Task 101

The Impact of Using Lidar Network Data for a Summertime Cold Front Case Study

Principal Investigator(s):

S. Rabenhorst

Sponsor(s):

D. Whiteman

Last Updated:

October 26, 2012 15:25:20

Description of Problem

Model simulations of the impact of space-based data on regional scale weather forecasts indicate space-based data alone are insufficient to improve short-term forecasting of convective systems. Smaller scale measurements that can only be acquired from either ground-based or airborne sensors are needed. This project responds to the national priorities to implement a mesoscale network capable of high resolution monitoring of humidity and planetary boundary layer structure. The focus is investigating the impact of a water vapor lidar network to improve short-term numerical forecasts for a summertime cold front case study during 1-5 August 2006. Emphasis is also placed on the cost-benefit component of these profilers. Network density, platform, system hardware, and associated market-value prices are all significant contributing factors. The results of this study should help inform decision-makers when considering a new or improved mesoscale observing network.

Scientific Objectives and Approach

Testing of the proposed lidar network is being done using Observing System Simulation Experiments (OSSE). Interpolated water vapor profiles are extracted from the Nature Run (NR) at proposed observation locations and then processed by a lidar model. The resulting profiles are realistic observations and associated errors which account for lidar system hardware specifications and quality. The profiles are then assimilated into perturbed impact runs to assess how effectively the lidar network constrains the model back to the NR, thus providing a measure of forecast impact.

Accomplishments

Operational centers had difficulty correctly predicting the cold front movement, timing, and quantitative precipitation forecast in this case study. The Weather Research and Forecasting (WRF) model has been used to conduct several runs of the case study. Optimization of initial conditions and other WRF parameters have been accomplished by verification against aircraft, upper air, and surface observations. Additionally, high-resolution measurements taken during the Water Vapor Variability – Satellite/Sondes (WAVES) 2006 field campaign are used for verification and a closer look at boundary layer processes. The verification has given insight into the representativeness of the model, and subsequently, the selection of an appropriate NR. Water vapor profiles from the NR have been extracted for lidar model input. Lidar model simulations of these profiles are ongoing.

Separate high-resolution runs have been performed to explore the meteorological dynamics behind the boundary progression, leading waves, and the interaction with the complex orography over the Mid-Atlantic Appalachian Mountains. WRF has offered possible explanations for interesting features present in the campaign measurement dataset. The model results have been quantified against the model. These findings are being gathered for a publication.

Testing of WRF’s Observation Nudging (FDDA), 3DVAR, and ETKF-3DVAR hybrid schemes has begun. Successful assimilation of radiosonde, aircraft (ACARS), profiler (MAP & NPN), and surface data has been done. Forecast improvement resulting from the assimilation of these conventional observations is a baseline to compare with forecast improvement from the proposed lidar observation network. Preliminary data assimilation of perfect lidar observations in the impact runs has demonstrated positive results.