Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Diseases and environment are closely related, especially with regard to air temperature and relative humidity. The weather data tend to follow a daily pattern of temporal variation, which derives mainly from the daily variation of the surface radiation balance. Based on this, this study aimed to make a qualitative and quantitative description of the database of the Automatic Meteorological Station of the Instituto Nacional de Meteorologia (INMET) in Santa Maria, RS, determine the hours of greatest frequency of occurrence of daily extreme air temperatures in Santa Maria, develop models to estimate hourly values of air temperature (T) and relative humidity (RH), based on values measured in "UTC standard times" and on the daily minimum air temperature and to determine the risk of fungal diseases occurrence during the developmental cycle of potato in different planting dates, using the Blitecast forecasting system. Models were fitted to estimate air T and partial vapor pressure of water at different times of the day. RH was indirectly obtained from partial vapor pressure of water and saturation vapor pressure, calculated from estimated T values. The database of Principal Climatological Station of Santa Maria was transformed to an hourly estimated T and RH database and the Blitecast's calculated severity values (CSV) were obtained for 14 planting dates in 43 years. The automatic weather station of Santa Maria, RS, belonging to INMET, shows 90.1% of the days of flawless data records in 10 years, from 2002 to 2011. The minimum daily air temperature in Santa Maria occurs more frequently at 09AM UTC, corresponding to 6AM on local official time, from September to February. From March through August, this time is delayed, and its occurrence to be more frequent at 10 h UTC. The precision of the estimates ranged from 0.7 ºC to 1.5 ºC, with average deviation of 0.65 °C for T and 0.7 % to 1.2 %, with average deviation of up to 4% for RH depending on the time. Potato crops during the Fall growing season require on average 54% more fungicide applications compared to the Spring season. In the Spring growing season the most probable number of sprayings ranged from two to four and from six to seven in the Fall growing season. Potato crops planted in August and March were demanded a lower economic, ecological and farmer health cost in crop protection against late blight than earlier crops (July and February). / Doenças e ambiente estão intimamente relacionados, especialmente no que se refere à temperatura e umidade relativa do ar. Os elementos meteorológicos tendem a seguir um padrão de variação temporal diária, que deriva principalmente da variação diária do balanço de radiação da superfície. Com base nisso, o objetivo desse trabalho foi descrever qualitativa e quantitativamente do banco de dados da Estação Meteorológica Automática do Instituto Nacional de Meteorologia de Santa Maria (INMET), determinar os horários de maior frequência de ocorrência das temperaturas extremas diárias do ar em Santa Maria, desenvolver modelos para estimativa dos valores horários de temperatura (T) e umidade relativa do ar (UR) e determinar o risco de ocorrência de requeima no ciclo de desenvolvimento da batata, em diferentes datas de plantio. Foram ajustados modelos analíticos para estimativa da T e da pressão parcial de vapor no ar nos diferentes horários do dia. A UR foi obtida indiretamente, com base nos valores de pressão parcial de vapor no ar estimados e nos valores de pressão de saturação de vapor no ar, calculados com base na T estimada. O banco de dados da Estação Climatológica Principal de Santa Maria foi transformado em um banco de dados horários estimados de T e UR e foram obtidos os valores de severidade calculada (VSC) pelo sistema Blitecast em 14 datas de plantio da batata em 43 anos agrícolas. A estação meteorológica automática de Santa Maria, RS, pertencente ao INMET, apresenta 90,1% dos dias sem falhas de registros de dados em 10 anos, de 2002 a 2011. A temperatura mínima diária do ar em Santa Maria ocorreu com maior frequência às 09 h UTC, correspondendo às 06 h no horário oficial local, nos meses de setembro a fevereiro. De março até agosto, esse horário é atrasado, sendo a sua ocorrência mais frequente às 10 h UTC. A precisão das estimativas variou de 0,7 oC a 1,5 oC, com desvio médio de até 0,65oC para T do ar e de 0,7 % a 1,2 %, com desvio médio de até 4 %, para UR, dependendo do horário. Cultivos de batata no período de safrinha necessitam, em média, de um número 54 % maior de aplicações de fungicida em relação ao período de safra. Na safra o número mais provável de pulverizações oscila entre duas e quatro e na safrinha entre seis e sete. Plantios de batata realizados em agosto e em março exigiram um menor custo econômico, ecológico e de saúde do produtor na proteção da cultura contra a requeima do que plantios mais precoces (julho e fevereiro).

<p dir="ltr">Methodologies for optimizing UAVs' control for varied environmental conditions have become crucial in the recent development for UAV control sector, yet they are lacking. This research focuses on the dynamism of the Gazebo simulator and PX4 Autopilot flight controller, frequently referenced in academic sectors for their versatility in generating close-to-reality digital environments. This thesis proposed an integrated simulation system that ensures realistic wind and gust interactions in the digital world and efficient data extraction by employing an industrial standard control communication protocol called MAVLink with the also the industry standard ground control software QGroundControl, using real and historical weather information from NOAA database. This study also looks into the potential of reinforcement learning, namely the DDPG algorithm, in determining optimal UAV safety distance, trajectory prediction, and mission planning under wind disruption. The overall goal is to enhance UAV stability and safety in various wind-disturbed conditions. Mainly focusing on minimizing potential collision risks in areas such as streets, valleys, tunnels, or really anywhere has winds and obstacles. The ROS network further enhanced these components, streamlining UAV response analysis in simulated conditions. This research presents a machine-learning approach to UAV flight safety and efficiency in dynamic environments by synthesizing an integrated simulation system with reinforcement learning. And the results model has a high accuracy, reaching 91%, 92%, and 97% accuracy on average in prediction of maximum shifting displacement, and left/right shifting displacement, when testing with real wind parameters from KLAF airport. </p>

This research was aimed to evaluate the possibility of using satellite precipitation products (SPPs) and Numerical Weather Prediction (NWP) of precipitation for better hydrologic simulations and flood forecasting in the trans-boundary Chenab River Basin (CRB) in Pakistan. This research was divided into three parts. In the first part, two renowned SPPs, i.e., global precipitation mission (GPM) IMERG-F v6 and tropical rainfall measuring mission (TRMM) 3B42 v7, were incorporated in a semidistributed hydrological model, i.e., the soil and water assessment tool (SWAT), to assess the daily and monthly runoff pattern in Chenab River at the Marala Barrage gauging site in Pakistan. The results exhibit higher correlation between observed and simulated discharges at monthly timescale simulations rather than daily timescale simulations. Moreover, results show that IMERG-F is superior to 3B42 by indicating higher R2, higher Nash–Sutcliffe efficiency (NSE), and lower percent bias (PBIAS) at both monthly and daily timescale. In the second part, three latest half-hourly (HH) and daily (D) SPPs, i.e., 'IMERG-E', 'IMERGL', and 'IMERG-F', were evaluated for daily and monthly flow simulations in the SWAT model. The study revealed that monthly flow simulation performance is better than daily flow simulation in all sub-daily and daily SPPs-based models. Results depict that IMERGHHF and IMERG-DF yield the best performance among the other latency levels of SPPs. However, the IMERG-HHF based model has a reasonably higher daily correlation coefficient (R) and lower daily root mean square error (RMSE) than IMERG-DF. IMERG-HHF displays the lowest PBIAS for daily and monthly flow validations and it also represents relatively higher values of R2 and NSE than any other model for daily and monthly model validation. Moreover, the sub-daily IMERG based model outperformed the daily IMERG based model for all calibration and validation scenarios. IMERG-DL based model demonstrates poor performance among all of the SPPs, in daily and monthly flow validation, with low R2, low NSE, and high PBIAS. Additionally, the IMERG-HHE model outperformed IMERG-HHL. In the third and last part of this research, coupled hydro-meteorological precipitation information was used to forecast the 2016 flood event in the Chenab River Basin. The gaugecalibrated SPP, i.e., Global Satellite Mapping of Precipitation (GSMaP_Gauge), was selected to calibrate the Integrated Flood Analysis System (IFAS) model for the 2016 flood event. Precipitation from the Global Forecast System (GFS) NWP, with nine different lead times up to 4 days, was used in the calibrated IFAS model. This study revealed that the hydrologic simulations in IFAS, with global GFS forecasts, were unable to predict the flood peak for all lead times. Later, the Weather Research and Forecasting (WRF) model was used to downscale the precipitation forecasts with one-way and two-way nesting approaches. It was found in this study that the simulated hydrographs in the IFAS model, at different lead times, from the precipitation of two-way WRF nesting exhibited superior performance with the highest R2, NSE and the lowest PBIAS compared with one-way nesting. Moreover, it was concluded that the combination of GFS forecast and two-way WRF nesting can provide high-quality precipitation prediction to simulate flood hydrographs with a remarkable lead time of 96 h when applying coupled hydrometeorological flow simulation.