339 research outputs found

    Impacts of convective treatment on tropical rainfall variability in realistic and idealized simulations

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    The prediction of precipitation in the tropics is a challenge for numerical weather prediction (NWP), meaning very low practical predictability there. However, previous studies indicated that intrinsic predictability in the tropics is up to a few weeks and thus longer than in the extratropics. Equatorial waves (EWs) from the linear shallow-water theory are considered the source of this long predictability. Most weather and climate models still struggle to accurately capture EWs, which is often attributed to parameterized convection. With advanced computing power, model development is moving toward high-resolution models with explicit convection. To evaluate the value of these high-resolution models, this thesis aims to provide important insights into the behavior of tropical precipitation due to the treatment of deep and shallow convection using the ICOsahedral Nonhydrostatic (ICON) model. First, we examine the sensitivity of EWs to model configuration using realistic ICON simulations with varying horizontal grid spacings (80-2.5 km) and with different convectivetreatments between parameterized versus explicit deep and shallow convection. To robustly identify wave signals, we use two objective methods, one filtering rainfall using a fast Fourier transform and the other projecting two-dimensional wind and geopotential onto theoretical wave patterns. The results demonstrate that large-scale EWs are surprisingly consistent in terms of phase speed and wave amplitude with little sensitivity to model resolution, convective treatment and wave identification method. Rainfall signals of westward inertio-gravity waves (WIGs), however, show a large difference between parameterized and explicit convection with the latter showing marked rainfall signals but with no corresponding wind patterns. A composite analysis to link rainfall and wind fields of waves reveals that the identified signals in rainfall appear to be associated with mesoscale convective systems, the spatiotemporal scales of which overlap with those of WIGs, and thus are isolated as waves through space-time filtering. Secondly, we analyze idealized ICON simulations in a tropical aquachannel configuration with zonally symmetric sea surface temperatures and with rigid walls at 30°N/S. The aquachannel simulations vary in the representation of deep and shallow convection but with the same horizontal grid spacing of 13 km. All aquachannel simulations have maximum rainfall at the equator, showing an intertropical convergence zone (ITCZ) there, but the rainfall amount increases by 35% with explicit deep convection. To physically understand this difference, we adapt a diagnostic based on a conceptual model by Emanuel (2019), assuming boundary-layer quasi-equilibrium (BLQE), the weak temperature gradient approximation, and mass and energy conservation. BLQE implies that moist entropy is in balance between surface enthalpy fluxes, which import high moist entropy to the BL, and convective downdrafts, which transport low moist entropy from the free troposphere into the BL. The results reveal that the rainfall differences are primarily associated with surface enthalpy fluxes through BLQE, while precipitation efficiency is surprisingly constant in the ITCZ. Further detailed analysis demonstrates that mean surface wind speed, which is closely related to the large-scale circulation, contributes most to the differences in surface enthalpy fluxes. Thus, the treatment of deep convection alters mean rainfall through tight links between surface winds, associated surface fluxes and convective mass flux. Lastly, variability associated with EWs is examined in the aquachannel simulations by using the same wave identification methods used for the realistic simulations. All simulations show prominent signals of Kelvin waves (KWs) with large variations among them. Parameterized deep convection produces various eastward propagation with speeds of 5–27 m/s, while explicit deep convection exhibits a dominance of KWs with a zonal wavenumber of one and with a propagation speed of 24 m/s. Furthermore, explicit deep convection causes more pronounced structures of zonal wind and temperature in the lower stratosphere and a stronger link of wind-induced surface enthalpy flux exchange to the development of convection. Meanwhile, the treatment of shallow convection plays a role for temperature variation below 2.5 km. However, BL warming is in phase with maximum rainfall associated with KWs, which is opposite to observations. Parameterized deep convection generates a feature sharing similarities with the Madden Julian Oscillation, which is not found in the other aquachannel simulations. The novelty of this thesis lies in understanding the behavior of tropical rainfall in both realistic and idealized simulations by using diagnostics adapted for systematic comparisons between different simulations, mainly due to different convective treatments. This allows us to obtain valuable insights into the sensitivity of tropical rainfall and its variability to model configuration, ultimately paving the way for developing more accurate weather and climate predictions in the tropics

    Advances in Modelling of Rainfall Fields

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    Rainfall is the main input for all hydrological models, such as rainfall–runoff models and the forecasting of landslides triggered by precipitation, with its comprehension being clearly essential for effective water resource management as well. The need to improve the modeling of rainfall fields constitutes a key aspect both for efficiently realizing early warning systems and for carrying out analyses of future scenarios related to occurrences and magnitudes for all induced phenomena. The aim of this Special Issue was hence to provide a collection of innovative contributions for rainfall modeling, focusing on hydrological scales and a context of climate changes. We believe that the contribution from the latest research outcomes presented in this Special Issue can shed novel insights on the comprehension of the hydrological cycle and all the phenomena that are a direct consequence of rainfall. Moreover, all these proposed papers can clearly constitute a valid base of knowledge for improving specific key aspects of rainfall modeling, mainly concerning climate change and how it induces modifications in properties such as magnitude, frequency, duration, and the spatial extension of different types of rainfall fields. The goal should also consider providing useful tools to practitioners for quantifying important design metrics in transient hydrological contexts (quantiles of assigned frequency, hazard functions, intensity–duration–frequency curves, etc.)

    Investigating the relationships between precipitable water vapor estimations and heavy rainfall over the Eastern Pacific Ocean and Ecuadorian regions

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    La lluvia es un fenĂłmeno atmosfĂ©rico difĂ­cil de predecir. MĂĄs allĂĄ de su importancia en las actividades humanas; existen dificultades teĂłricas y tĂ©cnicas que justifican el estudio de la lluvia y la lluvia intensa. Los Modelos NumĂ©ricos atmosfĂ©ricos de PredicciĂłn como el Weather Research & Forecasting Model (WRF), son las herramientas que se utilizan para predecir y estudiar su comportamiento, aunque presentan limitaciones al trabajar con lluvias intensas y topografĂ­as complejas y empinadas. Recientes investigaciones proponen a la estimaciĂłn vapor de agua troposfĂ©rico (Precipitable Water Vapor PWV), como una herramienta que puede ayudar a la predicciĂłn y entendimiento de los mecanismos que desencadenan lluvia intensa. Productos satelitales y su derivaciĂłn indirecta a travĂ©s del retraso de señales de Sistemas de Posicionamiento Global GNSS, son las principales fuentes actuales de PWV. El presente trabajo estudia la relaciĂłn entre la lluvia intensa y el PWV satelital sobre el ocĂ©ano, la relaciĂłn de PWV-GNSS sobre la Costa, Sierra y Oriente del Ecuador; asĂ­ como con los datos modelados en WRF sobre zonas andinas ecuatoriales. Como principales resultados, se tiene un modelo empĂ­rico entre el PWV satelital y los valores mĂĄximos de lluvia sobre el ocĂ©ano; ademĂĄs, se identifican perĂ­odos de carga y descarga del PWV-GNSS relacionados con el ciclo diurno de la lluvia sobre tierra, y relaciones con los eventos intensos de lluvia; y por Ășltimo, se encuentran las principales discrepancias entre los datos observados PWV- GNSS y lluvia con datos modelados de WRF sobre zonas de los Andes Ecuatoriales.Among the weather phenomena, rainfall is difficult to forecast, despite the theoretical and technical challenges inherently related to its prediction, its impact in economic and everyday activities, clearly justify its study. Numerical Weather Prediction Models are widely used to predict rainfall, such as the Weather Research & Forecasting Model (WRF), However, they underperform when is set to predict intense events and when working with complex and steep topographies. Recent studies have proposed the estimation of Precipitable Water Vapor PWV, as a tool that can help predict and understand the mechanisms that trigger intense rainfall. PWV is mainly sourced from satellite products and from indirectly measurements which derive it through the delay of the Global Navigation Positioning System (GNSS) signals quite accurately. Thus, the present work studies the relationship between intense rain and satellite sourced PWV over the ocean, the relationship of PWV-GNSS over the Coast, Sierra and Amazon of Ecuador, and the comparison of the PWV-GNSS with the data modeled in WRF. As main results, we point an empirical model between the satellite PWV and the maximum values of rainfall over the ocean. In addition, PWV-GNSS loading and unloading periods related to the diurnal cycle of rainfall over the land, and relationships with intense rain events were identified; and finally, the main discrepancies between the observed PWV-GNSS data and rainfall with WRF modeled data over areas of the Equatorial Andes.0000-0002-4496-73230000-0002-4408-85800000-0002-7205-5786Doctora (PhD) en Recursos HĂ­dricosCuenc

    Atti del XXV Convegno Nazionale di Agrometeorologia. L’Agrometeorologia per la gestione delle risorse e delle limitazioni ambientali in agricoltura

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    La razionale gestione delle risorse ambientali e naturali ha nella modellistica agrometeorologia il suo supporto di base. Del resto, le note e tristi vicende che negli ultimi giorni hanno duramente interessato i territori dell’Emilia-Romagna e delle Marche, impongono alla comunità scientifica e ai gestori del territorio un’attenta valutazione degli effetti che il cambiamento climatico ha sul territorio. Pertanto, i modelli agrometeorologici sono uno strumento essenziale per il processo gestionale e decisionale sia nell’ambito dei sistemi colturali che in quello zootecnico. Numerosi sono gli studi sui meccanismi e sulle relazioni che regolano le dinamiche ambientali e produttive del territorio stesso per descrivere le sue reali potenzialità produttive e quindi, pianificare e razionalizzare l’uso delle risorse utilizzate nel processo produttivo. La caratterizzazione meteorologica ù uno dei primi passi da intraprendere per la conoscenza di un territorio, valutando non solo l’andamento dei valori medi dei principali parametri misurati al suolo, ma soprattutto la loro variabilità spaziotemporale. AIAM 2023 ù l’appuntamento annuale tra i ricercatori e i tecnici dei servizi agrometeorologici regionali per presentare i risultati degli studi e dei progetti di ricerca per la gestione degli stress abiotici, dei mezzi di previsione e gestione delle avversità che interessano il mondo agricolo, con riferimento alle politiche di sviluppo agricolo del PSN 2023-27

    Earth Observation in the EMMENA Region: Scoping Review of Current Applications and Knowledge Gaps

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    Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services

    Improving Predictions of Precipitation Phase over Canada with the Development of a Spatially Aware Parameterization of Rain/Snow Partitioning

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    Partitioning precipitation into rain or snow is an important aspect of hydrologic and climatological modelling, affecting a wide variety of downstream processes (Harpold et al., 2017). Current conventional methods typically rely on surface temperature, either as a strict threshold or to inform a probability function which predicts a 0% chance of rain at one temperature to 100% chance of rain at another (Jennings et al., 2019, Feiccabrino et al., 2015). However, recent studies have shown that variables such as wind, pressure, humidity, and atmospheric profiles of temperature can all have a significant effect on pre- cipitation phase at the surface (Wang et al., 2019, Sims and Liu, 2015, Jennings et al., 2018). This study utilized CloudSat data of precipitation phase and associated environ- mental variables ground-truthed at ECCC stations across Canada to build an improved statistical parameterization of precipitation phase. Our results showed that using a ran- dom forest model with atmospheric profiles of wetbulb temperature in addition to surface wetbulb temperature, elevation, and wind resulted in a probability of detection of 97.8% across the -1, 4°C temperature interval, compared to a probability of detection of below 80% across the same interval for conventional methods. The random forest parameteri- zation was also spatially robust, performing well on stations it had not been trained on. Additionally, adding Sturm’s snow classes as an indicator variable to the model did not result in any significant improvement, indicating that a model trained on all available data is adequately able to capture spatial variability in rain-snow partitioning across the study area

    River landform dynamics detection and responses to morphology change in the rivers of North Luzon, the Philippines

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    River morphology detection has been improved considerably with the application of remote sensing and developments in computer science. However, applications that extract landforms within the active river channel remain limited, and there is a lack of studies from tropical regions. This thesis developed and then applied a workflow employing Sentinel-2 imagery for seasonal and annual river landform classification. Image downscaling approaches were investigated, and the performance of object-based image segmentation was assessed. The area to point regression kriging (ATPRK) approach was chosen to downscale coarser 20 m resolution Sentinel-2 bands to finer 10 m resolution bands. All features were set or processed at 10 m resolution before applying support vector machine (SVM) classification. To improve machine learning classification accuracy, Sentinel-2 acquisitions across one year, which incorporates multiple seasons, should be used. For rivers with different hydrological or geology settings, the thesis considered collecting river specific ground truth data to build a training model to avoid underfitting of models from other hydrological/geological settings. Applying the workflow, three landforms (water, unvegetated bars and vegetated bars) were classified within the active channel of the Bislak, Laoag, Abra and Cagayan Rivers, north Luzon, the Philippines, between 2016 to 2021, respectively. The spatial-temporal river landform datasets enabled the quantitative analysis of the river morphology changes. Water and unvegetated bars showed clear seasonal dynamics in all four rivers, whilst vegetated bars only showed seasonality in the rivers located in the northwest Luzon (the Bislak, Laoag and Abra Rivers). This thesis employed correlated coefficients to investigate the longitudinal correlation between river landforms and active width. It was found that vegetated bar areas always have strong significant correlations (≄0.67) with the active widths in all four rivers, whilst correlation coefficients between vegetated bar areas and active widths in the wet season are higher than that in the dry season. Ensemble empirical mode decomposition (EEMD) was applied to detect landform periodicity; this method indicated that water and vegetated bars commonly showed synchronised fluctuations with precipitation, while unvegetated bars had an anti-phase oscillation with precipitation. In the case of EEMD, deviations from periodic consistency in river pattern may reflect the influence of extreme events and/or human disturbance. Coefficient of variation (COV) was then used to evaluate the stability of the landforms; results suggested that the interplay of faults, elevation, confinement and tributary locations impacted landform stability. Finally, tributary inflow impacts on the mainstem river were investigated for eight tributaries of the lowland Cagayan River, also on Luzon Island. Longitudinal variations in channel morphology and stability, and temporal changes in landform frequency, using Simpson’s diversity index and COV, showed downstream widening associated with tributaries that was controlled by water discharge, with a secondary sediment flux effect. Overall, this thesis provided a novel example of combining remote sensing and GIS science, computing science, statistical science, and river morphology science to study the earth surface processes synthetically and quantitatively within river active channels in the tropical north Luzon, the Philippines. This work demonstrated how the fusion of techniques from these disciplines can be used to detect and analyse river landform changes, with potential applications for river management and restoration
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