Evaluation of Geopotential Height at 500 hpa with Rainfall Events: A Case Study of Iraq

Jabbar, Mohammed Abdul Raheem; Hassan, Ahmed S.

doi:10.23851/mjs.v33i4.1161

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Fall of geopotential height cause turbulent weather phenomena. This atmospheric condition favour instability and convection of cloud accompanied by heavy rainfall [18] . The variation of geopotential heights of 500 hPa level may impact the dynamics of climate change via the change in circulation pattern [19] .…”

Section: Introductionmentioning

confidence: 99%

Variation of Dynamical Parameters with Upper Tropospheric Potential Vorticity in Tropical Cyclone over the North Indian Ocean Using WRF Model

Rabbi,

Ishtiaque M. Syed,

Md. Abdullah Elias Akhter

et al. 2023

j. of atmos. sci. res.

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Meteorologists are experiencing many challenges in the reliable forecasting of the track and intensity of tropical cyclones (TC). Uses of the potential vorticity (PV) technique will enrich the current forecasting system. The use of PV analysis of TC intensification over the North Indian Ocean (NIO) is rare. In this study, the authors analyze the behaviour of upper-level PV with dynamic parameters of TCs over NIO. The authors used NCEP FNL reanalysis 1 × 1 degree data as input in WRF model version 4.0.3 with one-way nesting between the parent and child domains. The authors used a coupling of the Kain-Fritsch (new Eta) scheme and the WSM 6-class graupel scheme as cumulus and microphysics options to run the model. The authors found that at least 1 potential vorticity unit (PVU) (1 PVU = 10–6 m2 s –1KKg–1) upper PV is required to maintain the intensification of TC Titli. Larger upper PV accelerates the fall of central pressure. The high value of upper PV yields the intensification of TC. The wind shear and upper PV exhibited almost identical temporal evolution. Upper PV cannot intensify the TCs at negative wind shear and shear above the threshold value of 12 ms–1. The upper PV and geopotential heights of 500 hPa change mutually in opposite trends. The upper PV calculated by the model is comparable to that of ECMWF results. Therefore the findings of this study are admissible.

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Section: Introductionmentioning

confidence: 99%

Variation of Dynamical Parameters with Upper Tropospheric Potential Vorticity in Tropical Cyclone over the North Indian Ocean Using WRF Model

Rabbi,

Ishtiaque M. Syed,

Md. Abdullah Elias Akhter

et al. 2023

j. of atmos. sci. res.

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Daily reservoir inflow prediction using stacking ensemble of machine learning algorithms

Deb,

Arunachalam,

Raju

2024

Journal of Hydroinformatics

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The present study aims to evaluate the potentiality of Bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Networks (CNNs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Mechine (LGBM), and Random Forest (RF) for predicting daily inflows to the Sri Ram Sagar Project (SRSP), Telangana, India. Inputs to the model are rainfall, evaporation, time lag inflows, and climate indices. Seven combinations (S1–S7) of inputs were made. Fifteen and a half years of data were considered, out of which 11 years were used for training. Hyperparameter tuning is performed with the Tree-Structured Parzen Estimator. The performance of the algorithms is assessed using Kling–Gupta efficiency (KGE). Results indicate that Bi-LSTM with combination S7 performed better than others, as evident from KGE values of 0.92 and 0.87 during the training and testing, respectively. Furthermore, Stacking Ensemble Mechanism (SEM) has also been employed to ascertain its efficacy over other chosen algorithms, resulting in KGE values of 0.94 and 0.89 during training and testing. It has also been able to simulate peak inflow events satisfactorily. Thus, SEM is a better alternative for reservoir inflow predictions.

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Evaluation of Geopotential Height at 500 hpa with Rainfall Events: A Case Study of Iraq

Cited by 2 publications

References 20 publications

Variation of Dynamical Parameters with Upper Tropospheric Potential Vorticity in Tropical Cyclone over the North Indian Ocean Using WRF Model

Variation of Dynamical Parameters with Upper Tropospheric Potential Vorticity in Tropical Cyclone over the North Indian Ocean Using WRF Model

Daily reservoir inflow prediction using stacking ensemble of machine learning algorithms

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