IMDAA Regional Reanalysis: Performance Evaluation During Indian Summer Monsoon Season

Ashrit, Raghavendra; Rani, S. Indira; Kumar, Sushant; Karunasagar, S.; Arulalan, T.; Francis, Timmy; Routray, Aurobinda; Laskar, S. I.; Mahmood, Sana; Jermey, Peter; Maycock, Adam; Renshaw, Richard; George, John P.; Rajagopal, E. N.

doi:10.1029/2019jd030973

Cited by 78 publications

(48 citation statements)

References 66 publications

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“…To check the robustness of our results, we will also use precipitation data from the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) reanalysis (Ashrit et al 2020). This reanalysis is available at an hourly sampling frequency from 1979-2018 over the South Asian region (158S-458N, 308-1208E), at a spatial resolution of 12 km, and simulated precipitation well when verified against individual gauges (Ashrit et al 2020).…”

Section: Data and Methodology A Datamentioning

confidence: 99%

How interactions between tropical depressions and western disturbances affect heavy precipitation in South Asia

Hunt

Turner

Schiemann

2021

Monthly Weather Review

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Interactions over South Asia between tropical depressions (TDs) and extratropical storms known as western disturbances (WDs) are known to cause extreme precipitation events, including those responsible for the 2013 floods over northern India. In this study, existing databases of WD and TD tracks are used to identify potential WD–TD interactions from 1979–2015; these are filtered according to proximity and intensity, leaving 59 cases which form the basis of this paper. Synoptic charts, vorticity budgets, and moisture trajectory analyses are employed to identify and elucidate common interaction types among these cases. Two broad families of interaction emerge. Firstly, a dynamical coupling of the WD and TD, whereby either the upper- and lower-level vortices superpose (a vortex merger), or the TD is intensified as it passes into the entrance region of a jet streak associated with the WD (a jet-streak excitation). Secondly, a moisture exchange between the WD and TD, whereby either anomalous moisture is advected from the TD to the WD, resulting in anomalous precipitation near the WD (a TD-to-WD moisture exchange), or anomalous moisture is advected from the WD to the TD (a WD-to-TD moisture exchange). Interactions are most common in the post-monsoon period as the subtropical jet, which brings WDs to the subcontinent, returns south; there is a smaller peak in May and June, driven by monsoon onset vortices. Precipitation is heaviest in dynamically-coupled interactions, particularly jet-streak excitations. Criteria for automated identification of interaction types are proposed, and schematics for each type are presented to highlight key mechanisms.

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Section: Data and Methodology A Datamentioning

confidence: 99%

How interactions between tropical depressions and western disturbances affect heavy precipitation in South Asia

Hunt

Turner

Schiemann

2021

Monthly Weather Review

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“…The output variables from simulations have to be validated with the observed data to verify the accuracy of the simulations. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi‐satellite Precipitation Analysis (TMPA) at 0.25° × 0.25° resolution (Huffman & Savtchenko, 2017) daily accumulated precipitation data is used to validate the rainfall data and the Indian monsoon data assimilation and analysis (IMDAA) regional reanalysis data (Ashrit et al., 2020; Rani et al., 2021) at 0.12° × 0.12° resolution is used to validate surface air temperature, wind speed, and surface pressure. Root Mean Square Error (RMSE) is used as the objective function to verify the closeness of the simulation to the observation for surface air temperature, wind speed, and surface pressure.…”

Section: Design Of Experimentsmentioning

confidence: 99%

Assessment of WRF Model Parameter Sensitivity for High‐Intensity Precipitation Events During the Indian Summer Monsoon

Chinta

Sai

Balaji

2021

Earth and Space Science

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Default values for many parameters in Numerical Weather Prediction models are typically adopted based on theoretical or experimental investigations by scheme designers. Short‐range forecasts are substantially affected by the specification of parameters in the Weather Research and Forecasting (WRF) model. The presence of a multitude of parameters and several output variables in the WRF model renders appropriate parameter value identification quite challenging. This study aims to identify the parameters that most strongly influence the model output variables using a Global Sensitivity Analysis (GSA) method. Morris One‐At‐a‐Time (MOAT), a GSA method, is used to identify the sensitivities of 23 chosen tunable parameters corresponding to seven physical parameterization schemes of the WRF model. The sensitivity measures (MOAT mean and standard deviation) are evaluated for 11 output variables simulated by the WRF model, corresponding to different parameters. Twelve high‐intensity 4‐day precipitation events during the Indian summer monsoon during 2015, 2016, and 2017 over India's monsoon core region are considered for the study. Though the parameter sensitivities vary depending on the model output variable, overall results suggest a general trend. The consistency of sensitivity analysis results with different initial and lateral boundary conditions is also assessed.

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“…The simulations are validated against the Indian Monsoon Data Assimilation and Analysis (IMDAA) data (Ashrit et al, 2020) and Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset (Huffman, G and Savtchenko, AK, 2019). The IMDAA data is available at 0.12 • × 0.12 • resolution with a six-hour latency and the IMERG data is available at 0.1…”

Section: Simulation Events Wrf Model Output Variables and Observational Datamentioning

confidence: 99%

Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning

Baki

Chinta

Balaji

et al. 2021

Preprint

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Abstract. The present study focuses on identifying the parameters from the Weather Research and Forecasting (WRF) model that strongly influence the prediction of tropical cyclones over the Bay of Bengal (BoB) region. Three global sensitivity analysis (SA) methods, namely the Morris One-at-A-Time (MOAT), Multivariate Adaptive Regression Splines (MARS), and surrogate-based Sobol' are employed to identify the most sensitive parameters out of 24 tunable parameters corresponding to seven parameterization schemes of the WRF model. Ten tropical cyclones across different categories, such as cyclonic storms, severe cyclonic storms, and very severe cyclonic storms over BoB between 2011 and 2018, are selected in this study. The sensitivity scores of 24 parameters are evaluated for eight meteorological variables. The parameter sensitivity results are consistent across three SA methods for all the variables, and 8 out of the 24 parameters contribute 80 %–90 % to the overall sensitivity scores. It is found that the Sobol' method with Gaussian progress regression as a surrogate model can produce reliable sensitivity results when the available samples exceed 200. The parameters with which the model simulations have the least RMSE values when compared with the observations are considered as the optimal parameters. Comparing observations and model simulations with the default and optimal parameters shows that predictions with the optimal set of parameters yield a 16.74 % improvement in the 10 m wind speed, 3.13 % in surface air temperature, 0.73 % in surface air pressure, and 9.18 % in precipitation predictions compared to the default set of parameters.

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IMDAA Regional Reanalysis: Performance Evaluation During Indian Summer Monsoon Season

Cited by 78 publications

References 66 publications

How interactions between tropical depressions and western disturbances affect heavy precipitation in South Asia

How interactions between tropical depressions and western disturbances affect heavy precipitation in South Asia

Assessment of WRF Model Parameter Sensitivity for High‐Intensity Precipitation Events During the Indian Summer Monsoon

Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning

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