Numerical Weather Forecast System Research Articles

Comparison of Spring Forecasting Ability between GY-WRF and GRAPES in Danghe South Mountain

The Plateau Numerical Weather Forecast System (GY-WRF) was evaluated for its ability to forecast spring weather in Danghe South Mountain. The forecasted temperature, relative humidity, and wind were compared with GRAPES using data from four automatic weather stations in March and April 2022. The results showed that GY-WRF had the smallest mean absolute error (MAE) for each factor at station 4, and a small MAE for temperature, relative humidity, and wind speed at station 2, but a slightly larger MAE for wind direction. GRAPES had a small MAE for wind direction, and a similar trend in wind speed MAE to GY-WRF. GY-WRF was better at predicting the diurnal variation trend of temperature, while GRAPES had a better effect on forecasting the minimum temperature. Both GY-WRF and GRAPES were capable of pre-dicting the trend of wind direction conversion at each station, with GY-WRF being better at predicting the diurnal variation trend of wind speed.

Improved nonlinear mapping network for wind power forecasting in renewable energy power system dispatch

Wind power forecast (WPF) plays vital role in renewable energy power system dispatch. Inaccurate WPF caused by the stochastics and fluctuation of wind energy would lead to frequently modify power system dispatch scheduling and even endanger power system stability operation. Time-series analysis method can find hidden patters of historical wind power time-series but neglects the influence of meteorological factor on WPF accuracy, so it cannot deal with unexpected scenarios such as no wind scenario. Numerical weather forecast (NWF) method cannot accurately forecast wind power in wind curtailment scenario. To overcome these drawbacks mentioned above, a new WPF model based on nonlinear mapping network (NMN–WPF) is proposed to forecast wind power. By combining the advantages of time-series analysis and NWF methods, the proposed NMN–WPF method not only models hidden pattern of wind power time-series, but also adopts meteorological data from NWF system as external factor. Hence, the proposed method can effectively deal with the problem of wind curtailment and no wind scenarios because it balances both interior factor of wind power time-series data itself and external factor of meteorological data. Test cases are performed for practical wind plants. WPF results show that the proposed method can accurately forecast wind power and it is very suitable for renewable energy power system dispatch.

River Routing in the INM RAS-MSU Land Surface Model: Numerical Scheme and Parallel Implementation on Hybrid Supercomputers

The land surface model (LSM) is a necessary compartment of any numerical weather forecast system or the Earth system model. This paper presents a new version of the INM RAS-MSU land surface model where the river hydrodynamic and thermodynamic scheme is embedded into the parallel execution framework using MPI and OpenMP. Numerical experiments have been performed for the East European domain with resolution 0.5°× 0.5°. The soil model parallel efficiency at 1–144 MPI cores was 0.52–0.79 and limited by the presence of ocean area, and by imbalance of computational load between soil columns. The acceleration of the river model at MPI level was defined by the size of the largest river basin in the domain. At the OpenMP level, the potential for acceleration of large river basin simulation is shown to be close to number of threads used, based on fractal properties of the river networks. This acceleration was hindered in our numerical experiments by the reduced river orders at the coarse land surface model resolution, so that the optimal speedup for the Volga river basin was 2.5–3 times attained at 4–6 threads. This performance is projected to improve with refinement of the LSM spatial resolution.

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

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.

A NOVEL HIGH-RESOLUTION STORM SURGE FORECAST FOR THE GERMAN BIGHT

Storm surges are one of the most dangerous natural hazards in coastal areas and have the ability to cause great damages including fatalities. To be prepared when another storm surge hits the coast, reliable storm surge forecasts are indispensable. Storm surge warnings are routinely provided for selected tide gauge locations along a coastline through state-of-the-art forecast systems. In Germany, the Federal Maritime and Hydrographic Agency (BSH) (in cooperation with the German Weather Service (DWD)) have the responsibility for storm surge forecasts and warnings along the German North and Baltic Sea coastlines. The operational system in place for the North Sea consists of numerical weather forecast systems, a surge model and model output statistics. It provides accurate high frequency water level forecasts up to six days ahead at selected tide gauge sites (Müller-Navarra and Knüpfer, 2010), but not for the coastline in between. Spatial forecasts are, however, currently not available for two reasons: first, the shallow coast with complex morphological structures leads to strong non-linearities between individual sites hampering simple interpolation schemes (Arns et al. 2015). Second, tidal predictions are limited to tide gauge locations, which do not fall dry during low tide, since the traditional estimation of tidal coefficients requires complete time series covering both low and high waters.

Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation

In recent years, adopting renewable energy, such as wind power, has become a national energy policy for many countries due to concerns of pollution and climate change from fossil fuel consumption. However, accurate prediction of wind is crucial in managing the power load. Numerical weather prediction (NWP) models are essential tools for wind prediction, but they need accurate initial conditions in order to produce an accurate forecast. However, NWP models are not guaranteed to have accurate initial conditions over wind farms in isolated locations. This study hypothesizes that short-term, 0–3 h, wind forecast can be improved by assimilating anemometer wind speed observations from wind farm turbines into a numerical weather forecast system. A technique was developed to circumvent the requirement of simultaneously ingesting the wind speed and direction in a data assimilation/weather forecasting system. A six-day case study revealed that assimilating wind speed can improve the 0–3 h wind speed (power) forecast by reducing the mean absolute error up to 0.5–0.6 m s−1 (30–40%).

Flood vulnerability of the Karun River System and short‐term mitigation measures

AbstractKarun River Basin in south‐west of Iran has accommodated a large number of hydro projects including reservoirs, hydropower plants, water convey systems, and irrigation networks. Recent extreme floods of the basin have resulted in damage to the vulnerable flood plain areas and under‐construction dam sites. Structural and non‐structural approach was pursued to reduce the vulnerability of the region after extreme floods of 2005 and 2006. The paper describes the historical floods and the solutions that were recently implemented in the basin. Resistant cofferdams along with early flood forecasting system (EFFS) and emergency action plan (EAP) have been identified as adoptive short‐term solutions. The EFFS incorporates a regional numerical weather forecast system, rainfall–run‐off models, a reservoir flood control simulation model, flood routing, and two‐dimensional inundation models. Depending on the location of the sites, maximum lead time of forecast is 4–5 days, which is sufficient to evacuate the area and damage centres. An EAP was made for dam break and extreme flood events including maps of hazardous zones and access maps to safe areas in the damage centres, mainly in the large cities of the flood plain area.

Evidence of growing bred vector associated with the tropical intraseasonal oscillation

The stability property of the tropical intraseasonal oscillation (ISO) during 1 November 2003 to 31 January 2004 is examined using tropical bred vectors obtained from the operational numerical weather forecast system of the Japan Meteorological Agency. The tropical bred vectors are produced by a modified operational breeding cycle in which the perturbation is damped over the extratropics and rescaled by 3.3% of the climatological variance of the 200‐hPa velocity potential in the tropics. At least two growing tropical bred vectors that have similar spatial structure to the observed dry Kelvin waves are obtained: dominant zonal wave number 1 components propagating eastward with phase speed of 30 m s−1. The time‐mean growth rate of the fastest growing tropical bred vector has a positive value of 0.1 day−1. Although this growth rate is smaller than that of extratropical baroclinic instability, this result suggests that the tropical ISO is unstable to infinitesimal perturbations.

The Hirlam project [meteorology

In 1985 the national meteorological institutes of Denmark, Finland, Iceland, The Netherlands, Norway, and Sweden joined forces to develop and maintain a numerical short range weather forecasting system. Later Ireland and Spain joined the project, and the Hirlam (high resolution limited area modeling) project is now in its third phase. Version 2 of the Hirlam system, built during the second phase of the project, is now used in routine weather forecasting at almost all participating institutes. Modern numerical weather forecasting systems have three basic components: an analysis unit, a forecast model, and a postprocessor. Analysis provides the initial conditions for the model, based on recent observations and other sources of information. The forecast model spatially and temporally discretizes and then integrates the classical equations-Newton's second law, mass conservation, and thermodynamics. In the postprocessing step, the relevant weather phenomena (for example, wind speed at 10 m height) are calculated from the model variables. The article outlines the analysis unit, forecast model, and postprocessor of the Hirlam system, as well as its implementation on massively parallel processing systems.

Esso Energy Award Lecture, 1986 Advances in numerical weather prediction for aviation forecasting

In recent years, there have been notable advances in the science of forecasting the weather by solving the thermohydrodynamic equations governing atmospheric flow by using numerical methods. The development of an operational numerical weather-forecasting system requires a high level of expertise in areas such as fluid dynamics, atmospheric physics, numerical methods, supercomputers, space-based remote sensing, and radar. By incorporating the better forecasts into planning and decision making, improved levels of efficiency have been achieved within weather-sensitive areas of both industrial and service sectors of the economy. The Meteorological Office has developed a computer-based weather-forecasting system for civil aviation that permits the provision of weather forecasts for any aircraft anywhere in the world. Information provided by airlines has indicated that they are saving an additional £50M per year through reduced aviation fuel consumption directly related to the improved forecasts. Current research indicates that further substantial improvements to weather forecasting will be achieved within the coming decade, enabling meteorology to be integrated even more closely into air traffic control and airline operations.