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IMS Model Suite Post-Processing Complex Monitoring and Forecasting Suite IMS4 Model Suite Post-Processing IMS CLDB FEATURES: • High Availability FEATURES: • • High resolution, short term weather forecasting and nowcasting • Radiation and pollutant crisis modelling • Road condition modelling • Visibility modelling • Sand storm modelling The IMS Model Suite provides a composition of modern models and technologies for meteorology, hydrology, radiation and air quality. Models run both operatively and inresearch mode. IMS4 Model Suite is an integrated, easy-to-use powerful system for modeling, processing, visualization and model validation. support of servers centers for demanding meteorological simulations • Basic and Advanced Training, Support, System monitoring the statistical achievement. Visualization of model outputs can be set up to easy inter- IMS Model Suite is a complex system for supporting forecasters as well as crisis managers. It is designed from regular daily forecasting purposes, to emergency crisis regime. The decision system consists of several models and tools: • Nowcasting integrating radar, satellite and field data • Nowcasting integrating radar, satellite and flash data: Nowcasting utilizes time sequences of radar, satellite or lightning data to identify thunderstorms (intensive precipitation) and extrapolate their positions a few steps into the future • Hydrological Model • Model Validation Tools for easy monitoring of simulation quality • High Performance Computing - installation and MicroStep-MIS, www.microstep-mis.com © 2015. All specifications subject to change without prior notice. Model Output Statistics IMS Model Suite MOS It-Processing MOS (Model Output Statistics) is a computational post-processing scheme applied to the output of a numerical weather prediction model. It is an objective weather forecasting technique. way, products form Severe Weather Nowcasting Network will be converted to predictors. Climatic data can be used as additional predictors in the IMS Model Suite MOS. IMS Model Suite MOS applies 2 kinds of methods: • Statistical (linear) • Data mining (machine learning) • Corrects for systematic model biases • Probability forecast calculation • Calculation of elements not directly predicted by model (e.g. agrometeorological variables, evaporation, indices, departure from normals) The statistical method develops a set of equations by statistical regression that correlate model outputs and real observations. It utilizes the forecast and measurements database. Once a global model has completed the forecast (ECMWF, NCEP etc...), specific data is collected from model GRIB fields and the MOS equations are applied to it. Before publishing the product, the IMS Model Suite MOS output is post-processed to check for meteorological and statistical consistency; then categorical forecasts are generated. The statistical approach is successfully applied to predict locally adjusted continuous weather parameters: • Air pressure (station surface elevation and mean sea level) • Air and ground temperature, including extremes (high and low temperatures) • Relative humidity • Dew point • Wind speed and direction and gusts • Cloud cover • Precipitation occurrence and amount (accumulation over 1hr, 3hr, 6hr, 12hr, 24hr and other intervals) • Global solar radiation (surface insolation) • Others Functions Forecast spatial resolution, lead times, time resolution and update times The MOS methods are applied to exact location of forecasting site. For spatial calculations, a grid of 10x10 km resolution is used. Optionally, it can be reconfigured into 4x4 km network or other resolutions. • Lead time - Short and medium range (1-10 days) • 6 hourly forecast output for the 0 - 10 day forecast period, and hourly forecast output for at least the 0 – 5 day forecast period. • Update times: 1 hour The parameters are configurable. The data mining approach is based on neural networks and decision trees. These methods of artificial intelligence has been successfully applied by many centres worldwide to improve meteorological forecasts. In MicroStep-MIS, data mining was successfully applied to Fog Probability, Visibility and Low Clouds detection and prediction (Bartok et all, 2012). In addition, the IMS Model Suite MOS neural networks are trained on past observation to predict Probability of Precipitation, Thunderstorm probability, Precipitation Type Probability (Hail, Rain, Ice, Snow probability). The key terms of the process are predictors (the variables at the input of MOS module) and predictand (the weather element that is being predicted).The longer is the observational database, the better equations can be developed and the better neural network can be trained. One of IMS Model Suite MOS advantages is inclusion of real-time measurements from meteorological networks collected and distributed via IMS UDCS/CLDB. In a similar MicroStep-MIS, www.microstep-mis.com © 2015. All specifications subject to change without prior notice. IMS4 Model Suite Post-Processing IMS Model Suite Post-Processing IMS4 Model Suite Post-Processing IMS Model Suite Post-Processing IMS Model Suite Ensemble-MOS Centers such as ECMWF, NCEP, UK Met Office and others produce also ensemble forecasts and publish them (free or charged) in form of GRIB files. FM 92 GRIB format editions 1 and 2 are defined by standard of WMO (WMO No. 306) and are read by MicroStep-MIS decoders. IMS Model Suite Ensemble-MOS utilizes the advantages of ensemble forecasts and calibrates them using past forecast error, as IMS Model Suite does for deterministic forecasts in the MOS process. Training datasets • NCEP reforecast dataset 1979 to the present (Hamil et. all 2006) • ECMWF forecast archive Continuous improvement As the forecast and measurements database grows for country of interest (Zambia), the IMS Model Suite Ensemble-MOS continually utilizes these data to make better calibration and consequently, better forecasts. The quality of forecasts is continuously reviewed by Verification Tool. It-Processing extreme events. Some methods are better in shorter term forecast, some in longer term (6-10 day forecast). Other predictors (actual data, nowcasts) can be included into the process by employing additional Statistical (linear) and Data mining methods known from non-ensemble MOS (firstly, calibrated probabilities are calculated, then used as predictors together with actual data and nowcasts). Forecast spatial resolution, lead times, time resolution and update times: The MOS methods are applied to exact location of forecasting site. For spatial calculations, a grid of 10x10 km resolution is used. • Lead time - Short and medium range (1-10 days) • 6 hourly forecast output for the 0 - 10 day forecast period, and hourly forecast output for at least the 0 – 5 day forecast period. • Update times: 1 hour The parameters are configurable. Methods The IMS Model Suite Ensemble-MOS is open and configurable system that uses several known methods and is prepared for incorporation of other methods if they prove advantageous. MicroStep-MIS has successfully operated data mining (machine learning) methods neural networks and decision trees for fog forecasting. Currently used methods in Ensemble-MOS are: • Logistic regression • Gaussian ensemble dressing • Nonhomogeneous Gaussian regression Each method has the task of learning a target function that maps each attribute set to one of the predefined classes. In meteorological terms it means, that we can fit a function that gives us the probability of weather event, based on current ensemble mean and spread and verification results of previous ensemble forecasts. Mathematically, the regression functions is fitted iteratively by the means of maximum likelihood in case of logistic regression. Gaussian ensemble dressing equation is expressed analytically. In the case of Nonhomogeneous Gaussian regression the parameters of the equation are fit also iteratively to minimize the error for the training data. The results from individual calibration methods are combined in the resulting forecast, as some methods have best overall results, while others exhibit greater accuracy in MicroStep-MIS, www.microstep-mis.com © 2015. All specifications subject to change without prior notice. Integration of Nowcasting It-Processing Standard MOS - Learning phase Historical Forecast (Hindcast) Developing Regression Equations and/or Training of Neural Network Historical Observations Standard MOS - Operation Actual Forecast Regression Equations Site Specific Forecast Neural Network Actual Observations Lightning Detection and Nowcasting Site Specific Warning After enough data is collected Advanced MOS - Learning phase Historical Forecast (Hindcast) Developing Regression Equations and/or Ensemble-MOS Coeficients Historical Observations Training of Neural Network Historical Lightning Detection and Historical Nowcasting Advanced MOS - Operation Actual Forecast Actual Observations Regression Equations and/or Ensemble-MOS Equations Training of Neural Network Actual Lightning Detection and Nowcasting Contact us for more information Cavojskeho 1, 841 04 Bratislava, Slovak Republic tel.: +421 2 602 00 100, fax: +421 2 602 00 180 www.microstep-mis.com, [email protected] © 2015, All specifications subject to change without prior notice Monitoring and Information Systems Site Specific Forecast and Warning IMS4 Model Suite Post-Processing IMS Model Suite Post-Processing