Thermodynamic and liquid profiling during the 2010 Winter Olympics

Ware, Randolph; Cimini, Domenico; Campos, Edwin; Giuliani, Graziano; Albers, Steve; Nelson, Marta; Koch, Steven E.; Joe, Paul; Cober, Stewart G.

doi:10.1016/j.atmosres.2013.05.019

Cited by 56 publications

(66 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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).…”

Section: Methodsmentioning

confidence: 99%

“…The NN is trained by using a series of historical radiosonde data at Tateno, including modeled liquid profile (Decker et al 1978;Ware et al 2013). The vertical resolutions of NN-derived profiles are 50 m from the surface to 500 m, 100 m to 2 km, and 250 m to 10 km.…”

Section: Methodsmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Araki¹,

Murakami²,

Ishimoto³

et al. 2015

SOLA

View full text Add to dashboard Cite

Ground-based microwave radiometer (MWR) has been used for high-frequency retrievals of thermodynamic environments. However, raindrops on the radome of MWR and in the air cause errors in retrievals during precipitation events. Although a recent study has noted that off-zenith observations with neural networks (NN) reduce the retrieval errors, the effect of off-zenith observations with one-dimensional variational (1DVAR) technique, which is known to be more accurate than other methods, has not been studied. We developed a new 1DVAR technique that considers the effect of cloud liquid water. We statistically investigated the accuracy of vertical profiles of atmospheric temperature and water vapor retrieved by NN and 1DVAR techniques by using zenith and off-zenith observation at 15° elevation angle under no-rain and rainy conditions and compared them with results of radiosonde observations. The results showed that the 1DVAR technique outperforms NN and numerical model simulation in the estimation of thermodynamic profiles under no-rain conditions. The results also indicated that the error in retrieved profiles in the low-level troposphere can be reduced by the 1DVAR technique by using off-zenith observations even under rainy conditions with rainfall rate less than 1.0 mm h −1 , especially when the environment cannot be accurately reproduced by a numerical model.(Citation: Araki, K., M. Murakami, H. Ishimoto, and T. Tajiri, 2015: Ground-based microwave radiometer variational analysis during no-rain and rain conditions. SOLA, 11, 108−112,

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Araki¹,

Murakami²,

Ishimoto³

et al. 2015

SOLA

View full text Add to dashboard Cite

show abstract

The Mesoscale Heavy Rainfall Observing System (MHROS) over the middle region of the Yangtze River in China

et al. 2015

View full text Add to dashboard Cite

The Mesoscale Heavy Rainfall Observing System (MHROS), supported by the Institute of Heavy Rain (IHR), Chinese Meteorology Administration, is one of the major systems to observe mesoscale convective systems (MCSs) over the middle region of the Yangtze River in China. The IHR MHROS consists of mobile C-POL and X-POL precipitation radars, millimeter wavelength cloud radar, fixed S-band precipitation radars, GPS network, microwave radiometers, radio soundings, wind profiler radars, and disdrometers. The atmospheric variables observed or retrieved by these instruments include the profiles of atmospheric temperature, moisture, wind speed and direction, vertical structures of MCS clouds and precipitation, atmospheric water vapor, and cloud liquid water. These quality-controlled observations and retrievals have been used in mesoscale numerical weather prediction to improve the accuracy of weather forecasting and MCS research since 2007. These long-term observations have provided the most comprehensive data sets for researchers to investigate the formation-dissipation processes of MCSs and for modelers to improve their simulations of MCSs. As the first paper of a series, we briefly introduce the IHR MHROS and describe the specifications of its major instruments. Then, we provide an integrative analysis of the IHR MHROS observations for a heavy rain case on 3-5 July 2014 as well as the application of IHR MHROS observations in improving the model simulations. In a series of papers, we will tentatively answer several key scientific questions related to the MCS and Meiyu frontal systems over the middle region of the Yangtze River using the IHR MHROS observations.

show abstract

Comparison of atmospheric profiles between microwave radiometer retrievals and radiosonde soundings

Zhang

et al. 2015

JGR Atmospheres

View full text Add to dashboard Cite

Atmospheric profiles of temperature (T), vapor density (ρv), and relative humidity (RH) retrieved from ground‐based microwave radiometer (MWR) measurements are compared with radiosonde soundings at Wuhan, China. The MWR retrievals were averaged in the ±30 min period centered at sounding times of 00 and 12 UTC. A total of 403 and 760 profiles under clear and cloudy skies were selected. Based on the comparisons, temperature profiles have better consistency than the ρv and RH profiles, lower levels are better than upper levels, and the cloudy are better than the clear‐sky profiles. Three cloud types (low, middle, and high) were identified by matching the infrared radiation thermometer‐detected cloud base temperature to the MWR‐retrieved temperature‐height profiles. Temperature profile under high cloud has the highest correlation coefficient (R) and the lowest bias and RMS, but under low cloud is in the opposite direction. The ρv profile under middle cloud has the highest R and the lowest bias but under high cloud has the lowest R, the largest bias, and RMS. Based on the radiosonde soundings, both clear and cloudy wind speeds and drifting distances increase with height but increase much faster under clear than cloudy above 4 km. The increased wind speeds and drifting distances with height have resulted in decreased correlation coefficient and increased temperature biases and RMSs with height for both clear and cloudy skies. The differences in R, bias, and RMS between clear and cloudy skies are primarily resulted from their wind speeds and drifting distances.

show abstract

Thermodynamic and liquid profiling during the 2010 Winter Olympics

Cited by 56 publications

References 17 publications

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

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

The Mesoscale Heavy Rainfall Observing System (MHROS) over the middle region of the Yangtze River in China

Comparison of atmospheric profiles between microwave radiometer retrievals and radiosonde soundings

Contact Info

Product

Resources

About