GNSS-Based Machine Learning Storm Nowcasting

Marcelina Łoś,Guergana Guerova,Witold Rohm,Kamil Smolak

doi:10.3390/rs12162536

Abstract

Nowcasting of severe weather events and summer storms, in particular, are intensively studied as they have great potential for large economic and societal losses. Use of Global Navigation Satellite Systems (GNSS) observations for weather nowcasting has been investigated in various regions. However, combining the vertically integrated water vapour (IWV) with vertical profiles of wet refractivity derived from GNSS tomography has not been exploited for short-range forecasts of storms. In this study, we introduce a methodology to use the synergy of IWV and tomography-based vertical profiles to predict 0–2 h of storms using a machine learning approach for Poland. Moreover, we present an analysis of the importance of features that take part in the prediction process. The accuracy of the model reached over 87%, and the precision of prediction was about 30%. The results show that wet refractivity below 6 km and IWV on the west of the storm are among the significant parameters with potential for predicting storm location. The analysis of IWV demonstrated a correlation between IWV changes and storm occurrence.

Highlights

Nowcasting was defined in 2010 by the World Meteorologic Organisation (WMO) as (1) forecasting with local detail, (2) by any method, (3) over a period from the present to 6 h ahead and (4) including a detailed description of the present weather [1]
The results show that wet refractivity below 6 km and Integrated Water Vapour (IWV) on the west of the storm are among the significant parameters with potential for predicting storm location
After Introduction we demonstrate the methodology used to develop the hyper-parameters for ML methods, in the section Results, we demonstrate the outputs from our Random Forest Classifier for storm nowcasting

Summary

Introduction

Nowcasting was defined in 2010 by the World Meteorologic Organisation (WMO) as (1) forecasting with local detail, (2) by any method, (3) over a period from the present to 6 h ahead and (4) including a detailed description of the present weather [1]. GNSS (Global Navigation Satellite Systems)-derived tropospheric products were used in monitoring severe weather events as reviewed in [4]. Two GNSS campaigns took place within the COST Action ES1206 “Advanced Global Navigation Satellite Systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC)”. The second campaign [6] targeted operational provision of GNSS tropospheric products with a temporal resolution of 5 min. Both campaigns demonstrated the potential of GNSS troposphere products in monitoring the atmospheric water vapour distribution with high spatiotemporal resolution suitable for nowcasting. The model includes the rate of change, variation and monthly values of PWV

Methods

Results

Discussion

Conclusion