Multi-instrument, ground-based measurements provide unique and comprehensive
data sets of the atmosphere for a specific location over long periods of time
and resulting data compliment past and existing global satellite
observations. This paper explores the effect of ice hydrometeors on
ground-based, high-frequency passive microwave measurements and attempts to
isolate an ice signature for summer seasons at Summit, Greenland, from 2010
to 2013. Data from a combination of passive microwave, cloud radar,
radiosonde, and ceilometer were examined to isolate the ice signature at
microwave wavelengths. By limiting the study to a cloud liquid water path of
40 g m
Better characterization of precipitation in the Arctic is fundamental to improve our understanding of the hydrological cycle and mass balance of the polar ice sheets. The Greenland Ice Sheet (GIS) is of particular interest as it has relatively large impacts on the Earth's climate system (Church, 2001). Understanding the characteristics of precipitation above the GIS is a key factor in quantifying the full energy and ice mass balance. Accurate atmospheric measurements and remote sensing precipitation retrievals from multiple instruments are essential to resolving and refining precipitation estimates over the GIS.
Microwave radiometers (MWRs) are a common remote sensing instrument, which make passive measurements of radiance at specific frequencies. Typically, MWR measurements are used to retrieve atmospheric properties, specifically liquid water path and precipitable water vapor (LWP and PWV, respectively). A frequently implemented technique for characterizing ice hydrometers from satellites and aircraft is to use high-frequency microwave channels (89 GHz and greater) and look for depressed brightness temperatures due to scattering of the upwelling radiation to calculate an ice water path (Hong et al., 2005; Bennartz and Bauer, 2003; Kulie and Bennartz, 2009; Deeter and Evans, 2000). While liquid and gas in the atmospheric column absorb and emit microwave radiation, ice hydrometeors scatter surface radiation away from the satellite sensor and depress the observed brightness temperature (BT). The same technique can be used from the ground looking up with the opposite effect, as ice scatters the upwelling radiation back towards the MWR sensor. Kneifel et al. (2010; hereafter K10) demonstrated the presence of an enhanced BT signature from ice hydrometeors in downwelling microwave radiance observations for a case study of snowfall in the Alps using ground-based MWRs. The high-frequency channels (90 and 150 GHz) are considered “window channels”, since these frequencies are free of strong gas absorption lines. At these frequencies the clear-sky downwelling radiance is very small, so when ice or liquid water is present these channels see a warmer BT, as seen by the K10 study.
If there are ice hydrometeors present in the atmosphere column, they will have two effects on the observed downwelling radiance at the surface: emission of radiation and scattering of the surface-emitted radiation back to the instrument. In general, ice hydrometeors have fairly high single scatter albedo (SSA) at high microwave frequencies, regardless of habit and size distribution. Typically the SSA will be in the range 0.8–0.9 (Liu, 2008), which implies that scattered radiation is likely the larger effect, but there may still be significant emitted radiation from the ice hydrometeors. Since some of the ice signature is scattered surface radiation, the magnitude of the effect is related to both the surface temperature and emissivity. The surface emissivity of different types of snow seen at Summit varies in the range of 0.60 to 0.91 for the higher-frequency passive microwave channels used in this study (Yan et al., 2008). This makes the ice signature challenging to model because it depends on both properties of the ice hydrometeors (habit, size distribution, amount, etc.) and the surface (temperature, roughness, emissivity).
We propose that the enhanced BT from the ice hydrometeors can be isolated and quantified by combining the observed data from instruments in the Integrated Characterization of Energy, Clouds, Atmospheric State, and Precipitation at Summit project (ICECAPS; Shupe et al., 2013) with radiative transfer models of the gas and liquid in the atmosphere. By doing this we are enhancing the K10 study by expanding it to multiple years of data in an Arctic environment with very low amounts of liquid water and precipitable water vapor, which present unique challenges. Additionally, since the temperatures at Summit Station are below freezing, we are implementing a newly developed cloud liquid water model for more accurate retrievals in the presence of supercooled water (Kneifel et al., 2014; Turner et al., 2015). Because the ice signature is also dependent on ice crystal habit and size distribution, relying on a small number of precipitation events to derive the ice signature may bias the result toward specific precipitation situations. The large data set from the ICECAPS Project allows for the average ice signature to be computed over many precipitation events, thus reducing this potential sampling bias.
In this paper we use the ICECAPS instrument suite (described in Sect. 2) to resolve a signal from the ice hydrometeors present in the high frequency, ground-based MWRs (90, 150, 225 GHz) for multiple years of summer season data at Summit, Greenland. We modeled the gas and liquid present in the column and compared that to observations from the MWRs (Sect. 3). We had to develop a technique to accurately model the absorption/emission from the liquid water and atmospheric gases; this is described in Sect. 4. Finally, we demonstrate an initial scattering model of the ice and compare these results to the observed signature (Sect. 5).
Studying the seasonal characteristics of the ice hydrometeors above the GIS is made possible with observations from the ICECAPS instrument suite from 2010 to 2013. Model results are then combined or compared with observations from specific instruments in the ICECAPS suite.
Sub-group of ICECAPS suite instruments used in this study (modified from Table 1 in Shupe et al., 2013).
Summit Station was the site of the Greenland Ice Sheet Project 2 (GISP2) ice core project, and has been expanded to a continuously operational science facility dedicated to studying the atmosphere and ice sheet properties of the GIS (Dansgaard et al., 1993). Summit Station is home to many atmospheric and snow science instruments, including ICECAPS, which is purposely co-located at Summit Station to aid in understanding the cloud and atmosphere properties over the GIS and their interaction with the cryosphere. Since 2010, the ICECAPS suite of instruments has been monitoring a variety of atmospheric parameters to further our knowledge of atmospheric processes above the GIS (Shupe et al., 2013). The ICECAPS project will remain at Summit until at least 2018, thus providing a comprehensive data set and analyses of the atmosphere over central Greenland. Additionally, ICECAPS is expanding the network of past and existing high-latitude atmospheric suites (i.e., Eureka, Canada and Barrow, Alaska, Ny-Ålesund) already helping to characterize Arctic atmospheric and cloud processes (Shupe et al., 2011; Uttal et al., 2015).
ICECAPS is modeled after other successful Arctic observatories and is similar in scope to facilities run by the Department of Energy's Atmospheric Radiation Measurement (ARM) program (Ackerman and Stokes, 2003; Shupe et al., 2013). The ICECAPS instrument suite is supported by year-round technicians and support staff at Summit Station and is updated with new instruments, upgrades, and repairs by researchers every summer. Table 1 illustrates a brief overview of the ICECAPS instruments used in this study, including key specifications, measurements, and retrieved parameters. We employed data from a subgroup of the ICECAPS suite and a co-located 225 GHz MWR. The available measurements and retrieved values are further described in the following sections.
The Millimeter Wavelength Cloud Radar (MMCR) is a zenith pointing, 35 GHz
(Ka band) radar with processed measurements provided every 10 s at a height
resolution of 45 m (Moran et al., 1998). The MMCR measures the profile of
reflectivity, Doppler velocity, and Doppler spectral width in the column
above. For the MMCR, hydrometeors with geometric diameters less than
approximately 3 mm are in the Rayleigh scattering region (Kneifel et al.,
2011). However, for ice hydrometeors larger than
The Doppler velocity measures the fall speed of particles toward the radar – this is dependent on the mass and projected area of the ice hydrometer population; thus, some micro-physical insight is gained from these fall speed values. However, the particles are embedded with a vertical wind field that will affect the measured fall speed.
Finally, the variance of the velocity in a given pulse volume, the Doppler spectral width, aids in determining turbulence and contains indicators of hydrometeor phase. Strong turbulence or multiple phases/habits in a cloud layer leads to large a Doppler spectral width. On the other hand, uniform particle populations, such as for those precipitating out of a cloud, exhibit relatively low Doppler spectral width. By combining these measured quantities from the MMCR, we can infer many properties of the hydrometeors observed at Summit.
ICECAPS also gathers observations from three different passive MWRs all built by Radiometer Physics GmbH. The Humidity and Temperature Profiler (HATPRO) has seven channels from 22 to 32 GHz (near 22.24 GHz water vapor absorption line) and seven channels from 51 to 58 GHz (near oxygen absorption line; Rose et al., 2005). The high-frequency microwave MWR (MWRHF) has two high-frequency channels: 90 and 150 GHz. The two radiometers are run in a master–slave configuration and make coincident measurements every 4 s. Data from the third co-located MWR, which is sponsored by the Academia Sinica Institute of Astronomy and Astrophysics (ASIAA) group, observes downwelling radiation at 225 GHz, and takes measurements every 4 s (Matsushita et al., 2013). Although all of the MWRs measure the downwelling atmospheric radiance at several elevation angles, in this study we only use data from zenith pointing.
Passive microwave radiometry is commonly used to derive liquid water path
(LWP; Crewell et al., 2009). By combining the BTs observed from specific
channels, PWV and LWP are derived. Historically, LWP and PWV at ARM sites are
derived using the 23.84 and 31.40 GHz channels using a version of the MWR
retrieval (MWRRET) algorithm (Turner et al., 2007a). The physical retrieval
method employs the MonoRTM radiative transfer model (Clough et al., 2005) and
the Liebe91 liquid water model (Liebe, 1991). It was found that the addition
of high-frequency channels to the retrieval algorithm improves LWP accuracy,
particularly for low LWP amounts. By adding the 90 GHz channel, the
uncertainty is reduced from 20 to 30 g m
The reduced uncertainty at low LWPs is important to this study, as the cloud
liquid water path on average at Summit (and the Arctic as a whole) is small
as 80 % of liquid-bearing clouds in the Arctic have less than
100 g m
The MWRRET retrieval gives the integrated cloud liquid water amount but no information about cloud altitude. Cloud-base height (CBH) is estimated from a Vaisala Ceilometer (VCEIL). The VCEIL is a vertically pointing 905 nm pulsed laser system with 15 m height resolution and takes a measurement every 15 s. Cloud-base heights (up to three layers) are determined based on the backscattered signal received by the instrument. We use the first cloud-base height retrieved from the VCEIL to define the base of the cloud liquid water layer in this study.
This study also uses data from twice daily balloon-borne radiosondes (manufactured by Vaisala, models RS-92K and RS-92SGP) launched at Summit Station. The launches occur at approximately 1200 and 2400 Coordinated Universal Time (UTC), and gather in situ measurements of temperature, pressure, relative humidity, and, in some cases, horizontal wind speed and direction. These thermodynamic profiles provide critical input for the radiative transfer modeling (see Sect. 2.2).
The data sets described above are merged together to a common sampling time, defined by the MWRRET retrieval (every 100 s). The slower data stream (twice daily radiosonde) is linearly interpolated to the common sampling time, and the faster data streams are simply subsampled at the MWRRET retrieval times. We interpolate all the data to the fixed height grid defined by the MMCR.
For an example day, we use data from the prior day's radiosonde launch (day – 1, 24:00 UTC) along with the two radiosondes launched for the given day (12:00 and 24:00 UTC) and linearly interpolate the temperature, pressure, and relative humidity of each layer in the column throughout the day to the MWRRET temporal grid. The vertical layering uses the MMCR vertical grid up to 7.5 km altitude above ground level (a.g.l.). Above this altitude, the layering becomes gradually coarser and extends to up 30 km a.g.l. Next, the MWR retrieved PWV is used to scale the interpolated relative humidity from the radiosonde – this is because the PWV retrieved value is higher temporal resolution and more accurate than the radiosonde data (Turner et al., 2003). Finally, a single layer cloud is inserted into the vertical grid at the first cloud-base height (CBH1) detected by the VCEIL, with the MWR retrieved LWP value.
The emission and absorption of the gases and liquid water in the atmospheric column are modeled using in situ observations of temperature and pressure and remotely sensed values of integrated water vapor, liquid water content, and cloud-base height from the ICECAPS instruments. To compute the volume absorption coefficients of dry air and water vapor in the atmospheric column, we employed the MonoRTM (v5.0; Clough et al., 2005) using inputs of layer temperature, pressure, and scaled water vapor. The liquid water absorption and emission is modeled using the TKC15 model (Turner et al., 2015) with inputs of liquid water content (LWC) at a defined cloud height and temperature. For altitudes above the radiosonde profile, a subarctic standard atmosphere profile is assumed. The simulated emission is not sensitive to the details of the upper atmosphere profile, but systematic biases would be present if the atmosphere was artificially truncated at too low an altitude.
In ice-cloud-free atmospheres, the RT model need only consider the absorption and emission of atmospheric gases and liquid water. When ice is introduced into the column, multiple scattering can occur and we then must employ a radiative transfer model that accounts for scattering. The successive-order-of-interaction (SOI) RT model accurately simulates scattering for the infrared and microwave spectral region (Heidinger et al., 2006; O'Dell et al., 2006). The SOI model combines the layer-averaged optical properties and temperature in order to compute downwelling radiance at selected frequencies. The layer-averaged optical properties are calculated from the gas and liquid water absorption models (described above) and ice optical properties (further discussed in Sect. 5). The SOI modeled BTs can then be compared to MWR observations. For all cases used in this study we employed the SOI radiative transfer model, even when modeling non-scattering atmospheres that only include gases and cloud liquid water absorption. As is further discussed in the subsequent section, comparing the measured and modeled BTs at specific frequencies lends insight into the hydrometers present in the atmospheric column.
Similar to K10, we compared the BTs in the high-frequency channels of the MWRs to the output from the radiative transfer model calculation. The K10 study employed a radiative transfer model that included absorption/emission and scattering to simulate the behavior of the ice signature based on the habit, surface emissivity, etc. Different from K10, we do not initially include an ice scattering model for the purpose of identifying the ice signature. We instead attempt to isolate the ice radiative signature in the observations by accounting for any other potential emission or absorption sources within the column. If we compare the calculated BT using only gas and liquid water to the observed BTs from the MWRHF, any difference should be due to the ice signature. Consequently, the average ice hydrometeor radiative signature can be computed over many precipitation events by extending the analysis to the full available ICECAPS data set.
We can acquire statistics of different precipitation regimes at Summit by merging all available MMCR data and plotting contoured frequency by altitude diagrams (CFADs). CFADs depict all data as a two-dimensional occurrence histogram, with the vertical axis representing the height dimension and the horizontal axis representing a radar measurement (for example, reflectivity). Figure 1a is a CFAD of all the reflectivity values measured by the MMCR for any given time within the summer months – June, July, August (JJA) – 2010 through 2013. We can highlight the types of hydrometeors observed during specific atmospheric conditions by filtering the MMCR reflectivity CFAD, illustrated in Fig. 1a, as a function of other ICECAPS instrument measurements or derived parameters.
CFADs of MMCR reflectivity for summer (JJA) at Summit, Greenland,
from June 2010 to August 2013 with a sample resolution rate every 10 s.
Panel
Filtering the MMCR CFADs by the corresponding MWR-derived LWP for the same
time period can identify regimes in which ice hydrometeors are likely
present. We partition the data with a threshold LWP value in order to select
cases that have low LWP. The exact threshold value is arbitrary, as the
resulting CFADs are not sensitive to the particular threshold value. We tried
values of 5, 10, and 40 g m
As depicted in Fig. 1b and c, the MMCR reflectivity CFAD for JJA has been
filtered by cases when LWP was less than and greater than 40 g m
The frequency of cases in JJA where the LWP is greater than 40 g m
We argue that the large radar reflectivity values are directly correlated to
ice backscatter and cannot be from liquid precipitation, as Summit is never
above freezing and thus large liquid hydrometeors (greater than
80
As postulated from previous case studies in K10, the higher-frequency
channels in the ground-based zenith-pointing MWRs will see an enhanced BT in
the presence of ice in the column. Thus, we examine the difference between
the measured BTs from the 90 and 150 GHz channels and the SOI model outputs
(with no ice included, gas and liquid water contributions only) at that same
frequency. As illustrated in the contour plot of the JJA comparison in
Fig. 2c and d, there is an increase in the difference of the observed minus
modeled BTs as a function of the MMCR reflectivity converted to what we refer
to as “
Brightness temperature differences between observations minus the
modeled gas and liquid contributions in the 23.84, 31.40, 90, and 150 GHz
channels as a function of
The
The observed minus modeled BT differences at 90 and 150 GHz have a clear
positive dependence on
The lower-frequency channels (23.84 and 31.40 GHz) should exhibit little to
no effect from the presence of ice hydrometeors in the atmospheric column, as
the microwave radiation at these frequencies is comparatively insensitive to
ice hydrometeors (Johnson et al., 2012). Thus, we expect the histogram
contours to be nearly vertical at the 23.84 and 31.40 GHz for the
relationship between the BT differences and the integrated reflectivity
(
Panel
Two of the inputs for the radiative transfer model are retrieved values based on BTs from the MWRs: the PWV and LWP. As explained in Sect. 2.1.2., the retrieval for the PWV and LWP employ a three-channel algorithm, which includes the 90 GHz channel. Though we tried to mitigate the effect of the ice by using the three-channel algorithm, the enhanced BT in the 90 GHz still has a significant impact on the retrieved LWP and PWV. More precisely, the retrieval will tend to adjust the LWP and PWV in order to account for the enhanced BT from the ice hydrometeors, leading to an overestimate of LWP and underestimate of PWV.
As postulated in the previous section, we believe that the MWR retrieved LWP
(PWV) values are biased high (low) when a significant ice signature is
present in the column due to the retrieval incorporating the 90 GHz MWR
channel. However, if we use only a retrieval based on the lower frequencies
of 23.84 and 31.40 GHz, the random error in LWP increases dramatically to
20–30 g m
As illustrated in Fig. 2, the difference between measured and modeled BTs as
a function of The presence of ice increases the observed BTs at 90 GHz but has little
effect on the lower frequencies. Since the retrieval does not include effects from ice, the retrieval
accounts for this enhanced signal in the 90 GHz channel by increasing
(decreasing) the retrieved LWP (PWV) thus producing a positively (negatively)
biased LWP (PWV) estimate. Since the spectral absorption for the three water states (vapor, liquid,
ice) have different shapes, the retrieval cannot reduce the modeled–measured
BT bias to zero for all channels.
To better illustrate this idea it is useful to look at Fig. 2 from K10, where
the optical thickness as a function of frequency is plotted for several
absorption models – water vapor, liquid water, ice by habit,
etc. The liquid water and ice total optical depths (
Histograms of the MMCR
The bias in the simulated BT, shown in Fig. 2, suggests that the MWRRET retrieved PWV and LWP may be influenced by the presence of ice hydrometeor signature in the 90 GHz channel used in the retrieval. Since the MWRRET does not include ice hydrometeors in the radiative transfer calculation, it can only fit retrieval channel observations by adjusting the PWV and LWP. The higher optical depth for liquid water at 90 GHz suggests that MWRRET adds extra LWP to account for the observed microwave ice signature. This will increase the forward modeled BT for the 23.84 and 31.40 GHz channels as well. Since there will be effectively zero ice signature at the low-frequency MWR observations, the extra LWP will cause the low-frequency BTs to be biased high. The retrieval partially compensates for the high BT bias at low frequencies by decreasing the PWV, which will reduce the simulated BT primarily at the 23.84 GHz channel, which is near the water vapor absorption line. Figure 3 shows these biases in a schematic fashion. Because the liquid absorption model uses the MWR retrieved LWP and PWV as inputs to the SOI, a correction for the retrieved LWP and PWV in the presence of ice is necessary to accurately quantify the ice impact on passive microwave BTs.
The lower-frequency channels are comparably insensitive to ice (Johnson et
al., 2012), so we focus on the 23.84 and 31.40 GHz channels to derive a
first-order estimate for the MWRRET LWP and PWV biases from the ice
signature. In order to correct for the apparently biased PWV and LWP, we make
an ad hoc linear correction to the retrieved values. We assume the PWV and
LWP bias are linearly related to the
To utilize these corrections in our modeling framework, the
Returning to Fig. 3, we show the effect of these corrections for a standard
profile at Summit with 0.1 cm PWV and 20 g m
Brightness temperature differences between the
MWRHF and the MWRHF-225
observations and the modeled gas and liquid contributions after implementing
the LWP correction for ice for the 90, 150, and 225 GHz channels. The count
histogram is binned logarithmically in
Comparison of the MWR observed data with the radiative transfer model –
using the LWP and PWV corrections for ice – for the JJA season from 2010
through 2013 for LWP of less than 40 g m
We present the LWP and PWV corrected results for the 23.84, 31.40, 90, and 150 GHz channels. The lower-frequency MWR channels exhibit insensitivity to the ice (Fig. 4b and d), while the higher-frequency MWR channels exhibit enhanced BTs when ice is present (Fig. 5). Additionally, we present data from a co-located 225 GHz MWR, which exhibits even larger BT differences with respect to the ice. Finally, we recast the results from these five MWR channels and compare them to each other. We also show preliminary results from a simple radiative transfer simulation as a first-order comparison of modeled results against the MWR observed ice signature enhanced BTs in the 90, 150, and 225 GHz channels.
All data presented are events in JJA with LWP of less than 40 g m
Co-located with the ICECAPS measurements is the ASIAA a very high-frequency
MWRHF-225, which allows us to extend this study to include a 225 GHz
channel. As the effect of ice on this frequency from ground observations has
not yet been explored, the observed ice effect in the 225 GHz channel is a
new application of this instrument. As expected, the 225 GHz exhibits a
large BT enhancement due to ice (Fig. 5c). The MWRHF-225 was deployed in mid-2011, so the data set is somewhat smaller than the ICECAPS data set already
described. In addition, the MWRHF-225 does have slightly different time
coverage (e.g., the instrument downtime and QC flags are disjoint from the
HATPRO and MWRHF). The data set with all five MWR channels covers only the union
where all instruments collected good data. At the highest
By plotting the difference in the observed minus calculated BTs in the MWR
channels as a function of each other, one may gain insight about the spectral
character of the ice signature in the microwave. Figure 6 depicts the BT
difference of four of the MWR channels with respect to that of the 90 GHz:
23.84, 31.40, 150, and 225 GHz. Additionally, the binned values of the BT
differences are colored by logarithm of the average
In the top of Fig. 6 (panels a and b), the 23.84 and 31.40 GHz BT
differences are plotted and binned on the
Now that we have an estimate of the passive microwave ice signature, we can compare it to modeled results with our SOI framework, described in Sect. 2.3. We can find the difference in modeled BTs in the presence of ice using SOI by running the model twice: once including ice with contributions from the atmospheric gases and once with only the gases. The difference between these two runs produce differences in BTs that allow for direct comparison with our multi-frequency results (Fig. 6), and an assessment of the ice microwave optical property models for the ice hydrometeors at Summit, Greenland.
Multi-frequency plots of the BT difference in channels 23.84, 31.40,
150, and 225 GHz as compared to the 90 GHz channel. The binned values of BT
difference are colored according to logarithm of the average
For a first-order ice habit study, we used the temperature-dependent ice particle size distribution parameterization from Field et al. (2007) (hereafter F07) for the particle size distribution (PSD), which is developed from airborne stratiform ice cloud in situ measurements in the midlatitudes. Additionally, we used information from the Liu database of microwave single-scattering properties for three-bullet rosettes (LR3), sectored snowflakes (LSS), and dendrites (LDS) for ice habit characteristics (Liu, 2008; note that these are the same ice habits used in the K10 study). The PSD, ice habit, and radar backscatter cross section information are used to convert the MMCR reflectivity measurements to ice water content (IWC). This IWC is then recombined with the PSD and ice habit information and the microwave optical properties at the specific MWR frequencies, yielding the layer optical properties needed to simulate the passive MWR measurements (see Kulie et al., 2010, for further details). The SOI model uses these layer optical properties to calculate BTs at MWR frequencies. Finally, the emissivity of the snow surface is assumed to be 0.6, consistent with Yan et al. (2008) based on common snow surface conditions at Summit Station.
SOI simulated BT differences plotted on top of the observations for
the 150 versus 90 GHz and 225 versus 90 GHz channels (
For an initial test of the model, we generate a synthetic 1 km thick ice
cloud with a range in MMCR
The small differences between the SOI model results and the observations with
regard to equivalent
The above results are based on our first-order assessment of the
ice-influenced LWP and PWV biases. Our current correction is defined in terms
of the three-channel MWRRET retrieved LWP. As noted in Sect. 2.1.2, this
retrieval is used for this study as it is more sensitive to and has better
precision for low LWPs. One possible BT correction can be estimated by
examining specific “dry snow” cases (i.e., extremely low LWP and high
This study first examined cloud and precipitation statistics derived from the
MMCR and partitioned the data with a specified LWP derived from the MWR. By
limiting our study to low LWP (less than 40 g m
We identified a bias in the current MWRRET retrieved LWP and PWV caused by
the ice signature and utilization of 23.84, 31.40, and 90 GHz channels as
part of this study, and developed and applied a first-order correction
(described in Sect. 4). The bias correction to the three-channel retrieval is
not the focus of this study, but had to be addressed to quantify the ice
signature in at microwave frequencies. Overall, the LWP and PWV bias due to
ice occurs in a small fraction of the total data, and is relatively small in
magnitude. For example, the high
The multi-frequency relationships in the high-frequency MWR channels,
illustrated in our results in Sect. 5.3, show a linear relationship between
the 90 GHz channel versus both the 150 and 225 GHz channels and increasing
To accurately retrieve IWP from the measured ice signature, we need accurate descriptions of the ice habit, surface temperature and emissivity, and ice PSDs more representative of conditions at Summit. For future work, we hope to employ a PSD with a better fit to the Summit conditions and eventually have ICECAPS instrumentation capable of measuring a PSD in situ. The measured ice signature technique outlined in this work is a novel approach to better understand ice hydrometeors and could prove to be a powerful tool in future ground and remote sensing applications.
ICECAPS and associated research in this study is supported by NSF PLR1304544, PLR1355654, and PLR1303879. Partial support is also provided by NASA NNX12AQ76G and NNX13AG47G. We appreciate the advice and contributions from Stefan Kneifel, the use of the ASIAA MWR at Summit Station (P.I. Ming-Tang Chen), our colleague V. P. Walden, and all the technicians and support staff that keep the ICECAPS suite running. Ceilometer measurements were provided by the US Department of Energy's Atmospheric Radiation Measurement Program. Edited by: J.-Y. C. Chiu