The wind power is primarily induced by the local wind speed, whose accurate daily forecasts are important for planning and utilization of the unstable power generation and its integration into the electrical grid. The main problem of the wind speed or direct output power forecasting is its intermittent nature due to the high correlation with chaotic large-scale pattern atmospheric circulation processes, which together with local characteristics and anomalies largely influence its temporal flow. Numerical global weather systems solve sets of differential equations to describe the time change of each three-dimensional grid cell in several atmospheric layers. They can provide as a rule only rough short-term surface wind speed prognoses, which are not entirely adequate to specific local conditions, e.g., the wind farm siting, surrounding terrain, and ground level (hub height). Statistical methods using historical observations can particularize the daily forecasts or provide independent predictions in several hours. Extended polynomial networks can produce fraction substitution sum terms in all the nodes, in consideration of data samples, to decompose and substitute for the general linear partial differential equation, being able to describe the local atmospheric dynamics. The designed method using the inverse Laplace transformation aims at the formation of stand-alone spatial derivative models, which can represent current local weather conditions for a trained input–output time shift to predict the daily wind power up to 12 h ahead. The proposed intraday multistep predictions are more precise than those based on middle-term numerical forecasts or adaptive intelligence techniques using local time series, which are worthless beyond a few hours.

Direct Wind Power Forecasting Using a Polynomial Decomposition of the General Differential Equation

Since the early 20th century numerical weather prediction (NWP) has increasingly become one of the most important and complicated problems of modern science. With the advent of computers, increased observations, and progress in theoretical understanding, numerical models were developed. Since then, such models are playing an increasing role in under‐ standing and predicting weather and climate and have been a driving force in the advance‐ ment of the meteorological sciences. Numerical models are a mathematical representation of the earth’s climate system including the atmosphere, ocean, cryosphere and land, among others. The models divide the area of interest into a set of grids and then make use of obser‐ vations of variables such as surface pressure, winds, temperature and humidity at numer‐ ous locations throughout the globe. The observed values are then assimilated and used by the model to predict future evolution of the earth’s weather and climate. In the mid 20th cen‐ tury, models evolved from a simple model with a single atmospheric layer to a multi-layer primitive equation model capable of predicting cyclone development [1].

/pdf/grids-in-numerical-weather-and-climate-models-2euq7u9b3m.pdf

Grids in Numerical Weather and Climate Models

Accurate forecasting of heavy rainfall is crucial for the improvement of flood warning to prevent loss of life and property damage due to flash-flood-related landslides in the hilly region of Bangladesh. Forecasting heavy rainfall events is challenging where microphysics and cumulus parameterization schemes of Weather Research and Forecast (WRF) model play an important role. In this study, a comparison was made between observed and simulated rainfall using 19 different combinations of microphysics and cumulus schemes available in WRF over Bangladesh. Two severe rainfall events during 11th June 2007 and 24–27th June 2012, over the eastern hilly region of Bangladesh, were selected for performance evaluation using a number of indicators. A combination of the Stony Brook University microphysics scheme with Tiedtke cumulus scheme is found as the most suitable scheme for reproducing those events. Another combination of the single-moment 6-class microphysics scheme with New Grell 3D cumulus schemes also showed reasonable performance in forecasting heavy rainfall over this region. The sensitivity analysis confirms that cumulus schemes play a greater role than microphysics schemes for reproducing the heavy rainfall events using WRF.

Evaluation of Microphysics and Cumulus Schemes of WRF for Forecasting of Heavy Monsoon Rainfall over the Southeastern Hilly Region of Bangladesh

Climate change scenario simulations over Eritrea by using a fine resolution limited area climate model : temperature and moisture sensitivity

The local thunderstorms and gale weather occurring frequently has brought huge losses to the agriculture and transportation industries. This paper presents a method of forecasting the local thunderstorms and gale weather, in which a multisource convolution neural network is constructed to extract the features of weather-related data with multiple types from Doppler radar. To improve the discriminative power of features, Center-Loss and Softmax were jointly used as loss function in the training, and then the features obtained are combined with SVM for classification. Furthermore, a comparative experiment of multisource convolution neural network based on CNN-4, ResNet30, ResNet50, and VGG16 is designed, in which the ResNet30 achieves the highest accuracy. The experimental results show that the multisource convolution neural network avoids the limitation of using one type of data and improves the accuracy of forecasting local thunderstorms and gale.

/pdf/a-method-of-forecasting-thunderstorms-and-gale-weather-based-55uwhh1m5c.pdf

A Method of Forecasting Thunderstorms and Gale Weather Based on Multisource Convolution Neural Network

An Observing System Experiment is carried out using the Met Office threedimensional variational data assimilation scheme (3D-Var) and global forecast model. The experiment investigates the impact on forecasts of up to 6-days of denying whole observing systems (radiosonde, satellite, aircraft and surface). A total of 60 forecasts from two 30-day periods July 2001 and January 2002 are verified. The results show that overall all observing systems have a positive impact on mean forecast quality indicating that the Met Office 3D-Var system performs well. The experiment also gives an estimate of the relative benefit that the different components of the Global Observing System are having on Met Office NWP forecasts. Results indicate: • Satellite data have the largest positive impact of all observation types on forecasts in the southern hemisphere and have a significant positive impact in the tropics and northern hemisphere. • Satellite radiance data have a larger impact than data from Atmospheric Motion Vectors (AMV) which have a measurable impact in the extra-tropics only when satellite radiance data are not present. The benefit of AMV data can be most clearly seen in the tropics. • Surface data have a significant impact on surface pressure forecasts, but there may be some redundancy in the network for global Met Office NWP forecasts run at 90 km resolution. • Radiosonde data are important for northern hemisphere forecasts and also have benefit in the tropics and southern hemisphere. • Aircraft data play a relatively important role, particularly for short-range wind forecasts in the northern hemisphere and tropics. The results from this experiment are in broad agreement with those from a similar experiment, using four-dimensional variational assimilation, carried out by ECMWF. A further experiment investigates the impact of sub-components of the surface network (reports from land surface stations, ships and buoys). 30 forecasts from July 2001 are verified. The results from this experiment show that direct 'in-situ' observations of surface pressure have a large positive impact on NWP forecasts of mean sea level pressure. But removing whole sub-components of the surface network does not lead to a noticeable reduction in forecast quality.

Numerical Weather Prediction

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Numerical Weather Prediction