The predictive capacity of the high resolution weather research and forecasting model: a year-long verification over Italy

Abstract

Numerical models are operationally used for weather forecasting activities to reduce the risks of several hydro-meteorological disasters. The overarching goal of this work is to evaluate the Weather Research and Forecasting (WRF) model predictive capabilities over the Italian national territory in the year 2018, in two specific cloud resolving configurations. The validation is carried out with a fuzzy logic approach, by comparing the precipitation predicted by the WRF model, and the precipitation observed by the national network. The fuzzy logic technique, by considering different intensity thresholds, allows to identify the reliable spatial scales of the forecasts. The same approach is applied to evaluate the performances of COSMO-2I model, a state-of-the-art numerical model configuration used for operational activities. For the entire year, except for summer, the model predictive capabilities are high, with useful forecasts for structures of medium intensities down to O(10 km) length scales. In summer the skills decrease mainly because of localization errors. The work aims to provide a robust evaluation of the forecast performances of another convection permitting operational meteorological models currently available in Italy.

IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021)

“…2). This modified conditional merging spatializes in situ precipitation data using an approach similar to kriging (called GRISO, from the Italian version of Random Generator of Spatial Interpolation from uncertain Observations), where the covariance structure is estimated for each precipitation gauge and each hour using radar data (see full details in Sinclair and Pegram, 2005;Apicella et al, 2021;Bruno et al, 2021;Lagasio et al, 2022). Final maps have a resolution of ∼ 1 km 2 , with a median root mean square error of less than 1 mm for a selection of 70 heavy precipitation events in Italy with accumulation greater than 100 mm or a maximum precipitation rate greater than 50 mm h −1 during the 2011-2014 period (see details in Bruno et al, 2021).…”

Classification for Big Data Driven Marine Weather Forecasting Using Machine Learning Techniques

“…Over the past few years several numerical methods are currently utilized for forecasting weather pursuits to minimize the risks of numerous disasters related to hydrometeorological data. A Weather Research and Forecasting (WRF) model capable of predicting the potentialities over the Italian national territory in 2018 using fuzzy was proposed in [10].…”

IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2010–2021)

“…Total precipitation fields are the result of a modified conditional merging approach applied to precipitation gauges (spatial density of ∼1/100 km 2 ) and radar observations (Bruno et al, 2021), so no further spatialization is performed in S3M Italy (Figure 2). This modified conditional merging spatializes in-situ precipitation data using an approach similar to Kriging (called GRISO, from the Italian version of Random Generator of Spatial Interpolation from Uncertain Observations) where, however, the covariance structure is estimated for each precipitation gauge and each hour using radar data (see full details in Sinclair and Pegram, 2005;Apicella et al, 2021;Bruno et al, 2021). Final maps have a resolution of ∼1 km 2 , with a median Root Mean Square Error of less than 1 mm for a selection of 70 heavy precipitation events (see details in Bruno et al, 2021).…”