Awadaljeed, M.

Crocetti, L.

Klopotek, G.

Rothacher, M.

See, L.

Soja, B.

Sturn, T.

Weinacker, R.

International Institute for Applied Systems Analysis (IIASA)

Partner/Sponsor: Reference: Navarro, V., Grieco, R., Soja, B., Nugnes, M., Klopotek, G., Tagliaferro, G., See, L., Falzarano, R., Weinacker, R., Ventura Traveset, J., "Data Fusion and Machine Learning for Innovative GNSS Science Use Cases," Proceedings ofthe 34th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2021), St. Louis, Missouri, September 2021, pp. 2656-2669. https://doi.org/10.33012/2021.18115Machine learning based modelling of tropospheric parameterswith GNSS enhanced by meteorological data1 Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, Switzerland2 International Institute for Applied Systems Analysis, Laxenburg, Austria3 European Space Agency, European Space Astronomy Centre, Spain1. INTRODUCTIONGlobal Navigation Satellite System (GNSS) is mostly known forautonomous global positioning, navigation, and timing services.However, it is also a key tool for many areas of science such asquantification of global-scale geodynamical phenomena orstudies on the atmosphere.Over the past years, modern smartphones facilitate the collectionof raw GNSS measurements. These affordable smart devices areutilized by billions of people making crowdsourcing of GNSSobservations possible. This forms a great opportunity forinvestigating the usage of smartphone-based GNSS data fordifferent subjects such as weather forecasting or atmospherestudies at large. By combining the observations acquired by high-quality GNSS stations with a dynamic network of GNSS-capableInternet-of-Things (IoT) devices, the sample of observations canbe significantly increased and an unprecedented spatio-temporalresolution can be achieved.4. SCIENCE USE CASE2. PROJECT OVERVIEW3. CROWDSOURCING CAMPAIGNApplication of Machine Learning Technology for GNSS IoT datafusion (CAMALIOT) is an ongoing project funded by theEuropean Space Agency (ESA) that aims to collect largeamounts of GNSS observations by developing an Androidapplication and conducting a dedicated crowdsourcing campaign.Fig. 1: A high-level overview of the concept exploited within the CAMALIOT projectCampaign to collect data from satellites for scientific research• Started on 17th March 2022• Android app with 35k+ installations• 10k registered users• 10 billion GPS observations collectedFig. 2: Map showing the number of files uploaded from different countries (until 29th April 2022)5. SUMMARY• CAMALIOT will advance current knowledge and experienceconcerning GNSS observations as collected from smartphonesand applied for scientific purposes• Global ML-based ZWD model can make predictions for everypoint on Earth (assuming meteorological data is available) andachieves an average RMSE of 12.5 mmLaura Crocetti1, B. Soja1, G. Kłopotek1, M. Awadaljeed 1, Y. Pan1, M. Rothacher 1, L. See2, R. Weinacker2, T. Sturn2, I. McCallum2, V. Navarro 3camaliot.orgBrazilGermanyUnited StatesSpainUnited KingdomItalyFrancePolandPortugalAustria45412283038123679450104548412232882367227112345678910ML ALGORITHMXGBoost• standardize features• hyperparameter optimization,cross-validation• train modelEVALUATION• make ZWD predictions for testing data (y_test_pred)• calculate performance metrics− RMSE = 12.5 mm− R2 = 0.97 TRAINING DATA - 80%ZWD (y_train)features (X_train)TESTING DATA - 20%ZWD (y_test)features (X_test)output: ML modelapply ML model globallyFig. 3: ML-based global ZWD predictions for the 5th July 2019, 00:00 UTCML-based forecasting of tropospheric parametersOne science use case is to utilize the IoT GNSS data toinvestigate its feasibility for augmenting modelling and predictionof tropospheric parameters such as Zenith Wet Delay (ZWD).ZWD is strongly related to the water vapour content in theatmosphere, which in turn drives weather systems and climatechange to a great extent.The aim is to derive a global ML-based model enhanced by EarthObservation data that can predict ZWD in space and time.TARGETZenith Wet Delaysource: Nevada Geodetic LaboratoryFEATURESMeteorological Data, Latitude, Longitude, Height, Timesource: ECMWF ERA5• Specific humidity• Relative humidity• Temperature• Surface pressure• Total precipitation• Geopotential• Wind speedDATA

Machine learning based modelling of tropospheric parameters with GNSS enhanced by meteorological data

https://pure.iiasa.ac.at/18035/1/20220527_LPS_Crocetti_A0.pdf

Machine learning based modelling of tropospheric parameters with GNSS enhanced by meteorological data

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