Browsing by Author "Raghavendra Ashrit"

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Assessment of extreme rainfall events over Kerala using EVA and NCUM-G model forecasts
(Springer, 2023) V. Abhijith; Raghavendra Ashrit; Anumeha Dube; Sunita Verma
Assessment of extreme rainfall events (ERE) is crucial for disaster management. Numerical weather prediction (NWP) model-based predictions often fail to predict the extremes. This could be due to several reasons, including insufficient model resolution to capture the sub-grid scale processes, inadequate high-quality observational data for assimilation, uncertainty in initial conditions and approximations in model physics. Estimation of rainfall for different return periods (RP) using extreme value analysis (EVA) can aid in better decision-making. RP of an event indicates its probability and rarity over the region. The current study shows how EVA can be used to supplement model predictions. This study uses the high-resolution (0.25×0.25) gridded observed rainfall data from India Meteorological Department (IMD), which has been available for 117 years (1901–2017). The generalised extreme value (GEV) distribution is applied with suitable goodness-of-fit tests. Rainfall amounts corresponding to 100-year RP are estimated using EVA over the entire data period (1901–2017) and three epochs (1901–1940, 1941–1980, and 1981–2017). The results indicate increasing rainfall amounts corresponding to 100-year RP. Similarly, rainfall amounts for 25, 50, 100, and 200-year RP over Kerala are computed to compare with the extremely heavy rainfall (≤21 cm/day) amounts reported during JJAS 2018 and 2019. Further, this approach supplements the operational forecasts of NCUM-G model forecasts. © 2023, Indian Academy of Sciences.
Assessment of Met Office Unified Model (UM) quantitative precipitation forecasts during the Indian summer monsoon: Contiguous Rain Area (CRA) approach
(Springer, 2019) Kuldeep Sharma; Raghavendra Ashrit; Elizabeth Ebert; Ashis Mitra; R. Bhatla; Gopal Iyengar; E.N. Rajagopal
The operational medium range rainfall forecasts of the Met Office Unified Model (UM) are evaluated over India using the Contiguous Rainfall Area (CRA) verification technique. In the CRA method, forecast and observed weather systems (defined by a user-specified rain threshold) are objectively matched to estimate location, volume, and pattern errors. In this study, UM rainfall forecasts from nine (2007–2015) Indian monsoon seasons are evaluated against 0. 5 ∘× 0. 5 ∘ IMD–NCMRWF gridded observed rainfall over India (6. 5 ∘- 38. 5 ∘N , 66. 5 ∘- 100. 5 ∘E). The model forecasts show a wet bias due to excessive number of rainy days particularly of low amounts (<1mmd-1). Verification scores consistently suggest good skill the forecasts at threshold of 10mmd-1, while moderate (poor) skill at thresholds of <20mmd-1(<40mmd-1). Spatial verification of rainfall forecasts is carried out for 10, 20, 40 and 80mmd-1 CRA thresholds for four sub-regions namely (i) northwest (NW), (ii) southwest (SW), (iii) eastern (E), and (iv) northeast (NE) sub-region. Over the SW sub-region, the forecasts tend to underestimate rain intensity. In the SW region, the forecast events tended to be displaced to the west and southwest of the observed position on an average by about 1 ∘ distance. Over eastern India (E) forecasts of light (heavy) rainfall events, like 10mmd-1 (20 and 40mmd-1) tend to be displaced to the south on an average by about 1 ∘ (southeast by 1 - 2 ∘). In all four regions, the relative contribution to total error due to displacement increases with increasing CRA threshold. These findings can be useful for forecasters and for model developers with regard to the model systematic errors associated with the monsoon rainfall over different parts of India. © 2018, Indian Academy of Sciences.
Forecasting of monsoon heavy rains: Challenges in NWP
(SPIE, 2016) Kuldeep Sharma; Raghavendra Ashrit; Gopal Iyengar; R. Bhatla; E.N. Rajagopal
Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction (NWP) models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution. Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The unified model operational at National Center for Medium Range Weather Forecasting (NCUM) and the unified model operational at the Australian Bureau of Meteorology (Australian Community Climate and Earth-System Simulator - Global (ACCESS-G)) are used in this study. The recent (JJAS 2015) Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding. The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least. © 2016 SPIE.
Skill of Predicting Heavy Rainfall Over India: Improvement in Recent Years Using UKMO Global Model
(Birkhauser Verlag AG, 2017) Kuldeep Sharma; Raghavendra Ashrit; R. Bhatla; A.K. Mitra; G.R. Iyengar; E.N. Rajagopal
The quantitative precipitation forecast (QPF) performance for heavy rains is still a challenge, even for the most advanced state-of-art high-resolution Numerical Weather Prediction (NWP) modeling systems. This study aims to evaluate the performance of UK Met Office Unified Model (UKMO) over India for prediction of high rainfall amounts (>2 and >5 cm/day) during the monsoon period (JJAS) from 2007 to 2015 in short range forecast up to Day 3. Among the various modeling upgrades and improvements in the parameterizations during this period, the model horizontal resolution has seen an improvement from 40 km in 2007 to 17 km in 2015. Skill of short range rainfall forecast has improved in UKMO model in recent years mainly due to increased horizontal and vertical resolution along with improved physics schemes. Categorical verification carried out using the four verification metrics, namely, probability of detection (POD), false alarm ratio (FAR), frequency bias (Bias) and Critical Success Index, indicates that QPF has improved by >29 and >24% in case of POD and FAR. Additionally, verification scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and SEDI (Symmetric EDI) are used with special emphasis on verification of extreme and rare rainfall events. These scores also show an improvement by 60% (EDS) and >34% (EDI and SEDI) during the period of study, suggesting an improved skill of predicting heavy rains. © 2017, Springer International Publishing AG.
Verification of heavy rainfall in NWP models: A case study
(India Meteorological Department, 2017) Kuldeep Sharma; Raghavendra Ashrit; R. Bhatla; R. Rakhi; G.R. Iyengar; E.N. Rajagopal
Forecasting of heavy rainfall events is still a challenge even for the most advanced state-of-art high resolution NWP modelling systems. Very often the models fail to accurately predict the track and movement of the low pressure systems leading to large spatial errors in the predicted rain. Quantification of errors in forecast rainfall location and amounts is important for forecasters (to choose a forecast and interpret) and modelers for monitoring the impact of changes and improvements in model physics and dynamics configurations. This study aims to quantify and summarize errors in rainfall forecast for heavy rains associated with a Bay of Bengal (BOB) low pressure systems. The verification analysis is based on three heavy rain events during June to September (JJAS) 2015. The performance of the three deterministic models, NCMRWF’s Global Forecast Systems (NGFS), NCMRWF’s Unified Model (NCUM) and Australian Community Climate and Earth-System Simulator – Global (ACCESS-G) in predicting these heavy rainfall events has been analysed. In addition to standard verification metrics like RMSE, ETS, POD and HK Score, this paper also uses new family of scores like EDS (Extreme Dependency Score), EDI (Extremal Dependence Index) and Symmetric EDI with special emphasis on verification of extreme rainfall to bring out the relative performance of the models for these three rainfall events. The results indicate that Unified modeling framework in NCUM and ACCESS-G by and large performs better than NGFS in rainfall forecasts over India specially at higher lead times. Relatively improved skill in NCUM forecasts can be attributed to (i) improved resolution (~17 km) and (ii) END Game dynamics of NCUM. © 2017, India Meteorological Department. All rights reserved.