An high resolution coupled ocean-atmosphere numerical simulation for the Adriatic Sea
Francesco MemmolaAlessandro ColuccelliFrancesca NeriAngela GarziaAntonio RicchiRossella FerrettiPierpaolo Falco
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The role of Sea Surface Temperature in the weather forecast has been investigated in the past years and its role in modulating local circulation has been assessed. In this context, a coupled ocean-atmosphere numerical simulation has been developed. The three models WRF, ROMS and SWAN are coupled together within the Coupled Ocean-Atmosphere-Wave-Sediment Transport modeling system (COAWST) for the Adriatic Sea. For what concerns the atmospheric component the WRF-ARW model is used. Two domains run independently: a low resolution domain (15 km) initialized using NCEP analyses at 0.25 degrees and a high resolution domain (3 km) covering the Adriatic regions, initialized using the WRP low- resolution output. For what concern the oceanographic component, the circulation model ROMS and the wave driver SWAN run on the same high horizontal resolution grid (1km). The vertical discretization of ROMS is performed using 30 sigma layers while the river inputs consist of 67 sources of fresh water (Po river included). The exchange of data between models it is set to happen every 1O minutes of model time. The exchange is two-ways, therefore each component of the system influences the dynamics of the other components. In order to resolve the coastal dynamics, the oceanographic component above described was also used as parent grid for a one-way nested grid covering the area of sea facing the Marche rigion (child grid) with an horizontal resolution of 200 m. In this effort, the model system validation and results from a case study will be presented. The impact of the ocean-atmosphere coupling will be briefly addressed by comparing results with the ones obtained by a weather forecast (no coupled) and an ocean forecast (only ROMS+SWAN coupling). While the capability of the system in resolving the coastal dynamics is it assessed comparing results from the parent grid and results from the child grid with observed data.Keywords:
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The COSMO-Model is a nonhydrostatic limited-area atmospheric prediction model, designed for both operational numerical weather prediction and various scientific applications on the meso − β and meso − γ scale. The COSMO-Model is based on the primitive thermohydrodynamical equations describing compressible flow in a moist atmosphere. Model equations are formulated in rotated geographical coordinates and a generalized terrain following height coordinate. A variety of physical processes are taken into account by parameterization schemes. The purpose of this paper is to present the results of the comparative evaluation of the quality of high resolution weather forecasts from numerical weather prediction models COSMO 2.8km (Consortium for Small Scale Modelling) and WRF - 3km (Weather Research and Forecast model). The numerical weather prediction model COSMO - 2.8km is currently being run at the National Meteorological Administration once a day, at 00 UTC. WRF is a non-hidrostatic numerical weather prediction model developed by NCEP (National Centre for Environmental Prediction) in collaboration with the international meteorological comunity. In order to compare the performance of the two models, the WRF model was implemented at the 3km resolution and integrated with 00 UTC data for a test period. The integration domains of the two models cover the entire Romanian territory (COSMO-2.8km 361 × 291 grid points and WRF-3km 261 × 191 grid points, see figure 1).
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WHAT: Scientists from Korea, Japan, and the United States discuss recent developments in the parameterizations of physical processes in nextgeneration, high-resolution numerical weather prediction models WHEN: 17–18 May 2004 WHERE: Yonsei University, Seoul, Korea he Second International Workshop on NextGeneration Numerical Weather Prediction (NWP) Models1 met to discuss the impact of recent developments in modeling for next-generation, high-resolution NWP models, and to exchange ideas for improving the prediction of high-impact weather. In 1999, the Laboratory for Atmospheric Modeling Research (LAMOR) of Yonsei University (YSU) embarked on a national project developing a next-generation NWP model focusing on the parameterization of physical processes in high-resolution models (see information online at http://lamor.yonsei.ac.kr). The ultimate goal of the project is in line with that of the Weather Research and Forecast (WRF) model initiative (see information online at http://wrf-model. org), both of which are to develop a state-of-the-art mesoscale model that is suitable for grid spacing in the range of 1–30 km. The director of LAMOR, Professor Tae-Young Lee, told participants that the focus of this workshop was to discuss the progress in our understanding of the physical parameterizations and the outcome of collaboration between the LAMOR and U.S. groups for the past three years. Major outcomes of collaborations include the Yonsei University PBL (YSUPBL) and WRF single-moment microphysics (WSM) schemes implemented in the WRF model.
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Abstract—The model of Weather Research and ForecastingAdvanced Research WRF (WRF-ARW) is one of Numerical Weather Prediction (NWP) model which often used to study and to predict weather phenomenon in the atmosphere. Initial condition and boundary condition are two essential elements which needed by WRF model in order to produce forecast. Initial condition is a part of WRF that needs to be corrected in order to make the prediction more accurate. Various methods have been developed to improve the initial condition one of them through data assimilation. There are several methods of assimilation data process which combines NWP products with information from different types of observation, one of them is Three Dimensional Variational (3D-VAR). The purpose of this research is to analyze and compare the accuracy of Weather Research Forecasting (WRF) prediction before and after assimilation the Global Positioning System Radio Occultation (GPS RO) Refractivity data, where the GPS data will be assimilated into the WRF-ARW model by 3D-VAR technique to simulate rain event in Jakarta area on 14 until 16 February 2018. Verification technique to quantify the accuracy of the assimilation model was conducted towards 24 hours accumulated rainfall. The result of this research shows that by applying the data assimilation procedure of the GPS RO Refractivity which goes into WRF-ARW model can increase the accuracy predictions level of heavy rainfall phenomenon which is occurred at that time where able to predict the occurrence for the first category correctly through the percentage of average POD reaching 66% with a prediction error rate of average rainfall (POFD) of 26.1%. Furthermore, for the light rain category, on average only around 59.2% of events can be predicted correctly and with an average percentage of 12.5% prediction errors. Keywords—Weather Prediction, WRF-ARW, GPS RO Refractivity I
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The northeast Caribbean as other insular regions lack reliable high-resolution weather forecast data of 10meter winds to serve as the engine of numerical ocean models targeting high resolution waves and currents forecasts for nearshore areas. This issue is exacerbated due to the complex orographic features of the Antilles, which govern topographic shadowing and incoming solar radiation fields responsible for the diurnally forced convection typical of tropical island weather. A solution to this problem is currently under evaluation for the CariCOOS region. This solution employs both dynamical numerical solvers of the Weather Research and Forecasting (WRF) Model, which are referred to as the ARW (Advanced Research WRF), and the NMM (Non-hydrostatic Mesoscale Model) cores. CariCOOS research and development efforts are executed in collaboration with the National Weather Service Weather Forecast Office San Juan (NWS WFO SJU). CariCOOS current operational WRF model setups are based on the NMM core at resolutions of 6-km, 2-km, and 1-km. These models target short and medium range forecast; the latest experimental WRF model setup is based on the ARW with resolutions of up to 500-m. The WRF-ARW setup is currently under evaluation to improve very-short-term weather forecast, in support of maritime operations of high-traffic ports and harbors (Bay of San Juan, PR, and Port of Yabucoa, PR). Noteworthy improvement have been achieved as evidenced by model skill assessments of forecasted wind speed, and wind direction of these WRF model setups when compared to in-situ observations of CariCOOS assets (land base weather stations and coastal buoys). The numerical weather prediction forecasting improvements realized via the implementation of both WRF dynamical cores are primarily driven by the increase in horizontal resolution. The most prominent improvements in weather forecasts is were achieved for the leeward side of Puerto Rico and U.S. Virgin Islands and harbor regions when simulated at very fine horizontal grid spacing resolution (less than 1-km). Considering forecast lead time requirements of the NWS WFO SJU, CariCOOS researchers constantly strive to optimize WRF model setups to its limit. Details of the various CariCOOS WRF model setups and implementations will be presented in this paper along with validation statistics.
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Prediction of cumulonimbus (Cb) clouds in Indonesia is a necessity for civil aviation operations. The ability comparison of numerical weather prediction (NWP) multimodel to predict Cb clouds in order to obtain the best model was investigated in this study. NWP models used in this research were Australian Community Climate and Earth-System Simulator (ACCESS), Integrated Forecast System High Resolution (IFS HRES), Icosahedral Nonhydrostatic (ICON), Weather Research Forecast-Central Information and Processing System (WRF-CIPS), and Weather Research Forecast-Indonesia NWP (WRF-INA) whose performance is tested by calculating the probability of detection (POD), probability of false detection (1-POFD) and proportion correct (PC). The results show that the IFS HRES model is the best model compared to other models. For further studies, the research should be conducted at another time that has a different Cb cloud frequency distribution from this study.
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The influence of the turbulent atmosphere is seen as the main performance limitation for high-quality Interferometric Synthetic Aperture Radar (InSAR) techniques in ground deformation monitoring applications. Atmospheric correction using numerical weather prediction (NWP) models is widely seen as a promising emerging technology for mitigation of atmospheric signals. First results showed promising capabilities for correction of stratified delay yet have revealed limited performance for modeling and mitigating turbulent atmospheric water vapor signals from SAR [1, 2]. This paper presents an integration of InSAR observations with predictions from the high-resolution Weather Research and Forecasting Model (WRF). Special focus is put on investigating improvements in the weather model parameterization to achieve enhanced performance in atmospheric correction. First, a statistical analysis of the quality of absolute delay predictions is presented based on a comparison of vertically integrated WRF delays with radiosonde measurements. Second, the performance of WRF for atmospheric correction of InSAR data is analyzed by comparing WRF phase delay maps to SAR interferograms and analyzing structure functions and variances of the residual atmospheric delay signal. Here, significant improvements could be achieved through modifications of the WRF model parameterization, which are highlighted in Section 3.2. From our study, we conclude that the performance of latest generation high-resolution NWPs can be significantly improved if the setup and parameterization of the model domain is optimized.
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Computational experiments were carried out using the WRF model version 4.2. The influence of different sets of parameterizations on the results of calculating the surface values of air temperature, wind speed and direction is considered. A set of parameterizations providing the best accuracy of numerical prediction (with a resolution of 1 km) of local meteorological characteristics for the conditions of Western Siberia, is selected. It was found that the set of parameterizations affects the simulation quality, but it is not the main aspect in ensuring prediction accuracy. To test the WRF model, the observations obtained using meteorological instruments of the JUC Atmosphere of the V.E. Zuev Institution of Atmospheric Optics SB RAS, the airfield information and measurement system of the Tomsk Airport, and the Tomsk weather station were employed.
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Numerical weather prediction (NWP) models are frequently used tools in operational weather forecasting. The NWP bases on current weather observations and processing of this data using computational models to forecast possible weather conditions. The aim of the study was to determine the optimal configuration of the Weather Research and Forecasting (WRF) model , version 4.2 (Skamarock et al. 2008), for more effective weather forecasting for the area of Poland. For model evaluation, we used observations from the IMWM-NRI network (above 50 meteorological stations). Numerical simulations were run using GFS model data was obtained from NOAA's NCEP servers. The WRF model was configured for a 3 km horizontal resolution grid, using unique parameterization settings for this model. Validation of forecast data was performed using statistical measures recommended by the WMO, e.g. mean error, mean absolute error, mean squared error, showing the values of forecast error. In this study, the model settings were configured based of other papers for Europe (Stergiou et al. 2017, Mooney et al. 2013, Kioutsioukis et al. 2016, Garcia-Diez et al. 2015, Carvalho et al. 2014, Santos Alamillos 2013), especially from its central part (Wałaszek et al. 2014, Kryza et al. 2017). The results of the work present statistical summaries of optimal model parameterization schemes, depending on their verifiability. Model configuration characterized by the best performance will be further examined over a longer time period (in the study, the average MAE for air temperature was 0.8°C). The research was funded by National Science Center (project number: 2017/27/N/ST10/00565)
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Numerical Weather Prediction (NWP) models over limited areas enable the simulation of local atmospheric processes in more detail and with a higher degree of accuracy when compared to global models. Limited-area NWP models can outperform their global counterparts due to higher resolution (ability to explicitly simulate processes) and tailored physics (global models, unless run as a physics ensemble, have one set of parameterization schemes for the whole globe). However, increased accuracy from an NWP model is not guaranteed and can vary based on the location and variable of interest. In this paper, we present a method for combining the output of a limited-area NWP model, the Weather Research and Forecasting model (WRF) and its global model—the European Center for Medium Range Weather Forecasting (ECMWF) deterministic model. We simulate day-ahead global horizontal irradiance for a location in Qinghai, China. WRF model configurations optimized by the type of day (cloud amount) are then implemented based on the ECMWF model forecast of cloud amount. A regression model to combined ECMWF and WRF model forecasts is then trained. The optimized coefficients (weights) of ECMWF and WRF show increasing WRF importance with higher cloud amounts and the combination out-performs the ECMWF input by 5.2% and the best WRF configuration by 7.2% on a 2.5-month testing set. The performance of the combined model increased with observed cloud amount where the combined model out-performed the ECMWF model by 12.6% for cloudy days indicating the relative importance of physical downscaling for the simulation of clouds.
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