The NASA Kennedy Space Center (KSC) is situated along the east coast of central Florida, where a high frequency of lightning occurs annually. Although cloud-to-ground (CG) lightning forecasting using radar echoes has been thoroughly analyzed, few studies have examined intracloud (IC) and/or total (IC CG) lightning. In addition to CG lightning, IC flashes are of great concern to KSC launch operations. Four years (2006-2009) of summer (June, July, August) daytime (about 14-00 Z) Weather Surveillance Radar – 1988 Doppler data for Melbourne, FL were analyzed. Convective cells were tracked using a modified version of the Storm Cell Identification and Tracking (SCIT) algorithm and then correlated to CG lightning data from the National Lightning Detection Network (NLDN), as well as grouped IC flash data acquired from the KSC Lightning Detection and Ranging (LDAR) networks I and II. Pairs of reflectivity values (30, 35, and 40 dBZ) at isothermal levels (-10, -15, -20 and updraft -10 degrees C), as well as a vertically integrated ice (VII) product were used to optimize criteria for radar-based forecasting of both IC and CG lightning within storms. Results indicate that the best radar-derived predictor of CG lightning according to CSI was 25 dBZ at -20 degrees C, while the best reflectivity at isothermal predictor for IC was 25 dBZ at -15 degrees C. Meanwhile, the best VII predictor of CG lightning was the 30th percentile (0.840 kg m-2), while the best VII predictor of IC was the 5th percentile (0.143 kg m-2), or nearly 6 times lower than for CG! VII at both CG and IC initiation was higher than at both CG and IC cessation. VII was also found to be lower at IC occurrence, including at initiation, than at CG occurrence. Seventy-six percent of cells had IC initiation before CG initiation; using the first IC flash as a predictor of CG occurrence also statistically outperformed other predictors of CG lightning. Even though average lead time for using IC as a predictor of CG was only 2.4 minutes, when taking into account automation processing and radar scan time for the other methods, lead times are much more comparable.

Submitted by Diogo Saraiva (diogosaraivarj@gmail.com) on 2016-06-21T18:22:50Z No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) / Approved for entry into archive by GILSON ROCHA MIRANDA (gilson.miranda@fgv.br) on 2016-06-30T14:47:59Z (GMT) No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) / Approved for entry into archive by Maria Almeida (maria.socorro@fgv.br) on 2016-07-13T12:51:22Z (GMT) No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) / Made available in DSpace on 2016-07-13T12:51:41Z (GMT). No. of bitstreams: 1 Tese_Diogo_Saraiva.pdf: 1438960 bytes, checksum: 75bf0c000613e5b32c7480ba2c0a1f9b (MD5) Previous issue date: 2015-11-27 / The knowledge of the current state of the economy is crucial for policy makers, economists and analysts. However, a key economic variable, the gross domestic product (GDP), are typically colected on a quartely basis and released with substancial delays by the national statistical agencies. The first aim of this paper is to use a dynamic factor model to forecast the current russian GDP, using a set of timely monthly information. This approach can cope with the typical data flow problems of non-synchronous releases, mixed frequency and the curse of dimensionality. Given that Russian economy is largely dependent on the commodity market, our second motivation relates to study the effects of innovations in the russian macroeconomic fundamentals on commodity price predictability. We identify these innovations through a news index which summarizes deviations of offical data releases from the expectations generated by the DFM and perform a forecasting exercise comparing the performance of different models.

Made available in DSpace on 2014-06-11T19:31:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2008-06-09Bitstream added on 2014-06-13T20:02:24Z : No. of bitstreams: 1 lima_ma_dr_botfca.pdf: 6910754 bytes, checksum: 12da98a63ff4ae3c0c8a93b721f86f4f (MD5) / Outros / Para determinar como se inicia a convecção na região sudoeste da Amazônia, foram analisados dados do TRMM/LBA (Tropical Rainfall Measuring Mission / Large-scale Biosphere Atmosphere). A base para determinar onde e quando a convecção iniciou foi o radar banda-S, com polarização dual (S-Pol), do National Center for Atmospheric Research (NCAR). Utilizaram-se, adicionalmente, dados do canal visível do satélite GOES-8 para identificar piscinas frias produzidas pela precipitação convectiva. Essas informações, em conjunto com dados topográficos de alta resolução, foram utilizadas na determinação dos mecanismos possíveis de disparos da convecção. A elevação do terreno na área de estudo varia de 100 a 600m. Este estudo apresenta os resultados de 5 de fevereiro de 1999. Um total de 315 tempestades iniciou-se dentro do raio de 130km do radar S-Pol. Nesse dia, classificado como de fraco regime de monção, a convecção desenvolveu-se em resposta ao ciclo diurno do aquecimento solar. Cúmulos rasos espalhados durante a manhã desenvolveram-se em convecção profunda no início da tarde. As tempestades tiveram início após as 11h, com um pico de iniciação entre 15 e 16h. As causas de início de tempestades foram classificadas em 4 categorias. O modo mais comum de iniciação foi o levantamento forçado por frente de rajada (36%). A categoria, que inclui forçantes topográficas (>300m), sem a influência de nenhum outro mecanismo, é responsável por 21% das iniciações e a colisão de frentes de rajada por 16%. Nos 27% restantes, não foi possível a identificação de nenhum mecanismo. O exame de todos os dias do experimento TRMM/LBA mostrou que o dia estudado em detalhe foi representativo de muitos outros dias. Um modelo conceitual para o início e a evolução de tempestades é apresentado. Esses resultados, que devem ter implicações para outros... / Radar and satellite data from the Tropical Rainfall Measuring Mission / Large-scale Biosphere Atmosphere (TRMM/LBA) project have been examined to determine causes for convective storm initiation in the southwest Amazon region. The locations and times of storm initiation were based on the National Center for Atmospheric Research (NCAR) S-band dual-polarization Doppler radar (S-Pol). Both the radar and GOES-8 visible data were used to identify cold pools produced by convective precipitation. This data along with high-resolution topographic data were used to determine possible convective storm triggering mechanisms. The terrain elevation varied from 100 – 600 m. Tropical forests cover the area with numerous clear cut areas used for cattle grazing and farming. This study presents the results from 5 February 1999. A total of 315 storms initiated within 130 km of the S-Pol radar. This day was classified as a weak monsoon regime where convection developed in response to the diurnal cycle of solar heating. Scattered shallow cumulus during the morning developed into deep convection by early afternoon. Storm initiation began about 1100 LST and peaked around 1500-1600 LST. The causes of storm initiation were classified into 4 categories. The most common initiation mechanism was caused by forced lifting by a gust front (36%). Forcing by terrain (>300 m) without any other triggering mechanism accounted for 21% of the initiations and colliding gust fronts 16%. For the remaining 27% a triggering mechanism was not identified. Examination of all days during TRMM/LBA showed that this one detailed study day was representative of many days. A conceptual model of storm initiation and evolution is presented. The results of this study should have implications for other locations when synoptic scale forcing mechanisms are at a minimum... (Complete abstract click electronic access below)

Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-06-29Bitstream added on 2014-06-13T19:02:02Z : No. of bitstreams: 1 held_amg_dr_botfca.pdf: 4163192 bytes, checksum: db80ee83ff94cbb037cbf6cd62d8a9f0 (MD5) / Foi realizada uma análise para se obter a caracterização das tempestades sob o aspecto climatológico para a área central do Estado de São Paulo cujos parâmetros foram obtidos com o software TITAN, desenvolvido no NCAR e implementado nos computadores do IPMet. Os parâmetros que caracterizam as propriedades das tempestades tais como volume médio, área média, altura dos topos dos ecos, refletividade máxima e média bem como velocidade e deslocamento dos sistemas precipitantes foram determinados considerando o limiar de refletividade>30 dBZ, e volume>30 km3. A distribuição espacial de parâmetros tais como volume médio, área média, refletividade média e máxima mostrou, pela primeira vez para a área central do Estado de São Paulo, como os mesmos se distribuíram pela área monitorada pelo radar de Bauru e também a existência de regiões preferenciais onde se concentra a maior atividade convectiva, durante os verões analisados. Todas as varreduras observadas pelo radar de Bauru foram também processadas para se determinar as áreas de tempestades definidas pelo limiar de refletividade maior que 25 dBZ, para a partir daí obter a integração dessas áreas para o tempo de duração de cada tempestade e calcular a IAT, que é a integral área-tempo. O método da Integral-Área-Tempo (IAT), para se medir precipitação volumétrica baseada na informação de cobertura da precipitação em área foi aplicado aos dados de radar meteorológico de Bauru, para dois períodos de verão, o de 1998-1999 e 1999-2000. A premissa de que a porção crescente do conglomerado convectivo seria suficiente para calcular uma IAT que ainda seria altamente correlacionada com o volume total de chuva resultando, portanto numa técnica para o nowcasting é testada e verificada para os dois períodos analisados. Os resultados das análises mostraram que as células de tempestade... / A climatological characterization of storm properties during two summer seasons, viz. 1998-1999 and 1999-2000, based on observations from the Bauru S-band Doppler radar, was obtained with the TITAN Software of NCAR, implemented at IPMet. Parameters, such as mean volume, mean area, mean and maximum echo tops, mean and maximum reflectivity, as well as speed and direction of precipitating systems were determined using the reflectivity> 30 dBZ and a volume> 30 km3 as a threshold for storm identification. The spatial distribution for parameters such as mean volume, mean area, mean and maximum reflectivity, mean and maximum echo top, etc, were determined for the first time in the central State of São Paulo, based on radar data information. It has shown some preferential areas where most of the convective activity was concentrated during the study period. The Area Time-Integration (ATI) method was then applied to these observations using the 25 dBZ thresholds, to determine the rainfall volume in the central area of the State of São Paulo, taking into account the entire lifetime of all observed storms that exceeded the threshold considered. Furthermore, it was also investigated, if it would be possible to estimate the ATIs only for the growth period of a convective storm and still obtain a good correlation. This method could then be applied to obtain the total rain volume of a convective system at the stage of its maximum development, which could be considered as a nowcasting tool to be explored in subsequent studies. The time span for the storms reaching their maximum area was found to be about 2 hours on average and this was reached within...(Complete abstract, click electronic access below)

Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-04-03T23:20:24Z No. of bitstreams: 1 GilvanLuteroDeCarvalhoFilho_DISSERT.pdf: 2534911 bytes, checksum: 6e5346023d8898ac72d6969006150e15 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-04-12T00:06:08Z (GMT) No. of bitstreams: 1 GilvanLuteroDeCarvalhoFilho_DISSERT.pdf: 2534911 bytes, checksum: 6e5346023d8898ac72d6969006150e15 (MD5) / Made available in DSpace on 2017-04-12T00:06:08Z (GMT). No. of bitstreams: 1 GilvanLuteroDeCarvalhoFilho_DISSERT.pdf: 2534911 bytes, checksum: 6e5346023d8898ac72d6969006150e15 (MD5) Previous issue date: 2016-09-28 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior (CAPES) / A t?cnica de an?lise de sinais GNSS (Global Navigation Satellite System) emitidos por sat?lites tem sido largamente utilizada no campo da geodin?mica e da geodesia, como sensor para medidas de velocidades e deslocamentos de placas tect?nicas e da representa??o da forma e da superf?cie da Terra. No entanto, o sinal proveniente do sat?lite sofre atrasos ao atravessar a atmosfera terrestre, especificamente em duas das suas camadas: (a) a camada ionosf?rica, na qual o sinal sofre atraso pela presen?a de ?tomos ionizados nesta regi?o, e (b) a camada troposf?rica, onde o atraso acontece devido a presen?a de vapor d??gua na regi?o, sendo fortemente relacionado ? quantidade de vapor d??gua precipit?vel presente na mesma. Neste trabalho apresenta-se uma an?lise de dados de sinais GNSS, coletados em esta??es receptoras de superf?cie, visando aplica??es relacionadas ao c?lculo da quantidade de vapor d??gua na troposfera. Os dados dos sinais GNSS foram obtidos diretamente do IBGE (Instituto Brasileiro de Geografia e Estat?stica) atrav?s da RBMC (Rede Brasileira de Monitoramento Continuo dos Sistemas GNSS). O processamento dos dados foi realizado utilizando-se o software GIPSY (GPS Inferred Positioning System) do JPL-NASA (Jet Propulsion Laboratory), que processa os dados observados dos sat?lites e fornece os valores de ZTD (Zenital Tropospheric Delay) ou Atraso Zenital Troposf?rico. A partir do conhecimento da temperatura e da press?o na posi??o da antena receptora dos sinais, determinou-se o IWV (Integrated Water Vapor), que representa o vapor d??gua integr?vel na coluna atmosf?rica e est? relacionado ? pluviometria local. Aplica??es foram feitas para as cidades de Natal e Mossor?. Das s?ries temporais dos par?metros ZWD, IWV e Pluviometria ? obtidas do INMET (Instituto Nacional de Meteorologia) - foram realizadas as correla??es estat?sticas entre estas vari?veis, utilizando-se o software R. Correla??es estat?sticas entre sinais de GNSS e de Pluviometria v?m sendo usada como ferramenta de aux?lio para a PNT (Previs?o Num?rica de Tempo). Este trabalho mostra, sem sombra de d?vida, que o par?metro IWV pode ser utilizado como dado de entrada para aplica??es de Nowcasting. / The GNSS signal analysis (Global Navigation Satellite System) issued by satellites has been widely used in the field of geodynamics and geodesy, as a sensor for speed measurements and displacement of tectonic plates and the representation of the shape and the Earth's surface. However, the satellite signal is delayed as it crosses the earth's atmosphere, specifically in two of its layers: (a) the ionospheric layer, where the signal is delayed by ionized atoms present in this region, and (b) the tropospheric layer, due to the presence of water vapor, and is strongly related to the amount of water vapor precipitable present in that region. This work presents data analysis of GNSS signals obtained from surface gauge stations, aiming applications related to the amount of water vapor in the troposphere. Data from the GNSS signals were obtained directly from the IBGE (Instituto Brasileiro de Geografia e Estat?stica) through its link with RBMC (Rede Brasileira de Monitoramento Continuo dos Sistemas GNSS). Data processing was performed using the GIPSY (GPS Inferred Positioning System) software, from JPL-NASA (Jet Propulsion Laboratory), which processes the observed data from satellites and provides ZTD values (Zenithal Tropospheric Delay). From the knowledge of temperature and pressure in the gauge station antenna, one can estimate IWV (Integrated Water Vapor), that means the water vapor in the atmospheric column and is related to the local pluviometry. Applications has been made on Natal and Mossor? cities and were made correlations between the variables from the time series obtained from INMET (Instituto Nacional de Meteorologia), for the ZTD parameters, IWV and Pluviometry, using statistical analysis from the R-software. Statistical correlations between GNSS and Pluviometry data could be used as a tool for NWP (Numerical Weather Prediction). This work shows, without a doubt, that this happen when IWV is used as input data for Nowcasting applications.

Using high frequency data for forecasting or nowcasting, we have to deal with three major problems: the mixed frequency problem, the high dimensionality (fat re- gression, parameter proliferation) problem, and the unbalanced data problem (miss- ing observations, ragged edge data). We propose a BSTS-U-MIDAS model (Bayesian Structural Time Series-Unlimited-Mixed-Data Sampling model) to handle these prob- lem. This model consists of four parts. First of all, a structural time series with regressors model (STM) is used to capture the dynamics of target variable, and the regressors are chosen to boost the forecast accuracy. Second, a MIDAS model is adopted to handle the mixed frequency of the regressors in the STM. Third, spike- and-slab regression is used to implement variable selection. Fourth, Bayesian model averaging (BMA) is used for nowcasting. We use this model to nowcast quarterly GDP for Canada, and find that this model outperform benchmark models: ARIMA model and Boosting model, in terms of MAE (mean absolute error) and MAPE (mean absolute percentage error). / Graduate / 0501 / 0508 / 0463 / jonduan@uvic.ca

The gross domestic product (GDP) is an essential measure of the state of economic activity and serves as a crucial tool for policymakers, investors, or businesses. However, the official GDP estimate in the Czech Republic is only available with a lag of approximately 60 days, and the Czech National Bank (CNB) announces its GDP forecast once in each quarter. This thesis focuses on predicting GDP growth in the current quarter, referred to as nowcasting. I employ several methods to nowcast the real GDP growth in the Czech Republic in a pseudo-real-time setting and compare their performance. Additionally, I investigate the possibility of creating an ensemble model by using a weighted average of several nowcasting models. The results suggest that the Dynamic Factor Model (DFM) performs best in the GDP nowcasting task, and its predictive accuracy is comparable with the official CNB nowcast. Furthermore, the model averaging process yields accuracy close to the best individual model while addressing model uncertainty. The GDP nowcast of the DFM will be made available to the public in real-time on a website and updated with a daily frequency.

Adjustment of the drinking water treatment process as a simultaneous response to climate variations and water quality impact has been a grand challenge in water resource management in recent years. This desired and preferred capability depends on timely and quantitative knowledge to monitor the quality and availability of water. This issue is of great importance for the largest reservoir in the United States, Lake Mead, which is located in the proximity of a big metropolitan region - Las Vegas, Nevada. The water quality in Lake Mead is impaired by forest fires, soil erosion, and land use changes in nearby watersheds and wastewater effluents from the Las Vegas Wash. In addition, more than a decade of drought has caused a sharp drop by about 100 feet in the elevation of Lake Mead. These hydrological processes in the drought event led to the increased concentration of total organic carbon (TOC) and total suspended solids (TSS) in the lake. TOC in surface water is known as a precursor of disinfection byproducts in drinking water, and high TSS concentration in source water is a threat leading to possible clogging in the water treatment process. Since Lake Mead is a principal source of drinking water for over 25 million people, high concentrations of TOC and TSS may have a potential health impact. Therefore, it is crucial to develop an early warning system which is able to support rapid forecasting of water quality and availability. In this study, the creation of the nowcasting water quality model with satellite remote sensing technologies lays down the foundation for monitoring TSS and TOC, on a near real-time basis. Yet the novelty of this study lies in the development of a forecasting model to predict TOC and TSS values with the aid of remote sensing technologies on a daily basis. The forecasting process is aided by an iterative scheme via updating the daily satellite imagery in concert with retrieving the long-term memory from the past states with the aid of nonlinear autoregressive neural network with external input on a rolling basis onward. To account for the potential impact of long-term hydrological droughts, teleconnection signals were included on a seasonal basis in the Upper Colorado River basin which provides 97% of the inflow into Lake Mead. Identification of teleconnection patterns at a local scale is challenging, largely due to the coexistence of non-stationary and non-linear signals embedded within the ocean-atmosphere system. Empirical mode decomposition as well as wavelet analysis are utilized to extract the intrinsic trend and the dominant oscillation of the sea surface temperature (SST) and precipitation time series. After finding possible associations between the dominant oscillation of seasonal precipitation and global SST through lagged correlation analysis, the statistically significant index regions in the oceans are extracted. With these characterized associations, individual contribution of these SST forcing regions that are linked to the related precipitation responses are further quantified through the use of the extreme learning machine. Results indicate that the non-leading SST regions also contribute saliently to the terrestrial precipitation variability compared to some of the known leading SST regions and confirm the capability of predicting the hydrological drought events one season ahead of time. With such an integrated advancement, an early warning system can be constructed to bridge the current gap in source water monitoring for water supply.

Nowcasting – short-term forecasting using current observations – is a key challenge that human activities have to face on a daily basis. We heavily rely on short-term meteorological predictions in domains such as aviation, agriculture, mobility, and energy production. One of the most important and challenging task for meteorology is the nowcasting of extreme events, whose anticipation is highly needed to mitigate risk in terms of social or economic costs and human safety. The goal of this thesis is to contribute with new machine learning methods to improve the spatio-temporal precision of nowcasting of extreme precipitation events. This work relies on recent advances in deep learning for nowcasting, adding methods targeted at improving nowcasting using ensembles and trained on novel original data resources. Indeed, the new curated multi-year radar scan dataset (TAASRAD19) is introduced that contains more than 350.000 labelled precipitation records over 10 years, to provide a baseline benchmark, and foster reproducibility of machine learning modeling. A TrajGRU model is applied to TAASRAD19, and implemented in an operational prototype. The thesis also introduces a novel method for fast analog search based on manifold learning: the tool leverages the entire dataset history in less than 5 seconds and demonstrates the feasibility of predictive ensembles. In the final part of the thesis, the new deep learning architecture ConvSG based on stacked generalization is presented, introducing novel concepts for deep learning in precipitation nowcasting: ConvSG is specifically designed to improve predictions of extreme precipitation regimes over published methods, and shows a 117% skill improvement on extreme rain regimes over a single member. Moreover, ConvSG shows superior or equal skills compared to Lagrangian Extrapolation models for all rain rates, achieving a 49% average improvement in predictive skill over extrapolation on the higher precipitation regimes.

[pt] Este estudo explora fontes de dados não convencionais como dados textuais de jornais e pesquisas de internet do Google Trends em dois problemas empíricos: (i) analisar o impacto da mobilidade sobre o número de casos e mortes por Covid-19; (ii) nowcasting do PIB em alta-frequência. O primeiro artigo usa fontes de dados não estruturados como controle para fatores comportamentais não observados e encontra que um aumento na mobilidade residencial diminui significativamente o número de casos e mortes num horizonte de quatro semanas. O segundo artigo usa fontes de dados não estruturadas para fazer um nowcasting semanal do PIB, mostrando que dados textuais e Google Trends pode aumentar a qualidade das projeções (medido pelo EQM, EAM e outras métricas) comparado com as expectativas de mercado do Focus como base. Em ambos casos, dados não estruturados reveleram-se fontes ricas de informação não codificadas em indicadores estruturados convencionais. / [en] This study exploits non-conventional data sources such as newspaper textual data and internet searches from Google Trends in two empirical problems: (i) analysing the impacts of mobility on cases and deaths due to Covid-19; (ii) nowcasting GDP in high-frequency. The first paper resorts to unstructured data to control for non-observable behavioural effects and finds that an increase in residential mobility significantly reduces Covid-19 cases and deaths over a 4-week horizon. The second paper uses unstructured data sources to nowcast GDP on a weekly basis, showing that textual data and Google Trends can significantly enhance the quality of nowcasts (measured by MSE, MAE and other metrics) compared to Focus s market expectations as a benchmark. In both cases, unstructured data was revealed to be a valuable source of information not encoded in structured indicators.