The Italian open data meteorological portal: MISTRAL

AbstractAt the national level, in Italy, observational and forecast data are collected by various public bodies and are often kept in various small, heterogeneous and non‐interoperable repositories, released under different licenses, thus limiting the usability for external users. In this context, MISTRAL (the Meteo Italian SupercompuTing PoRtAL) was launched as the first Italian meteorological open data portal, with the aim of promoting the reuse of meteorological data sets available at national level coverage. The MISTRAL portal provides (and archives) meteorological data from various observation networks, both public and private, and forecast data that are generated and post‐processed within the Consortium for Small‐scale Modeling‐Limited Area Model Italia (COSMO‐LAMI) agreement using high performance computing (HPC) facilities. Also incorporated is the Italy Flash Flood use case, implemented with the collaboration of European Centre for Medium‐Range Weather Forecasts (ECMWF), which exploits cutting edge advances in HPC‐based post‐processing of ensemble precipitation forecasts, for different model resolutions, and applies those to deliver novel blended‐resolution forecasts specifically for Italy. Finally, in addition to providing architectures for the acquisition and display of observational data, MISTRAL also delivers an interactive system for visualizing forecast data of different resolutions as superimposed multi‐layer maps

Introducing the Bulletin of Atmospheric Science and Technology

The rapid technological development of the past few decades has allowed for an unprecedented wealth of data about ourselves and our planet. The cost reduction of space platforms, the microelectronic revolution and the nearly exponential increase in computer power have been generating novel opportunities to explore and understand the world around us. Tools and theoretical approaches, capable of putting together all the insights we may possibly gain from all these new streams of data in a multidisciplinary framework, are still being developed. We are hence faced with both a unique challenge and an opportunity to make a significant progress in many scientific fields, first and foremost in the atmospheric and climate sciences. We are pleased to announce here the launch of the Bulletin of Atmospheric Science and Technology (BAST), a new peer-reviewed journal which is meant to bridge this gap in the broad area of the atmospheric sciences. The journal encourages a cross-disciplinary approach with an emphasis on new sensor technologies and systems, combined observational and modeling techniques, innovative numerical methods, data analysis, and retrieval techniques. BAST offers a platform to share new ideas and fresh developments to stimulate research activities focusing on urban, coastal, marine, rural, and mountain environments. Particular attention will be given to cross-disciplinary studies, especially those involving citizens for the collection of crowd-sourced data and those devoted to the characterization of uncertainties and homogenization of methods. BAST aims at connecting weather and climate communities using both observational and modeling approaches, creating a forum hosting discussion and brainstorming activities. The journal also hopes to attract contributions reporting approaches or techniques from other scientific fields that can be applicable to atmospheric sciences, as well as contributions where technological developments are discussed alongside with their scientific and societal impacts. In this sense BAST will provide a new platform to support the technological revolution towards a climate-smart society through the collection and exploitation of big data. The journal will give visibility to international experiments and projects in atmospheric science and technology, illustrating preliminary or consolidated results from these initiatives. Additional fields of interest are : environmental protection; observation, understanding, and modeling of hazardous and extreme events and mitigation of their impacts; development of new sensing tools integrating satellite information with surface or airborne measurements; operation of unmanned and remotely piloted air vehicles equipped with sensors of small size and weight, especially remote sensors, pushing electro-optical-mechanical components towards a continuously increasing miniaturization. Research articles, Review articles, Technical reports, Brief reports, Letters and News are welcome. While keeping the focus of the journal on scientific research, the “Bulletin” format provides appropriate visibility to contributions from the operational side, i.e., meteorological services and private companies developing sensors and products of interest to the atmospheric science and technology community. Below we provide a more detailed description of the topics that will be emphasized and fostered in BAST

Data assimilation of radar reflectivity volumes in a LETKF scheme

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used

Ensemble forecasting: status and perspectives

One of the main challenges for Numerical Weather Prediction is the Quantitative Precipitation Forecasting (QPF). The accurate forecast of high-impact weather still remains difficult beyond day 2 and many limited-area ensemble prediction systems have been recently developed so as to provide more reliable forecasts than achievable with a single deterministic forecast. As a consequence the calibration of ensemble precipitation forecasts has become a very demanding task, for improving the QPF, especially as an input to hydrological models. Different calibration techniques are compared: cumulative distribution function, linear regression and analogues method

Environmental Factors and Kawasaki Disease Onset in Emilia-Romagna, Italy

Kawasaki disease (KD)is an idiopathic acute febrile illness that primarily occurs in children <5 years of age and can lead to artery lesions if not promptly treated. Recent studies have shown possible correlations between KD onset and currents and air pollutants.The present study describes results on the correlation between environmental conditions, frequency, and variability ofKD onset in children under five years of age in Emilia-Romagna, a region of Northern Italy, over the period from 2000 to 2017. Since there are substantial climatic differences between the western-central (Emilia) and the eastern area (Romagna) of the region, the data for these areas are analyzed separately. The environmental factors considered are observed local surface daily temperature, daily precipitation, upper air wind regimes, and local air pollution. The results indicate that in Emilia-Romagna, KD onset occurs mainly during late autumn and early spring, which is in agreement with the literature. The frequency of KD onset in Emilia is significantly higher in months characterized by a high frequency of southerly flow, which is associated with milder than average night-time temperature, and in years with a prevailing south-westerly mean flow. These results are consistent with other studies, suggesting that certain wind conditions are more favorable for disease onset, which are possibly associated with one or more airborne agents

Downscaling with an unstructured coastal-ocean model to the Goro Lagoon and the Po river delta branches

The Goro Lagoon Finite Element Model (GOLFEM) presented in this paper concentrates on the high-resolution downscaled model of the Goro Lagoon, along with five Po river branches and the coastal area of the Po delta in the northern Adriatic Sea (Italy) where crucial socio-economic activities take place. GOLFEM was validated by means of validation scores (bias – BIAS, root mean square error – RMSE, and mean absolute error – MAE) for the water level, current velocity, salinity and temperature measured at several fixed stations in the lagoon. The range of scores at the stations are: for temperature between −0.8 to +1.2◦C, for salinity from −0.2 to 5 PSU, for sea level 0.1 m. The lagoon is dominated by an estuarine vertical circulation due to a double opening at the lagoon mouth and sustained by multiple sources of freshwater inputs. The non-linear interactions among the tidal forcing, the wind and the freshwater inputs affect the lagoon circulation at both seasonal and daily time scales. The sensitivity of the circulation to the forcings was analyzed with several sensitivity experiments done with the exclusion of the tidal forcing and different configurations of the river connections. GOLFEM was designed to resolve the lagoon dynamics at high resolution in order to evaluate the potential effects on the clam farming of two proposed scenarios of human intervention on the morphology of the connection with the sea. We calculated the changes of the lagoon current speed and salinity, and using opportune fitness indexes related to the clams physiology, we quantified analytically the effects of the interventions in terms of extension and persistence of areas of the clams optimal growth. The results demonstrate that the correct management of this kind of fragile environment relies on both long-term (intervention scenarios) and short-term (coastal flooding forecasts and potential anoxic conditions) modeling, based on a flexible tool that is able to consider all the recorded human interventions on the river connections. This study also demonstrates the importance of designing a seamless chain of models that are capable of integrating local effects into the coarser operational oceanographic models