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EN
The examination and integration of numerical forecast products are essential for using and developing numerical forecasts and hydrological forecasts. In this paper, the control forecast products from 2010 to 2014 of four model data (China Meteorological Administration (CMA), the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the United Kingdom Meteorological Office (UKMO)) from The Interactive Grand Global Ensemble (TIGGE) data center were evaluated comprehensively. On this basis, a study of runoff forecasting based on multi-model (multiple regression (MR), random forest (RF), and convolutional neural network-gradient boosting decision tree (CNN-GBDT)) precipitation integration is carried out. The results show that the CMA model performs the worst, while the other models have their advantages and disadvantages in different evaluation indexes. Compared with the single-index optimal model, CMA model had a higher root-mean-square error (RMSE) of 18.4%, and a lower determination coefficient (R2 ) of 14.7%, respectively. The integration of multiple numerical forecast information is better than that of a single model, and CNN-GBDT method is superior to the multiple regression method and random forest method in improving the precision of rainfall forecast. Compared with the original model, the RMSE decreases by 13.1 ~27.9%, PO decreases to 0.538 at heavy rainfall, and the R2 increases by 4~15.2%, but the degree of improvement decreases gradually with the increase in rainfall order. The method of multi-model ensemble rainfall forecasting based on a machine learning model is feasible and can improve the accuracy of short-term rainfall forecasting. The runoff forecast based on multi-model precipitation integration has been improved, and NSE increases from 0.88 to 0.935, but there is still great uncertainty about food peaks during the food season.
EN
Charter Party agreements underpin the relationship between ship owners and charterers. The agreement guarantees the performance of a vessel in terms of speed and fuel consumption. On this basis the charterers plan the arrival of their cargo and their profit margin. However, ship performance is degraded by age, periods between maintenance and many vessels fail to perform as expected. Moreover the performance is only warranted during the specific conditions stated in the charter party which are not always clear. These usually refer to Beaufort Force (BF) and the Douglas Sea and Swell (DSS) scale which is archaic in the age of Numerical Weather Prediction. Given these conditions, the stage is set for conflict and there are often disputes over the weather conditions experienced. Moreover ships’ often do not arrive on time because the charterer has assumed that the ship will make good its warranted speed and not taken account of the forecast weather conditions. The authors propose a new way of approaching charter agreements with the emphasis on consultation rather than confrontation facilitated by a new web based software platform.
EN
In this paper a series of estimations has been performed in order to establish the actual cost-effectiveness of small wind turbines (SWTs). Different design solutions have been evaluated and based on their power curves and installation costs, using accurate wind data, a rate of investment return (ROI) period has been calculated for each one of them. The chosen turbines are: a classic three bladed horizontal axis wind turbine (HAWT), an advanced diffuser augmented HAWT and a Darrieus type vertical axis wind turbine (VAWT). The conclusions drawn from this study entertain the idea that from the economical point of view, a price reduction of SWT systems is more important, than aerodynamic complexity and efficiency.
EN
High temporal and spatial resolution of radar measurements enables to continuously observe dynamically evolving meteorological phenomena. Three-dimensional (3D) weather radar reflectivity data assimilated into the numerical weather prediction model has the potential to improve initial description of the atmospheric model state. The paper is concentrated on the development of radar reflectivity assimilation technique into COAMPS mesoscale model using an Ensemble Kalman Filter (EnKF) type assimilation schemes available in Data Assimilation Research Testbed (DART) programming environment. Before weather radar data enter into the assimilation system, the measurement errors are eliminated through quality control procedures. At first artifacts associated with non-meteorological errors are removed using the algorithms based on analysis of reflectivity field pattern. Then procedures for correction of the reflectivity data are employed, especially due to radar beam blockage and attenuation in rain. Each of the correction algorithms is connected with generation of the data quality characteristic expressed quantitatively by so called quality index (QI). In order to avoid transformation of data uncertainty into assimilation scheme only the radar gates successfully verified by means of the quality algorithms were employed in the assimilation. The proposed methodology has been applied to simulate selected intense precipitation events in Poland in May and August 2010.
5
Content available remote A comparison of ASCAT wind measurements and the HIRLAM model over the Baltic Sea
EN
This paper presents a comparison of the wind data measured by the ASCAT polar-orbiting satellite scatterometer and winds forecast by the numerical weather prediction model HIRLAM in the Baltic Sea region during the stormy season in 2009. Two different resolution models were used in the comparison. Mutual quality and uncertainty characteristics of the measurements and predictions are determined. The results of the study show that the ASCAT wind data are well correlated with the HIRLAM predicted winds, which raises the credibility of both data sources in operational and hindcasting applications over the Baltic Sea. A case of phase error in a HIRLAM forecast of cyclonic activity over the Baltic Sea is discussed.
EN
The increasing resolution of contemporary regional numerical weather prediction (NWP) models, reaching horizontal grid sizes of O(1 km), requires robust and reliable dynamical cores, working well beyond the approximation of quasi-horizontal flows. That stimulates an interest in an application for NWP purposes of dynamical cores based on the anelastic, or - more generally - sound-proof flow equations, and characterized by appropriate robustness and reliability. The paper presents results from testing the dynamical core of EULAG, the anelastic research model for multi-scale flows, as a prospective NWP dynamical core. The model simulates the semi-realistic frictionless and adiabatic flow over realistic steep Alpine topographies, employing horizontal grid sizes of 2.2, 1.1, and 0.55 km. The paper demonstrates not only the numerical robustness of EULAG, but also studies the influence of the varying horizontal resolution on the simulated flow. Results show that the increased horizontal resolution increases orographic drag on the flow. While the general flow pattern remains the same, increased resolution influences the flow on scales from hundreds of kilometers to meso-gamma scales. The differences are especially apparent in the near-surface layer of 1.5 to 3 km deep, and in the distribution and amplitudes of the orographically-induced gravity waves.
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