Adaptation of a particle filtering method for data assimilation in a 1D numerical model used for fog forecasting

We propose an efficient algorithm to perform nonlinear data assimilation for Korteweg–de Vries solitons. In particular we develop a reduced particle filtering method to reduce the dimension of the problem. The method decomposes a solitonic pulse into a clean soliton and small radiative noise, and instead of inferring the complete pulse profile, we only infer the two soliton parameters with particle filter. Numerical examples are provided to demonstrate that the proposed method can provide rather accurate results, while being much more computationally affordable than a standard particle filter.

Download Full-text

Dynamic Indoor Localization Using Maximum Likelihood Particle Filtering

Sensors ◽

10.3390/s21041090 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1090

Author(s):

Wenxu Wang ◽

Damián Marelli ◽

Minyue Fu

Keyword(s):

Maximum Likelihood ◽

Particle Filtering ◽

Indoor Localization ◽

Real Data ◽

High Accuracy ◽

Classical Particle ◽

Popular Approach ◽

Filtering Method ◽

Essential Idea ◽

Number Of Particles

A popular approach for solving the indoor dynamic localization problem based on WiFi measurements consists of using particle filtering. However, a drawback of this approach is that a very large number of particles are needed to achieve accurate results in real environments. The reason for this drawback is that, in this particular application, classical particle filtering wastes many unnecessary particles. To remedy this, we propose a novel particle filtering method which we call maximum likelihood particle filter (MLPF). The essential idea consists of combining the particle prediction and update steps into a single one in which all particles are efficiently used. This drastically reduces the number of particles, leading to numerically feasible algorithms with high accuracy. We provide experimental results, using real data, confirming our claim.

Download Full-text

Evaluation of Operational Monsoon Moisture Surveillance and Severe Weather Prediction Utilizing COSMIC-2/FORMOSAT-7 Radio Occultation Observations

Remote Sensing ◽

10.3390/rs13152979 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2979

Author(s):

Yu-Chun Chen ◽

Chih-Chien Tsai ◽

Yi-Chao Wu ◽

An-Hsiang Wang ◽

Chieh-Ju Wang ◽

...

Keyword(s):

Numerical Model ◽

Data Assimilation ◽

Water Resource Management ◽

Radio Occultation ◽

Positive Impact ◽

Weather Prediction ◽

Severe Weather ◽

Model Experiments ◽

Typhoon Structure ◽

Numerical Model Experiments

Operational monsoon moisture surveillance and severe weather prediction is essential for timely water resource management and disaster risk reduction. For these purposes, this study suggests a moisture indicator using the COSMIC-2/FORMOSAT-7 radio occultation (RO) observations and evaluates numerical model experiments with RO data assimilation. The RO data quality is validated by a comparison between sampled RO profiles and nearby radiosonde profiles around Taiwan prior to the experiments. The suggested moisture indicator accurately monitors daily moisture variations in the South China Sea and the Bay of Bengal throughout the 2020 monsoon rainy season. For the numerical model experiments, the statistics of 152 moisture and rainfall forecasts for the 2020 Meiyu season in Taiwan show a neutral to slightly positive impact brought by RO data assimilation. A forecast sample with the most significant improvement reveals that both thermodynamic and dynamic fields are appropriately adjusted by model integration posterior to data assimilation. The statistics of 17 track forecasts for typhoon Hagupit (2020) also show the positive effect of RO data assimilation. A forecast sample reveals that the member with RO data assimilation simulates better typhoon structure and intensity than the member without, and the effect can be larger and faster via multi-cycle RO data assimilation.

Download Full-text