Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6593-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-13-6593-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Dec 2020
Research article |
| 07 Dec 2020
| 07 Dec 2020
Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study
Pauline Martinet
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Domenico Cimini
Institute of Methodologies for Environmental Analysis (IMAA-CNR), Potenza, Italy
Frédéric Burnet
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Benjamin Ménétrier
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Yann Michel
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Vinciane Unger
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
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Cited
25 citations as recorded by crossref.
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al. 10.5194/amt-16-433-2023
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- Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations I. Djalalova et al. 10.5194/amt-15-521-2022
- EUMETNET opens to microwave radiometers for operational thermodynamical profiling in Europe R. Rüfenacht et al. 10.1007/s42865-021-00033-w
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- Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy P. Vishwakarma et al. 10.5194/amt-16-1211-2023
- Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements P. Marquet et al. 10.5194/amt-15-2021-2022
- Formation of fog due to stratus lowering: An observational and modelling case study M. Fathalli et al. 10.1002/qj.4304
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- Meteorological characteristics of fog events in Korean smart cities and machine learning based visibility estimation J. Kim et al. 10.1016/j.atmosres.2022.106239
- Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall Y. Cao et al. 10.3390/rs15174314
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- Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field H. Li et al. 10.1109/JSTARS.2023.3298489
- Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models L. Bianco et al. 10.5194/amt-17-3933-2024
- The First Characterization of Fog Microphysics in the United Arab Emirates, an Arid Region on the Arabian Peninsula M. Weston et al. 10.1029/2021EA002032
- Fog Decision Support Systems: A Review of the Current Perspectives D. Bari et al. 10.3390/atmos14081314
- Effects of Assimilating Ground-Based Microwave Radiometer and FY-3D MWTS-2/MWHS-2 Data in Precipitation Forecasting B. Wang et al. 10.3390/rs16142682
- Experimental study on the evolution of droplet size distribution during the fog life cycle M. Mazoyer et al. 10.5194/acp-22-11305-2022
- Review on Parameterization Schemes of Visibility in Fog and Brief Discussion of Applications Performance Q. Long et al. 10.3390/atmos12121666
- TP-PROFILE: Monitoring the Thermodynamic Structure of the Troposphere over the Third Pole X. Chen et al. 10.1007/s00376-023-3199-y
- Analysis of Agglomerate Fog Meteorological Characteristics in Anhui Province Based on Traffic Accident Data J. Gao et al. 10.1007/s00024-022-03187-6
25 citations as recorded by crossref.
- Atmospheric boundary layer height from ground-based remote sensing: a review of capabilities and limitations S. Kotthaus et al. 10.5194/amt-16-433-2023
- A dataset of temperature, humidity, and liquid water path retrievals from a network of ground-based microwave radiometers dedicated to fog investigation P. Martinet et al. 10.1007/s42865-022-00049-w
- Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations I. Djalalova et al. 10.5194/amt-15-521-2022
- EUMETNET opens to microwave radiometers for operational thermodynamical profiling in Europe R. Rüfenacht et al. 10.1007/s42865-021-00033-w
- Passive ground-based remote sensing of radiation fog H. Guy et al. 10.5194/amt-15-5095-2022
- Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy P. Vishwakarma et al. 10.5194/amt-16-1211-2023
- Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements P. Marquet et al. 10.5194/amt-15-2021-2022
- Formation of fog due to stratus lowering: An observational and modelling case study M. Fathalli et al. 10.1002/qj.4304
- PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations S. Larosa et al. 10.5194/gmd-17-2053-2024
- W-band radar observations for fog forecast improvement: an analysis of model and forward operator errors A. Bell et al. 10.5194/amt-14-4929-2021
- Meteorological characteristics of fog events in Korean smart cities and machine learning based visibility estimation J. Kim et al. 10.1016/j.atmosres.2022.106239
- Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall Y. Cao et al. 10.3390/rs15174314
- Applicability of the low-cost OPC-N3 optical particle counter for microphysical measurements of fog K. Nurowska et al. 10.5194/amt-16-2415-2023
- Assimilation of Ground‐Based Microwave Radiometer Temperature Observations Into a Convective‐Scale NWP Model for Fog Forecast Improvement G. Thomas et al. 10.1002/qj.4893
- Improving the representation of the atmospheric boundary layer by direct assimilation of ground‐based microwave radiometer observations J. Vural et al. 10.1002/qj.4634
- An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties A. Bell et al. 10.5194/amt-15-5415-2022
- Investigating the Optimal Spatial Resolution for Assimilating GNSS PWV into an NWP System to Improve the Accuracy of Humidity Field H. Li et al. 10.1109/JSTARS.2023.3298489
- Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models L. Bianco et al. 10.5194/amt-17-3933-2024
- The First Characterization of Fog Microphysics in the United Arab Emirates, an Arid Region on the Arabian Peninsula M. Weston et al. 10.1029/2021EA002032
- Fog Decision Support Systems: A Review of the Current Perspectives D. Bari et al. 10.3390/atmos14081314
- Effects of Assimilating Ground-Based Microwave Radiometer and FY-3D MWTS-2/MWHS-2 Data in Precipitation Forecasting B. Wang et al. 10.3390/rs16142682
- Experimental study on the evolution of droplet size distribution during the fog life cycle M. Mazoyer et al. 10.5194/acp-22-11305-2022
- Review on Parameterization Schemes of Visibility in Fog and Brief Discussion of Applications Performance Q. Long et al. 10.3390/atmos12121666
- TP-PROFILE: Monitoring the Thermodynamic Structure of the Troposphere over the Third Pole X. Chen et al. 10.1007/s00376-023-3199-y
- Analysis of Agglomerate Fog Meteorological Characteristics in Anhui Province Based on Traffic Accident Data J. Gao et al. 10.1007/s00024-022-03187-6
Latest update: 25 Dec 2024
Short summary
Each year large human and economical losses are due to fog episodes. However, fog forecasts remain quite inaccurate, partly due to a lack of observations in the atmospheric boundary layer. The benefit of ground-based microwave radiometers has been investigated and has demonstrated their capability of significantly improving the initial state of temperature and liquid water content profiles in current numerical weather prediction models, paving the way for improved fog forecasts in the future.
Each year large human and economical losses are due to fog episodes. However, fog forecasts...
