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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-65
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-65
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model evaluation paper 06 May 2019

Model evaluation paper | 06 May 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Evaluation of Unified Model Rainfall Forecasts over the Western Ghats and North East states of India

Kuldeep Sharma1, Sushant Kumar1, Raghavendra Ashrit1, Sean Milton2, Ashis K. Mitra1, and Ekkattil N. Rajagopal1 Kuldeep Sharma et al.
  • 1National Centre for Medium Range Weather Forecasting, A-50, Sector-62, Noida 201309
  • 2Met Office, FitzRoy Road, Exeter Devon, EX1 3PB, UK

Abstract. Prediction of heavy rains associated with orography is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. The aim of this study is to evaluate the performance of UK Met Office Unified Model (UM) in predicting heavy and very heavy rainfall exceeding 80th and 90th percentiles which occurs mainly due to the forced ascent of air parcels over the mountainous regions of the Western Ghats (WGs) and North East (NE) – states of India during the monsoon seasons of 2007 to 2018. Apart from the major upgrades in the dynamical core of UM from New Dynamics (ND) to Even Newer Dynamics for General Atmospheric Modeling of the environment (ENDGame), the horizontal resolution of the model has been increased from 40 km and 50 vertical levels in 2007 to 10 km and 70 vertical levels in 2018. In general, it is expected that the prediction of heavy rainfall events improves with increased horizontal resolution of the model. The evaluation based on verification metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), Frequency Bias (Bias) and Critical Success Index (CSI), indicate that model rainfall forecasts from 2007 to 2018 have improved from 0.29 to 0.38 (CSI), 0.45 to 0.55 (POD) and 0.55 to 0.45 in the case of FAR over WGs for rainfall exceeding the 80th percentile (CAT-1) in the Day-1 forecast. Additionally, the Symmetric Extremal Dependence Index (SEDI) is also used with special emphasis on verification of extreme and rare events. SEDI also shows an improvement from 0.47 to 0.62 and 0.16 to 0.41 over WGs and NE-states during the period of study, suggesting an improved skill of predicting heavy rains over the mountains. It has also been found that the improvement is consistent and comparatively higher over WGs than NE-states.

Kuldeep Sharma et al.
Interactive discussion
Status: open (until 01 Jul 2019)
Status: open (until 01 Jul 2019)
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Kuldeep Sharma et al.
Data sets

Data sets A. K. Mitra, NCMWRF in collaboration with IMD https://github.com/kuldeep-ncmrwf/ABC/blob/master/rain07-18.tar

Model code and software

Verification Codes K. Sharma and R. Ashrit https://github.com/kuldeep-ncmrwf/ABC/tree/master/GMDD_CODES

Kuldeep Sharma et al.
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Latest update: 20 May 2019
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Short summary
This study is based on the long record (2007–2018) of UM model's real time rainfall forecasts over India to highlight the improved skill of forecasts over orographic regions of India. Some of these improvements are attributed to the increased horizontal and vertical resolutions as well as improved physics parameterization schemes while major credit to the substantial improvements in weather forecasting goes to the sophisticated data assimilation systems which utilizes satellite data.
This study is based on the long record (2007–2018) of UM model's real time rainfall forecasts...
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