This paper deals with the UAS trajectory planning and optimization using the operative meteorological data. The algorithm and software for UAS operators when preparation to UAS flight and for possible UAS flight correction during the mission realization were developed and studied. The detailed explanation of the algorithm operation was done. The interface of the developed software was analyzed from the position of the convenience to percept information. The simulation of the application operation was done and the results of simulation shows the correspondence of the current conditions and decision proposed by the DSS software

UAS Flight Trajectory Optimization Algorithm Based on Operative Meteorological Information

In the realm of state estimation for the majority of practical engineering applications, nonlinear systems have been prevalent. Traditional Kalman filtering, rooted in model feature constraint theory, exhibited limitations by being restricted to linear systems and proved inadequate for addressing nonlinear systems. In response to this challenge, this paper introduces a novel Nonlinear Adaptive Kalman Filtering approach that extends the application of model feature constraint theory to nonlinear systems. Firstly, the paper leverages the EKF filtering framework as a foundation and derives the optimal estimation theory for Gaussian noise statistical feature constraints (model parameter ratios) in nonlinear systems. A series of theoretical proofs are presented to verify the existence of Gaussian model parameter ratio features in nonlinear systems. Secondly, the paper combines model parameter ratios with covariance matching and introduces corresponding parameter estimation criteria for nonlinear systems. This approach transforms the adaptive filtering problem into an optimization problem with constraints. To obtain the optimal solution for estimating the system model, the Wild Horse Optimizer is employed to solve this optimization problem. Ultimately, by revealing and validating the existence of model parameter ratios in optimal nonlinear Kalman filtering, the feasibility of the proposed method is demonstrated. Experimental results further affirm the applicability of model feature constraint theory in nonlinear systems.

Adaptive EKF Filtering Based on Model Feature Constraints

Understanding air pollution dynamics of Antalya Manavgat forest fires: a WRF-Chem analysis.

ABSTRACT Data‐driven weather models have shown the potential to match the accuracy of state‐of‐the‐art numerical weather predictions (NWPs). However, existing data‐driven forecasting models still have limitations in operational applications. For example, most of them are predominantly trained via fifth‐generation climate reanalysis data (ERA5). However, in actual forecasting operations, the models are usually initiated by analysis fields instead of reanalysis data; this leads to a mismatch between the training data used by machine learning (ML) forecasting models and the actual operational data. To address this issue, we attempt to fine‐tune the data‐driven model with the initiation fields in operation. This study first develops a fine‐tuned Pangu Weather Model (PGW) by integrating forecasting system (IFS) analysis data from 2021 to 2022 and conducts a comprehensive evaluation of its performance. By comparing the fine‐tuned version (PGW_O) with the public version (PGW_P) against IFS models with different resolutions (IFS_L at 0.25° and IFS_H at 0.1°), this research highlights advancements in data‐driven forecasting methodologies. The models are tested on data from South China, a region with dense meteorological observation networks, over a three‐month period, encompassing a detailed case study of Tropical Cyclone Haikui (2023). The findings show that with the forecast activity (FA) level comparable to PGW_P, PGW_O significantly reduces the root mean square error (RMSE) and mean error (ME) across upper atmospheric variables and demonstrates superior accuracy in predicting surface elements. The operational relevance of these models is evaluated through both ERA5 reanalysis and surface observations, revealing that fine‐tuning with IFS data enhances PGW compatibility and forecasting precision, particularly for severe weather events. 

A Fine‐Tuned Pangu Weather Model and Its Performance Based on an Operational Framework in South China

Abstract The emissions from South Asia and Southeast Asia significantly impact air quality and meteorological conditions in China. However, the individual or joint contributions of anthropogenic emissions and/or biomass burning from outside China to surface particulate matter with aerodynamic diameters less than 2.5 μm (PM 2.5 ) and aerosol optical depth (AOD) over southern China have not been fully investigated. Here, five experiments were designed to investigate the impacts of these emissions in January (winter), March (pre‐monsoon), and October (post‐monsoon) for 2017. Aerosols from South Asia and Southeast Asia contributed less to southern China during winter and post‐monsoon seasons, whereas the wind patterns and emission intensity during the pre‐monsoon season were conducive to the transport of aerosols. During pre‐monsoon season, the total emissions contributed approximately 5.0 μg m −3 to surface PM 2.5 in Xizang Province. Biomass burning in Southeast Asia increased PM 2.5 in Yunnan Province by 37.9 μg m −3 , while anthropogenic emissions increased it by 8.9 μg m −3 . Transboundary aerosols can be transported to Xizang Province and Yunnan Province, primarily influencing PM 2.5 below 2 km height. It mainly affected PM 2.5 levels above the planetary boundary layer over southeast China. Aerosols from outside China can account for 79.5% and 54.8% AOD in Yunnan Province and southeast China, respectively. These aerosols reduced surface incident solar radiation by approximately 6%, leading to decreases in air temperature, wind speed, and boundary layer height. The findings are only applicable to the pre‐monsoon season. 

Impacts of Anthropogenic Emissions and Open Biomass Burning in South Asia and Southeast Asia on Air Quality and Meteorology Over Southern China

With global warming, extreme high temperatures become more frequent and are an important factor affecting aircrafts’ takeoff performance. Using Community Earth System Model version 1 (CESM1) and Boeing Performance Software (BPS), the influence of increasing temperature on aircrafts’ maximum takeoff weight (MTOW) and takeoff distance is evaluated quantitatively in this study. The results show that the distribution of summer daily maximum temperature shifts obviously to higher temperature at all the chosen airports and the variation in temperature is larger at four airports (Jinan, Shanghai, Lhasa, and Urumqi) during 2071−2080 under the RCP8.5 scenario than that in the historical run (1991−2000). The warming air leads to the MTOW reducing and takeoff distance increasing. Taking the Boeing 737–800 aircraft as an illustration, the number of weight-restriction days increases significantly across the airports, which can influence airlines’ economic benefit and flight operations in the future. It is also found that the takeoff distance does not change linearly with temperature, but shows a stronger increase with higher temperature. The takeoff distance increases about 6.2% on average during 2071−2080 compared to 1991−2000 for the low-altitude airports, calling for an additional 113−222 m takeoff distance in future summers.

/pdf/decreased-aircraft-takeoff-performance-under-global-warming-43zssc7a.pdf

Decreased Aircraft Takeoff Performance under Global Warming

Seasonal transition of Black carbon aerosols over Qinghai-Tibet Plateau: Simulations with WRF-Chem

Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was first applied in the mesoscale version of the Global/Regional Assimilation and Prediction System (GRAPES_Meso). To evaluate the performance of the HGDA method in the GRAPES_Meso model, different DA schemes, including the three-dimensional variational (3DVAR), local ensemble transform Kalman filter (LETKF), and HGDA schemes, were compared across eight tropical cyclone (TC) cases, and FY-4A atmospheric motion vectors were assimilated. The results indicated that the HYBRID scheme outperformed the 3DVAR and LETKF schemes in TC position forecasting, and with ensemble forecasting techniques, the HYBRID scheme promoted the accuracy of the prediction TC intensity. The threat score (TS) values for the light and medium precipitation forecasts obtained in the HYBRID experiment were higher than those for the forecasts obtained in the 3DVAR and LETKF experiments, which may be attributed to the forecasting accuracy for the TC position. Regarding heavy and extreme rainfall, the HYBRID scheme achieved a more stable effect than those of the 3DVAR and LETKF schemes. The results demonstrated the superiority of the HGDA scheme in TC prediction with the GRAPES_Meso model.

/pdf/performance-of-a-hybrid-gain-ensemble-data-assimilation-38d18m0o.pdf

Performance of a Hybrid Gain Ensemble Data Assimilation Scheme in Tropical Cyclone Forecasting with the GRAPES Model

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Kun Wang

Author Tools

Chat about Author

Papers

Decreased Aircraft Takeoff Performance under Global Warming

Seasonal transition of Black carbon aerosols over Qinghai-Tibet Plateau: Simulations with WRF-Chem

Performance of a Hybrid Gain Ensemble Data Assimilation Scheme in Tropical Cyclone Forecasting with the GRAPES Model