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Microwave profiling radiometer retrievals of a winter upslope snowstorm (North American Central High Plains) at Boulder, Colorado (USA), on 14 Feb 2008. Panel descriptions are as in Fig. 3. Black vertical lines indicate a wet radiometer (in this case, snow is melting on its warm rain sensor).
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This work presents observations of water phase dynamics that demonstrate the theoretical Wegener-Bergeron-Findeisen concepts in mixed-phase winter storms. The work analyzes vertical profiles of air vapor pressure, and equilibrium vapor pressure over liquid water and ice. Based only on the magnitude ranking of these vapor pressures, we identified co...
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... and dynamics of winter upslope snow- storms, along the eastern margin of the Colorado Rockies, are well understood at the synoptic scale (e.g., Dunn, 1987;Mahoney et al., 1995). Concentrating here on the cloud scale, Fig. 7 shows radiometer observations for the onset and development of a winter upslope snowstorm, on 14 February 2008 at Boulder. The top and middle panels in this figure correspond to vertical profiles of air temperature and vapor density, respectively. The bottom panel shows retrievals of vertically integrated water vapor (WV path, black ...Context 2
... 2014). Positive error resulting from ice accrual during freezing rain that occurs on the top and the windward side of the radiometer radome can be minimized using off-zenith observations on the lee side of the radiometer radome (e.g., Ware et al., 2013). For the cases analyzed in this study, however, off-zenith sampling methods were not needed. Fig. 7a shows a cold front that arrived at the radiometer site around 0415 UTC. The retrieved profiles show the sharp drop in temperature and rise in vapor density (Fig. 7b) that occurred below 3 km height. By definition, the frontal boundary around 0415 UTC implies strong advection of a different air mass into the radiometer sampling volume, ...Context 3
... off-zenith observations on the lee side of the radiometer radome (e.g., Ware et al., 2013). For the cases analyzed in this study, however, off-zenith sampling methods were not needed. Fig. 7a shows a cold front that arrived at the radiometer site around 0415 UTC. The retrieved profiles show the sharp drop in temperature and rise in vapor density (Fig. 7b) that occurred below 3 km height. By definition, the frontal boundary around 0415 UTC implies strong advection of a different air mass into the radiometer sampling volume, and it complicates the analyses of cloud water-phase dynamics for this ...Context 4
... increase of vapor density that follows the frontal passage (Fig. 7b) is due primarily to advection. After that, we can assume that local microphysical processes are driving the water-phase dynamics. Characterization of our three theoret- ical scenarios (droplet-ice growth, evaporation deposition, and droplet-ice depletion), in combination with radar observations, allows a reasonable qualitative analysis ...Context 5
... about 06 UTC, the water vapor path starts to decrease when the liquid water path increases (Fig. 7c). Condensation of cloud liquid appears to deplete the water vapor density. However, the following questions arise: (1) Why is cloud liquid water not observed in the period between 0430 and 06 UTC (i.e., right after the front passes over the radiometer site)? and (2) What makes the cloud liquid water disappear after about 0930 UTC? As ...Context 6
... period between 06 and 0930 UTC corresponds to droplet-ice growth (Fig. 8b). This agrees with the liquid water paths indicated in Fig. 7c and with the discernible liquid water contents in Fig. 8a. The Fig. 7a indicates that this cloud liquid water is supercooled (between 0 °C and − 15 °C). Conversely, the period between 0440 and 06 UTC and the one after 0930 UTC have regions of droplet-ice growth (Fig. 8b), but radiometer observations do not indicate cloud liquid water ...Context 7
... agrees with the liquid water paths indicated in Fig. 7c and with the discernible liquid water contents in Fig. 8a. The Fig. 7a indicates that this cloud liquid water is supercooled (between 0 °C and − 15 °C). Conversely, the period between 0440 and 06 UTC and the one after 0930 UTC have regions of droplet-ice growth (Fig. 8b), but radiometer observations do not indicate cloud liquid water within these periods ( Fig. 7c; as before, all times are obtained from the radiometer Level2 output files, with a 1 min resolution). ...Context 8
... discernible liquid water contents in Fig. 8a. The Fig. 7a indicates that this cloud liquid water is supercooled (between 0 °C and − 15 °C). Conversely, the period between 0440 and 06 UTC and the one after 0930 UTC have regions of droplet-ice growth (Fig. 8b), but radiometer observations do not indicate cloud liquid water within these periods ( Fig. 7c; as before, all times are obtained from the radiometer Level2 output files, with a 1 min ...Context 9
... for the observations on 14 Feb 2008, we believe that it is only after 06 UTC that the droplets are large enough (in number and size) to be detected by the radiometer ( Fig. 7c). The same reason explains why the droplet-ice growth scenario appears much earlier than the discernible amounts of liquid water content (Figs. 4a and 8a). The positive outcome is that our analysis of vapor-pressure classes (Figs. 4b and 8b) are providing more than one-hour lead time in the forecasting of supercooled liquid ...Context 10
... analysis of the entire event is possible by using co-located observations from vertically pointing radar. Fig. 9b). This ascending air is responsible for transporting new amounts of water vapor aloft (vapor density increasing in Fig. 7b and water vapor path increasing in Fig. 7c). The radar signals detected during this period are actually from clear-air targets (i.e., from sharp discontinuities in the index of refraction, an index that depends on air temperature, vapor pressure and air pressure; e.g., Röttger and Larsen, 1990). These targets can be detected at the UHF ...Context 11
... analysis of the entire event is possible by using co-located observations from vertically pointing radar. Fig. 9b). This ascending air is responsible for transporting new amounts of water vapor aloft (vapor density increasing in Fig. 7b and water vapor path increasing in Fig. 7c). The radar signals detected during this period are actually from clear-air targets (i.e., from sharp discontinuities in the index of refraction, an index that depends on air temperature, vapor pressure and air pressure; e.g., Röttger and Larsen, 1990). These targets can be detected at the UHF band but not the X band. For example, the ...Context 12
... from sharp discontinuities in the index of refraction, an index that depends on air temperature, vapor pressure and air pressure; e.g., Röttger and Larsen, 1990). These targets can be detected at the UHF band but not the X band. For example, the sharp spatial gradients of temperature and vapor pressure retrieved by the radiometer at 0415 UTC in Fig. 7 (due to the frontal passage over the radiometer site) are matched by sharp UHF reflectivity values right after 0415 UTC (Fig. ...Context 13
... m s −1 . These magni- tudes are typical fall velocities for riming snow and small raindrops (diameters around 0.8 mm; e.g., Gunn and Kinzer, 1949). Rimed snow implies the presence of cloud droplets. Thus, the UHF radar observations agree during this period with our radiometer estimates on the presence of liquid water (i.e., liquid water paths in Fig. 7c, droplet-ice growth scenario in Fig. 8b, and discernible liquid water in Fig. 8a). All these suggest that, for the period roughly after 0830 UTC, supercooled droplets are being captured by snow particles during riming, right from its first formation at levels above the 2 ...Context 14
... at about 0830-0840 UTC. Then, roughly after 0930 UTC, the associated Doppler velocities (Fig. 9b) become smaller than 2 m s −1 (downwards). These magni- tudes are typical fall velocities for unrimed snow and imply that most of the liquid water has been eliminated by this time. This is in agreement with the vanishing of liquid water paths in Fig. 7c, and the disappearance of discernible liquid water in Fig. ...Similar publications
The evaporation flux Jev(H2O) of H2O
from HCl-doped typically 1.5 µm or so thick vapor-deposited
ice films has been measured in a combined quartz crystal
microbalance (QCMB)–residual gas mass spectrometry (MS) experiment.
Jev(H2O) has been found to show complex behavior and to
be a function of the average mole fraction χHCl of HCl in
the ice film r...
The evaporation flux J ev (H 2 O) of H 2 O from HCl-doped typically 1.5 μm or so thick vapor-deposited ice films has been measured in a combined quartz crystal microbalance (QCMB)-residual gas mass spectrometry (MS) experiment. J ev (H 2 O) has been found to show complex behaviour and to be a function of the average mole fraction χ (HCl) of HCl in...
A theoretical framework has been developed describing nonequilibrium formation and maintenance of mixed-phase clouds. The necessary and sufficient conditions required to activate liquid water within a preexisting ice cloud, and thus convert it to mixed phase, are considered for three scenarios: (i) uniform ascent, (ii) harmonic vertical oscillation...
For a better utilization of the ground-based microwave radiometer, it is important to detect the cloud presence in the measured
data. Here, we introduce a simple and fast cloud detection algorithm by using the optical characteristics of the clouds in the
infrared atmospheric window region. The new algorithm utilizes the brightness temperature (Tb)...
Citations
... Ground-based microwave radiometers are commonly used for atmospheric observations [1] and can be operated continuously with a typical temporal resolution of 1 s. They can be used to monitor the temperature and humidity profiles of the atmospheric boundary layer [2]- [9], and unique liquid water content profiles [10] [11] [12] [13] and to detect lightning [14]. The number of ground-based microwave radiometers in use in China for both research and quasi-operational observations has increased rapidly in recent years [15]- [20] and long time data have been obtained. ...
... In that such a case, we can first identify water on the radiometer by analyzing spikes in the integrated water vapor because IWV is smoothly varying when the radome is dry (Ware et al., 2004). Then if precipitation is detected, accurate off-zenith MWR observations during precipitation (Chan, 2009;Cimini et al., 2011;Ware et al., 2004Ware et al., , 2013Campos et al., 2014;Serke et al., 2014;Xu et al., 2014Xu et al., , 2015 can be used to constrain the cloud retrievals. The rainfall properties could also be derived from the MWR observations of brightness temperature (Marzano et al., 2005;Won et al., 2009). ...
Microphysical properties of low level liquid clouds at the Naqu site over the Tibetan Plateau (TP) are characterized using empirical regression algorithms based on ground-based millimeter cloud radar (MMCR) boundary mode observations in July 6–31, 2014. Monthly averaged temperature profiles measured over the Naqu site by radiosondes at Beijing local time 8:00 and 20:00 and diurnal variation of microwave radiometer (MWR) temperature profiles indicate a 0 °C layer above 1.2 km. Only clouds below 1.2 km are examined in this study and they are assumed as pure liquid in phase. The parameters used in the regression equations have been scaled based on MWR liquid water path when there are only low non-precipitating liquid clouds and MWR measurements are available. Statistically, the characteristic properties of liquid clouds show a single mode distribution for cloud droplet effective radius (re) with most frequent values around 5–7 μm, and for cloud liquid water content (LWC) with most frequent values below 0.2 g/m³. The diurnal distribution shows weak variation with slightly low values in the morning and evening time; and the vertical distribution shows increasing cloud droplet re and LWC with height. Especially, cloud droplet re increases from approximately 4–6 μm to 8–12 μm with height. The monthly mean cloud droplet re and LWC are approximately 5.7 μm and 0.07 g/m³ in July 2014. The liquid cloud properties characterized here have been shown comparable to those obtained from MODIS satellite observations, which has an average value of 5.1 μm for the same observation period.
... As a result of latest technical improvements, radiometers can be used for profiling both temperature and humidity simultaneously (Solheim et al., 1998;Güldner and Spänkuch, 2001;Cadeddu et al., 2013;Renju et al., 2015;Harikishan et al., 2014). An additional advantage of MWR is the high accuracy of measurement of integrated liquid water (Westwater, 1978;Peter and Kämpfer, 1992) and measurement of the liquid water profile (Politovich et al., 1995;Solheim et al., 1998;Ware et al., 2003;Crewell et al., 2009;Ebell et al., 2010;Calheiros and Machado, 2014;Campos et al., 2014;Serke et al., 2014). This instrument measures the radiation intensity at a number of frequency channels in the microwave spectrum that are dominated by atmospheric water vapor and molecular oxygen emissions (Rose and Czekala, 2003;Knupp et al., 2009;Cadeddu et al., 2013;Wulfmeyer et al., 2015). ...
Microwave radiometer is an effective instrument to monitor the atmosphere continuously in different weather conditions. It measures brightness temperatures at different frequency bands which are subjected to standard retrieval methods to obtain real time profiles of various atmospheric parameters such as temperature and humidity. But the retrieval techniques used by radiometer have to be adaptive to changing weather condition and location. In the present study, three retrieval techniques have been used to obtain the temperature and relative humidity profiles from brightness temperatures, namely; piecewise linear regression, feed forward neural network and neural back propagation network. The simulated results are compared with radiosonde observations using correlation analysis and error distribution. The analysis reveals that neural network with back propagation is the most accurate technique amongst the three retrieval methods utilized in this study.
... The system is currently positioned near John Hopkins Airport at the NASA Glenn Research Center in Cleveland, Ohio. Campos et al. (2014) utilized a method to detect the water phase dynamics of mixed-phase winter storms and applied the method to several winter cases. These results were Combining ground-based microwave radiometer data with radar retrievals has shown great promise, however deficiencies with the current generation of radiometers have somewhat limited their capabilities. ...
A one degree beamwidth, multi-frequency (20 to 30 and 89GHz), dual-polarization radiometer with full azimuth and elevation scanning capabilities was built with the purpose of improving the detection of in-flight icing hazards to aircraft in the near airport environment. This goal was achieved by collocating the radiometer with Colorado State University's CHILL polarized Doppler radar and leveraging the similar beamwidth and volume scan regiments of the two instruments. The collocated instruments allowed for the liquid water path and water vapor measurements derived from the radiometer to be merged with the radar moment fields to determine microphysical and water phase characteristics aloft. The radiometer was field tested at Colorado State University's CHILL radar site near Greeley, Colorado during the summer of 2009. Instrument design, calibration, and initial field testing results are discussed in this paper.
... rapid destabilization occurred in a shallow layer within 300 m of the surface, beginning at 1630 UTC and ending at 1800 UTC, being most pronounced near the time of the nearby tornado passage. These analyses were compared to the radiometer liquid water time-height data (Campos et al. 2014;Serke et al. 2014) and satellite imagery sequences (as in Fig. 3a). That intercomparison revealed that the cause of this destabilization was due in part to the breakup of clouds in the Windsor CI region at this time, allowing solar heating to warm the surface layer and create superadiabatic conditions, but it also is attributable to lowlevel moistening (Fig. 10a). ...
This study documents a very rapid increase in convective instability, vertical wind shear, and mesoscale forcing for ascent leading to the formation of a highly unusual tornado as detected by a ground-based microwave radiometer and wind profiler, and in 1-km resolution mesoanalyses. Mesoscale forcing for the rapid development of severe convection began with the arrival of a strong upper-level jet streak with pronounced divergence in its left exit region and associated intensification of the low-level flow to the south of a pronounced warm front. The resultant increase in stretching deformation along the front occurred in association with warming immediately to its south as low-level clouds dissipated. This created a narrow ribbon of intense frontogenesis and a rapid increase in convective available potential energy (CAPE) within 75 min of tornadogenesis. The Windsor, Colorado, storm formed at the juncture of this warm frontogenesis zone and a developing dryline. Storm-relative helicity suddenly increased to large values during this pretornadic period as a midtropospheric layer of strong southeasterly winds descended to low levels. The following events also occurred simultaneously within this short period of time: a pronounced decrease in midtropospheric equivalent potential temperature θe accompanying the descending jet, an increase in low-level θe associated with the surface sensible heating, and elimination of the capping inversion and convective inhibition. The simultaneous nature of these rapid changes over such a short period of time, not fully captured in Storm Prediction Center mesoanalyses, was likely critical in generating this unusual tornadic event.
... Ground-based microwave radiometer has become a kind of instrument for atmospheric observations with remote-sensing technique [1] and can operate continuously with a typical temporal resolution of 1 s and satisfy the requirements in order to continuously monitor the atmospheric boundary layer temperature and humidity profiles [2][3][4][5][6][7][8][9] and unique liquid profiles [10][11][12][13], and may even detect lightning [14]. It is therefore very important to know the working state of a ground-based microwave radiometer. ...
Time series analysis on clear-sky brightness temperature (TB) data observed in the morning with ground-based microwave radiometer for atmospheric remote sensing is adapted to judge the working state of the radiometer system according to meteorological data variation features in terms of radiative transfer. The TB data taken as the first example in this study was for the ground-based microwave radiometer at Nanjing during the period from Nov. 27, 2010 to May 29, 2011. The radiometer has 12 channels including five channels at 22.235, 23.035, 23.835, 26.235, and 30 GHz for sensing air humidity and liquid water content and seven channels at 51.25, 52.28, 53.85, 54.94, 56.66, 57.29, and 58.80 GHz for sensing air temperature profiles. The correlation coefficients between the TB readouts from the radiometer and the simulated TB with radiosonde temperature and humidity profiles as input to radiative transfer calculation are greater than 0.9 for the first five channels while the correlation coefficients for the last seven channels are quite poor, especially for the channels at lower frequency such as 51.25, 52.28, and 53.38 GHz, at which the TB readout values in time series do not show the right atmospheric temperature variation features as time goes from November (winter) through spring to early summer (late May). The results show that the first five channels worked well in the period while the last seven channels didn’t, implying that the radiometer need to be maintained or repaired by manufacture. The methodology suggested by this paper has been applied to the similar radiometers at Wuhan and Beijing for quality control and calibration on observed TB data.
... It is also well established that microwave measurements alone are relatively insensitive to vertical liquid water distribution (Crewell, Ebell, Loehnert, & Turner, 2009). However, infrared cloud base temperature measurements and saturated humidity height distribution climatology (from historical radiosondes) contain additional liquid profile information (Campos, Ware, Joe, & Hudak, 2014;Serke et al., 2014). The neural network makes use of integrated liquid water (from microwave), cloud base temperature and height (from infrared and microwave) and liquid profile climatology (from historical radiosondes) in liquid profile retrievals. ...
Mountains, through atmospheric processes, play an integrated role in weather systems and climate systems. In turn, weather and climate over and nearby mountains are modified depending on environmental conditions. Mountain size, shape, slope, aspect ratio, and orientation along the water sources affect the weather systems; they can intensify fronts, eddies, vortex, rotors and turbulence, visibility, and precipitation. Depending on their location and height, precipitation type as snow over the mountains contributes significantly to hydrometeorological cycle and ecosystem. Increasing temperatures because of a possible climate change can reduce river discharges during warm seasons. Therefore, accurate measurements of climate change impact on the mountains are required. On the other hand, obtaining measurements over the mountainous regions are extremely difficult because of strong winds, cold temperatures (<0°C), mountain slopes, and heavy snow precipitation as well as instrumental issues. New developments on measurement strategies and instruments, and developing multiscale numerical models with detailed cloud microphysical processes and boundary layer parameterizations can reduce uncertainties in the weather and climate prediction. In this review, the challenges related to collecting measurements, numerical model predictions, and climate change issues over and nearby mountains will be emphasized.
... The MWR has become a popular and efficient instrument for remotely sensing the atmospheric temperature and humidity profiles as well as path-integrated cloud liquid water content in last few decades. MWRs are operated in many countries for monitoring climate and meteorological phenomena (e.g., nowcasting convective activity, boundary layer meteorology, and clouds) [Spänkuch et al., 2011;Xie et al., 2011;Cimini et al., 2013;Cadeddu et al., 2013;Madhulatha et al., 2013;Raju et al., 2013;Venkat Ratnam et al., 2013;Ware et al., 2013;Calheiros and Machado, 2014;Campos et al., 2014;Clements and Oliphant, 2014;Serke et al., 2014;Xu et al., 2014;Gultepe et al., 2015]. ...
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 (IRT) 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 increases 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.
... To understand and predict severe storms such as local heavy rainfall, thunderstorms, heavy snowfall, and tornadoes, temporally and spatially high-resolution measurements of thermodynamic and dynamic environments are required (e.g., Araki et al. 2015;Illingworth et al. 2015;Bodeker et al. 2015). 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). ...
... Another method to obtain the LWC profile, the neural network method, combines liquid profile climatology from historical radiosondes (Decker et al. 1978) with microwave and infrared observations ). The neural network method has demonstrated a LWC profile retrieval accuracy of 50% or better when compared to balloon-borne supercooled liquid sensors (Ware et al. 2003;Serke et al. 2013) and radar (Campos et al. 2014). Differences between fixed volume radiometric observations and balloon-borne liquid sensor point measurements along an uncontrolled flight path contribute to the uncertainty. ...
... 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). ...
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.
... The utility of this distinction is dependent upon the total COD (thinner clouds polarize more), the solar and observational viewing geometry (scattering angles between 40 and 70 • are best), ice crystal aspect ratio (values close to AR = 1.0 polarize least), and the instrument accuracy with respect to Q. Additionally, the ability to determine cloud thermodynamic phase with polarization is insensitive to the altitude of the cloud or the surface reflectance. There are many ways to determine cloud thermodynamic phase from the ground, such as with active measurements (Sassen, 1991), spectral ratios (Martins et al., 2011;LeBlanc et al., 2014), hyperspectral infrared measurements (Turner et al., 2003), and microwave radiometers (Shupe et al., 2005;Campos et al., 2014). While it may not be appropriate for all conditions, this method is well suited for low CODs, which may be a useful addition to the observational toolset. ...
The primary goal of this project has been to investigate if ground-based visible and near-infrared passive radiometers that have polarization sensitivity can determine the thermodynamic phase of overlying clouds, i.e., if they are comprised of liquid droplets or ice particles. While this knowledge is important by itself for our understanding of the global climate, it can also help improve cloud property retrieval algorithms that use total (unpolarized) radiance to determine cloud optical depth (COD). This is a potentially unexploited capability of some instruments in the NASA Aerosol Robotic Network (AERONET), which, if practical, could expand the products of that global instrument network at minimal additional cost.
We performed simulations that found, for zenith observations, that cloud thermodynamic phase is often expressed in the sign of the Q component of the Stokes polarization vector. We chose our reference frame as the plane containing solar and observation vectors, so the sign of Q indicates the polarization direction, parallel (positive) or perpendicular (parallel) to that plane. Since the fraction of linearly polarized to total light is inversely proportional to COD, optically thin clouds are most likely to create a signal greater than instrument noise. Besides COD and instrument accuracy, other important factors for the determination of cloud thermodynamic phase are the solar and observation geometry (scattering angles between 40 and 60° are best), and the properties of ice particles (pristine particles may have halos or other features that make them difficult to distinguish from water droplets at specific scattering angles, while extreme ice crystal aspect ratios polarize more than compact particles).
We tested the conclusions of our simulations using data from polarimetrically sensitive versions of the Cimel 318 sun photometer/radiometer that compose a portion of AERONET. Most algorithms that exploit Cimel polarized observations use the degree of linear polarization (DoLP), not the individual Stokes vector elements (such as Q). Ability to determine cloud thermodynamic phase depends on Q measurement accuracy, which has not been rigorously assessed for Cimel instruments. For this reason, we did not know if cloud phase could be determined from Cimel observations successfully. Indeed, comparisons to ceilometer observations with a single polarized spectral channel version of the Cimel at a site in the Netherlands showed little correlation. Comparisons to lidar observations with a more recently developed, multi-wavelength polarized Cimel in Maryland, USA, show more promise. The lack of well-characterized observations has prompted us to begin the development of a small test instrument called the Sky Polarization Radiometric Instrument for Test and Evaluation (SPRITE). This instrument is specifically devoted to the accurate observation of Q, and the testing of calibration and uncertainty assessment techniques, with the ultimate goal of understanding the practical feasibility of these measurements.
