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Parana Meteorological Servicewww.simepar.br
SIMEPAR modeling group activities
Bianca Buss [email protected]
About SIMEPARSIMEPAR is a regional meteorological center located in Curitiba-PR. SIMEPAR hasthe mission to provide data, forecasts, products and services of meteorological, hy-drological and environmental nature and promote scientific research and technologi-cal development in the atmospheric sciences and environmental issues.Our main services are:
# Weather monitoring and forecasting
# Climate monitoring and forecasting
# Environmental monitoring andhydrological forecasting
# Hydrometeorology, operation andenergy distribution
# Environmental impacts
# Technical reports of meteorologicalevents
Numerical Weather Forecast in SIMEPARSIMEPAR uses the Weather Research and Forecasting (WRF) model, followingNCAR updates, serving customers with diverse applications such as:
# SIMEPAR operational monitoring
# Energy Production and generation
# Wind energy
# Agricultural
# Civil defense
# Mining
# RefineryThe WRF model uses the Global Forecast System (GFS) as a contour. Currently, werun 5 grids, in the areas marked in figure 1, at 00Z and 12Z with resolution of: 1) 8km,2) 2km, 3) 9km, 4) 5km and 5) 1km. Model outputs are available through files (bin,nc, grib), text files, tables, graphics and maps.
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4, 5
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Figure 1: WRF model grids in SIMEPAR and model data distribution forms
Post-Processing of Wind ForecastPrecise wind conditions can be critical to the most diverse applications nowadays,with the most prominent application of wind forecasting being the use of clean andrenewable energies, which has received more attention in recent years.Despite the advance of meteorological models they are still susceptible to errors andsystematic trends. In the face of all this, we have chosen a machine-learning ap-proach to forecast correction. We provide models with forecasting and other vari-ables on the weather forecast, allowing each model to decide how best to do post-processing correction.Have been tested 6 methods: Linear Regression, M5P Tree, Random Forest, MultiLayer Perceptron, Support Vector Regression Machines and Gradient Boosting. Thevariables which had its correction performed were U component, V component, speedand direction of the wind. In order to achieve reliable results, several tests weremade with the training data, exploiting the variables as machine learning attributes,disregarding the physics in their relationships. The experiments are listed in the table1.
The data was collected from 12 September 2016 to 25 July 2017, every 15 minutes,with direction and speed values at both heights in each point. The WRF forecast cov-ers a period of 3 days, with predictions for every 30 minutes, totalizing 144 momentsof forecast. Due to this interval of 30 minutes, the observed data used were onlyobservations that coincided with the time of forecast provided by the WRF model.The results presented in the table 2 indicate that the M5P tree handled very well thecorrection, losing only in the U component correction to the SVR. But as the testsprogressed and more variables were added as predictors, we can see the RandomForest algorithm is able to better predict the corrected forecast. We also note thatthe third round of test, the one with more predictors, is where the performance washigher for all target variables.
Ensemble Multi-ModelQuantitative precipitation is one of the variables of greatest interest in the weatherprediction and great difficulty of a precise forecast and this uncertainty is propagatedto the hydrological models which are highly dependent on the precipitation. The useof ensemble forecast is widely used in operational centers to reduce error. There areseveral ways to compose the members of the ensemble, among these: perturbationof the initial condition, different parameterizations, time-lagged, different models.The use of ensemble composed of several models has been explored, being calledsuperensemble or multimodel ensemble. In SIMEPAR we use 27 models and simu-lations started 3 days before the target date to start forecast, each model receives acoefficient that are calculated dynamically by minimizing the mean square error. As areference to calcule the error we use the precipitation from SIPREC, which includesrain gauge, radar and satellite estimation.Because of the use of simulations up to 3 days earlier, the first forecast times havethe largest number of members, as shown in figure 2, after 3 days the number ofmembers begins to fall again (Fig.3). The figure 4 shows an example of amount ofprecipitation and probability of precipitation generated by the ensemble.
Figure 2: Scheme of ensemble members.
Figure 3: Number of members of each forecast hour.
Figure 4: Quantitative Precipitation and Precipitation Probability.
