ISSN 0252-1075 Contribution from IITM Research Report No. RR-145 ESSO/IITM/SERP/SR/01(2019)/196 Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay Indian Institute of Tropical Meteorology (IITM) Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES) PUNE, INDIA http://www.tropmet.res.in/ ISSN 0252-1075 Contribution from IITM Research Report No. RR-145 ESSO/IITM/SERP/SR/01(2019)/196 Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay *Corresponding Author Address: Dr. A.K. Sahai Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune – 411008, India. E-mail: [email protected] Phone: +91-(0)20-25904520 Indian Institute of Tropical Meteorology (IITM) Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES) PUNE, INDIA http://www.tropmet.res.in/ DOCUMENT CONTROL SHEET -------------------------------------------------------------------------------------------------------------------------------------- Earth System Science Organization (ESSO) Ministry of Earth Sciences (MoES) Indian Institute of Tropical Meteorology (IITM) ESSO Document Number ESSO/IITM/SERP/SR/01(2019)/196 Title of the Report Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Authors Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay Type of Document Scientific Report (Research Report) Number of pages and figures 37, 17 Number of references 37 Keywords Dynamical downscaling, Bias-correction, Extended range prediction Security classification Open Distribution Unrestricted Date of Publication January 2019 Abstract This study aims to document the strengths and shortcomings of forecast strategy being developed based on dynamical downscaling for better extended range prediction of extreme weather events. One heavy rainfall event and two cyclone cases are selected to dynamically downscale the global forecasts of Extended Range Prediction (ERP) system using Weather Research and forecasting (WRF) model at 9km for Indian domain. The ERP outputs are bias-corrected and fed to WRF as lateral boundary conditions to minimize migrated errors from parent model via boundary conditions. Results show bias-corrected downscaled ERP is more efficient in predicting extreme weather with 10-12 days lead time. Summary Indian subcontinent is prone to weather extremes like cyclones and heavy rainfall events causing severe damages due to floods and landslides. General circulation models (GCMs) running at coarse resolution are unable to forewarn the intensity of these catastrophic rains at sufficient lead time. In this study, a forecast strategy of dynamical downscaling is adopted to improve the extended range predictions of high impact weather events. Ensemble members of an Extended Range Prediction (raw-ERP) system based on higher (T382) and lower (T126) resolution of Climate Forecast System (CFSv2) and Global Forecast system (GFS) are downscaled individually using Weather Research and Forecasting (WRF) model. WRF run at 9 km resolution for Indian region such that the selected model domain is large enough to include ocean-atmospheric interactions in lateral boundary conditions. Downscaled ERP (ERPWRF) will take advantage of good prediction skills of raw-ERP in capturing the large scale signals and will help to reduce spatio-temporal errors in regional detailing. For the present study we have selected three different events - two cyclone cases and one heavy rainfall event. Global raw-ERP ensemble forecasts are bias-corrected to minimize GCM intrigued biases via boundary conditions. Bias-corrected and downscaled forecasts show improvement in predicting the spatio-temporal evolution of rainfall associated with the selected severe weather cases. This study affirms that dynamical downscaling can be an effective tool to generate useful high resolution predictions at 10-12 days lead time. Contents Abstract 1 Summary 2 1 Introduction 3 2 Data and Methodology 5 2.1 Models 5 2.2 Bias-correction & dynamical downscaling 5 2.3 Objective tracking algorithm 6 3 Results and Discussions 6 3.1 Cyclonic Storm Roanu of 2016 6 3.1.1 Results from Experiments 7 3.1.2 Results from final setup 8 3.2 Uttarakhand Heavy Rainfall 2013 8 3.3 Severe Cyclonic Storm Mora of 2017 9 4 Conclusions and Remarks 10 Acknowledgements 11 References 12 Figures 15 Bias-Correction and Dynamical Downscaling Strategy to Improve the Prediction of Extreme Weather Events on Extended Range Manpreet Kaur, R Phani, Susmitha Joseph, A.K. Sahai*, R. Mandal, A. Dey and R. Chattopadhyay Indian Institute of Tropical Meteorology, Pune-411008 Abstract This study aims to document the strengths and shortcomings of forecast strategy being developed based on dynamical downscaling for better extended range prediction of extreme weather events. One heavy rainfall event and two cyclone cases are selected to dynamically downscale the global forecasts of Extended Range Prediction (ERP) system using Weather Research and forecasting (WRF) model at 9km for Indian domain. The ERP outputs are bias-corrected and fed to WRF as lateral boundary conditions to minimize migrated errors from parent model via boundary conditions. Results show bias-corrected downscaled ERP is more efficient in predicting extreme weather with 10-12 days lead time. *Corresponding Author: Dr. A.K. Sahai Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune – 411008, India. E-mail: [email protected] Phone: +91-(0)20-25904520 1 Summary Indian subcontinent is prone to weather extremes like cyclones and heavy rainfall events causing severe damages due to floods and landslides. General circulation models (GCMs) running at coarse resolution are unable to forewarn the intensity of these catastrophic rains at sufficient lead time. In this study, a forecast strategy of dynamical downscaling is adopted to improve the extended range predictions of high impact weather events. Ensemble members of an Extended Range Prediction (raw-ERP) system based on higher (T382) and lower (T126) resolution of Climate Forecast System(CFSv2) and Global Forecast system (GFS) are downscaled individually using Weather Research and Forecasting (WRF) model. WRF run at 9 km resolution for Indian region such that the selected model domain is large enough to include ocean-atmospheric interactions in lateral boundary conditions. Downscaled ERP (ERPWRF) will take advantage of good prediction skills of raw-ERP in capturing the large scale signals and will help to reduce spatio-temporal errors in regional detailing. For the present study we have selected three different events - two cyclone cases and one heavy rainfall event. Global raw-ERP ensemble forecasts are bias-corrected to minimize GCM intrigued biases via boundary conditions. Bias-corrected and downscaled forecasts show improvement in predicting the spatio-temporal evolution of rainfall associated with the selected severe weather cases. This study affirms that dynamical downscaling can be an effective tool to generate useful high resolution predictions at 10-12 days lead time. Key-Words: Dynamical downscaling, Bias-correction, Extended range prediction 2 1. Introduction Extreme weather events are devastative due to their impacts on society such as excessive loss of lives, severe economic collapse and endangered ecosystems. Cyclonic storms and low pressure systems causing exceptionally heavy precipitation are few examples of extreme events. Studies have shown increasing trends of heavy rainfall( ≥ 100 mm/day) and very heavy rainfall (≥150 mm/day) over Indian region1. Increased frequency of such severe weather events are suspected to be linked to changes in hydro-cycle due to enhanced green-house effect2. Forewarning of these extreme cases at least 10-12 days in advance can help in reducing socio-economic loss. The forecasting of meteorological parameters with 2-3 weeks lead time is referred as extended range prediction. This sub seasonal forecast range has predictability due to the presence of quasi-periodicities in intra-seasonal scales; and is important for different sectors like agriculture, health, water management etc. A real time forecast system for extended range prediction (ERP) of active and break spells of Indian summer monsoon is developed at Indian Institute of Tropical Meteorology (IITM). This system is now operational at India Meteorological Department (hereafter termed as IITM/IMD ERP). ERP system is a combination of deterministic as well as probabilistic forecast which includes coupled model Climate Forecast System (CFSv2)3 and Atmospheric model Global Forecast System (GFS) at higher (T382) and lower (T126) resolutions with ensembles of perturbed initial conditions (ICs)4. ERP has impressive skill in predicting Monsoon onset & progression, cyclone genesis, Madden-Julian Oscillation, heat and cold waves with a lead time of two weeks5. It has been found in previous studies that ERP system is successful in providing outlooks on impending heavy rainfall events, albeit there are some spatio-temporal errors5,6. Therefore, it is hypothesized in this study that if the model could reasonably predict the large scale features associated with heavy