Advancements in the Assimilation of Lidar Data in Numerical Weather Prediction and Climate Forecast Models
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".
Deadline for manuscript submissions: closed (23 April 2022) | Viewed by 315
Special Issue Editors
Interests: remote sensing; Arctic; meteorology; climate; lidar; boundary layer dynamics; forecasting; numerical weather prediction
Interests: atmospheric aerosol optical properties; in situ aerosol sampling; low cost sensors; indoor air quality; Fabry Perot interferometers; lidar optics; high altitude mountain station atmospheric measurements; climate change
Special Issue Information
Dear Colleagues,
Atmospheric profiling lidars have only recently been made more autonomous, widely commercially available, and cost-effective, potentially making them ideally suited for deployment as part of a larger operational network and to complement operational radiosonde observations. Different types of lidar technologies have been developed and introduced into the commercial market with a mature technological readiness level. They were shown to accurately measure vertical profiles (and, for some designs, horizontal scanning) of atmospheric winds, humidity (or water vapour mixing ratio), temperature, and trace gases such as O3, CH4, and CO2 with higher spatial and temporal resolution than alternative technologies. At the same time, current operational observing systems fail to provide sufficient observations, particularly in the planetary boundary layer where high-impact weather occurs. Thus, there is increasing interest among international modelling centres regarding the potential impact of lidar data on numerical weather prediction (NWP) forecast models in order to fill this gap. Recent studies have demonstrated the utility of lidar data for the use of NWP model verification and to considerably improve analyses and short- to medium-term forecasts of high-impact weather, such as thunderstorms and severe rainfall events in an operational, convective-scale NWP framework. Even the assimilation of a single lidar, despite its limited range, can improve the forecast of heavy rainfall events via improving water vapour transportation in the model. This Special Issue invites the submission of research letters and articles that have tested the assimilation of atmospheric lidar data (real or simulated) under any assimilation scheme (1DVAR, ensemble adjustment Kalman filter, etc.) in NWP or climate forecast models. Submissions that discuss methodologies of assimilation, the lidar observation and data assimilation approach, impact assessments and spatial representativeness, lidar network density and optimal locations, and other related assimilated datasets are encouraged.
Dr. Zen Mariani
Dr. Prodromos Fetfatzis
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing
- lidar
- assimilation
- high impact
- network
- numerical weather prediction
- forecast
- model
