scholarly journals Sensitivity of a long-range numerical weather forecast model to small changes of model parameters

2011 ◽  
Vol 6 (1) ◽  
pp. 13-18 ◽  
Author(s):  
M. B. Gavrilov ◽  
G. R. Jovanović ◽  
Z. Janjić
Keyword(s):  
Initial Conditions ◽  
Weather Forecast ◽  
Forecast Model ◽  
Model Parameters ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
First Case ◽  
Weather Forecast Model ◽  
Model Setup ◽  
Numerical Weather Forecast

Abstract. Sensitivity of extended-range numerical weather forecasts to small changes of model parameters is studied for two cases. In the first case the Earth radius was perturbed. In the other case changes of the gravity were introduced. The results for the 500 hPa geopotential fields are presented on hemispheric maps and intercompared visually and using RMS differences of the perturbed and reference forecasts. During about the first 10 days of integration the results indicate modest sensitivity of the forecasts to the parameter variation. After this period the forecasts diverge rapidly and start to differ significantly. Repeated integrations on the same computer using the same model setup and the same initial conditions yield identical results.

DeepPE: Emulating Parameterization in Numerical Weather Forecast Model Through Bidirectional Network

2021 ◽  
pp. 87-101
Author(s):  
Fengyang Xu ◽  
Wencheng Shi ◽  
Yunfei Du ◽  
Zhiguang Chen ◽  
Yutong Lu
Keyword(s):  
Weather Forecast ◽  
Forecast Model ◽  
Numerical Weather ◽  
Weather Forecast Model ◽  
Numerical Weather Forecast

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

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

2020 ◽  
Author(s):  
Sarah Sparrow ◽  
Andrew Bowery ◽  
Glenn D. Carver ◽  
Marcus O. Köhler ◽  
Pirkka Ollinaho ◽  
...  
Keyword(s):  
Citizen Science ◽  
General Circulation ◽  
Weather Forecast ◽  
Forecast Model ◽  
General Circulation Models ◽  
Operational Environment ◽  
Computing Systems ◽  
Weather Forecasts ◽  
Operational Forecasts ◽  
Weather Forecast Model

Abstract. 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.

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

2021 ◽  
Vol 14 (6) ◽  
pp. 3473-3486
Author(s):  
Sarah Sparrow ◽  
Andrew Bowery ◽  
Glenn D. Carver ◽  
Marcus O. Köhler ◽  
Pirkka Ollinaho ◽  
...  
Keyword(s):  
Citizen Science ◽  
General Circulation ◽  
Weather Forecast ◽  
Forecast Model ◽  
General Circulation Models ◽  
Operational Environment ◽  
Computing Systems ◽  
Weather Forecasts ◽  
Operational Forecasts ◽  
Weather Forecast Model

Abstract. 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.

scholarly journals Technical note: Lowermost-stratosphere moist bias in ECMWF IFS model diagnosed from airborne GLORIA observations during winter–spring 2016

2020 ◽  
Vol 20 (23) ◽  
pp. 15379-15387
Author(s):  
Wolfgang Woiwode ◽  
Andreas Dörnbrack ◽  
Inna Polichtchouk ◽  
Sören Johansson ◽  
Ben Harvey ◽  
...  
Keyword(s):  
Water Vapor ◽  
Initial Conditions ◽  
Weather Forecast ◽  
Horizontal Resolution ◽  
Technical Note ◽  
Cold Bias ◽  
Weather Forecasts ◽  
Time Period ◽  
Forecasting System ◽  
Numerical Weather Forecast

Abstract. Numerical weather forecast systems like the ECMWF IFS (European Centre for Medium-Range Weather Forecasts – Integrated Forecasting System) are known to be affected by a moist bias in the extratropical lowermost stratosphere (LMS) which results in a systematic cold bias there. We use high-spatial-resolution water vapor measurements by the airborne infrared limb-imager GLORIA (Gimballed Limb Observer for Radiance Imaging of the Atmosphere) during the PGS (POLSTRACC/GW-LCYCLE-II/SALSA) campaign to study the LMS moist bias in ECMWF analyses and 12 h forecasts from January to March 2016. Thereby, we exploit the two-dimensional observational capabilities of GLORIA, when compared to in situ observations, and the higher vertical and horizontal resolution, when compared to satellite observations. Using GLORIA observations taken during five flights in the polar sub-vortex region around Scandinavia and Greenland, we diagnose a systematic moist bias in the LMS exceeding +50 % (January) to +30 % (March) at potential vorticity levels from 10 PVU (∼ highest level accessed with suitable coverage) to 7 PVU. In the diagnosed time period, the moist bias decreases at the highest and driest air masses observed but clearly persists at lower levels until mid-March. Sensitivity experiments with more frequent temporal output, and lower or higher horizontal and vertical resolution, show the short-term forecasts to be practically insensitive to these parameters on timescales of < 12 h. Our results confirm that the diagnosed moist bias is already present in the initial conditions (i.e., the analysis) and thus support the hypothesis that the cold bias develops as a result of forecast initialization. The moist bias in the analysis might be explained by a model bias together with the lack of water vapor observations suitable for assimilation above the tropopause.

scholarly journals How well do we forecast the aurora?

2019 ◽  
Vol 60 (5) ◽  
pp. 5.22-5.25
Author(s):  
Michaela K Mooney ◽  
Colin Forsyth ◽  
Mike Marsh ◽  
Michael Sharpe ◽  
Teresa Hughes ◽  
...  
Keyword(s):  
Space Weather ◽  
Weather Forecast ◽  
Forecast Model ◽  
Weather Forecasts ◽  
Weather Forecast Model

Abstract Michaela K Mooney and co-authors evaluate a space weather forecast model in the same way that weather forecasts are assessed, work that won the 2019 Rishbeth Prize for best poster.

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