Physical Initialisation of Precipitation in a Mesoscale Numerical Weather Forecast Model

Short term quantitative precipitation forecast (QPF) is an important task for numerical weather prediction (NWP) models, particularly in summer. The increase of model resolution requires the understanding of the initiation and evolution of convection. Initialisation schemes based on radar derived precipitation fields can reduce the model forecast error in convective cases. Any improvement of QPF denotes a correct forecast of the dynamics and the moisture content of the atmosphere, thus upgrading QPF generically improves NWP forecast. The method, which we call Physical initialisation Bonn (PIB), uses as the most important input the precipitation estimation from the German weather service (DWD) radar network and assimilates the data into the operational non-hydrostatic COSMO model. During the assimilation window, PIB converts the input data (radar precipitation and cloud top height from satellite data) into prognostic COSMO variables, which are relevant for the development of rain events. PIB directly adjusts vertical wind, humidity, cloud water, and cloud ice in order to force the model state towards the measurements. The most distinctive feature of the algorithm is the adjustment of the vertical wind profile in the framework of a simple precipitation generation scheme. In a first study we performed an identical twin experiment with three convective cases. The consistency of PIB with the physics of the NWP model is proved using qualitative comparisons and quantitative evaluations (e.g. objective skill scores). The performance of PIB, using real data, is investigated by applying the scheme to the whole month of August 2007, with three simulations every day, at 00, 08 and 16 UTC. Every simulation consists of two hours of data assimilation followed by seven hours of free forecast. The comparison with the Control run and with Latent heat nudging, the operational radar data assimilation scheme from the DWD, is also made. PIB succeeds in improving QPF for up to six hours. Its results are comparable to the forecast by LHN. The sensitive of PIB to different assimilation windows is tested. An assimilation window of only 15 minutes is enough to provide the trigger for convection and to enhance the forecast quality. Thus PIB is much more time efficient than LHN and need much less observation values

Auditoria and Public Halls. The preserved Architectonic Heritage, in the Perspective of Sustainability

AbstractTheatres and auditoria have only rarely been "virtuous" examples for energy use both for their peculiar space requirements and for the discontinuity of their use; such statement is even more true when such spaces are located and hosted within monumental buildings such as former churches or industrial areas; that is a quite frequent case in the Italian urban contest. To re-think critically the entire process - from political-planning decision-making to the managing phase – does represent a key step to prevent the decay and abandonment of works of great value and great architectonic and cultural significance. To that aim, three cases of architectonic and historic quality, located in Torino (Italy), are reviewed

Design, Construction and Testing of a 3-Component Force Balance for Educational Wind Tunnels in Undergraduate Aerodynamics

This article is focused on providing detailed instructions on how to build and use a force balance for educational wind tunnels. The article’s objective is to encourage undergraduate students in underfunded programs to engage in the field of aerodynamics. The discussed force balance represents an affordable device that only requires basic components like Arduino board, a servo motor, and acrylic and aluminum as construction materials. A simple data collection example is included at the end of the article showing that this simple force balance can collect meaningful data about lift, drag, and moment coefficient of a tested airfoil

RL-IoT: Reinforcement Learning to Interact with IoT Devices

Our life is getting filled by Internet of Things (IoT) devices. These devices often rely on closed or poorly documented protocols, with unknown formats and semantics. Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities. In this paper, we propose RL-IoT, a system that explores how to automatically interact with possibly unknown IoT devices. We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions. We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown. RL-IoT exchanges messages with the target IoT device, learning those commands that are useful to reach the given goal. Our results show that RL-IoT is able to solve both simple and complex tasks. With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions. RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems