Improved Rainfall Simulation by Assimilating Oceansat-2 Surface Winds Using Ensemble Kalman Filter for a Heavy Rainfall Event over South India

Madhu, Dhanya; Chandrasekar, A.

doi:10.1109/tgrs.2014.2317501

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…(18) and decreasing trend: Fig. (19). The increasing group had 29 subsets, while the decreasing group had 16 subsets.…”

Section: Relative Biasmentioning

confidence: 92%

“…As an important category of data-driven models, deeplearning models have made significant contributions to meteorological forecasting in recent years [15], [16], [17], [18], [19]. In 2023, Jongyun Byun et al introduced a new research direction that utilizes cloud image data as input, employing a deep-learning model for analysis and prediction of rainfall amount [20].…”

Section: Introductionmentioning

confidence: 99%

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Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting

Zheng,

Miyamoto,

Shimizu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Lagrangian persistence method in nowcasting is a highly effective method for short-term weather forecasting. However, its performance is not as robust in long-term forecasting or situations of rapid weather changes due to the difficulty in analyzing the intensity variation of meteorological physical quantities. To address this shortcoming, in this study, we incorporated Dynamic Mode Decomposition into the Lagrangian persistence method. Specifically, we proposed a Coordinate-Transformed Dynamic Mode Decomposition (CT-DMD) model by integrating an Optical Flow model with a Dynamic Mode Decomposition model, providing an effective method for analyzing the intensity variation of meteorological physical quantities in the Lagrangian persistence method. The integration of the Optical Flow model and the Dynamic Mode Decomposition model involves the transformation of data between Eulerian and Lagrangian coordinate systems. The CT-DMD model was evaluated using radar-observed rainfall data from the Kanto region of Japan, with the Rainymotion model used as a benchmark. When the lead time was 5 min, 22.22% of the subsets in the experimental dataset showed that the CT-DMD model had a higher forecast accuracy compared to the Rainymotion model. When the lead time was 25 min, 88.89% of the subsets in the experimental dataset showed that the CT-DMD model had a higher forecast accuracy compared to the Rainymotion model. The accuracy advantage of the CT-DMD model became apparent after a lead time of 15 min and became increasingly significant as the lead time increased. The results demonstrated the validity of the CT-DMD model.

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“…(18) and decreasing trend: Fig. (19). The increasing group had 29 subsets, while the decreasing group had 16 subsets.…”

Section: Relative Biasmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting

Zheng,

Miyamoto,

Shimizu

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…M ODERN scientific tools and technologies often involve sensors that give noisy data [1], [2]. In nonengineering applications, survey and experimental data are often noisy [3]. Very often, noisy data are used for determining the hidden states of a system (or process).…”

Section: Introductionmentioning

confidence: 99%

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

et al. 2022

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Approximate filtering algorithms in nonlinear systems assume Gaussian prior and predictive density and remain popular due to ease of implementation as well as acceptable performance. However, these algorithms are restricted by two major assumptions: they assume no missing or delayed measurements. However, practical measurements are frequently delayed and intermittently missing. In this paper, we introduce a new extension of the Gaussian filtering to handle the simultaneous occurrence of the delay in measurements and intermittently missing measurements. Our proposed algorithm uses a novel modified measurement model to incorporate the possibility of the delayed and intermittently missing measurements. Subsequently, it redesigns the traditional Gaussian filtering for the modified measurement model. Our algorithm is a generalized extension of the Gaussian filtering, which applies to any of the traditional Gaussian filters, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF). A further contribution of this paper is that we study the stochastic stability of the proposed method for its EKF-based formulation. We compared the performance of the proposed filtering method with the traditional Gaussian filtering (particularly the CKF) and three extensions of the traditional Gaussian filtering that are designed to handle the delayed and missing measurements individually or simultaneously.INDEX TERMS Delayed measurements, Gaussian filtering, missing measurements, nonlinear Bayesian filtering.

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“…Generally, the initial and lateral boundary conditions for a WRF model are provided by large scale global analyses but with the constraints of low resolution and insufficient depiction of regional mesoscale characteristics. Different data assimilation techniques, which ingest the localized observational data, such as variational and ensemble data assimilation methods are integral to WRF and have been shown to have a considerable effect on the rainfall forecast in India [13,14].…”

Section: Introductionmentioning

confidence: 99%

Assessment of WRF model parameter sensitivity for high-intensity precipitation events during the Indian summer monsoon

Chinta,

Jetti,

Balaji

2020

Preprint

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Default values for many model parameters in Numerical Weather Prediction models aretypically 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. The objective of the current study is to reduce the uncertainty in the model outcomes through the recognition of parameters which most strongly influence the model performance using a Global Sensitivity Analysis (GSA) method. Morris one-step-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 eleven output variables, which are simulated by the WRF model, with respect to different parameters. Twelve high-intensity four-day precipitation events during the Indian summer monsoon (ISM) for the years 2015, 2016, and 2017 over the monsoon core region in India are considered for the study. Though the sensitivities of each parameter vary from one model output variable to the other, overall results suggest a

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Improved Rainfall Simulation by Assimilating Oceansat-2 Surface Winds Using Ensemble Kalman Filter for a Heavy Rainfall Event over South India

Cited by 9 publications

References 12 publications

Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting

Coordinate-Transformed Dynamic Mode Decomposition for Short-Term Rainfall Forecasting

Gaussian Filtering for Simultaneously Occurring Delayed and Missing Measurements

Assessment of WRF model parameter sensitivity for high-intensity precipitation events during the Indian summer monsoon

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