Thermodynamic Atmospheric Profiling During the 2010 Winter Olympics Using Ground-Based Microwave Radiometry

Abstract

Ground-based microwave radiometer profilers in the 20-60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecast-ing during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events (∼235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied "quasi-operationally," i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5 g/m 3 for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8 kg/m 2 , with small bias (−0.1 kg/m 2 ) and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within ∼0.60 km.

Ground-Based Microwave Radiometer Variational Analysis during No-Rain and Rain Conditions

“…Differences between fixed volume radiometric observations and balloon-borne liquid sensor point measurements along an uncontrolled flight path contribute to the uncertainty. Example neural network liquid profile retrievals are provided by Ware et al 2003Ware et al , 2013Knupp et al 2009;Madonna et al 2011;Cimini et al 2011;Madhulatha et al 2013;Campos et al 2014;Serke et al 2014;and Gultepe et al 2015. The other method for the LWP profiling combines microwave radiometer and cloud radar measurements, but this method has 60% or larger liquid profile retrieval uncertainty (Ebell et al 2010).…”

“…The observation data by the ground-based microwave radiometer profiler (MWR) has been used to retrieve vertically integrated water vapor (precipitable water vapor: PWV) and liquid water (liquid water path: LWP) (e.g., Hogg et al 1983a;Wei et al 1989;Cadeddu et al 2013), and vertical profiles of atmospheric temperature, water vapor density, and liquid water content (LWC) at time intervals within a few minutes (e.g., Ware et al 2003Ware et al , 2013Campos et al 2014;Serke et al 2014;Gultepe et al 2015). For the retrieval of vertical thermodynamic profiles, various inversion methods have been proposed such as statistical inversion methods, multivariate regressions, neural networks (NN; e.g., Hogg et al 1983b;Solheim et al 1998;Cadeddu et al 2009), and variational techniques (e.g., Löhnert et al 2004;Hewison et al 2007;Cimini et al 2006Cimini et al , 2010Cimini et al , 2011Cimini et al , 2015Ishimoto 2015). The NN technique is a popular method that combines MWR observations with radiosonde climatology.…”

“…Recently, variational approaches have been shown to outperform other methods in retrieving thermodynamic profiles of atmospheric temperature and water vapor, especially in the lower troposphere (e.g., Cimini et al 2011). These approaches are based on data assimilation with a one-dimensional variational (1DVAR) technique, which combines radiometric observations with output from numerical weather prediction models.…”

“…To reduce the effect of raindrops on the radome, several techniques have been tested such as hydrophobic coatings on the radome and forced airflow over the radome surface from a blower (e.g., Marzano et al 2005). In recent years, several studies have reported that off-zenith observations at an elevation angle of 15° successfully reduce the error caused by raindrops on the radome (e.g., Cimini et al 2011;Ware et al 2013). Xu et al (2014) investigated the error in thermodynamic profiles retrieved by NNs under non-rainy and rainy conditions, and noted that off-zenith observations at an elevation angle of 15° reduced the retrieval error due to raindrops on the radome during rain periods.…”

“…They noted that the absorption by rain water was important for improving vertical profiles under rainy conditions. Off-zenith 1DVAR, NN, and brightness temperature comparisons with 6-h radiosondes during conditions including rain, sleet, and snow were reported by Cimini et al (2011) and Ware et al (2013). No study, however, has examined the accuracy of thermodynamic profiles retrieved by 1DVAR techniques using off-zenith observations under rainy conditions.…”