RAGHAVENDRA ASHRIT
Articles written in Journal of Earth System Science
Volume 115 Issue 3 June 2006 pp 299-313
Simulation of a Himalayan cloudburst event
Someshwar Das Raghavendra Ashrit M W Moncrieff
Intense rainfall often leads to floods and landslides in the Himalayan region even with rainfall amounts that are considered comparatively moderate over the plains; for example, ‘cloudbursts’, which are devastating convective phenomena producing sudden high-intensity rainfall (∼10 cm per hour) over a small area. Early prediction and warning of such severe local weather systems is crucial to mitigate societal impact arising from the accompanying flash floods. We examine a cloudburst event in the Himalayan region at Shillagarh village in the early hours of 16 July 2003. The storm lasted for less than half an hour, followed by flash floods that affected hundreds of people. We examine the fidelity of MM5 configured with multiple-nested domains (81, 27, 9 and 3 km grid-resolution) for predicting a cloudburst event with attention to horizontal resolution and the cloud microphysics parameterization. The MM5 model predicts the rainfall amount 24 hours in advance. However, the location of the cloudburst is displaced by tens of kilometers
Volume 117 Issue 5 October 2008 pp 603-620
Skills of different mesoscale models over Indian region during monsoon season: Forecast errors
Someshwar Das Raghavendra Ashrit Gopal Raman Iyengar Saji Mohandas M Das Gupta John P George E N Rajagopal Surya Kanti Dutta
Performance of four mesoscale models namely,the MM5,ETA,RSM and WRF,run at NCMRWF for short range weather forecasting has been examined during monsoon-2006.Evaluation is carried out based upon comparisons between observations and day-1 and day-3 forecasts of wind,temperature,specific humidity,geopotential height,rainfall,systematic errors,root mean square errors and specific events like the monsoon depressions.
It is very difficult to address the question of which model performs best over the Indian region? An honest answer is ‘none ’.Perhaps an ensemble approach would be the best.However, if we must make a final verdict,it can be stated that in general,(i)the WRF is able to produce best All India rainfall prediction compared to observations in the day-1 forecast and,the MM5 is able to produce best All India rainfall forecasts in day-3,but ETA and RSM are able to depict the best distribution of rainfall maxima along the west coast of India,(ii)the MM5 is able to produce least RMSE of wind and geopotential fields at most of the time,and (iii)the RSM is able to produce least errors in the day-1 forecasts of the tracks,while the ETA model produces least errors in the day-3 forecasts.
Volume 119 Issue 4 August 2010 pp 417-446
Mesoscale model forecast verification during monsoon 2008
Raghavendra Ashrit Saji Mohandas
There have been very few mesoscale modelling studies of the Indian monsoon, with focus on the verification and intercomparison of the operational real time forecasts. With the exception of Das
Here we present a mesoscale model forecast verification and intercomparison study over India involving three mesoscale models: (i) the Weather Research and Forecast (WRF) model developed at the National Center for Atmospheric Research (NCAR), USA, (ii) the MM5 model developed by NCAR, and (iii) the Eta model of the NCEP, USA. The analysis is carried out for the monsoon season, June to September 2008. This study is unique since it is based entirely on the real time global model forecasts of the National Centre for Medium Range Weather Forecasting (NCMRWF) T254 global analysis and forecast system. Based on the evaluation and intercomparison of the mesoscale model forecasts, we recommend the best model for operational real-time forecasts over the Indian region.
Although the forecast mean 850 hPa circulation shows realistic monsoon flow and the monsoon trough, the systematic errors over the Arabian Sea indicate an easterly bias to the north (of mean flow) and westerly bias to the south (of mean flow). This suggests that the forecasts feature a southward shift in the monsoon current. The systematic error in the 850 hPa temperature indicates that largely the WRF model forecasts feature warm bias and the MM5 model forecasts feature cold bias. Features common to all the three models include warm bias over northwest India and cold bias over southeast peninsula. The 850 hPa specific humidity forecast errors clearly show that the Eta model features dry bias mostly over the sea, while MM5 features moist bias over large part of domain. The RMSE computed at different levels clearly establish that WRF model forecasts feature least errors in the predicted free atmospheric fields. Detailed rainfall forecast verification further establishes that the WRF model forecast rainfall skill remains more or less same in day-2 and day-3 as in day-1, while the forecast skill in the MM5 and Eta models, deteriorates in day-2 and day-3 forecasts.
Volume 128 Issue 1 February 2019 Article ID 0004 Research Article
Kuldeep Sharma Raghavendra Ashrit Elizabeth Ebert Ashis Mitra Bhatla R Gopal Iyengar Rajagopal E N
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$^{\circ }\times$ 0.5$^{\circ }$ IMD–NCMRWF gridded observed rainfall over India (6.5$^{\circ }{-}$38.5$^{\circ }$N, 66.5$^{\circ }{-}$100.5$^{\circ }$E). The model forecasts show a wet bias due to excessive number of rainy days particularly of low amounts (<1 mm d$^{-1}$). Verification scores consistently suggest good skill the forecasts at threshold of 10 mm d$^{-1}$, while moderate (poor) skill at thresholds of <20 mm d$^{-1}$ (<40 mm d$^{-1}$). Spatial verification of rainfall forecasts is carried out for 10, 20, 40 and 80 mm d$^{-1}$ 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$^{\circ }$ distance. Over eastern India (E) forecasts of light (heavy) rainfall events, like 10 mm d$^{-1}$ (20 and 40 mm d$^{-1}$) tend to be displaced to the south on an average by about 1$^{\circ }$ (southeast by 1$-2^{\circ }$). 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.
Volume 129 All articles Published: 1 January 2020 Article ID 0013 Research Article
Bias correction of maximum temperature forecasts over India during March–May 2017
HARVIR SINGH ANUMEHA DUBE SUSHANT KUMAR RAGHAVENDRA ASHRIT
In recent times, instances of intense heat waves have increased over the Indian subcontinent. This increase in temperature has an adverse effect on human health and the economy. Over India, such high temperatures are usually seen during the months of March–May (summer). For weather forecasters, it is a challenging job to accurately predict the timing and intensity of this anomalous high temperature. The difficulty in the accurate prediction of weather is increased because of the presence of systematic biases inthe models. These biases are present because of improper parameterizations or model physics. For increasing the reliability or accuracy of a forecast it is essential to remove these biases by using a process called post-processing. In this study the biases in the surface temperature maximum are corrected using two methods, namely, the moving average and the decaying average. One of the main advantages of both the methods is that they do not require a large amount of past data for calibration and they take into account the most recent behaviour of the forecasting system. Verification, for maximum surface temperature during March–May 2017, was carried out in order to decide upon the method giving the best temperature forecast. It was found that both the bias correction methods lead to a decrease in the mean error in maximum surface temperature ($T_{max}$). However, the decaying average method showed a higher decrease in the mean error. Scores obtained from a contingency table like POD, FAR and PSS, showed that for $T_{max}$, the decaying average method outperforms the forecasts, i.e., raw and moving average in terms of having high POD and PSS and a low FAR.
Volume 130 All articles Published: 6 May 2021 Article ID 0082 Research Article
KULDEEP SHARMA RAGHAVENDRA ASHRIT SUSHANT KUMAR SEAN MILTON EKKATTIL N RAJAGOPAL ASHIS K MITRA
Prediction of heavy/extreme rains is still a challenge, even for the most advanced state-of-the-art high-resolution Numerical Weather Prediction (NWP) modelling systems. Hydrological models use the rainfall forecasts from the NWP models as input. This study evaluates the performance of the UK Met Office Unified Model (UM) in predicting the rainfall exceeding 80th and 90th percentiles. Such high rainfall amounts occur over the Western Ghats (WGs) and North East (NE) India mainly due to the forced ascent of air parcels. Apart from the significant upgrades in the UM's dynamical core, the model features an increased horizontal grid (40–10 km) and vertical resolution (50–70 levels). The prediction skill of heavy rainfall events improves with an increased horizontal resolution of the model. The probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) are the verification metrics used. As per these metrics, model rainfall forecasts have improved during 2007–2018 (increase in CSI from 0.29 to 0.38, POD from 0.45 to 0.55, and decrease in FAR from 0.55 to 0.45). Additionally, to verify extreme and rare events, the symmetric extremal dependence index (SEDI) is also used. SEDI also shows an increase from 0.47 to 0.62 and 0.16 to 0.41 over WGs and NE India during the study period, suggesting an improved skill of predicting heavy rains over the mountains. The improved forecast performance is consistent and relatively higher over WGs than over NE states.
Volume 131 All articles Published: 9 May 2022 Article ID 0114 Research article
SUSHANT KUMAR ANUMEHA DUBE SUMIT KUMAR S INDIRA RANI KULDEEP SHARMA S KARUNASAGAR SAJI MOHANDAS RAGHAVENDRA ASHRIT JOHN P GEORGE ASHIS K MITRA
Operational forecasting of tropical cyclone (TC) track and intensity in the India Meteorological Department (IMD) relies more and more on the numerical weather prediction (NWP) model guidance from national and international agencies particularly, on the medium range (24–120 h). Any improvement in TC forecasts by the NWPmodels enhances the operational forecaster’s confidence and capability. The real-time information from the National Centre for Medium Range Weather Forecasting (NCMRWF) global NWP model (NCUM-G) is routinely used by operational forecasters at IMD as model guidance. The present study documents the improved skill of NCUM-G in forecasting the North Indian Ocean (NIO) TCs during 2015–2019, based on a collection of 1810 forecasts involving 22 TC cases. The study highlights three significant changes in the modelling system during the recent five years, namely (i) increased grid resolution from 17 to 12 km, (ii) use of hybrid 4D-Var data assimilation (DA), and (iii) increased volume of assimilated data. The study results indicate a consistent improvement in the NCUM-G model forecasts during the premonsoon (April–May,AM)and post-monsoon (October–December,OND)TCseasons. In addition to a 44% reduction in the initial position error, the study also reports a statistically significant decrease in the direct position error (DPE) and error in the intensity forecast, resulting in a forecast gain of 24 hrs. Comparing NWP models with IMDs official track error shows that NCUM-G and ECMWF model forecasts feature lower DPE than IMD in 2019, particularly at higher (96, 108, and 120 h) lead times.
Volume 131 All articles Published: 15 December 2022 Article ID 0259 Research article
Evaluation of five high-resolution global model rainfall forecasts over India during monsoon 2020
RAGHAVENDRA ASHRIT MOHAN S THOTA ANUMEHA DUBE KONDAPALLI NIRANJAN KUMAR S KARUNASAGAR SUSHANT KUMAR HARVIR SINGH RAJASEKHAR MEKA R PHANI MURALI KRISHNA ASHIS K MITRA
This study aims to evaluate the performance of five global medium-range operational NWP model rainfall forecasts, namely NCUM, UKMO, IMD GFS, NCEP GFS and ECMWF to provide an intercomparison of rainfall forecasts over India in terms of skill in predicting daily rainfall (24-hr accumulated rainfall). Veri- fication and intercomparison of rainfall forecasts over India during monsoon 2020 (JJAS) are carried out using both (i) standard traditional verification methods (POD, FAR, RMSE, etc.) and (ii) advanced spatial verification methods (MODE, FSS). The evaluation also includes assessment of large-scale mean patterns, temporal evolution of spells during the season, dominant modes using spectral analysis, basin-scale rainfall time series and isolated heavy rainfall cases. Our analysis suggests that some of the key large-scale aspects of monsoon (seasonal mean, active/break spells, and northward propagation) are realistically represented in all the models, with slight discrepancies. In addition, the spectral analysis of rainfall is in association with observed rainfall in Day-1 forecast and deteriorates with lead times. Synoptic variance in NCUM on longer leading times is closer to observations. While the standard categorical verification over India as a whole (spatial averaged) suggests that ECMWF forecast skill is relatively high among the Bve models, the veri-fication over the sub-regions shows mixed results with no clear unique higher performer among the models. In addition, basin-scale verification of rainfall forecasts for five rivers over the Indian subcontinent shows a fairly good amount of skill in terms of CC and RMSE up to Day-3 with comparable scores among the models. The advanced spatial verification metrics, like MODE and FSS, applied to the models show varying skills with different attributes. However, for FSS, forecast skill was high (low) for lower (higher) rainfall thresholds of 20 mm/day (100 mm/day). Though different models with different spatial resolutions show reasonable skill scores for larger regions, for high-impact heavy rainfall events, which are generally localised, the models have very comparable poor skill with no clear edge by a model among the five models.
Volume 131 All articles Published: 16 December 2022 Article ID 0260 Research article
ABHIJIT SARKAR S KIRAN PRASAD ASHU MAMGAIN ANUMEHA DUBE PAROMITA CHAKRABORTY SUSHANT KUMAR SAGILI KARUNASAGAR MOHANA S THOTA GAURI SHANKER RAGHAVENDRA ASHRIT A K MITRA
Probabilistic forecasting of tropical cyclone (TC) from ensemble prediction systems provides flowdependent uncertainty associated with the model forecast and helps in better decision making. NCMRWF global and regional ensemble prediction systems (NEPS-G and NEPS-R) have been used in forecasting the intensity and track of the Super Cyclone ‘Amphan’, which hit eastern India and Bangladesh in May 2020. Observation shows very heavy rainfall (${\ge}$11.5 cm/day) over West Bengal and Bangladesh on 20th May 2020. NEPS-R predicted very heavy rainfall in day-2 forecasts with a probability in the range of 50–70%. NEPS-G also could predict very heavy rainfall in day-2 forecast with probability lying in the same range but over a small area. In its day-5 forecast also, NEPS-G was able to predict very heavy rainfall with a probability lying in the range of 30–50%. The prediction of time and magnitude of the maximum intensity by NEPS-R was predicted better from the initial condition of 00 UTC 16th May 2020. Prediction of intensi-fication is better in NEPS-R forecast. Both NEPS-G and NEPS-R are under-dispersive in 10 m maximum wind forecasting. The RMSE-spread relationship of maximum wind speed is better in the NEPS-R forecast till 72 hrs forecast lead time. The reliability of both NEPS-Gand NEPS-R strike probability forecasts is good and NEPS-G shows better reliability at lower probability values. The mean direct position error (DPE) from NEPS-G does not exceed 350 kmin day-5 and 270km in day-3 forecast lead time. The meanDPEof NEPS-R forecast is about 175 km in day-3 forecast lead time. Both NEPS-G and NEPS-R predicted early landfall, but the error in the landfall time does not exceed 3 hrs within the last 72 hrs before landfall. NEPS-R at short range and NEPS-G at longer range show reasonable skill in prediction of tropical cyclone ‘Amphan’.
Volume 132, 2023
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