Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Buzzi, Matteo

Guidicelli, Matteo

Liniger, Mark A.

English

Agencia Estatal de Meteorología

1Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology  MeteoSwissNowcasting wind using machine learning from the stations to the gridMatteo Buzzi, Matteo Guidicelli, Mark Liniger2Motivation: INCA-CH seamless Nowcasting SystemCurrent INCA-CH system for mean wind• Analysis: • COSMO-1 error interpolated in space using inverse distance weighting (horizontal and vertical).• leave one out cross-validation: equivalent or sometimes even worse performance as COSMO-1.• Forecast:• Linear blending  between analysis and COSMO-1 (0-6h): unrealistic transitions.INCA-CHCVGoal: evaluate machine learning techniques• Improve analysis and nowcasting of the model on the whole grid• Add wind gust3Project flowchart4Dataset description: observation• Hourly mean wind, wind gusts• 10.2017 and 12.2018• Instruments at 10 m from the ground• Switzerland• SMN• IMIS (SLF)• Private network, Cantonal network • France (FR), Italy (IY), Germany (DL), Austria (OS)• Data quality check: removed suspicious stations 5Dataset description: predictors• COSMO-1 variables:- Hourly mean wind, gust, u, v- Pressure- Boundary layer height- Relative humidity- …• Seasonality parameters:- Hour and Day of the year• Topographical parameters (resolutions 15 km – 100 m):- Altitude- Slope- Aspect- Directional derivatives- Topographic Position Index- Maximum slope dependent on wind direction6Dataset description: topographical predictorsDEM E-W derivative N-S derivativeSlope Aspect TPIResolutions:15km, 7km, 3km, 1km, 500m, 250m, 100mSxWinstral et al. (2016)7Machine learning model for stations8Model for stationsFeedforward Artificial Neural Network:• Multi-layer perceptron• Number of observations > 1 billion (365 days)• 38 predictors (10 COSMO-1, 4 seasonality, 20 topographical, 4 observations)• 2 hidden layers with (200,100) neurons• Loss function: Mean Squared Error• 5-fold cross-validation with grid-search for hyperparameter tuning• Early-stopping to avoid overfitting• Test independent in TIME• Training: 10.2017-09.2018 (30 % for validation); Test: 09.2018 –12.2018• Feature importance from Random ForestLearning curve:9Results at stations: mean wind performance102 steps model for the whole grid11Model on the grid: step 1Generic model trained on 1 year of data• Correction of the systematic error• Trained not only on independent period of time, but also on independent stations (unknown points, 20-fold cross-validation)• Trying to keep the same performance at stations included and not included (grid), avoiding overfitting• Loss function: Logarithm of the hyperbolic cosine of the prediction error• log cosh 𝑥 ≅  𝑥2 𝑓𝑜𝑟 𝑠𝑚𝑎𝑙𝑙 𝑥𝑎𝑏𝑠 𝑥 − log 2 𝑓𝑜𝑟 𝑙𝑎𝑟𝑔𝑒 𝑥• Mostly like MSE, but less affected by the occasional wildly incorrect prediction• The validation curve shows the same performance on known and unknown points, early-stopping to avoid overfitting at stationsValidation curveUnknown points: performance on the grid12Results step 1: mean wind performance13Results step 2: mean wind• Prediction values limited forRandom forest• Better at stations but crossvalidation doesn’t improveconsistently: • RMSE LR at stations = 1.62• RMSE RF at stations = 1.40• LR improves the dispersion, distribution closer to observationsStep 2 RFStep 2 LR COSMO-1Step 114Results step 2: wind gust15Model at stations• 1 unique ML model leads to a strong improvement for all lead-times (0-24h).• Persistence of wind is very important for nowcasting.Model on the grid• Machine learning for wind on locations without measurements remains a very difficult task in the Alpine region.• Multi-step approach aims to correct systematic and forecasting errors on the whole grid.• The efficacy of step 2 is evident for wind gusts, less significant for mean wind.Possible improvements• Step 1: data size vs performance (learning curve)• Step 1: further optimisation of the Neural Network• Step 1: convolutional Neural Networks (better representativeness of topographical parameters) or/and more realistic ground model (accounting for buildings and vegetation)• Step 2: increase the number of high quality stations, try to give more importance to coordinatesFuture work• Implementation of the model in real time and evaluate performanceSummary and Outlook16Federal Department of Home Affairs FDHAFederal Office of Meteorology and Climatology  MeteoSwissThanks for the attention!17Results step 2: mean wind

Nowcasting wind using machine learning from the stations to the grid

https://repositorio.aemet.es/bitstream/20.500.11765/10623/1/NTSP9_Buzzi_3ENC2019.pdf

Nowcasting wind using machine learning from the stations to the grid

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