Mass accumulation is a key geophysical parameter in understanding the Antarctic climate and its role in the global system. The local mass variation is driven by a number of different mechanisms: the deposition of snow and ice crystals on the surface from the atmosphere is generally modified by strong surface winds and variations in temperature and humidity at the ground, making it difficult to measure directly the accumulation by a sparse network of ground based instruments. Moreover, the low cloud total water/ice content and the varying radiative properties of the ground pose problems in the retrieval of precipitation from passive space-borne sensors at all frequencies. Finally, numerical models, despite their high spatial and temporal resolution, show discordant results and are difficult to be validated using ground-based measurements.
A significant improvement in the knowledge of the atmospheric
contribution to the mass balance over Antarctica is possible by
using active space-borne instruments, such as the Cloud Profiling
Radar (CPR) on board the low earth orbit CloudSat satellite,
launched in 2006 and still operating. The radar measures the
vertical profile of reflectivity at 94
The aim of this work is to show that, after accounting for the characteristics of precipitation and the effect of surface on reflectivity in Antarctica, the CPR can retrieve snowfall rates on a single event temporal scale. Furthermore, the CPR, despite its limited temporal and spatial sampling capabilities, also effectively observes the annual snowfall cycle in this region. Two years of CloudSat data over Antarctica are analyzed and converted in water equivalent snowfall rate. Two different approaches for precipitation estimates are considered in this work. The results are analyzed in terms of annual and monthly averages, as well as in terms of instantaneous values. The derived snowfall maps are compared with ERA-Interim reanalysis and with in situ measurements, showing overall agreement. The effects of coastlines in enhancing precipitation rates and cloud precipitation efficiency are recognized. A significant seasonal signal also affects the averaged spatial extent of snowfall patterns. A comparison with snow accumulation ground measurements of single snowfall events shows consistency with the CPR retrievals: all the retrieved snowfall episodes correspond to an increase of snow accumulation at the ground, while several episodes of increase of snow stack height are not related to significant retrieved snowfall rate, likely indicating the local contribution of blowing snow. The results show that CPR can be a valuable source of snowfall rate data in Antarctica that can be used at different temporal scales, providing support to the sparse network of ground-based instruments both for numerical model validation and climatological studies.
The study and characterization of atmospheric phenomena in polar environments is a crucial aspect in solving the uncertainties of Earth's water cycle, energy budget, and climate studies. However, many limits affect the analyses of meteo-climatic parameters and of their variability at high latitudes (Lubin and Massom, 2007). Among these, precipitation is a key geophysical parameter with the highest spatial and temporal variability, and its measure and monitoring using either ground based measurements or satellite observations, is a challenging task over Antarctica, where mostly weak solid precipitation (snow and ice crystals) occurs (Bromwich, 1988).
The knowledge of precipitation characteristics over Antarctica relies on a sparse network of instruments based on different principles (snowpits, heated raingauges, snow stakes, acoustic sounders), measuring different properties of solid precipitation with different sensitivity and errors. The spatial interpolation of such sparse data is also difficult, with results depending on the data/instruments and techniques used (Arthern et al., 2006; Eisen et al., 2008). Moreover, ground based measurements are affected by severe experimental problems, such as the difficulty to measure relatively small snow deposits with an acceptable degree of accuracy and reliability (Eisen et al., 2008; Knuth et al., 2010), ensuring also effective instrument maintenance for the long time frames necessary to study precipitation characteristics. Recently, an experimental site devoted to the intensive study of clouds and precipitation microphysics has been established (Gorodetskaya et al., 2014).
The use of numerical models, in general, shows discordant results, with large bias and relatively high errors if compared with ground measurements (Genthon and Krinner, 2001). Modeled precipitation fields, however, are still extremely valuable to assess annual trends for climatic studies (Bromwich et al., 2011). At finer scales, the polar version of the MM5 model (Guo et al., 2003), has been applied to infer spatial and temporal distribution of Antarctic precipitation (Bromwich et al., 2004), addressing also the role of blowing snow. Also the RACMO2 regional atmospheric climate model has recently updated its physical package giving some improvements on Surface Energy Balance, including precipitation (Van Wessem et al., 2014).
The detection from passive space-borne Low Earth Orbiting (LEO)
sensors of light and solid precipitation is made difficult over
Antarctica at any wavelength, due to the highly reflecting and
emitting cold background and the relatively low ice content of the
clouds. Several theoretical studies have been carried out to analyze
the potential of passive microwave observations to detect and retrieve
snowfall and to relate microphysical properties of iced hydrometeors
to the observations (e.g., Skofronick-Jackson and Johnson, 2011;
Munchak and Skofronick-Jackson, 2014). The use of high frequency
channels (
A new tool for clouds and precipitation remote sensing at high latitudes became available with the launch of the Cloud Profiling Radar (CPR) onboard the low earth orbit CloudSat satellite in 2006. The W-band, nadir pointing radar is designed to provide vertical profiles of reflectivity of the atmosphere, allowing a physical retrieval of the vertical structure of clouds and precipitation (Stephens et al., 2002; Liu, 2008), and, under some assumptions, the quantitative estimate of the snowfall rate near the ground (Hudak et al., 2008; Liu, 2008; Kulie and Bennartz, 2009). After the first operational year of CloudSat, some studies have been done over the Antarctic region and Liu (2008) and Hiley et al. (2011) showed the first remotely sensed precipitation maps, together with extensive discussion on the Antarctic snowfall estimate using CloudSat data. Since early 2013, the CloudSat Project releases a global product (2C-SNOW-PROFILE) where the “near surface” snowfall rate is reported for each CPR profile (Wood, 2013), and these data were used by Palerme et al. (2014) to compute a multi-year precipitation map of the Antarctica ice sheet.
Along this line, we used the snowfall rate retrieval algorithm from Kulie and Bennartz (2009) from raw CPR reflectivity data, tailored to the characteristics of the Antarctic precipitation and to account for the highly reflecting background surface, obtaining a precipitation product suited for Antarctica. Furthermore, we used the 2C-SNOW-PROFILE product to extend the Palerme et al. (2014) precipitation analysis over the ocean. We analyzed two years of data (2009 and 2010) and studied the spatial distribution of annual snow amount, also as function of cloud cover. The results were compared with ERA-Interim (Dee et al., 2011) reanalysis and with the data from 6 Acoustic Depth Gauges (ADG) available at the ground. ADG data are also compared to single snowfall events, showing consistency between the CPR snowfall retrievals and the ground-based measurements. Moreover, we evaluated the seasonal cycle of precipitation and the influence of the orography on snowfall rate.
It has to be considered that a systematic comparison between the two snowfall products (the one obtained from Kulie and Bennartz (2009) and 2C-SNOW-PROFILE product) is not the primary focus of this study. The two different approaches for CloudSat precipitation estimates over Antarctica are shown to highlight their potentials, and possible explanations for differences between the two are discussed. As opposed to the 2C-SNOW-PROFILE product used in Palerm et al. (2014) where also wet snow and mixed phase precipitation is included, with Kulie and Bennartz (2009) algorithm we focus on the dry snow only. From the analysis of the results and from the comparison with ERA-Interim reanalysis it is evident that the impact of wet snow (and mixed phase precipitation) is significant over ocean. Depending on the application, the 2C-SNOW-PROFILE product can be used to study the climatology of the total precipitation, while the estimates from Kulie and Bennartz (2009) can be used to study the contribution to the precipitation due only to dry snow. Further studies dedicated to systematic comparisons between different snowfall retrieval algorithms are currently being undertaken.
The CloudSat is an experimental satellite equipped with a W-band
(94
Two years of CloudSat CPR data from January 2009 to December 2010,
hereafter referred to as Observing Period (OP), are considered in this
study. A significant gap, due to CPR battery problems, occurred from
7 December 2009 to 16 January 2010 reducing the number of satellite
tracks to 9541 (about 560 tracks less than the expected number). We
selected the area south of 60
The CPR data used in this work are the radar reflectivity factor
vertical profiles, hereafter referred to as reflectivity (
The algorithm here considered to retrieve snowfall properties over
Antarctica is based on the technique developed by Kulie and Bennartz
(2009) (hereafter referred as KB09) that makes use of CPR reflectivity
profiles to select clouds with precipitation and then assigns
a snowfall rate to the profiles. The algorithm first selects profiles
with near-surface reflectivity (
A further study (Hiley et al., 2011) addressed the sensitivity of the
algorithm to the choice of some parameters, such as the depth of the
cloud vertical continuity, the reflectivity thresholds, and the
parameters of the
Several values of the
The definition of a correct value of the near-surface bin, i.e. the
lowest bin not affected by ground clutter signal, is particularly
relevant over Antarctica for two reasons: (1) the coastal areas are
very steep in Eastern Antarctica and thus ground clutter could affect
higher bins in the CPR vertical profile; (2) very often snow could be
drifting by strong low-level winds resulting in the local deposit of
snow not related to snowfall (Li and Pomeroy, 1997). Following Kulie
and Bennartz (2009), a further test is carried out on the reflectivity
of the lowest CPR bins. In particular, we assumed the value of 20 dBZ
as the theoretical upper limit for clutter-free reflectivity: when
reflectivity at the 6th bin exceeds the threshold, it means that it is
very likely affected by ground clutter, and the retrieval algorithm is
applied starting at the 8th bin of the CPR, checking again for
continuity of 5 vertical layers with reflectivity larger than
The CloudSat snow profile product (2C-SNOW-PROFILE, hereafter 2C-SNOW)
provides estimates of vertical profiles along with surface snowfall
rate (Wood, 2013; Palerme et al., 2014). As KB09, this CloudSat
product does not consider the lower bins close to the surface in order
to avoid ground clutter effects. The 2C-SNOW product does not consider
the bins below the 3rd bin from the surface if the profile is over
ocean without sea ice, or over inland water, and below the 5th bin if
the profile is over land, sea ice, or unknown surface. Also in this
case a reflectivity threshold of
After a deeper investigation of the SRS flag behavior over Antarctica,
we found that profiles with SRS bit 3 active are affected by ground
clutter and are then corrected using the snowrate value of the bin
immediately above the near surface bin. In Fig. 3 the PDF of the
reflectivity used in the 2C-SNOW product retrieval is shown (black
line), together with the PDF of profiles marked as affected by ground
clutter (red line) from the 2C-SNOW product. As for the KB09
algorithm, the number of bins affected by ground clutter is not very
high (also in this case only 10
The optimal
Long term ground based precipitation measurements over Antarctica rely on a sparse number of stations equipped with heated raingauges and/or Acoustic Depth Gauges (ADG), the latter measuring the distance from the snow covered surface to the instrument. Both instruments have strong limitations when a reliable measure of snowfall rate is needed (Eisen et al., 2008). For raingauge measurements in polar regions most of the uncertainties are related to wetting, evaporation losses, and strong winds (Sugiura et al., 2003): in particular winds induce undercatch, but also an overestimation bias due to blowing snow. The relative impact of these two effects depends on the type of wind-shield used. The measure of accumulated snow by ADG is influenced by many other factors than precipitation itself, since other different processes can change the height of the snow at the ground: blowing snow, deposition of hoar frost, surface sublimation, snowdrift sublimation, snow settling and compaction over time, meltwater (Knuth et al., 2010; Li and Pomeroy, 1997).
In this work we make use of the data from 6 ADG stations, whose
location is shown in Fig. 4, managed by the University of
Wisconsin-Madison Automatic Weather Station Program during the OP
(Knuth et al., 2010). We processed the ADG 10 min data in order to
evaluate the snow amount accumulated at the ground at two different
time scales, monthly and at the single snowfall event time scale. The
monthly distance variation between instrument and snow covered ground
is computed by subtracting the ADG distance measurement at the end of
the month by the one at the beginning of the month. The distance at
the beginning (and at the end) of each month is computed as the mean
distance over the first (last) five days of the month and the last
(first) five days of the previous (following) month. After the
screening of the outliers (i.e. very large distance variations over
10
CloudSat has overpasses within a 10
The analysis of solid precipitation over Antarctica and surrounding
ocean is carried out at two different spatial and temporal
resolutions. The CPR retrievals (both KB09 and 2C-SNOW) are analyzed
at the CPR footprint scale, discussing statistical properties of
snowrate at the highest resolution available. A parallel analysis is
performed, sampling the CPR data on 1
As first result, we computed the mean snowfall rate over the OP,
expressed in mm of water equivalent per year, estimated in each
Comparing the estimate maps with the forecast reanalysis map, the
first distinguishing feature is that the order of magnitude of the
mean annual snowfall rate is very similar over the grounded ice sheet
for both CloudSat-based methodologies. The KB09 estimates, however,
exhibit a larger extension of the very dry area (snowfall rate below
50
These results indicate that the CPR observation, even subject to low spatial and temporal sampling, are able to reconstruct the main features of the mean annual snowfall patterns, due also to the expected low variability of snowfall fields. The patchy snowfall pattern over the oceans, where more homogeneous fields are expected given the lack of surface forcing, could be due to the poor sampling of the CPR (see Fig. 1).
The scatterplot between ERA-I and CPR retrieved mean annual snowfall,
all remapped on the
Cloudiness over Antarctica has been studied from both in situ and
remote observations (Spinhirne et al., 2005; Lachlan-Cope, 2010;
Bromwich et al., 2012), providing information on cloud horizontal and
vertical structures, and cloud microphysical characteristics. In this
work, we use the cloud top height information of the CPR_echo_top
product to compute a cloud cover map for the OP. Figure 7a shows the
percentage of profiles classified as cloudy by the CPR_echo_top
product (hereafter “cloud” profiles) with respect to the total
number of profiles in each grid box. The cloud cover is highly
correlated with topography: it is higher over the ocean and low
elevation land, while is low over the Plateau. Over the ocean cloud
cover patterns are more related to atmospheric circulation with
a significant dependence on longitude, with wide areas, such as the
Weddell Sea (around 30–50
A comparison between satellite estimated monthly mean snowfall rate (computed in the same way as the annual mean snowfall rate, but on a monthly basis) and the measurements from the 6 ADG is performed in order to provide a qualitative measure of the accuracy of the CPR ability to detect snowfall.
The monthly distance variation from ADG, computed as described in
Sect. 2.2, is compared to the mean snowfall rate
(
Results from KB09 and from 2C-SNOW estimates present high qualitative
similarities. The large majority of the months with snowfall (as
derived from CPR) have a negative monthly distance variation measured
by the ADG (positive snow accumulation variation, i.e., average
accumulation at the end of the month more than average accumulation at
the beginning of the month), especially for snowfall rates higher than
25
In the validation of satellite precipitation retrievals (i.e., Puca et al., 2014), often a contingency table is constructed by matching estimates with ground reference measurements, where the number of hits (when estimates and observation both detect precipitation), misses (when only observations measure precipitation), false alarms (when only the estimate detects precipitation), and correct negatives (when both do not detect precipitation) are reported. In this case, for the reasons explained in Sect. 2.2, we can not assume the distance variation as “true” representation of the precipitated snow amount. However, an inspection of the resulting contingency tables (reported in Tables 2 and 3) could help in evaluating the overall matching between the two compared fields.
We define a hit when a month with a negative distance variation (increase of the snow accumulation) corresponds to a monthly mean snowfall rate above zero, a miss when it corresponds to estimated snowfall equal to zero, a false alarm when an estimated snowfall above zero corresponds to a distance variation below or equal to zero (decrease or no variation of the snow accumulation), and the correct negatives accordingly.
From the total number of 140 couples of monthly estimates and observations (computed in the 6 grid boxes containing ADG stations for each month, with the exception of four months during the OP when ground based data are missing), few indicators can be computed to summarize the results. For both estimate algorithms, most of the grid boxes where the CPR estimate detects precipitation corresponds to an increase of the snow accumulation (78 out of 114, i.e., 68 % as estimated by KB09 and 89 out of 132, i.e. 67 % as estimated by 2C-SNOW), while 86 % (78 out of 91) of grid boxes with an increase in the snow accumulation have an estimated snowfall amount above zero as estimated by KB09 and 98 % (89 out of 91) as estimated by 2C-SNOW. The 13 KB09 and the 2 2C-SNOW cases where the increase of snow accumulation does not correspond to estimated snowfall amount above zero could be due to blowing snow episodes, carrying snow from other sites. The main error is associated to the 36 (KB09) and to the 43 cases (2C-SNOW) where snowfall amount above zero is estimated, but no increase of the snow accumulation is measured at the ground: again blowing snow (in this case removing the snow) could be an important factor, but as mentioned, many other factors contribute to the decrease (or no variation) of the snow accumulation between the end and the beginning of the month, and these mechanisms may be active in the same months where snowfall occurs. The larger occurrence of this cases for the 2C-SNOW (43) with respect to KB09 (36) could be due to the absence of a well defined temperature threshold for the 2C-SNOW snowfall estimates, including cases of mixed precipitation that are not well or not at all measured by ADG instruments.
To study the relationship between the snow accumulation as measured by the ADG instruments and the estimated snowfall rate from CPR, we performed the comparison also at the single event temporal scale, and the results are presented in the next Section.
A comparison between instantaneous snowfall rate estimated from CPR
and snow accumulation variation at the time of the CPR overpass as
measured by the 5 ADG close to the CloudSat overpasses (station
n. 08915 is out of the CPR coverage) has been carried out. A total of
195 CloudSat overpasses occurred at a distance lower than
10
As found for the monthly values, instantaneous snowfall detected by the CPR (for both algorithms) corresponds to negative variations of the distance difference for most of the cases (increase of the snow accumulation). The relatively small number of misses, i.e. cases when the satellite does not detect snowfall but the snow accumulation increases, can be attributed to blowing snow episodes. Most of the significant snowfall episodes are related to negative distance differences, with one exception (station n. 30374) when relatively high snowfall rate corresponds to slightly positive distance variation. A closer inspection of the corresponding CPR profiles (not shown) shows a marked tilting of the snow column just above the ADG, indicating a likely occurrence of strong low level winds, able to weaken the local relationship between measurements at the ground from the ADG and the retrieval from CPR from the atmospheric layers aloft.
From the analysis of annual snowfall maps it is evident a clear signal
of the influence of the topography on the precipitation patterns (see
Fig. 5a and b and Bromwich, 1988). For a deeper discussion of the impact
of topography on snow spatial and temporal distribution we divided the
domain in three different background surface classes: (1) ocean and
sea ice, (2) coastal areas and lowlands and (3) plateau (hereafter
ocean, land, and plateau, respectively), according to the height above
the sea level. The separation is based on the CPR elevation flag,
choosing 2000
A further analysis on CPR snowrate at single profile scale was attempted to highlight seasonal signals in the snowfall rate distribution: we computed, for each of the three surface type classes (land, ocean, and plateau) the average snowfall rate for three-month periods (MAM, JJA, SON and DJF) of the OP and the results are reported in Fig. 11. We used here the common months grouping used to define seasons (as in Bromwich, 1988), considering though that the number of daylight hours vary substantially across the months, and thus the signal due to the change of daily insolation is embedded in the seasonal cycle. Over ocean and land both snowfall products have the highest average snowfall rate in the austral autumn (MAM), in agreement with Bromwich (1988), while the driest season is the summer (DJF). For the plateau region, the behavior is different: higher averaged snowfall rate is estimated during DJF and lower in SON.
A further investigation was focused on the estimation of the spatial extension of precipitation pattern over Antarctica. The use of point-like ground measurements allows the estimation of the duration of snowfall events (Knuth et al., 2010), but no information can be derived on the spatial extension of snowfall patterns, given the sparse distribution of ground instruments. On the other side, the CPR does not provide information about temporal features, but, from the analysis of the lengths of the track segments with snowfall, it is possible to infer the 1-D horizontal extension of the snowfall events.
Within the dataset of CloudSat orbits with detected snowfall the 1-D
horizontal extension (hereafter “length”) of the event was defined
as the number of contiguous CPR footprints with snowfall rate above
0.1
Two different approaches for snowfall rate estimate from the CloudSat W-band space-borne Cloud Profiling Radar reflectivity profiles were applied to the Antarctic region to study solid precipitation characteristics at different spatial and temporal scales. The algorithm of Kulie and Bennartz (2009) has been used to convert CPR reflectivity profiles for the years 2009 and 2010 to instantaneous water equivalent snowfall rates. The algorithm has been tailored to the characteristics of precipitation in Antarctica. In addition, the official CloudSat 2C-SNOW-PROFILE product with different characteristics and based on different assumptions has also been considered. For both products the significant impact of ground clutter contamination in this region has been taken into account.
The instantaneous, footprint scale, retrieved snowfall rates are first
up-scaled and integrated in time to obtain spatially continuous mean
annual snowfall rate maps, to be compared with ERA-I reanalysis of
snowfall. The KB09 map shows general agreement with the ERA-I mean
annual snowfall rate map, both in terms of numerical values and
spatial distribution, while the 2C-SNOW shows good agreement with the
ERA-I mean annual rate map of the total precipitation. Because of the
similar behavior of 2C-SNOW and ERA-I over the grounded continent
(where the surface temperature is always below 0
A deeper analysis at finer scale (considering instantaneous,
footprint-scale CPR estimates) shows that for KB09, higher snowfall
rates (always above 1.5
Finally, the influence of orography on the precipitation intensity, in relation to the seasonal variation, was also analyzed, showing that mean snowfall rate is higher in MAM over ocean and land, while on the Plateau higher mean snowfall rate is found in DJF. The results showed also that the seasonal cycle affects the spatial extension of snowfall pattern: relatively larger snowfall fields are found in JJA, while shorter paths are found in DJF and MAM.
This study is not intended for suggesting the best algorithm for
precipitation estimation over the Antarctic region, but for evaluating
the potentials of CPR for snowfall retrieval in Antarctica
independently of the approach used, relating the differences in the
results to the different assumptions and methodologies (i.e.,
temperature threshold used, height of near surface bin, and to the
This work shows that, in spite of its limited temporal and spatial sampling capabilities, CPR can be used as a valuable source of snowfall rate data in Antarctica that can be analyzed at different temporal scales, providing support to the sparse network of ground-based instruments in this region, as well as to numerical models used to simulate single snowfall events (at high temporal and spatial resolution), or used for climatological studies. CPR snowfall products tailored to the characteristic of the Antarctic region could then be used in the analysis and interpretation of passive measurements from the large number of radiometers on board polar satellites orbiting around the globe providing full spatial coverage of the whole region.
This work has been funded by the Italian Ministry of University, Research and Education under the National Plan for Antarctic Research PNRA (PEA 2009) “Bilancio della sostanza ghiaccio e caratterizzazione delle precipitazioni solide in Antartide”.
We would like to acknowledge for the use of ERA-Interim dataset
produced by ECMWF and freely available at:
The authors appreciate the support of the University of Wisconsin-Madison Automatic Weather Station Program for the data set, data display, and information, NSF grant numbers ANT-0944018 and ANT-1245663.
Finally, we also appreciate the valuable support of Mark Kulie, Norman Wood and Tristan L'Ecuyer on the use of 2C-SNOW-PROFILE product.
Values of the
Contingency table for monthly mean snowfall
rate estimated by KB09 algorithm and ADG monthly distance variations
(distance variation
Contingency table for monthly mean snowfall
rate estimated by 2C-SNOW product and ADG monthly
distance variations (distance variation
Averaged daily number of CloudSat CPR tracks computed over
1
PDF of the reflectivity corrected for ground clutter
contamination (black line). The PDF of 6th bin reflectivity for
PDF of the near surface bin (black line) used by 2C-SNOW for the snowfall rate estimate. The PDF of the profiles with bit 3 of the SRS binary value active is also shown (red line). Counts are normalized to the total number of occurrences.
Location of the 6 ADG stations used in this study.
Mean annual snowfall rate over the Antarctica and surrounding
ocean computed over the OP, as estimated by KB09
Scatterplot on the
Maps of the cloud cover
CPR estimated mean monthly KB09
Estimated instantaneous KB09
Normalized PDFs of the instantaneous snowfall rate as estimated by KB09 (solid lines) and by 2C-SNOW (dashed lines) for the different background surface classes.
Average instantaneous snowfall rates as estimated by KB09
Distribution of continuous snowfall pattern lengths over the
three length classes, normalized for the seasons, for the OP, as
estimated by KB09