OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organizations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales by running these models at high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home, and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net (https://www.climateprediction.net/, last access: 1 June 2021) and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of Tropical Cyclone Karl from September 2016 studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet streak near Scotland and heavy rainfall over Norway. For the validation we use a 2000-member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF's forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts and discuss the use of large ensembles in the context of forecasting extreme events.

Real-Time Weather Analytics

Weather forecasting is one of the biggest challenges that modern science is still contending with. The advent of high-power computing, technical advancement of data storage devices, and incumbent reduction in the storage cost have accelerated data collection to turmoil. In this background, many artificial intelligence techniques have been developed and opened interesting window of opportunity in hitherto difficult areas. India is on the cusp of a major technology overhaul with millions of people's data availability who were earlier unconnected with the internet. The country needs to fast forward the innovative use of available data. The proposed model endeavors to forecast temperature, precipitation, and other vital information for usability in the agrarian sector. This project intends to develop a robust weather forecast model that learns automatically from the daily feed of weather data that is input through a third-party API source. The weather feed is sourced from openweathermap, an online service that provides weather data, and is streamed into the forecast model through Kafka components. The LSTM neural network used by the forecast model is designed to continuously learn from predictions and perform actual analysis. The model can be architected to be implemented across very large applications having the capability to process large volumes of streamed or stored data.

OpenIFS@home version 1: a citizen science project for ensemble weather and climate forecasting

Weather forecasts rely heavily on general circulation models of the atmosphere and other components of the Earth system. National meteorological and hydrological services and intergovernmental organisations, such as the European Centre for Medium-Range Weather Forecasts (ECMWF), provide routine operational forecasts on a range of spatio-temporal scales, by running these models in high resolution on state-of-the-art high-performance computing systems. Such operational forecasts are very demanding in terms of computing resources. To facilitate the use of a weather forecast model for research and training purposes outside the operational environment, ECMWF provides a portable version of its numerical weather forecast model, OpenIFS, for use by universities and other research institutes on their own computing systems. In this paper, we describe a new project (OpenIFS@home) that combines OpenIFS with a citizen science approach to involve the general public in helping conduct scientific experiments. Volunteers from across the world can run OpenIFS@home on their computers at home and the results of these simulations can be combined into large forecast ensembles. The infrastructure of such distributed computing experiments is based on our experience and expertise with the climateprediction.net and weather@home systems. In order to validate this first use of OpenIFS in a volunteer computing framework, we present results from ensembles of forecast simulations of tropical cyclone Karl from September 2016, studied during the NAWDEX field campaign. This cyclone underwent extratropical transition and intensified in mid-latitudes to give rise to an intense jet-streak near Scotland and heavy rainfall over Norway. For the validation we use a two thousand member ensemble of OpenIFS run on the OpenIFS@home volunteer framework and a smaller ensemble of the size of operational forecasts using ECMWF’s forecast model in 2016 run on the ECMWF supercomputer with the same horizontal resolution as OpenIFS@home. We present ensemble statistics that illustrate the reliability and accuracy of the OpenIFS@home forecasts as well as discussing the use of large ensembles in the context of forecasting extreme events.

Analysis of the severe weather outbreak of 5 June 2019 in The Netherlands

<div>In the evening of 5 June 2019, a severe thunderstorm passed through the Netherlands. The storm was so extreme in the westernmost part of the country, that entire tree lines were blown over and tree trunks were severed. In residential communities, this extreme weather event lead to damage to real estate and cars. From preliminary analysis, it follows that the wind gusts that were responsible for the mentioned damage were caused by gravity waves that had been forced by the interaction of the surface weather with a strong inversion layer aloft.</div><div> </div><div>In this work, we show a complimentary set of observations that include Doppler Radar and the Dutch microbarometer array network that are operated by the Royal Netherlands Meteorological Institute (KNMI). The radar measurements show wind gusts with speeds of over 125 km/h while the microbarometers measure associated pressure variations up to 8 hPa. The observations are compared with the non-hydrostatic HARMONIE weather forecast model.</div><div> </div><div>The use of high-resolution observations and forecast modelling is important for Early Warning Centers that report on such severe weather outbreaks that can be disruptive for society.</div>

Modelling maximum discharge of the catastrophic flood at the Iya River (Irkutsk region, Russia) in 2019

The aim of the study is to estimate the maximum discharge of the catastrophic flood in June 2019 at the Iya River (Irkutsk Region, Russia). The main cause of this flood was extreme precipitation (170 mm for 3 days). The distributed deterministic hydrological model Hydrograph was applied. The schematization of the Iya river basin, parametrization and verification of the Hydrograph model were performed. The median value of the Nash-Sutcliff criteria was 0.69 for the period 1970-1996 for three catchments of the Iya River basin. Based on the data of weather stations and global weather forecast model ICON, maximum daily discharge values of the flood were estimated as 6570 and 4780 m3s-1 respectively with the possible value range assessed by the dependence of Q(H) 6250-7500 m3s-1. The flood hydrograph estimated from weather station data coincides in magnitude of flood peak, but its formation is delayed by 1 day. ICON data underestimates maximum value but provides proper timing of the flood peak. The ensemble of input meteorological data from various sources could potentially be used to satisfactorily predict the magnitude and duration of the catastrophic flood and minimize the consequences of the flood.