Nothing Special   »   [go: up one dir, main page]

Jin et al., 2019 - Google Patents

Machine learning for observation bias correction with application to dust storm data assimilation

Jin et al., 2019

View HTML
Document ID
10365805383028035622
Author
Jin J
Lin H
Segers A
Xie Y
Heemink A
Publication year
Publication venue
Atmospheric Chemistry and Physics

External Links

Snippet

Data assimilation algorithms rely on a basic assumption of an unbiased observation error. However, the presence of inconsistent measurements with nontrivial biases or inseparable baselines is unavoidable in practice. Assimilation analysis might diverge from reality since …
Continue reading at acp.copernicus.org (HTML) (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00Investigating or analysing materials by the use of optical means, i.e. using infra-red, visible or ultra-violet light
    • G01N21/17Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infra-red light
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • G06Q10/0639Performance analysis
    • G06Q10/06393Score-carding, benchmarking or key performance indicator [KPI] analysis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01WMETEOROLOGY
    • G01W1/00Meteorology
    • G01W1/10Devices for predicting weather conditions
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations
    • G06F17/18Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by the preceding groups

Similar Documents

Publication Publication Date Title
Jin et al. Machine learning for observation bias correction with application to dust storm data assimilation
Kok et al. Improved representation of the global dust cycle using observational constraints on dust properties and abundance
Li et al. Predicting ground-level PM2. 5 concentrations in the Beijing-Tianjin-Hebei region: A hybrid remote sensing and machine learning approach
Benedetti et al. Status and future of numerical atmospheric aerosol prediction with a focus on data requirements
Kim et al. Development of a daily PM 10 and PM 2.5 prediction system using a deep long short-term memory neural network model
McCoy et al. Predicting decadal trends in cloud droplet number concentration using reanalysis and satellite data
Chimot et al. An exploratory study on the aerosol height retrieval from OMI measurements of the 477 nm O 2− O 2 spectral band using a neural network approach
Viatte et al. Atmospheric ammonia variability and link with particulate matter formation: a case study over the Paris area
Jin et al. Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth
Logothetis et al. 15-year variability of desert dust optical depth on global and regional scales
Lama et al. Quantifying burning efficiency in Megacities using NO 2/CO ratio from the Tropospheric Monitoring Instrument (TROPOMI)
McCoy et al. Untangling causality in midlatitude aerosol–cloud adjustments
Laughner et al. Evaluation of version 3.0 B of the BEHR OMI NO 2 product
Evangeliou et al. 10-year satellite-constrained fluxes of ammonia improve performance of chemistry transport models
Doherty et al. Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the Southeast Atlantic
Jin et al. Inverse modeling of the 2021 spring super dust storms in East Asia
Aleksankina et al. Advanced methods for uncertainty assessment and global sensitivity analysis of an Eulerian atmospheric chemistry transport model
Adam et al. Biomass burning events measured by lidars in EARLINET–Part 1: Data analysis methodology
Kurganskiy et al. Incorporation of pollen data in source maps is vital for pollen dispersion models
Burgos et al. A global model–measurement evaluation of particle light scattering coefficients at elevated relative humidity
Lund et al. Concentrations and radiative forcing of anthropogenic aerosols from 1750 to 2014 simulated with the Oslo CTM3 and CEDS emission inventory
Zaidan et al. Exploring non-linear associations between atmospheric new-particle formation and ambient variables: a mutual information approach
White et al. Quantifying the UK's carbon dioxide flux: an atmospheric inverse modelling approach using a regional measurement network
Betancourt et al. Global, high-resolution mapping of tropospheric ozone–explainable machine learning and impact of uncertainties
Saponaro et al. Evaluation of aerosol and cloud properties in three climate models using MODIS observations and its corresponding COSP simulator, as well as their application in aerosol–cloud interactions