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  1. This article contains an overview of Seasonal Forecasting and its challenges with a focus on the UK Met Office seasonal model GloSea and its sub-models. First some background: The Met Office has a range of models, each with a particular focus and ranging from daily weather forecasts to climate forecasts for the next century. Since 1990 their strategy has been to integrate the individual models into a Unified Model system that can be used for prediction across a range of timescales. This from their website: “The Unified Model applies this seamless modelling approach, which means that the same dynamical core and, where possible, the same parameterization schemes are used across a broad range of spatial and temporal scales on a traceable frame work. The model is suitable for numerical weather prediction (NWP), seasonal forecasting and climate modelling with forecast times ranging from a few days to hundreds of years. Furthermore, the Unified Model can be used both as a global and a regional model.” The Met Office’s Seasonal model, GloSea, sits within the Unified Model structure and is described as the main prediction system currently used for seasonal timescales (up to 6 months) and sub-seasonal timescales (between 2 to 6 weeks). It is an ensemble prediction system using a coupled ocean-atmosphere model (a variant of Met Office climate prediction model: HadGEM3 family), to generate probabilistic forecasts up to six months ahead. The model family is shown in this diagram: The latest iteration - GloSea5 - runs daily with 28 ensemble members for sub-seasonal forecasts and weekly with 42 members for seasonal forecasts. It has two components: the real-time forecast and a companion set of hindcasts, also called historical reforecasts, used for post-processing (bias correction and calibration) and skill assessment. In the forecast suite, initial states (start conditions) for the atmosphere, land surface and ocean are calculated daily. The atmosphere and land surface resolution (grid spacing) is 1.875° × 1.25° to 0.833° × 0.556°. The grid spacing in the ocean and sea‐ice models is 0.25°. System initialisation includes the following data: atmosphere and land surface components from the Met Office operational numerical weather prediction (NWP) model; fully resolved stratosphere as part of 38 vertical levels of the atmosphere; ocean surface temps (SST), ocean vertical temperature and salinity profiles, ocean currents, AMOC strength; atmosphere–ocean coupling; sea ice cover and thickness, ice-berg calving; solar forcing; climate forcings (e.g., aerosols, methane, CO2, Ozone, etc.). Specific teleconnections and other key components modeled include: ESNO (El Nino Southern Oscillation) MJO (Madden-Julian Oscillation) QBO (Quasi-Biennial Oscillation) NAO (North Atlantic Oscillation) AO (Arctic Oscillation) WNPSH (Western North Pacific Subtropical High) Tropical Storms (tropical storm number, spatial distribution, accumulated cyclone energy index, and landfall frequency) Global 1.5m Temps Global Precipitation Global SSTs (sea surface temps) Total cloud amount Pressure at mean sea level Insolation (solar radiation) Outgoing longwave and shortwave radiation Clear-sky outgoing shortwave and longwave radiation Shortwave and longwave cloud forcing Geopotential height at 200, 500, 850 hPa Temperature at 200, 500, 850 hPa Zonal wind at 200, 500, 850 hPa Meridional wind at 200, 500, 850 hPa Relative humidity at 200, 500, 850 hPa Specific humidity at 200, 500, 850 hPa The latest forecast accuracy of GloSea5 forecasts versus actual observations (measured as the correlation coefficient) for key teleconnections are: ESNO correlation of 0.80 MJO correlation of approx. 0.78 for a 5-day forecast and 0.70 for 10-day. NAO correlation of 0.62 AO correlation of 0.63 WNPSH correlation of 0.80 Below are GloSea5 overall skill maps for Precipitation, SSTs and 1.5m Temps. Values above 0.5 denote useful skill compared to climatology. Whilst forecast accuracy continues to improve, seasonal forecasting still presents many challenges, particularly: the very complex interaction of teleconnections, the unfolding impact of rapid Arctic Amplification (warming) on previous understanding and teleconnections impact; the challenge of getting initialisation data for the entire globe absolutely correct so that the “butterfly effect” of a small error in starting data doesn’t rapidly escalate to forecast errors. Further reading on the challenges facing seasonal forecasting can be found in the following short paper from the Imperial College London titled: The challenge of seasonal weather prediction. https://wwwf.imperial.ac.uk/blog/climate-at-imperial/2014/04/14/the-challenge-of-seasonal-weather-prediction/ Despite these challenges, good progress is being made, with the performance of GloSea5 notably improved from its predecessor GloSea4. Sources of information and further reading: GloSea4 https://journals.ametsoc.org/doi/10.1175/2010MWR3615.1 GloSea5 https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/qj.2396 Impact of Atmosphere and Land Surface Initial Conditions on Seasonal Forecasts of Global Surface Temperature https://journals.ametsoc.org/doi/10.1175/JCLI-D-14-00163.1 Statement of Guidance for Global Numerical Weather Prediction (NWP) https://www.wmo.int/pages/prog/www/OSY/SOG/SoG-Global-NWP.pdf Met Office Links: Unified Model: https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/index Seasonal and Climate Models: https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models GloSea5: Met Office seasonal prediction system: https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/climate-models/glosea5 Coupled forecasting development: https://www.metoffice.gov.uk/research/weather/ocean-forecasting/coupled-development Numerical weather prediction models: https://www.metoffice.gov.uk/research/approach/modelling-systems/unified-model/weather-forecasting
  2. Here are the current Papers & Articles under the research topic Numerical Weather Prediction Models (NWP, Seasonal and Climate Forecasting). Click on the title of a paper you are interested in to go straight to the full paper. Advances in weather prediction Published Jan 2019 by Science Magazine. No abstract. This is a top level, easy-to-read overview. UNFORTUNATELY NOW PLACED BEHIND PAYWALL. GFS Operational - Verification stats Graph of 500hPa geo potential heights - Day 5 verification for the GFS cycles 0z, 6z, 12z and 18z. The Global Forecast System (GFS) - Global Spectral Model (GSM) Intro: The following documentation is for the Global Spectral Model, which ran in the GFS from 1980-2019. Documentation on the current GFS (lots of info including resolution etc. Up to March 2021) The ECMWF Integrated Forecasting System - User Guide Intro: The aim of this User Guide is to help meteorologists make the best use of the forecast products from ECMWF - to increase understanding of the ensemble forecast process, to develop new products, to reach new sectors of society, to satisfy new demands. The User Guide presents the Integrated Forecasting System (IFS) and advises on how best to use the output, not least on how to build up trust in the forecast information. A good forecast that is not trusted is a worthless forecast. AROME: an overview AROME is a small scale numerical prediction model, operational at Meteo-France since December 2008. It was designed to improve short range forecasts of severe events such as intense Mediterranean precipitations (Cévenole events), severe storms, fog, urban heat during heat waves. AROME was developed in close collaboration with national and international institutes so as to benefit from the latest research in atmospheric modelling. The physical parametrizations of the model come mostly from the research Méso-NH model whereas the dynamic core is the Non-Hydrostratic ALADIN one. The size of the mesh, many time smaller than previous model, is 1.3km against 5km for ARPEGE over France. The model is initialized from data assimilation derived from the ARPEGE-IFS variational assimilation system and adapted to the AROME finer resolution. Besides available data from ARPEGE, AROME is supplied, for instance, by precise data from the ARAMIS radar network (doppler wind and precipitation), assimilated on an hourly basis. The making of : a weather forecast (how MeteoGroup prepare customer forecasts) Published 2018 Abstract: As a weather company, we at Meteogroup.com do our utmost to provide our customers with the most reliable forecasts possible. A chain of information processing and editing underlies all of the weather forecasts that we issue. The added value we have to offer is primarily due to our balanced model combinations and continuous, automatic quality checks to ensure that observations and model data are aligned optimally. MeteoGroup uses a combination of three weather prediction models to arrive at the best result. These are the ECMWF, NCEP/GFS, and UKMO. Our long-standing use of each of these models has taught us how well each scores on the various elements, which allows us to give a certain weighting in relation to each weather element to be calculated. The improvements we make to raw model data ensure that our forecasts are of the highest quality. A large number of colleagues — meteorologists, programmers, data specialists and IT professionals — dedicate all of their working hours to the above. Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system Global Seasonal forecast system version 5 (GloSea5): a high‐resolution seasonal forecast system Impact of Atmosphere and Land Surface Initial Conditions on Seasonal Forecasts of Global Surface TemperatureThe GloSea4 Ensemble Prediction System for Seasonal Forecasting The HadGEM2 family of Met Office Unified Model Climate configurations The challenge of seasonal weather prediction The quiet revolution of numerical weather prediction The Met Office Unified Model Global Atmosphere 3.0/3.1 and JULES Global Land 3.0/3.1 configurations The NCEP climate forecast system version 2 Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system Seasonal sea ice forecast skills and predictability of the KMA's GloSea5 Statement of Guidance for Global Numerical Weather Prediction (NWP) What Is the Predictability Limit of Midlatitude Weather? Information about the Met Office Global and Regional Ensemble Prediction System (MOGREPS) The Weather Research and Forecasting (WRF) Model
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