High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

Kioutsioukis, Ioannis; Meij, Alexander de; Jakobs, Hermann; Katragkou, Eleni; Vinuesa, J.-F.; Kazantzidis, Andreas

doi:10.1016/j.atmosres.2015.07.015

Cited by 44 publications

(35 citation statements)

References 50 publications

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“…The definition of RMSE and other statistical measures commonly used in model evaluation can be found in Willmott (1981) and the definitions and descriptions of the local meteorological parameters can be found in our previous study . The statistical results in Table 1 indicate that WRF could well reproduce the meteorological field in the study area and the performance is comparable with that in other complex terrain areas (Jimenez et al, 2005;Kioutsioukis et al, 2016).…”

Section: Model Datasupporting

confidence: 74%

Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

Xie

et al. 2016

Water Air Soil Pollut

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Knowledge of the relationship between air quality and impact factors is very important for air pollution control and urban environment management. Relationships between winter air pollutant concentrations and local meteorological parameters, synopticscale circulations and precipitation were investigated based on observed pollutant concentrations, highresolution meteorological data from the Weather Research and Forecast model and gridded reanalysis data. Artificial neural network (ANN) model was developed using a combination of numerical model derived meteorological variables and variables indicating emission and circulation type variations for estimating daily SO 2 , NO 2 , and PM 10 concentrations over urban Lanzhou, Northwestern China. Results indicated that the developed ANN model can satisfactorily reproduce the pollution level and their day-to-day variations, with correlation coefficients between the modeled and the observed daily SO 2 , NO 2 , and PM 10 ranging from 0.71 to 0.83. The effect of four factors, i.e., synoptic-scale circulation type, local meteorological condition, pollutant emission variation, and wet removal process, on the day-to-day variations of SO 2 , NO 2 , and PM 10 was quantified for winters of 2002-2007. Overall, local meteorological condition is the main factor causing the day-today variations of pollutant concentrations, followed by synoptic-scale circulation type, emission variation, and wet removal process. With limited data, this work provides a simple and effective method to identify the main factors causing air pollution, which could be widely used in other urban areas and regions for urban planning or air quality management purposes.

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Section: Model Datasupporting

confidence: 74%

Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

Xie

et al. 2016

Water Air Soil Pollut

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“…For both TX and TN, the largest negative biases, reaching −6 °C in the case of TN, are found primarily over the Alpine ridge and over regions characterized by steep orography. This is a feature that is rather common to WRF simulations (e.g., Kotlarski et al, ; Katragkou et al, ; Kioutsioukis et al, ), attributed, at least partially, to the treatment of physics over snow‐covered areas.…”

Section: Resultsmentioning

confidence: 99%

Climatology and trends of the Euro‐Mediterranean thermal bioclimate

Giannaros

Kotroni

Lagouvardos

et al. 2018

Intl Journal of Climatology

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High‐resolution numerical simulations were carried out for the most recent 30‐year period (1987–2016), focusing on the Euro‐Mediterranean region. A sophisticated thermo‐physiologically significant thermal index, namely the physiologically equivalent temperature (PET), was computed for assessing the thermal bioclimate of the study area and for investigating the presence of long‐term trends, focusing on bioclimatic extremes. Results indicate that the Euro‐Mediterranean thermal bioclimate follows a zonal pattern, upon which topography acts to delineate regional maxima and minima. The conducted time series analysis reveals statistically significant trends that are generally more pronounced for cold extremes. In particular, it is found that the number of days with cold (hot) stress has decreased (increased), with the distribution of PET exhibiting warming of both its cold and warm tail. The south–east Mediterranean and the Balkans are found to be the most responsive to bioclimatic changes, showing coherent and statistically significant warming trends. Overall, the results of this study provide a new point of view of the Euro‐Mediterranean climate, which could be of usefulness in a wide range of future applications. The detailed spatio‐temporal bioclimatic data could be exploited, for instance, to support applications related to tourism and recreation, highlighting regions with favourable or less favourable thermal comfort conditions. Furthermore, the trend data could be employed for quantifying the vulnerability of certain regions to cold and heat stress, in the context of public health applications.

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“…It should be noted that the combination of the Mellor−Yamada−Janjic planetary boundary layer scheme and the Noah Land Surface Model was decided on the basis of past experience with the WRF model, in particular focusing on the simulation of wind at high horizontal grid spacing (e.g. Kioutsioukis et al, 2016;Koletsis et al, 2016;Giannaros et al, 2017).…”

Section: Details Of the Aeolus-rio2016 Forecasting Servicementioning

confidence: 99%

Ultrahigh resolution wind forecasting for the sailing events at the Rio de Janeiro 2016 Summer Olympic Games

Giannaros

Kotroni

Lagouvardos

et al. 2017

Meteorological Applications

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ABSTRACT:The current study presents AEOLUS-RIO2016, an ultrahigh resolution wind forecasting system that was developed and implemented operationally for supporting the Hellenic Olympic Sailing Team during the sailing events at the Rio de Janeiro 2016 Summer Olympic Games. The forecasting system was built around the state-of-the-art numerical weather prediction Weather Research and Forecasting model, ported to a high performance computing infrastructure that ensured the reliability and timeliness of the provided service. Advanced web mapping tools were employed for communicating forecasts effectively to the athletes and coaches throughout the period of the sailing events. Prior to the operational deployment of AEOLUS-RIO2016, sensitivity experiments were carried out, focusing on the representation of topography and land use and aiming at improving wind forecasts. The results suggest that the employment of very high resolution and up-to-date terrestrial data allowed model performance to be improved, especially in terms of the wind direction guidance provided. Considering the period of the sailing events, the evaluation of AEOLUS-RIO2016 revealed an overall satisfactory performance. Notably, however, it was found that the system failed to provide reliable wind guidance on specific days. A posteriori implementation of the model using an alternative source for providing initial and lateral boundary conditions showed notable improvements in model performance.

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High resolution WRF ensemble forecasting for irrigation: Multi-variable evaluation

Cited by 44 publications

References 50 publications

Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

Numerical Model-Based Artificial Neural Network Model and Its Application for Quantifying Impact Factors of Urban Air Quality

Climatology and trends of the Euro‐Mediterranean thermal bioclimate

Ultrahigh resolution wind forecasting for the sailing events at the Rio de Janeiro 2016 Summer Olympic Games

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