Awadaljeed, M.

Crocetti, L.

Kłopotek, G.

McCallum, I.

Navarro, V.

Rothacher, M.

See, L.

Soja, B.

Sturn, T.

Weinacker, R.

International Institute for Applied Systems Analysis (IIASA)

Machine learning algorithms for global modelling of Zenith Wet Delay based on GNSS measurements and meteorological dataLaura Crocetti - lcrocetti@ethz.ch, B. Soja, G. Kłopotek, M. Awadaljeed, M. Rothacher, L. See, R. Weinacker, T. Sturn, I. McCallum, V. Navarro14th June 2022, D4G22Motivation• Global Navigation Satellite System (GNSS) – find also application in atmospheric research• GNSS signals traverse the atmosphere• Time delays → measurements of atmospheric propertiesGNSS14.06.2022 2Laura Crocetti – lcrocetti@ethz.chMotivation14.06.2022 2LongitudeLaura Crocetti – lcrocetti@ethz.ch• Global Navigation Satellite System (GNSS) – find also application in atmospheric research• GNSS signals traverse the atmosphere• Time delays → measurements of atmospheric properties• Model Zenith Wet Delay globallybased on meteorological datausing Machine Learning (ML)Motivation14.06.2022 2LongitudeTimeLaura Crocetti – lcrocetti@ethz.ch• Global Navigation Satellite System (GNSS) – find also application in atmospheric research• GNSS signals traverse the atmosphere• Time delays → measurements of atmospheric properties• Model Zenith Wet Delay globallybased on meteorological datausing Machine Learning (ML)DataZenith Wet Delay (ZWD)• Source: Nevada Geodetic Laboratory (NGL)− More than 19.000 GPS stations available• Temporal resolution: 5 min → hourly resolution• Spatial resolution: stations distributed globally• Time span: year 20193Meteorological data• Source: ECMWF ERA5• Temporal resolution: hourly• Spatial resolution: 0.25°• Time span: year 2019• Several variables:− Specific humidity− Relative humidity− Temperature− Surface pressure− Total precipitation− Geopotential− Wind speedTARGET• Aim: predict ZWD in space (and time) using ML algorithms14.06.2022Laura Crocetti – lcrocetti@ethz.chFEATURES− Latitude− Longitude− Height− TimeSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATALaura Crocetti – lcrocetti@ethz.chSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATADistribution of training and test stations for all available stations (2019)Global model for the year 2019Laura Crocetti – lcrocetti@ethz.chSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATALaura Crocetti – lcrocetti@ethz.chSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATALaura Crocetti – lcrocetti@ethz.ch• Train model based on 10752 training stations for the year 2019• Make predictions for 2688 testing stations for the year 2019 → Predictions for different stations for the same time period→ no predictions into the futureSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATALaura Crocetti – lcrocetti@ethz.chML algorithms:• XGBoost• Random ForestSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATALaura Crocetti – lcrocetti@ethz.chML algorithms:• XGBoost• Random Forest• HistGBoost• Multilayer Perceptron• Ridge Regression• Stochastic Gradient Decent• ElasticNet Regression• Lasso Regression• Linear Support Vector Machine• AdaBoostPerformance decreasesSetup14.06.2022 4ML ALGORITHM• standardize features• hyperparameter optimization• cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics• RMSE• R2TRAINING STATIONS - 80%ZWD (y_train)features (X_train)TESTING STATIONS - 20%ZWD (y_test)features (X_test)output: ML modelDATALaura Crocetti – lcrocetti@ethz.chPerformance of individual test stations514.06.2022Laura Crocetti – lcrocetti@ethz.chRMSE of ZWD [mm]RMSE of ZWD [mm] of testing stationsPerformance of individual test stations614.06.2022mean = 12.4 mmLaura Crocetti – lcrocetti@ethz.chRMSE of ZWD [mm] of testing stations714.06.2022ZWD predictions for 2019Laura Crocetti – lcrocetti@ethz.chUnderstanding the performance8Comparison between RMSE and … • minimum distance to next training station → 𝒄𝒐𝒓𝒓 = 𝟎. 𝟑𝟒• height difference to next training station → 𝒄𝒐𝒓𝒓 = 𝟎. 𝟑𝟏• height of GNSS station→ 𝒄𝒐𝒓𝒓 = −𝟎. 𝟎𝟔Laura Crocetti – lcrocetti@ethz.chComparison between RF and XGBoostMean difference over each month for the year 201914.06.2022 9• Calculated ZWD predictions for year 2019, once with RF and once with XGBoost• Took differences between RF predictions and XGBoost predictions• Took mean over the differences for each monthLaura Crocetti – lcrocetti@ethz.chMonthly models – performance for testing stationsWorld 20191014.06.2022RMSE [mm]Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Decall months8.8 8.9 8.9 9.8 10.7 12.1 12.7 13.2 12.0 10.9 9.4 9.4 12.5Laura Crocetti – lcrocetti@ethz.chRegional models for the year 2019RMSE [mm]#stationsNorth America 10.0 6605Europe 10.1 3004WORLD 12.5 13440Australia 14.1 665Africa 20.0 302South America 21.8 54211Validation of ZWD predictions with independent methods• Use radiosonde data to calculate ZWD• Several options for validation: − Compare ZWD at locations where radiosonde data are available− Compare ZWD at locations where additionally a GNSS station is available14.06.2022Overview of radiosonde stations for 2019• 790 radiosonde stations available for 2019• source:  − Integrated Global Radiosonde Archive (IGRA)− https://www.ncei.noaa.gov/pub/data/igra/• #samples differs for the stations12Laura Crocetti – lcrocetti@ethz.chValidation of ZWD predictions with independent methods• Use radiosonde data to calculate ZWD• Several options for validation: − Compare ZWD at locations where radiosonde data are available− Compare ZWD at locations where additionally a GNSS station is available14.06.2022 12Laura Crocetti – lcrocetti@ethz.chValidation of ZWD predictions with independent methods• Use radiosonde data to calculate ZWD• Several options for validation: − Compare ZWD at locations where radiosonde data are available− Compare ZWD at locations where additionally a GNSS station is available14.06.2022 12ZWD [mm]GNSS ZWDGNSS ZWDpredradiosonde ZWDZWD [mm] of radiosonde station 17607 and closest GNSS station NICOExample time seriesLaura Crocetti – lcrocetti@ethz.chGNSS ZWD… radiosonde station and closest GNSS station14.06.2022RMSE between ZWD of …ML ZWD… radiosonde station and ML prediction13Laura Crocetti – lcrocetti@ethz.ch… radiosonde station and closest GNSS station14.06.2022RMSE between ZWD of …… radiosonde station and ML prediction14Laura Crocetti – lcrocetti@ethz.chSummary• Global ML-based ZWD model based on meteorological data • Can make predictions of ZWD for every point on Earth (assuming meteorological data is available)• Achieves an average RMSE of 12.4 mm• ML algorithms: XGBoost and Random Forest14.06.2022 15Laura Crocetti – lcrocetti@ethz.chLaura Crocettilcrocetti@ethz.chETH ZurichSpace Geodesy Group Institute of Geodesy and PhotogrammetryZurich, Switzerlandwww.space.igp.ethz.ch

Machine learning algorithms for global modelling of Zenith Wet Delay based on GNSS measurements and meteorological data

https://pure.iiasa.ac.at/18058/1/20220614_D4G22_Crocetti.pdf

Machine learning algorithms for global modelling of Zenith Wet Delay based on GNSS measurements and meteorological data

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