Abstract There is a growing interest in the use of ground-based remote sensors for Numerical Weather Prediction (NWP), which is sparked by their potential to address the currently existing observation gap within the Planetary Boundary Layer (PBL). Nevertheless, open questions still exist regarding the relative importance of and synergy among various instrument types. To shed light on these important questions, the present study examines the forecast benefits associated with several different ground-based profiling networks using 10 diverse cases from the Plains Elevated Convection at Night (PECAN) field campaign. Aggregated verification statistics reveal that a combination of in situ and remote sensing profilers leads to the largest increase in forecast skill, both in terms of the parent mesoscale convective system and the explicitly resolved bore. These statistics also indicate that it is often advantageous to collocate thermodynamic and kinematic remote sensors. By contrast, the impacts of networks consisting of single profilers appear to be flow-dependent, with thermodynamic (kinematic) remote sensors being most useful in cases with relatively low (high) convective predictability. Deficiencies in the data assimilation method as well as inherent complexities in the governing moisture dynamics are two factors shown to limit the forecast value extracted from such networks.
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An Evaluation of the Impact of Assimilating AERI Retrievals, Kinematic Profilers, Rawinsondes, and Surface Observations on a Forecast of a Nocturnal Convection Initiation Event during the PECAN Field Campaign
Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection.
more » « less- NSF-PAR ID:
- 10114633
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Monthly Weather Review
- Volume:
- 147
- Issue:
- 8
- ISSN:
- 0027-0644
- Page Range / eLocation ID:
- p. 2739-2764
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The National Center for Atmospheric Research (NCAR) and Montana State University jointly developed water vapor micropulse differential absorption lidars (MPDs) that are a significant advance in eye-safe, unattended, lidar-based water vapor remote sensing. MPD is designed to provide continuous vertical water vapor profiles with high vertical (150 m) and temporal resolution (5 min) in the lower troposphere. This study aims to investigate MPD observation impacts and the scientific significance of MPDs for convective weather analyses and predictions using observation system simulation experiments (OSSEs). In this study, the Data Assimilation Research Testbed (DART) and the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model are used to conduct OSSEs for a case study of a mesoscale convective system (MCS) observed during the Plains Elevated Convection At Night (PECAN) experiment. A poor-performing control simulation that was drawn from a 40-member ensemble at 3-km resolution is markedly improved by assimilation of simulated observations drawn from a more skillful simulation that served as the nature run at 1-km resolution. In particular, assimilating surface observations corrected surface warm front structure errors, while MPD observations remedied errors in low- to midlevel moisture ahead of the MCS. Collectively, these analyses changes led to markedly improved short-term predictions of convection initiation, evolution, and precipitation of the MCS in the simulations on 15 July 2015. For this case study, the OSSE results indicate that a more dense MPD network results in better prediction performance for convective precipitation while degrading light precipitation prediction performance due to an imbalance of the analysis at large scales.
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