WeChat Mini Program
Old Version Features

A Revised Parameterization for Aerosol, Cloud and Precipitation Ph for Use in Chemical Forecasting Systems (Eqsam4clim-V12)

Swen Metzger, Samuel Rémy,Jason E. Williams, Vincent Huijnen, Johannes Flemming

openalex(2023)

Cited 0|Views2
Abstract
Abstract. The Equilibrium Simplified Aerosol Model for Climate version 12 (EQSAM4Clim-v12) has recently been revised to provide an accurate and efficient method for calculating the acidity of atmospheric particles. EQSAM4Clim is based on an analytical concept that is not only sufficiently fast for numerical weather prediction (NWP) applications, but also free of numerical noise, which makes it attractive also for air quality forecasting. EQSAM4Clim allows the calculation of aerosol composition based on the gas-liquid-solid and the reduced gas-liquid partitioning with the associated water uptake for both cases, and can therefore provide important information about the acidity of the aerosols. Here we provide a comprehensive description of the recent changes made to the aerosol acidity parameterization (referred to a version 12) which builds on the original EQSAM4Clim. We evaluate the pH improvements using a detailed box-model and compare against previous model calculations and both ground-based and aircraft observations from US and China covering different seasons and scenarios. We show that, in most cases, the simulated pH is within reasonable agreement with the results of the E-AIM reference model and of satisfactory accuracy.
More
Translated text
Key words
Atmospheric Composition,Aerosol Formation,Aerosols,Emission Modeling,Atmospheric Aerosols
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined