,<br>http://archive.org/details/globalscaleestim00fros<br>Lieutenant, United States Navy
30
Saw, Bun Liong. "Infrared and passive microwave satellite rainfall estimate over tropics." Diss., Columbia, Mo. : University of Missouri-Columbia, 2005. http://hdl.handle.net/10355/4267.
Testo completoAbstract (sommario):
Thesis (M.S.)--University of Missouri-Columbia, 2005.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (December 19, 2006) Includes bibliographical references.
31
Ma, Xiaoyan, Fangqun Yu, and Johannes Quaas. "Reassessment of satellite-based estimate of aerosol climate forcing." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-177222.
Testo completoAbstract (sommario):
Large uncertainties exist in estimations of aerosol direct radiative forcing and indirect radiative forcing, and the values derived from globalmodeling differ substantially with satellite-based calculations. Following the approach of Quaas et al. (2008; hereafter named Quaas2008),we reassess satellite-based clear- and cloudy-sky
radiative forcings and their seasonal variations by employing updated satellite products from 2004 to 2011 in combination with the anthropogenic aerosol optical depth (AOD) fraction obtained frommodel simulations using the Goddard Earth Observing System-Chemistry-Advanced ParticleMicrophysics (GEOS-Chem-APM). Our derived annual mean aerosol clear-sky forcing (-0.59 W m-2) is lower, while the cloudy-sky forcing (-0.34 W m-2) is
higher than the corresponding results (-0.9Wm-2 and -0.2W m-2, respectively) reported in Quaas2008. Our
study indicates that the derived forcings are sensitive to the anthropogenic AOD fraction and its spatial distribution but insensitive to the temporal resolution used to obtain the regression coefficients, i.e.,monthly or seasonal based. The forcing efficiency (i.e., the magnitude per anthropogenic AOD) for the clear-sky forcing
based on this study is 19.9Wm-2, which is about 5% smaller than Quaas2008’s value of 21.1Wm-2. In contrast, the efficiency for the cloudy-sky forcing of this study (11 W m-2) is more than a factor of 2 larger than Quaas2008’s value of 4.7 W m-2. Uncertainties tests indicate that anthropogenic fraction of AOD strongly affects the computed forcings while using aerosol index instead of AOD from satellite data as aerosol proxy does not appear to cause any significant differences in regression slopes and derived forcings.
32
Ma, Xiaoyan, Fangqun Yu, and Johannes Quaas. "Reassessment of satellite-based estimate of aerosol climate forcing." Wiley, 2014. https://ul.qucosa.de/id/qucosa%3A13449.
Testo completoAbstract (sommario):
Large uncertainties exist in estimations of aerosol direct radiative forcing and indirect radiative forcing, and the values derived from globalmodeling differ substantially with satellite-based calculations. Following the approach of Quaas et al. (2008; hereafter named Quaas2008),we reassess satellite-based clear- and cloudy-sky
radiative forcings and their seasonal variations by employing updated satellite products from 2004 to 2011 in combination with the anthropogenic aerosol optical depth (AOD) fraction obtained frommodel simulations using the Goddard Earth Observing System-Chemistry-Advanced ParticleMicrophysics (GEOS-Chem-APM). Our derived annual mean aerosol clear-sky forcing (-0.59 W m-2) is lower, while the cloudy-sky forcing (-0.34 W m-2) is
higher than the corresponding results (-0.9Wm-2 and -0.2W m-2, respectively) reported in Quaas2008. Our
study indicates that the derived forcings are sensitive to the anthropogenic AOD fraction and its spatial distribution but insensitive to the temporal resolution used to obtain the regression coefficients, i.e.,monthly or seasonal based. The forcing efficiency (i.e., the magnitude per anthropogenic AOD) for the clear-sky forcing
based on this study is 19.9Wm-2, which is about 5% smaller than Quaas2008’s value of 21.1Wm-2. In contrast, the efficiency for the cloudy-sky forcing of this study (11 W m-2) is more than a factor of 2 larger than Quaas2008’s value of 4.7 W m-2. Uncertainties tests indicate that anthropogenic fraction of AOD strongly affects the computed forcings while using aerosol index instead of AOD from satellite data as aerosol proxy does not appear to cause any significant differences in regression slopes and derived forcings.
33
MILANI, Lisa. "Multi-sensor Satellite Precipitation Estimate for Hydrogeological Hazard Mitigation." Doctoral thesis, Università degli studi di Ferrara, 2012. http://hdl.handle.net/11392/2388795.
Testo completoAbstract (sommario):
High-impact meteorological events have in the last decade received increasing
interest and considerable efforts are constantly undertaken to mitigate their
effects on human activities and environment.
Several projects addressing different aspects of the risk mitigation strategy have been financed in Europe, and PROSA (Prodotti di Osservazione
Satellitare per l'Allerta Meteorologica - Satellite products for meteorological alert), funded by the Italian Space Agency (ASI), represents the Italian
attempt to solve the meteorological side of the hazard mitigation scheme.
It is devoted to design, develop, test and demonstrate a prototype system
dedicated to the innovative dynamic characterization of meteorological parameters at the ground by means of satellite data.
This work is part of PROSA and the main objective is the implementation
and optimization of three di�erent satellite precipitation estimation algorithms. The algorithms are based on Artificial Neural Networks and correlate multi-sensors satellite data, in the Visible, Infrared (from the European
Geostationary satellite Meteosat) and Microwave bands (from polar orbiting
satellites), to the precipitation rate at ground. The ANNs are set up as classification problem and use rain-gauges data as true values of precipitation at
the ground for the training, testing an validation of the techniques.
The work is divided in three main steps: the first version of the algorithm
gives a binary classification of satellite pixel as rain and no-rain classes, with
seasonal and day-time characterization of the precipitation maps. The second
version gives a quantitative estimate, classifying the rain-rate in five precipitation intervals. Finally, the last version provides precipitation maps with
quantitative values expressed in mmh^-1, and also explicitly uses microwave
data.
To reach the main objective several sensitivity studies and intermediate goals
have been pursued, in order to refine and tune the technique. The sensitivity
to precipitation of the infrared channels with respect to the seasonal cycle and
the impact of the visible channels on the estimates have been assessed. The
relationship between the probability of precipitation, output of the neural network, and the rain-rate, as measured by rain-gauges, has been established
for warm and cold months, and the optimal way to ingest in the algorithm
the microwave estimates has been defined by analyzing the di�erent performances of microwave and visible-infrared techniques. Finally, the results
have been critically discussed in comparison with other algorithms taking
part of the PROSA system.
34
Joetzjer, E., M. Pillet, P. Ciais, et al. "Assimilating satellite-based canopy height within an ecosystem model to estimate aboveground forest biomass." AMER GEOPHYSICAL UNION, 2017. http://hdl.handle.net/10150/625358.
Testo completoAbstract (sommario):
Despite advances in Earth observation and modeling, estimating tropical biomass remains a challenge. Recent work suggests that integrating satellite measurements of canopy height within ecosystem models is a promising approach to infer biomass. We tested the feasibility of this approach to retrieve aboveground biomass (AGB) at three tropical forest sites by assimilating remotely sensed canopy height derived from a texture analysis algorithm applied to the high-resolution Pleiades imager in the Organizing Carbon and Hydrology in Dynamic Ecosystems Canopy (ORCHIDEE-CAN) ecosystem model. While mean AGB could be estimated within 10% of AGB derived from census data in average across sites, canopy height derived from Pleiades product was spatially too smooth, thus unable to accurately resolve large height (and biomass) variations within the site considered. The error budget was evaluated in details, and systematic errors related to the ORCHIDEE-CAN structure contribute as a secondary source of error and could be overcome by using improved allometric equations.
35
Quaas, Johannes, Olivier Boucher, Nicolas Bellouin, and Stefan Kinne. "Satellite-based estimate of the direct and indirect aerosol climate forcing." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-177289.
Testo completoAbstract (sommario):
The main uncertainty in anthropogenic forcing of the Earth’s climate stems from pollution aerosols, particularly their ‘‘indirect effect’’ whereby aerosols modify cloud properties. We develop a new methodology to derive a measurement-based estimate using
almost exclusively information from an Earth radiation budget instrument (CERES) and a radiometer (MODIS). We derive a statistical relationship between planetary albedo and cloud properties, and, further, between the cloud properties and column aerosol
concentration. Combining these relationships with a data set of satellite-derived anthropogenic aerosol fraction, we estimate an anthropogenic radiative forcing of -0.9 ± 0.4 Wm-2 for the aerosol direct effect and of -0.2 ± 0.1 Wm-2 for the cloud albedo effect. Because of uncertainties in both satellite data and the method, the uncertainty of this result is likely larger than the values given here which correspond only to the quantifiable error estimates. The results nevertheless indicate that current global climate models may overestimate the cloud albedo effect.
36
Quaas, Johannes, Olivier Boucher, Nicolas Bellouin, and Stefan Kinne. "Satellite-based estimate of the direct and indirect aerosol climate forcing." Wiley, 2008. https://ul.qucosa.de/id/qucosa%3A13453.
Testo completoAbstract (sommario):
The main uncertainty in anthropogenic forcing of the Earth’s climate stems from pollution aerosols, particularly their ‘‘indirect effect’’ whereby aerosols modify cloud properties. We develop a new methodology to derive a measurement-based estimate using
almost exclusively information from an Earth radiation budget instrument (CERES) and a radiometer (MODIS). We derive a statistical relationship between planetary albedo and cloud properties, and, further, between the cloud properties and column aerosol
concentration. Combining these relationships with a data set of satellite-derived anthropogenic aerosol fraction, we estimate an anthropogenic radiative forcing of -0.9 ± 0.4 Wm-2 for the aerosol direct effect and of -0.2 ± 0.1 Wm-2 for the cloud albedo effect. Because of uncertainties in both satellite data and the method, the uncertainty of this result is likely larger than the values given here which correspond only to the quantifiable error estimates. The results nevertheless indicate that current global climate models may overestimate the cloud albedo effect.
37
Ren, Weili. "A control-centralised multiaccess protocol exploiting non-selective fading for LEO satellite communications." Thesis, University of Surrey, 2001. http://epubs.surrey.ac.uk/892/.
Testo completo
38
Evans, Jessica. "Use of satellite imagery to estimate global mortality attributable to fine particulate air pollution." Thesis, University of Ottawa (Canada), 2010. http://hdl.handle.net/10393/28774.
Testo completoAbstract (sommario):
Background: Studies assessing the effects of air pollution on health have traditionally relied upon ground-monitoring stations to measure ambient concentrations. Use of satellite derived air pollution measures poses the advantage of providing global coverage.
Objective: To undertake a global assessment of mortality associated with chronic exposure to fine particulate air pollution using remote sensory data.
Methods: Global PM2.5 exposure levels were derived from MODIS and MISR satellite instruments. Relative risks and attributable fractions of mortality were modelled using previously developed concentration-response functions for the association between PM2.5 and mortality.
Results: The global fraction of adult mortality attributable to the anthropogenic component of PM2.5 was 4.5% (95% confidence interval: 2.9-6.0) for cardiopulmonary disease, 5.6% (2.5-8.3) for lung cancer, and 8.2% (5.6-10.4) for ischemic heart disease.
Conclusion: This study demonstrated that it is feasible to use satellite derived pollution concentrations in a global assessment of mortality and air pollution.
39
Lang, Andreas. "Adaptive channel estimators suitable for implementation in coherent digital receivers operating in a mobile satellite environment." Thesis, Edinburgh Napier University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286200.
Testo completo
40
Kuratomi, Alejandro. "GNSS Position Error Estimated by Machine Learning Techniques with Environmental Information Input." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262692.
Testo completoAbstract (sommario):
In Intelligent Transport Systems (ITS), specifically in autonomous driving operations, accurate vehicle localization is essential for safe operations. The localization accuracy depends on both position and positioning error estimates. Technologies aiming to improve positioning error estimation are required and are currently being researched. This project has investigated machine learning algorithms applied to positioning error estimation by assessing relevant information obtained from a GNSS receiver and adding environmental information coming from a camera mounted on a radio controlled vehicle testing platform. The research was done in two stages. The first stage consists of the machine learning algorithms training and testing on existing GNSS data coming from Waysure´s data base from tests ran in 2016, which did not consider the environment surrounding the GNSS receiver used during the tests. The second stage consists of the machine learning algorithms training and testing on GNSS data coming from new test runs carried on May 2019, which include the environment surrounding the GNSS receiver used. The results of both stages are compared. The relevant features are obtained as a result of the machine learning decision trees algorithm and are presented. This report concludes that there is no statistical evidence indicating that the tested environmental input from the camera could improve positioning error estimation accuracy with the built machine learning models.<br>Inom Intelligenta transportsystem (ITS), specifikt för självkörande fordon, så är en exakt fordonspositionering en nödvändighet för ökad trafiksäkerhet. Positionsnoggrannheten beror på estimering av både positionen samt positionsfelet. Olika tekniker och tillämpningar som siktar på att förbättra positionsfeluppskattningen behövs, vilket det nu forskas kring. Denna uppsats undersöker olika maskininlärningsalgoritmer inriktade på estimering av positionsfel. Algoritmerna utvärderar relevant information från en GNSS-mottagare, samt information från en kamera om den kringliggande miljön. En GNSS-mottagare och kamera monterades på en radiostyrd mobil testplattform för insamling av data. Examensarbetet består av två delar. Första delen innehåller träning och testning av valda maskininlärningsalgoritmer med GNSS-data tillhandahållen av Waysure från tester gjorda under 2016. Denna data inkluderar ingen information från den omkringliggande miljön runt GNSS-mottagaren. Andra delen består av träning och testning av valda maskininlärningsalgoritmer på GNSS-data som kommer från nya tester gjorda under maj 2019, vilka inkluderar miljöinformation runt GNSS-mottagaren. Resultaten från båda delar analyseras. De viktigaste egenskaper som erhålls från en trädbaserad modell, algoritmens beslutsträd, presenteras. Slutsatsen från denna rapport är att det inte går att statistiskt säkerställa att inkludering av information från den omkringliggande miljön från en kamera förbättrar noggrannheten vid estimering av positionsfelet med de valda maskininlärningsmodellerna.
41
Vitalich, John. "Design and simulation of a three-axis stabilized satellite and Kalman filter rate estimator." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2003. http://library.nps.navy.mil/uhtbin/hyperion-image/03Jun%5FVitalich.pdf.
Testo completo
42
Lamichhane, Gaurav Raj. "STATISTICAL ASSESSMENT OF PERFORMANCE OF INTEGRATED MULTI-SATELLITE RETRIEVALS FOR GPM (IMERG) TO ESTIMATE MONTHLY PRECIPITATION." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2559.
Testo completoAbstract (sommario):
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.
43
Spata, Alessandro. "SISTEM: a new method to integrate geodetic and satellite data to estimate 3D ground deformation maps." Thesis, Universita' degli Studi di Catania, 2011. http://hdl.handle.net/10761/376.
Testo completoAbstract (sommario):
We propose a new technique, based on the elastic theory, to efficiently produce an estimate of three-dimensional surface displacement maps by integrating sparse Global Positioning System (GPS) measurements of deformations and Differential Interferometric Synthetic Aperture Radar (DInSAR) maps of movements of the Earthà à à à à ¢ s surface.
Previous approaches known in literature to combine GPS and DInSAR data require two steps: a first step in which sparse GPS measurements are interpolated in order to fill in GPS displacements in the DInSAR grid, and a second step to estimate the three-dimensional surface displacement maps by using a suitable optimization technique.
One of the advantages of the proposed approach, with respect to the previous ones, is that it does not require preliminary interpolation of the observed deformation pattern. Indeed, we propose a linear matrix equation which accounts for both GPS and DInSAR data whose solution provides simultaneously the strain tensor, the displacement field and the rigid body rotation tensor. The mentioned linear matrix equation is solved by using the Weighted Least Square (WLS), thus assuring both numerical robustness and high computation efficiency.
The proposed methodology was tested on both synthetic and experimental data, these last from GPS and DInSAR measurements carried out on Mt. Etna during the 2003-2004 time interval. In order to appreciate the results accuracy standard estimated errors are provided. These tests also allow optimising the choice of specific parameters of this algorithm.
This method can be further exploited to account for other available data sets, such as additional interferograms, or other geodetic data (e.g. levelling, tilt, etc.), in order to achieve higher accuracy.
44
Dorij, Passang. "Satellite Remote Sensing Algorithm Development to Estimate Total Suspended Sediment Concentration for Highly Turbid Waters of Western Australia." Thesis, Curtin University, 2017. http://hdl.handle.net/20.500.11937/55102.
Testo completoAbstract (sommario):
Satellite-based remote sensing of total suspended sediment (TSS) concentration is studied for highly turbid waters of northern Western Australia. A robust multi-sensor red band semi-analytic sediment model was developed for coastal waters, and the implications of using a wide range of sensors with different spatial resolutions were also explored. In addition, historical TSS models were critically compared, and the feasibility of the geostationary satellite sensor AHI was also explored with respect to mapping TSS.
45
Côté, Jimmy. "Application des estimateurs de type Unscented pour l'estimation des états, des perturbations et des paramètres d'un satellite." Mémoire, Université de Sherbrooke, 2007. http://savoirs.usherbrooke.ca/handle/11143/1352.
Testo completoAbstract (sommario):
À l'ère de l'exploration spatiale et de l'étude de la Terre à partir de l'espace, il est encore surprenant de voir à quel point la conception de satellites est exigeante. Le développement de logiciels autonomes permettra de réduire considérablement le coût des missions spatiales et d'augmenter leurs chances de réussite. Avec ces logiciels autonomes, il sera possible de déterminer les états de l'orbite et de l'orientation d'un satellite à l'aide de filtres optimaux, tel que ceux de Kalman. Entre autres filtres, le filtre de Kalman étendu, (référé avec le sigle EKF pour sa version anglaise dite Extended Kalman Filter ) a démontré son efficacité à déterminer les états d'un système non linéaire en combinant des techniques linéaires et non linéaires. Depuis 1994, un nouveau type de filtre de Kalman, le filtre de Kalman non-parfumé (référé avec le sigle UKF pour sa version anglaise dite Unscented Kalman Filter ), a été développé en utilisant une approche complètement non linéaire. Les travaux de recherche décrits dans ce mémoire ont pour objectif d'utiliser les techniques d'estimation non linéaires de type non-parfumée ( unscented ) pour estimer les états et les perturbations d'un satellite. Afin de réaliser cet objectif, les techniques ont été élaborées dans le contexte réaliste du labosatellite TEAMSAT pour estimer le couple créé par la perturbation gravitationnelle. De plus, les techniques d'estimation de paramètre ont été utilisées pour déterminer la position du centre de masse qui engendre le couple de perturbation gravitationnelle afin de généraliser l'approche pour estimer les perturbations d'un satellite en orbite. Ce mémoire décrit la revue des techniques d'estimation ainsi que l'état de l'art. En outre, il présente en détail l'implémentation du système de validation ainsi que des techniques d'estimation, et il souligne les différences entre la théorie et la pratique. Enfin, des cas de tests sont définis en fonction des paramètres de simulation et du système à valider. Les résultats de ces tests sont présentés et analysés dans le but de dresser un tableau de comparaison des techniques et de conclure sur les performances des techniques d'estimation étudiées dans ces travaux de recherche.
46
Côté, Jimmy. "Application des estimateurs de type Unscented pour l'estimation des états, des perturbations et des paramètres d'un satellite." [S.l. : s.n.], 2007.
Cerca il testo completo
47
Wu, Ya-Lin, and Patrick D. Sheehan. "An ALMA Dynamical Mass Estimate of the Proposed Planetary-mass Companion FW Tau C." IOP PUBLISHING LTD, 2017. http://hdl.handle.net/10150/625808.
Testo completoAbstract (sommario):
Dynamical mass estimates down to the planet-mass regime can help to understand planet formation. We present Atacama Large Millimeter/submillimeter Array (ALMA) 1.3 mm observations of FW Tau C, a proposed similar to 10M(Jup) planet-mass companion at similar to 330 au from the host binary FW Tau AB. We spatially and spectrally resolve the accretion disk of FWTau C in (CO)-C-12 (2-1). By modeling the Keplerian rotation of gas, we derive a dynamical mass of similar to 0.1 M-circle dot. Therefore, FW Tau C is unlikely a planet, but rather a low-mass star with a highly inclined disk. This also suggests that FW Tau is a triple system consisting of three similar to 0.1. M-circle dot stars.
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Kerstetter, David. "Use of Pop-Up Satellite Tag Technology to Estimate Survival of Blue Marlin (Makaira nigricans) Released from Pelagic Longline Gear." W&M ScholarWorks, 2001. https://scholarworks.wm.edu/etd/1539617772.
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Weideman, Craig Ivan. "Linking satellite and point micrometeorological data to estimate : distributed evapotranspiration modelling based on MODIS LAI, Penman-Monteith and functional convergence theory." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1012078.
Testo completoAbstract (sommario):
Recent advances in satellite sensor technology and micrometeorological instrumentation for water flux measurement, coupled with the expansion of automatic weather station networks that provide routine measurements of near-surface climate variables, present new opportunities for combining satellite and ground-based instrumentation to obtain distributed estimates of vegetation water use over wide areas in South Africa. In this study, a novel approach is tested, which uses satellite leaf area index (LAI) data retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) to inform the FAO-56 Penman-Monteith equation for calculating reference evaporation (ET₀) of vegetation phenological activity. The model (ETMODIS) was validated at four sites in three different ecosystems across the country, including semi-arid savanna near Skukuza, mixed community grassland at Bellevue, near Pietermaritzburg, and Groenkop, a mixed evergreen indigenous forest near George, to determine potential for application over wider areas of the South African land surface towards meeting water resource management objectives. At Skukuza, evaluated against 170 days of flux data measured at a permanent eddy covariance (EC) flux tower in 2007, the model (ETMODIS) predicted 194.8 mm evapotranspiration relative to 148.9 mm measured fluxes, an overestimate of 31.7 %, (r² = 0.67). At an adjacent site, evaluated against flux data measured on two discrete periods of seven and eight days in February and May of 2005 using a large aperture scintillometer (SLS), ETMODIS predicted 27.4 mm and 6.7 mm evapotranspiration respectively, relative to measured fluxes of 32.5 and 8.2 mm, underestimates of 15.7 % and 18.3 % in each case (r² = 0.67 and 0.34, respectively). At Bellevue, evaluated against 235 days of evapotranspiration data measured using a surface layer scintillometer (SLS) in 2003, ETMODIS predicted 266.9 mm evapotranspiration relative to 460.2 mm measured fluxes, an underestimate of 42 % (r² = 0.67). At Groenkop, evaluated against data measured using a SLS over three discrete periods of four, seven and seven days in February, June and September/October respectively, ETMODIS predicted 9.7 mm, 10.3 mm and 17.0 mm evapotranspiration, relative to measured fluxes of 10.9 mm, 14.6 mm and 23. 9 mm, underestimates of 22.4 %, 11.2 % and 24.1 % in each case (r² = 0.98, 0.43 and 0.80, respectively). Total measured evapotranspiration exceeded total modelled evapotranspiration in all cases, with the exception of the flux tower site at Skukuza, where evapotranspiration was overestimated by ETMODIS by 31.7 % relative to measured (EC) values for the 170 days in 2007 where corresponding modelled and measured data were available. The most significant differences in measured versus predicted data were recorded at the Skukuza flux tower site in 2007 (31.7 % overestimate), and the Bellevue SLS flux site in 2003 (42 % underestimate); coefficients of determination, a measure of the extent to which modelled data are able to explain observed data at validation periods, with just two exceptions, were within a range of 0.67 – 0.98. Several sources of error and uncertainty were identified, relating predominantly to uncertainties in measured flux data used to evaluate ETMODIS, uncertainties in MODIS LAI submitted to ETMODIS, and uncertainties in ETMODIS itself, including model assumptions, and specific uncertainties relating to various inputs; further application of the model is required to test these uncertainties however, and establish confidence limits in performance. Nevertheless, the results of this study suggest that the technique is generally able to produce estimates of vegetation water use to within reasonably close approximations of measurements acquired using micrometeorological instruments, with r² values within the range of other peer-reviewed satellite remote sensing-based approaches.
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Yoon, Yeosang. "Evaluation of the potential to estimate river discharge using measurements from the upcoming SWOT mission." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376480417.
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