Large-scale influences on tropical cyclogenesis for selected storms in the 2005 Atlantic hurricane season

Unpreparedness of large and increasing populations to Atlantic tropical cyclones (TCs) in North and Central America often causes a significant percentage of human casualties and economic losses, which results in part from the difficulty of forecasting tropical cyclogenesis (TCG) and changes in TC track and intensity. Although the mechanisms that lead to TCG have been studied extensively, lack of knowledge still exists about the relative importance of the precursor factors responsible for TCG, especially in the Gulf of Mexico/Caribbean basin where no clear genesis mechanism has been identified for TCs. A series of studies in this dissertation examines influences of large-scale atmospheric circulation on TCG and intensity change mechanisms for Tropical Storm Arlene and Hurricanes Cindy, Dennis, and Wilma in 2005 by using various derived and observed data sets. To support the main analyses of the large-scale circulations GOES-12 satellite water vapor imagery and the Weather Research and Forecasting (WRF) model (V.3.2.1) are used. Six-hourly NCEP FNL (final) operational global analysis data and daily “real-time global” (RTG) sea surface temperature (SST) data are used as WRF model inputs. Results show that large-scale, low-level circulations incurred by subtropical high pressure systems in the surrounding ocean basins or triggered by mid-latitude troughs over northeastern North America play critical roles in the TCG process in the western North Atlantic. In particular, the convergence of temporary westerly winds from the eastern North Pacific and the southeasterly/easterly winds from the Atlantic under the orographic effects of Central America creates conditions in the lower atmosphere that favor the development of a meso-scale vortex over the warm sea surface, leading to TCG. WRF model simulation revealed that the interaction between the mid-latitude systems and tropical atmosphere determined the success or failure of the TCG forecast, which suggests that large-scale, low-level circulations heavily affect TCG and that every large-scale vortex and circulation component in the immediately-neighboring region of the storm development is important for TCG forecasting. This study shows that the global WRF has a potential to be used for operational short-range TCG forecasting

Heavy Rainfall Identification within the Framework of the LEXIS Project: The Italian Case Study

LEXIS (Large-scale EXecution for Industry and Society) H2020 project is currently developing an advanced system for Big Data analysis that takes advantage of interacting large-scale geographically-distributed HPC infrastructure and cloud services. More specifically, LEXIS Weather and Climate Large-Scale Pilot workflows ingest data coming from different sources, like global/regional weather models, conventional and unconventional meteorological observations, application models and socio-economic impact models, in order to provide enhanced meteorological information at the European scale. In the framework of LEXIS Weather and Climate Large-scale Pilot, CIMA Research Foundation is running a 7.5 km resolution WRF (Weather Research and Forecasting) model with European coverage, radar assimilation over the Italian area, and daily updates with 48 hours forecast. WRF data is then processed by ITHACA ERDS (Extreme Rainfall Detection System - http://erds.ithacaweb.org), an early warning system for the monitoring and forecasting of heavy rainfall events. The WRF model provides more detailed information compared to GFS (Global Forecast Systems) data, the most widely used source of rainfall forecasts, implemented in ERDS also. The entire WRF - ERDS workflow was applied to two of the most severe heavy rainfall events that affected Italy in 2020. The first case study is related to an intense rainfall event that affected Toscana during the afternoon and the evening of 4th June 2020. In this case, the Italian Civil Protection issued an orange alert for thunderstorms, on a scale from yellow (low) to orange (medium) to red (high). In several locations of the northern part of the Region more than 100 mm of rainfall were recorded in 3 hours, corresponding to an estimated return period equal to or greater than 200 years. As far as the 24-hours time interval concerns, instead, the estimated return period decreases to 10-50 years. Despite the slight underestimation, WRF model was able to properly forecast the spatial distribution of the rainfall pattern. In addition, thanks to WRF data, precise information about the locations that would be affected by the event were available in the early morning, several hours before the event affected these areas. The second case study is instead related to the heavy rainfall event that affected Palermo (Southern Italy) during the afternoon of 15th July 2020. According to SIAS (Servizio Informativo Agrometeorologico Siciliano) more than 130 mm of rain fell in about 2.5 hours, producing widespread damages due to urban flooding phenomena. The event was not properly forecasted by meteorological models operational at the time of the event, and the Italian Civil Protection did not issue an alert on that area (including Palermo). During that day, in fact, only a yellow alert for thunderstorms was issued on northern-central and western Sicily. Within LEXIS, no alert was issued using GFS data due to the severe underestimation of the amount of forecasted rainfall. Conversely, a WRF modelling experiment (three nested domain with 22.5, 7.5 and 2.5 km grid spacing, innermost over Italy) was executed, by assimilating the National radar reflectivity mosaic and in situ weather stations from the Italian Civil Protection Department, and it resulted in the prediction of a peak rainfall depth of about 35 mm in 1 hour and 55 mm in 3 hours, roughly 30 km far apart the actual affected area, thus values supportive at least a yellow alert over the Palermo area. Obtained results highlight how improved rainfall forecast, made available thanks to the use of HPC resources, significantly increases the capabilities of an operational early warning system in the extreme rainfall detection. Global-scale low-resolution rainfall forecasts like GFS one are in fact widely known as good sources of information for the identification of large-scale precipitation patterns but lack precision for local-scale applications

Master of Science

thesisThe Florida (FL) peninsula has the most frequent occurrence of warm-season thunderstorms in the US, with the majority of this convection initiated by the sea breeze (SB) circulation. Previous numerical studies of FL SB convection have emphasized either large mesoscale grid scales (tens of kilometers or greater) or much smaller large-eddy simulation (LES) grid scales (less than a hundred meters). Few studies have been conducted in the numerical gray-zone scale (e.g., 1-5 km). In this thesis, numerical simulations of a convective FL SB case study are conducted using an advanced research version of the Weather Research and Forecasting (WRF) model with gray-zone grid spacing and 40 different simulation configurations. Simulations are evaluated against surface observations and analysis data to determine the accuracy of the model-simulated SB convective initiation (CI). The dependence of the SB and its associated convection on variations in physics parameterizations, initial conditions (ICs), stochastic perturbations, and grid scale spacing is also evaluated. Results indicate that the WRF model can realistically reproduce the SB CI. However, large sensitivities of simulations to boundary layer parameterizations, ICs, grid scale, and stochastic perturbations of potential temperature and wind tendency fields are found in predicting the timing and intensity of the SB and its associated convective systems. Further analysis indicates that the specific representation of atmospheric variables (e.g., sensible surface heating, synoptic winds, and low-level convergence) and geophysical features (e.g., coastline shape and lake resolution) within the simulations are important for the accurate representation of the timing, location, and intensity of the SB and its associated convection

Quantitative precipitation estimation based on highresolution numerical weather prediction and data assimilation with WRF - a performance test

Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale bandlike precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data

Influence of input climatic data on simulations of annual energy needs of a building: energyplus and WRF modeling for a case study in Rome (Italy)

The simulation of the energy consumptions in an hourly regime is necessary in order to perform calculations on residential buildings of particular relevance for volume or for architectural features. In such cases, the simplified methodology provided by the regulations may be inadequate, and the use of software like EnergyPlus is needed. To obtain reliable results, usually, significant time is spent on the meticulous insertion of the geometrical inputs of the building, together with the properties of the envelope materials and systems. Less attention is paid to the climate database. The databases available on the EnergyPlus website refer to airports located in rural areas near major cities. If the building to be simulated is located in a metropolitan area, it may be affected by the local heat island, and the database used as input to the software should take this phenomenon into account. To this end, it is useful to use a meteorological model such as the Weather Research and Forecasting (WRF) model to construct an appropriate input climate file. A case study based on a building located in the city center of Rome (Italy) shows that, if the climatic forcing linked to the heat island is not considered, the estimated consumption due to the cooling is underestimated by 35–50%. In particular, the analysis and the seasonal comparison between the energy needs of the building simulated by EnergyPlus, with the climatic inputs related to two airports in the rural area of Rome and with the inputs provided by the WRF model related to the center of Rome, show discrepancies of about (i) WRF vs. Fiumicino (FCO): Δ = −3.48% for heating, Δ = 49.25% for cooling; (ii) WRF vs. Ciampino (CIA): Δ = −7.38% for heating, Δ = +35.52% for cooling