The currently available long-term snow depth data sets are either
from point-scale ground measurements or from gridded
satellite/modeled/reanalysis data with coarse spatial resolution, which
limits the applications in climate models, hydrological models, and regional
snow disaster monitoring. Benefitting from its unique advantages of
cost-effective and high spatiotemporal resolution (∼ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has
become a popular topic in recent years. However, due to complex
environmental and observation conditions, developing robust and operational
technology to produce long-term snow depth data sets using observations from
various GNSS station networks is still challenging. The two objectives of
this study are (1) to propose a comprehensive framework using raw data of the
complex GNSS station networks to retrieve snow depth and control its
quality automatically; and (2) to produce a long-term snow depth data set
over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022) using the
proposed framework and historical data from 80 stations. The data set has
high internal consistency with regards to different GNSS constellations
(mean r=0.98, RMSD = 0.99 cm, and nRMSD (snow depth > 5 cm)
= 0.11), different frequency bands (mean r= 0.97, RMSD = 1.46 cm, and
nRMSD (snow depth > 5 cm) = 0.16), and different GNSS receivers
(mean r= 0.62). The data set also has high external consistency with the
in situ measurements and the passive microwave (PMW) product, with a
consistent illustration of the interannual snow depth variability. Additionally, the
result show the potential of GNSS to derive hourly snow depth
observations for better monitoring of snow disasters. The proposed framework to
develop the data set provides comprehensive and supportive information for
users to process raw data of ground GNSS stations with complex environmental
conditions and various observation conditions. The resulting GSnow-CHINA
v1.0 data set is distinguished from the current point-scale in situ data or
coarse-gridded data, which can be used as an independent data source for
validation purposes. The data set is also useful for regional climate
research and other meteorological and hydrological applications. The
algorithm and data files will be maintained and updated as more data
become available in the future. The GSnow-CHINA v1.0 data set is available
at the National Tibetan Plateau/Third Pole Environment Data Center via
10.11888/Cryos.tpdc.271839 (Wan et al., 2021).
Introduction
Snow cover is one of the most active elements in the cryosphere, and the
maximum snow area during winter nearly occupies 50 % of the total land
surface area of the Northern Hemisphere (Frei and Robinson, 1999;
Armstrong and Brodzik, 2001; Robinson et al., 1993). The snow change plays a
significant role in the hydrological, ecological, and climatic systems
(Henderson et al., 2018). Therefore, accurately estimating snow
cover and snow depth including their variations is essential for studies on
climate and hydrology.
Currently, snow cover products derived from optical remote-sensing data
present high accuracy (Hao et al., 2021), but snow
depth products show significant uncertainties. Snow depth can be measured at
point-scale using ground-based ultrasonic snow depth sensors or laser snow
depth sensors, and mainly include observations from meteorological stations,
snow surveys, and hydrological stations (Kinar and Pomeroy,
2015). Large-scale snow depth can be retrieved from optical, passive
microwave (PMW), and active remote-sensing observations (Shi and Dozier, 2000;
Guerreiro et al., 2016; Leinss et al., 2014; Che et al., 2016), yet
currently operational observations have shortcomings. Optical remote sensing
is affected by solar radiation and cloud (Dai et al., 2017).
The PMW remote sensing has coarse spatial footprints
(> 25 km), and the observations saturate in deep snow
(> 0.8 m) (Lievens et al., 2019). Active microwave
remote sensing has a long revisiting period (> 20 d) and high
cost (Lievens et al., 2019).
The available global/hemispheric/regional snow depth data sets are mainly
derived from ground observations, microwave remote sensing, model
simulations, and reanalysis (Xiao et al., 2020). Representative snow
depth data sets include (1) in situ measurements from ground networks such
as SCAN and SNOTEL in the United States (point-scale,
hourly/daily/weekly/monthly; http://www.wcc.nrcs.usda.gov, last access: 29 July 2022), (2) data sets derived from satellite PMW brightness temperatures, e.g., the
Advanced Microwave Scanning Radiometer for the Earth Observing System
(AMSR-E), its follow-on, the Advanced Microwave Scanning Radiometer-2
(AMSR2) (25 km, daily, global/regional, 2002–, https://nsidc.org/, last access: 29 July 2022), and the
Global Snow Monitoring for Climate Research (GlobSnow) data set produced
from the data assimilation of microwave radiometer data and meteorological
station data (25 km, daily, hemispheric, 1979–, https://www.globsnow.info/, last access: 29 July 2022),
(3) snow depth data set simulated using models such as snow modules in the
Global Land Data Assimilation System (GLDAS-2.0, 1948,
0.25∘× 0.67∘, 3-hourly and monthly; https://ldas.gsfc.nasa.gov/gldas),
and (4) reanalysis of snow depth data sets from the ERA-Interim (1979–,
0.75∘, 6-hourly/daily/monthly; http://www.ecmwf.int/, last access: 29 July 2022) and the
Modern-Era Retrospective Analysis for Research and Applications (MERRA) as well as
their series data sets (MERRA-2/MERRA-Land, 1979, 0.5∘× 0.67∘; https://gmao.gsfc.nasa.gov/reanalysis/, last access: 29 July 2022).
The aforementioned long-term snow depth data sets are either point-scale or
gridded data with coarse spatial resolution. Previous studies also
demonstrated that current snow depth data sets and snow water equivalent
data sets show significant inconsistencies and uncertainties, which limit
their applications in climate change projections and simulations of hydrological processes (Xiao et al., 2020; Zhang et al., 2021; Shao et al., 2022).
Due to the complex spatiotemporal variability and the limitations of the
current observation approaches, it is still challenging to derive long-term snow depth data sets with high spatiotemporal resolution. In particular,
it lacks detailed observations of snow depth on a regional scale, which
limits the applications in climate models, hydrological models, and snow
disaster monitoring.
Estimating snow depth using the Global Navigation Satellite System
Interferometric Reflectometry (GNSS-IR) technique has become a popular topic
in recent years, ever since the principle was proposed by
Larson et al. (2009). Snow depth is determined by
calculating the relative change of the effective multipath reflector height
(i.e., the snow surface) to the snow-free surface. This technique is
cost-effective because it does not require an additional transmitter, and
instead, it continuously receives L-band microwave signals transmitted by
the GNSS satellites. The temporal resolution for snow sensing is expected to
be hourly, along with the increasing number of GNSS satellites in orbit
(Tabibi et al., 2017a). For typical GNSS-IR sites, the spatial
footprint is ∼ 1000 m2, which is a scale between
point-scale and satellite-scale (i.e., from tens of meters to tens of
kilometers) (Larson and Nievinski, 2013). Therefore, GNSS-IR could
provide new snow depth data sets which could be supplementary to the current
in situ and satellite data sets. However, developing robust and operational
technology to produce long-term snow depth data sets using data from various
GNSS station networks is still challenging due to complex environmental and
observation conditions.
This study, taking advantage of 80 sites from a continuously operating GNSS
network over northern China, develops a comprehensive framework to process
raw data from various stations, and subsequently develops a new GNSS-IR snow
depth data set (GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022). Northern China has a
widely distributed snow cover from October to April of the following year. China's annual mean snow extent is greater than 9 000 000 km2, with a stable
snow-covered area of ∼ 4 200 000 km2. This region is the
main snow-covered area in China, which also plays a vital role in the
climate research of the Northern Hemisphere and the cryosphere. The unique
characteristics of GSnow-CHINA v1.0 and the framework to develop it are
as follows.
GSnow-CHINA v1.0 is a snow depth data set developed using GNSS data
source, independent of the current satellite, modeled, reanalysis, and
in situ data sets. The spatial resolution of this data set is between the
in situ point-scale and the coarse-gridded data, which makes it a new data
set suitable for validation purposes.
GSnow-CHINA v1.0 is a long-term snow depth data set over China with high
temporal and spatial resolution, providing a new data source for
regional and global climate research. The data set is also helpful for
monitoring local snow disasters and water resource management.
The proposed framework to develop the data set provides comprehensive
and supportive information for users to process raw data of ground GNSS
stations with complex environmental conditions and various observation
conditions. The technique has the potential to provide a finer-resolution snow depth product (e.g., 1–2 h) with adequate observations from
multiple GNSS systems.
Study area and dataStudy area
Northern China lies between latitudes of 25 and 55∘ N
and longitudes of 70 and 140∘ E, and includes humid,
semi-humid, semi-arid, and arid zones. Snow is the primary freshwater
resource in this area. Sudden snowstorms or long-lasting deep snow is one of
the major natural disasters for pastoral areas because it affects livestock
grazing. The study area includes the three main stable snow accumulation
areas over China, i.e., Northeast China and Inner Mongolia (NCM), North
Xinjiang and Tianshan mountain (NXT), and Qinghai-Tibet Plateau (QTP)
(Fig. 1).
The NCM region has various geomorphic types. Mountains and hills surround
the east, west and north of this region, and the middle of this region is
plain. The mean minimum air temperature in January is below -30 ∘C. The annual mean snow depth is greater than 5 cm with a maximum value
greater than 30 cm. The mean snow density of this area is ∼ 0.15 g cm-3. The NXT region has abundant seasonal snow water
resources, vital to local irrigation and animal husbandry. The mean air
temperature is -4–9 ∘C with a long winter period.
The QTP region is the core region of “The Third Pole” with a mean altitude
of ∼ 4378 m. Rainfall of the QTP is concentrated chiefly from
May to September, while snowfall usually starts from September to April of
the following year.
Distributions of the GNSS sites over northern China. The symbols
are colored by the GNSS receiver type, such as Trimble, Leica, MinShiDa
(MSD), and SiNan (SNA).
Data
Observations from the GNSS station networks over northern China are the
primary data source to produce the snow depth data set. The networks include
two separate categories constructed by two organizations, i.e., the network
constructed by the China Meteorological Administration (CMA) and the Crustal
Movement Observation Network of China (CMONOC) constructed by the China Earthquake
Administration (CEA). China started to construct ground GNSS stations in
2009, and the building phase was initially completed in 2012 with some
regions later in 2015. The CMA stations were built to observe precipitable
water vapor, while the CEA stations were built to monitor crustal
deformation.
As shown in Fig. 1, raw data from all 174 CMA sites and 171 CEA sites are
acquired from the CMA's Center of Meteorological Observation to initially
evaluate the capability of retrieving snow depth site by site. The sites are
divided into three categories, i.e., high quality, medium quality, and low
quality, following the recognition rule used for site-quality determination.
The rule will be introduced in Sect. 3. Overall, there are 55 high-quality sites (52 for CMA and 3 for CEA) and 25
medium-quality sites (22 for CMA and 3 for CEA). The high-quality CMA sites
are composed of various types regarding the received data of different GNSS
systems, i.e., 47 GPS-only, 4 GPS/GLONASS-compatible, and 1 GPS/BDS-compatible. The CEA sites are GPS-only sites. Most of the high-quality sites
are located in the NCM region, while a few are located in the NXT and QTP
regions.
Figure 2 shows the periods of the high-quality and medium-quality GNSS sites
used for snow depth retrieval. For CMA, despite the possible raw data
missing for some sites, the majority time spans for the high-quality sites
are 2013–2022, 2015–2022, and 2016–2022, and those for the medium-quality
sites are 2015–2022. For CEA, the three high-quality sites are from
2016/2018/2019–2022, with one medium-quality site having the earliest record
from the year 2010. Each GNSS site has an irreplaceable value because of its
unique natural environment and characteristic of snow. Therefore, regardless
of the raw data incompleteness in some periods for some sites, we preserve
the high-quality and medium-quality sites as much as possible during the
production of the data set.
Periods of the GNSS sites used for snow depth retrieval. HS: start
year of the high-quality site; HE: end year of the high-quality site; MS:
start year of the medium-quality site; ME: end year of the medium-quality
site. CMA: China Meteorological Administration; CEA: China Earthquake
Administration.
The broadcast ephemeris was used to calculate each GNSS satellite's
position. For CMA and CEA sites, the minimum elevation angle of the GNSS
satellite was set to be 10∘ when the sites were built.
The Soil Moisture Active Passive (SMAP) L3 36 km soil moisture data are
used to estimate the penetration depth of GNSS signals to the soil layer
(O'Neill et al., 2019). It is a quality-control step to derive
a more accurate reflector height of the snow-free surface. The Moderate
Resolution Imaging Spectroradiometer (MODIS) 1 km Normalized Difference
Vegetation Index (NDVI) data are used to identify the vegetation effects on
snow depth retrieval (Didan, 2021). Two independent snow depth
data products are used to analyze the quality of the data set produced in
this study. One is the 1979–2020 snow depth product using PMW
remote sensing produced by Che and Dai (2015); Che et al. (2008); Dai et
al. (2015) (daily, 25 km). The snow depth of this product is derived using the SMMR and SSMI/S microwave brightness
temperature processed by the National Snow and Ice Data Center (NSIDC). The
other is the daily in-situ snow depth measurements using laser snow depth
sensors provided by the Meteorological Observation Center of CMA.
Methods
The flowchart to produce and validate the GSnow-CHINA v1.0 data set is shown in
Fig. 3. The raw GNSS data used for snow depth retrieval are the daily
Receiver Independent Exchange Format (RINEX) data derived directly from
individual CMA/CEA GNSS sites. Significant steps to produce the data set are
described as follows.
The observables for snow depth retrieval, i.e., satellite pseudorandom noise
(PRN) numbers, observation time, satellite elevation angle, satellite
azimuth angle, pseudorange, carrier phase (CP), and signal-to-noise ratio
(SNR), are extracted or calculated from the raw data.
The Lomb–Scargle periodogram (LSP) analysis (Lomb, 1976) is executed on several snow-free days to determine the mean reflector heights
for each GNSS satellite, each quadrant, and each GNSS frequency. For those
high- and medium-quality sites which will be distinguished in step (3), the mean reflector heights are used as reference heights when
calculating snow depth. Here, the reflector height refers to the vertical
distance between the antenna phase center and the surface.
A comprehensive evaluation of the quality of all the GNSS sites is done
based on the data quality of the snow-free surface reflector heights in step (2), and the sites are divided into high-, medium-, and low-quality
accordingly.
For high- and medium-quality sites, the model for deriving daily reflector
height is established, and the raw snow depth for each GNSS satellite, each
quadrant, and each GNSS frequency is subsequently calculated as the
difference value of the referenced height in step (2) and the height of this step.
Several quality-control strategies are used to further improve the quality
of the raw snow depth during the previous step, such as considering the
penetration depth of soil, considering the vegetation effects, filtering of
outliers, adding valid flags such as the standard error (SE) of snow depth
and the number of PRNs used to calculate a specific snow depth value.
Daily 24 h and sub-daily 12 h snow depths are derived for general high-
and medium-quality GNSS sites, and snow depths of finer resolution are additionally derived for several GPS/GLONASS compatible sites.
The GSnow-CHINA v1.0 data set is evaluated using the PMW product and the in situ
measurements. The advantages and limitations of the produced data set are
further analyzed to provide supportive information for future method
improvement or data set extension.
The following sections introduce detailed descriptions of the solutions of
several key steps in the processing framework.
Flowchart showing the production and validation of the GSnow-CHINA v1.0 data set.
Snow depth retrieval model
The state-of-the-art GNSS-IR snow depth retrieving models can be divided
into two categories according to the two types of observables (i.e., SNR
and CP). The principle of the SNR model is to establish
a linear relationship between the oscillation frequency of the SNR
observation sequence of the reflected signal and the height of the
reflection surface (Larson et al., 2009). This model was
later derived into several variants: e.g., the triple-frequency SNR
combination model (SNR_COM) (Zhou et al., 2019), the
SNR model based on raw SNR sequences (Peng et al., 2016),
the SNR model based on horizontal polarization antenna (Chen et
al., 2014), the SNR model considering the influence of construction
facilities (Vey et al., 2016), and the SNR model considering
the influence of terrain (Zhang et al., 2017). The CP
combination model was initially proposed to estimate snow depth when there
were no SNR data in the raw GNSS observation file (Ozeki and Heki,
2012). The initial form of this model used the geometry-free linear
combinations of the phase measurements (L4), and Yu et al. (2015, 2018) extended the model to use triple-frequency CP
observations (F3) as well as the combination of pseudorange and CP of dual-frequency signals (F2C).
The main formulas and applicability of the five models mentioned above to
the data of GNSS sites in this study are listed in Table 1, and Table 2
further shows the meanings of variables for the models in Table 1. The SNR,
L4 and F2C models are suitable for all sites because the observables used
as inputs for these models are available in the GNSS raw data. The SNR model
has been verified to have higher accuracy than the L4 and F2C models
(Liu et al., 2022). The applicability of the SNR_COM and F3 models is limited because most of the GNSS sites do not contain
three SNR or CP observables in a single raw data file. Considering both the
applicability and the accuracy, the SNR model is determined as the primary
model used to produce the snow depth data set.
Snow depth models and their corresponding formulas.
ModelMain formulasApplicabilitySNR (Larson et al., 2009)SNR2=Ac2=Ad2+Am2+2AdAmcosQAm=Acos4πhλsinE+φf=2hλSuitable for all sitesSNR_COM (Zhou et al., 2019)SNRcom,i=SNR1,iSNR2,iSNR3,iOnly suitable for several BDS sites (no triple SNR observations)L4 (Ozeki and Heki, 2012)L1=ρ+If1+T+ML1+noise1L2=ρ+If2+T+ML2+noise2L4=L1-L2=If1-If2+ML1-ML2+noise1-noise2Suitable for all sites but with relatively lower accuracyF3 (Yu et al., 2015)L3=ρ+If3+T+ML3+noise3f3=λ32(L1-L2)-λ22(L1-L3)+λ12(L2-L3)Suitable for one GPS/GLONASS siteF2C (Yu et al., 2018)c1=ρ+If1+T+Mc1f2c=λ12+λ22λ12-λ22(c1-L1)-2λ12λ12-λ22(c1-L2)Suitable for all sites but with relatively lower accuracy
Meanings of variables for the models in Table 1.
VariablesMeaningsAdAmplitudes of the direct signalAmAmplitudes of the reflected signalAcAmplitudes of the synthetic signalcosQCosine value of the angle between the direct signal and the reflected signalλCarrier wavelengthESatellite elevation anglehVertical reflection distancefFrequency of GNSS multipath reflection signalφPhase values less than an entire periodSNRcom,iSNR observation values of triple-frequencyλiWavelengthρThe true geometric range between the satellite and receiverTTropospheric delayIfiIonospheric delay for the signalMLiMultipath error for the signalnoiseiInteger ambiguities for the signalL4Multipath error sequence of L4f3Multipath error sequence of F3f2cMultipath error sequence of F2C
Geometry and principle of the SNR model. (a) The geometry of the
direct and reflected signal over the snow surface; (b1) example of the
recorded GNSS SNR data and the removal of the direct signal with a
second-order polynomial; (b2) residual of (b1) below elevation angle (E) of
30∘, converted from dB to linear units (for simplicity, Volts);
(b3) Lomb–Scargle analysis of (b2) to find out the dominant frequency of the transformation and the resulting reflector height.
The geometry and principle of the SNR model are shown in Fig. 4. As shown
in Fig. 4a, the snow depth (hsnow) is calculated using a simple
equation:
hsnow=h0-h,
where h0 is the reflector height of the snow-free surface, and
h is the reflector height of the snow-covered surface. The approaches to
derive h0 and h are similar, with Fig. 4b1–b3 showing the general technical process. Firstly, the time series of the GNSS SNR
observation is shown as a function of sine (elevation angle), and the
direct signal is removed using the polynomial fitting method. The residue is treated to be the contribution of the reflected signal from the land
surface. Secondly, the reflected signal is converted from dB-Hz to Volts. Thirdly, the LSP analysis is applied to the reflected signal curve to establish the dominant frequency of the transformation. In this
study, the peak-to-noise ratio (PNR) of LSP is set to be greater than 5
to filter out the quality-controlled satellite tracks. The h0 or h can be calculated by (Larson et al., 2009)
h=λf/2,
where λ is the wavelength of the GNSS signal and f is the
dominant frequency.
Determination of the snow-free surface reflector height
For each site, ∼ 10 d of data with no snow on the ground
are used to calculate the raw snow-free surface reflector height (h0).
According to the data availability, days of the year (DOYs)
110–119 or DOYs 274–283 are generally selected
since these days have no snow according to historical in situ data.
Specifically, for GLONASS, to deal with the non-repeating tracks, 1 month of snow-free data (DOYs 105–135) are used to calculate the raw
h0. The reflector height for each GNSS satellite, quadrant, and GNSS
frequency band is calculated using the Lomb–Scargle periodogram, and it is only the initial height being used for the quality evaluation of the GNSS sites.
Due to the complex natural environment of various sites, it is not clear
whether one site is suitable for snow depth retrieval. The following section
will define a rigorous rule to evaluate the quality of all the GNSS sites.
For those high- and medium-quality sites determined in the following section,
which are suitable for snow depth retrieval, the finalized snow-free surface
reflector height will be determined as the mean value of heights for the 10
days.
It is worth mentioning that GPS ground tracks have sidereal repeatability
and reappear at the same azimuth every day. In contrast, GLONASS satellite
and BDS MEO satellite have non-repeating ground tracks. The GLONASS orbits
repeat every 8 sidereal days, with the ground track shifted by
45∘ in longitude per day (Tabibi et al., 2017b). The BDS MEO
satellites repeat approximately every 7 sidereal days (Ye et al.,
2015). In this study, there are only four GLONASS sites (i.e., bfqe, bttl,
hltl, and hlhl) and one BDS site (e.g., qxdw). The strategy for processing
GLONASS data is slightly different from that of GPS, i.e., the snow-free
surface reflector heights are given in 12 azimuths separated by
30∘ for all available GLONASS satellite tracks and frequency
bands. While for the BDS satellite, due to the relatively low number of
available satellites, the reflector height is given by quadrant only, without
distinguishing tracks and frequency bands, to preserve as many observations
as possible. Previous research developed a multistep clustering algorithm to
handle the non-repeating ground tracks of GLONASS (Tabibi et al.,
2017a). We are also developing a new algorithm in an upcoming study
considering terrain effects, which will be particularly effective for
non-repeating tracks.
Quality evaluation of the GNSS sites
The CMA and CEA sites are built under various natural and manual
environmental conditions. Figure 5 shows several photos of typical CMA/CEA
sites. The CMA sites are mainly built on the ground with antenna height
ranging from 1.5 to 5 m. Some sites are located in relatively flat and
open land, while others are in yards and surrounded by buildings or
other artificial objects. The majority of the CEA antennas are set upon a
rooftop (e.g., Site qhdl in Fig. 5), with the GNSS receivers being put
in the accompanying housing. It explains why most of the CEA sites are not
suitable for snow depth retrieval.
Photos of typical GNSS sites: bumz and bgfc are two
high-quality CMA sites, and qhdl is a low-quality CEA site that is not
suitable for snow depth retrieval.
A rigorous rule is defined to evaluate the quality of all the GNSS sites.
For each site, the 10 d reflector heights of snow-free surface (i.e.,
h0) are calculated, sorted, and colored by azimuths to make a
“h0 plot”. Examples of the “h0 plot” are
shown at the bottom of each subfigure in Fig. 6. The “h0
plot” is visually checked carefully and determines whether it is suitable
for the retrieval of snow depth. If one site shows relatively long and
stable h0 values during the entire observation period, the “h0 plot” has a relatively “flat” segment
on the curve, which indicates that this site is qualified to determine the
initial range of the snow-free surface reflector height. Afterward, a range
of h0 is given manually to narrow the good h0
values. The difference of the minimum and maximum value of the range is set
to be no more than 0.5 m. The finalized snow-free surface reflector height
for each satellite, each quadrant, and each GNSS frequency are respectively
determined as the mean value of the good heights of the 10 days. In
contrast, if one site has no “flat” segment on the “h0
plot”, this site is determined as a low-quality site and will not be used
for snow depth retrieval. It should be noted that during this processing
step, it can only eliminate those sites with poor data quality for snow
depth retrieval rather than distinguishing high- and medium-quality sites. There are
no apparent differences for the high- and medium-quality sites regarding the
natural environment. Instead, the medium-quality site is defined using two
simple rules: (i) the site has good-quality data, but there is no
snow for almost all the years; (ii) the site's lack of data for most
of the years.
Figure 6 shows the defined rule applied to six individual sites with various
surroundings, i.e., bumz, bfhr, bgfc, uqwl, qhdl and qhbm. The top panel of
each subfigure shows the environmental conditions around the station on
Google Map, with different colors indicating the footprints for elevation
angles of 10, 15, 20, 25
and 30∘, respectively. The bottom panel of each subfigure shows
the sorted 10 d reflector heights of snow-free surface (i.e., h0).
The plots clearly show the differences in the heights for different sites.
The first two sites, i.e., bumz and bfhr, show relatively long and
stable h0 values for all the GNSS satellites, quadrants, and frequency
bands during the entire observation period. It indicates that these sites
are flat enough for all the orientations and are ideal for determining the
initial range of the snow-free surface reflector height, i.e., 2.5–2.8 m for bumz and 2.8–3.1 m for
bfhr. Unlike these two sites, the bgfc site has relatively stable
h0 values only in specific orientation, with a natural condition that is open
and flat. At the same time, it is impossible to derive correct h0
values for bgfc in other orientations that have buildings or trees. This
phenomenon can be verified from the photo of the site in Fig. 5. This site
is also good enough to determine the initial range of the snow-free surface
reflector height, i.e., 3.6–4.1 m. On the contrary, the
three sites at the bottom of Fig. 6, i.e., uqwl, qhdl and qhbm, show
continuously changed h0 values because of the poorly defined peaks for
most LSPs. It indicates that it is unreliable to
determine a true h0 due to complex environmental conditions.
The default temporal resolution of the snow depth data set is 24 h.
However, some sites have adequate satellite observations that make it
possible to produce finer resolution snow depth data. We have two different
solutions to produce snow depth of finer temporal resolution. For most sites
with only GPS observations, we try to produce 12 h snow depths if there
are no less than five valid observations from 00:00–12:00 UTC or 12:00–24:00 UTC within 1 specific day. The snow depth value for
each 12 h is defined as the mean of all the observations during this time
window. For a few sites with GPS/GLONASS compatible observations, we use the
exact processing solutions like the previous GPS-only sites and combine all
the observations from the GPS and GLONASS systems to derive finer temporal
resolution snow depth. Unlike the previous 12 h maximum resolution,
2, 3 and 6 h resolutions can be achieved using compatible
observations.
Examples showing the moving-average filtering of the snow depth
results over one snow season. The site presented in this figure is bfqe
which is a CMA site. The day of year is abbreviated as DOY.
Quality control of the snow depth data set
Several postprocessing steps are executed to accomplish the quality control
of the raw snow depth data set. This section gives detailed information on
these steps as follows:
Moving average filtering
For each site, as shown in Fig. 7, the raw snow depth values over a snow
season, i.e., from 1 October this year to 30 April the following year,
are gathered together. The moving average algorithm is executed to filter
out the snow depth outliers, probably due to the incorrect recognition of the
peak frequencies on the Lomb–Scargle periodograms. This moving average method is a traditional way to reject outliers (Wang et al., 2020; Tabibi et
al., 2017a; Nievinski and Larson, 2014a). Snow depth values out of the
95 % confidence interval are smoothed over a sliding window across
neighboring elements. The length of the moving window is set to be 12 h
in this study. In the finalized GSnow-CHINA v1.0 data set, we also provide the
original data set without filtering to allow users to check the initial form of the data. The following analyses in Sects. 4 and 5 are based on the
filtered data.
Modifying the system errors caused by the penetration depth of soil
The penetration depth of the GNSS signal through bare soil (hp) directly
influences the determination of the reflector height of the snow-free
surface. The hp is dependent on the soil permittivity and the GNSS
wavelength. The soil permittivity is related to soil moisture and soil
components. Figure 8a shows the relationship between penetration depth of
GPS L1 band and soil moisture/soil components calculated using parameters
provided in Hallikainen et al. (1985). The penetration depth
is deeper than 10 cm when soil is very dry (i.e., volumetric soil moisture
(VSM) < 0.1 cm3 cm-3). The penetration depth is around or
shallower than 5 cm under normal soil moisture conditions. In this study,
the data of soil components for each site, i.e., the percentages of sand and
clay, are approximatively derived from the China Soil Science Database
(http://vdb3.soil.csdb.cn/, last access: 29 July 2022) by the soil attributes of the specific city and
province that the site is located in. The average VSM of each site is
calculated as the multiyear mean value of the SMAP VSM. The penetration
depths of each site for GPS L1/L2, GLONASS B1/B2, and BDS B1/B2/B3 are
subsequently calculated using the prepared soil components and VSM
parameters. Figure 8b shows the number of GNSS sites categorized by the
soil penetration depths (hp). The majority has a shallow penetration
depth of 4–8 cm, with only a few being 10 cm or deeper. The
h0 is modified as (h0-hp)+C for the final production of the
snow depth data set. The C is an empirical constant set at 3 cm in this study to
represent the offset of the complicated land surface conditions.
Eliminating the vegetation effects
For densely vegetated surfaces, particularly in autumn, vegetation height is
usually calculated as “fake snow depth” due to similar responses on the
Lomb–Scargle periodogram. However, it is difficult to identify whether it is vegetation or snow. As for northern China, this phenomenon occurs mainly in
October and early November. In this study, for each site from 1 October to
15 November, if there are snow depth records from the GNSS data, we use
the NDVI from MODIS data and the historical weather report to determine
whether it is actual snow or not. After this round of checking, to ensure
the reliability of the snow depth for 15 sites that probably have “fake
snow depth” records, DOYs 270–300 are masked out from the
data set.
Quality flags
The number of GNSS satellites used for this calculation is used as a quality
flag for each snow depth data record. In this study, we set the threshold at 5 to preserve as much data as possible. According to this quality flag,
the users can decide whether to use a snow depth data record with a low
number of observations. For each snow depth data record, the SE of the snow
depths for different satellite tracks is treated as another qualifying flag.
The users can also determine their own rules for filtering the data according to
this quality flag. The 8 d MODIS NDVI is also included as a quality flag
in the data set to show the vegetation conditions of the site initially. The
8 d values are combinations of the MODIS MOD13Q1 and MYD13Q1 products. The
NDVI flag can provide supplementary information for the users to identify
the possible error due to vegetation. However, due to the coarse resolution
of MODIS data, it is not possible to use this flag to represent the actual
vegetation cover around the GNSS station.
(a) The penetration depth of GNSS signals over the soil layer,
taking GPS L1 band (wavelength = 19 cm) as an example. The red line
indicates the mean penetration depth for various soil types. (b) Statistics
of the number of GNSS sites categorized by the soil penetration depths (also
taking GPS L1 band as an example).
Error indicators used in this study
The root mean square difference (RMSD), normalized RMSD (nRMSD), SE and
normalized SE (nSE) are four error indicators used in this study. The RMSD
of two data (X and Y) are given by RMSD=∑(Xi-Yi)2/N, where N is the number of elements in the
sample. The nRMSD is given by nRMSD=RMSD/meanX. The SE of
one data (Z) is given by STE=σZ/NZ, where
σZ is the standard deviation of the data Z, and NZ is the
number of elements in Z. The nSE is given by nSE=SE/Z¯,
where Z¯ is the mean of the sample.
Validation of the data qualityIntra-comparisons of GNSS snow depth results
The intra-comparisons of the snow depths are executed from three aspects,
i.e., comparison of different GNSS constellations, frequency bands, and
receivers. If we compare one of the three factors, we should prevent the
other two and other random errors from cross-influence. In other words, we
should ensure a snow depth value is “accurate” under the defined
condition. Therefore, in this section, we use a rigorous threshold of SE
= 1 cm to filter out the outliers. We show the correlation coefficient
(r), RMSD and nRMSD values for each comparison. It should be noted that the
nRMSD (snow depth > 5 cm) is significantly lower than the nRMSD
(all). This is because the reference value (i.e., the mean snow depth) was
used to normalize the RMSD. A large portion of snow depths in the study area
is lower than 5 cm, yielding a lower mean value when involving all the data
than when only using the > 5 cm data. The same principle applies to Figs. 9, 10 and 11. Nevertheless, the metrics only
represent the comparison during the intermediate process of the data set
production. Users can define their own rules to use the data according to
the quality flags in the published data set.
Figure 9a and b show correlations of the snow depths between GPS and
GLONASS for 24 and 12 h respectively, using data from the four
GPS/GLONASS compatible sites. Both show good agreement, with the correlation
coefficient r= 0.98 and RMSD = 1.01 cm for the 24 h result and
RMSD = 0.97 cm for the 12 h results. Figure 9 also shows the RMSD and
nRMSD values of snow depths greater than 5 cm, which is within the accuracy
of the current GNSS-IR technology. The RMSD (nRMSD) of the 24 and
12 h results are respectively 1.65 cm (0.11) and 1.51 cm (0.10). The BDS
results are not used for comparison due to the limited number of
observations.
Correlations of 24 h (a) and 12 h (a) snow depths from GPS and GLONASS
observations. The error bar of each point is the
standard error (SE) of the snow depths for all the available tracks of this
point. Four available sites, i.e., hltl, hlhl, bfqe and bttl, during the
GPS/GLONASS overlapped periods (i.e., 2014 and 2015) are used to
plot this figure. For each point in the figure, the number of valid
observations is more than five. To prevent other possible effects besides
the GNSS constellation, the SE of snow depths is less than 1 cm (90 % for
the 24 h data and 76 % for the 12 h data). Blue points are with the
retrieved GPS and GLONASS snow depths greater than 5 cm. Root mean
square difference is abbreviated as RMSD and normalized RMSD as nRMSD.
Figure 10a1, a2 and b1, b2 show correlations of the snow depths
between GPS L1 and L2 and between GLONASS L1 and L2, respectively, using
data from the same four GPS/GLONASS compatible sites as in Fig. 9. The
results from different frequency bands show good consistency with each
other, where r= 0.94 (RMSD = 1.64 cm) for GPS, and r= 0.99 (RMSD =
1.28 cm) for GLONASS (Fig. 10a1 and b1). The RMSD (nRMSD) values of
snow depths greater than 5 cm are 2.68 cm (0.22) for GPS and 1.86 cm (0.10)
for GLONASS. It should be noted that a small part of the difference between
L1 and L2 is because the antenna phase centers are not in the same place.
The initial bias occurs on the raw L1 and L2 reflector heights. However, the
final bias becomes negligible because, during snow depth calculation, the
reflector height value of bare soil is subtracted. The BDS results are still
not used for comparison due to the limited number of observations.
Correlations of snow depth from different GNSS frequencies. (a1) GPS L1 vs. GPS L2; (b1) GLONASS L1 vs. GLONASS L2. The color bar represents
the density of points; (a2) same as (a1) but with snow depths greater than 5 cm; (b2) same as (b1) but with snow depths greater than 5 cm. Fifty-one
high-quality GPS sites of CMA and four GPS/GLONASS compatible sites are
respectively used to plot (a1, a2) and (b1, b2). For each point in the
figure, the number of valid observations is more than five. To prevent other
possible effects besides the GNSS frequency, the SE of each snow depth is
less than 1 cm in all the subfigures (61 % for the GPS data and 70 % for
the GLONASS data). Root mean square difference is abbreviated as RMSD and normalized RMSD as nRMSD.
Comparisons of the GNSS-derived snow depth and the in situ
measurements from different types of GNSS receivers: (a1) Trimble; (b1) Leica; (c1) Minshida (MSD), and the histogram of the standard error (SE)
and nSE of snow depths for different types of GNSS receivers: (a2) Trimble;
(b2) Leica; (c2) MSD. The number of sites representing Trimble, Leica and
MSD is 20, 5, and 24, respectively. The GNSS snow depth values are greater than 5 cm in
this figure. To prevent other possible effects besides the receiver type,
the SE of snow depths is less than 1 cm (63 % of the entire data) in
(a1), (b1) and (c1). Root mean
square difference is abbreviated as RMSD and normalized RMSD as nRMSD.
The CMA and CEA sites are set up with various brands of GNSS receivers. Most
of these receivers are from three brands, i.e., Trimble, Leica and MinShiDa
(MSD). In order to evaluate the snow
depth results from these three brands, Fig. 11a1, b1 and c1
respectively show the differences of the snow depths derived from the three
brands, using the in situ measurements as benchmarks. The results from the
three brands show good consistency with r= 0.60, 0.67 and 0.59, and RMSD
= 3.94, 3.98 and 4.63 cm, respectively. Figure 11a2, b2 and
c3 further show the histogram of the SEs and nSEs of the snow depths
from the three brands, and good consistency is also shown in these
subfigures. The nSTE for Trimble, Leica
and MSD is respectively around 1 cm (0.07), 0.6 cm (0.04) and 1 cm (0.07).
Due to the inconsistent footprint between the GNSS and in situ measurements,
the error metrics presented in Fig. 11 are for reference only and do not
represent factual accuracies.
From the comprehensive intra-comparisons shown in Figs. 9–11, we conclude that the snow depths derived from different GNSS
constellations, frequency bands, and receivers have overall good agreement.
The average values of the metrics shown in Figs. 9–11 are
summarized as follows: mean r= 0.98, mean RMSD = 0.99 cm, and mean
nRMSD (snow depth > 5 cm) = 0.11 for different GNSS
constellations; mean r= 0.97, mean RMSD = 1.46 cm, and mean nRMSD (snow
depth > 5 cm) = 0.16 for different frequency bands; and mean r= 0.62 for different GNSS receivers. Therefore, it is feasible to combine all these results to produce the snow depth data set in this study.
Comparison with in situ measurements and the PMW products
The GNSS snow depth data set, the PMW data set, and the in situ measurements
are not consistent in terms of the spatial footprint. The GNSS and in situ
data have a closer footprint than the 25 km PMW data. The footprint of GNSS
is approximately ∼ 30 m × 30 m, as illustrated in Fig. 17. Due to the discrepancy in footprint, it is impractical
to give factual accuracies when comparing these three data sets. Instead, we
present the performance of the three data sets at daily scale, multiyear
scale, and interannual variabilities. The RMSD and nRMSD values presented in
Figs. 13 and 14 are for reference only and do not represent factual
accuracies.
Figure 12 shows an example of the comparisons of daily snow depth derived
from GNSS, in situ, and PMW data sets. The data used in this figure are from 16 GNSS
sites in 2016–2022, with the least missing daily snow depth values. The
comparison period is from 2016 to 2022 due to the data discontinuity in
other periods. The three data sets have similar variation trends but with
apparent differences in absolute snow depth values. The GNSS-derived snow
depths are closer to the in situ values than the PMW values for most sites because
GNSS and in situ data have a closer footprint. However, for some sites (e.g.,
Site jldg in Fig. 12), the in situ measurements are much higher than
the GNSS and PMW, which need further in-depth analysis. Figure 12 presents
all the GNSS snow depth values of the 16 GNSS sites, regardless of its
quality, to give a comprehensive illustration of the data. It is recommended
that the users define their own rules to determine whether to use those snow
depth values with low numbers of GNSS tracks or high SEs.
Comparisons of daily snow depth derived from GNSS, in situ, and
PMW data sets. The data used in this figure are from 16 GNSS sites in 2016–2022, with
the least missing daily snow depth values.
Figure 13 shows an example of the comparisons of daily mean snow depth
derived from GNSS, in situ, and PMW data sets. The data used in this figure are from 17
GNSS sites with the most extended temporal coverage (i.e., from 2013 to
2022). As expected, the GNSS and in situ data have similar performance
compared to the PMW data, with RMSD = 2.37 cm and nRMSD = 0.23 for GNSS
vs. in situ, and RMSD = 3.55 cm and nRMSD = 0.35 for GNSS vs. PMW. In
addition, the peak of the PMW snow trend for each snow season is later in
the season, which is due to the change of snow grain size (Dai
et al., 2012).
Comparisons of daily mean snow depth derived from GNSS, in situ,
and PMW data sets for 17 GNSS sites with the most extended temporal coverage (i.e.,
from 2013 to 2022). Root mean
square difference is abbreviated as RMSD and normalized RMSD as nRMSD.
The annual mean and maximum snow depths are significant indicators that can
reflect the overall data quality and the variation trend over multiple
years. Sixteen sites with the least missing daily snow depth values (the
same as data used in Fig. 12) are used to compare the multiyear averages
of the annual maximum/mean snow depths derived from GNSS, in situ, and PMW.
Coincidentally, all these 16 sites are located in the NCM region,
making it possible to further analyze the interannual variability of the
multiyear maximum or mean snow depth. Figure 14 shows a site-by-site
comparison of the 5-year average of the annual maximum/mean snow depth
derived from GNSS, in situ, and PMW, respectively. Figure 14a1 and b1
respectively show the spatial distribution of 16 sites marked by their
corresponding values of the average annual (a1) maximum and (b1) mean
snow depth. The snow depth values are classified into five categories to
show consistency and discrepancy better. It shows high consistency for the
three data sets in general but with discrepancies for some sites. Figure 14a2 and b2 respectively show the site-by-site comparison of the average annual (a2) maximum and (b2) mean snow depth.
The maximum values are consistent for the three data sets without regard for
the in situ data that have one outlier at Site jldg. This data point is an
outlier because the historical weather reports showed no significant
snowfall events before or after these dates. This result is a reminder that
operational laser measurements of snow depth are not always reliable. For
the mean values shown in (b2), the GNSS and in situ have a better agreement
than the PMW because of the significant difference in their spatial
footprint. Most sites are located in the region with evergreen coniferous
forest, which prevents the PMW data from acquiring reliable snow depth
values due to its wider observation extent of 25 km. Figure 14a3 and
b3 further show the correlation between the GNSS and in situ or PMW data.
Accordingly, higher consistencies are achieved from GNSS vs. in situ than
GNSS vs. PMW, with r= 0.75 (RMSD = 4.08 cm) vs. r= 0.57 (RMSD = 6.10 cm) for the maximum and r= 0.90 (RMSD = 1.22 cm) vs. r= 0.75
(RMSD = 3.59 cm) for the mean. The outliers are not involved during the
correlations.
Site-by-site comparison of the 5-year average annual
maximum/mean snow depth derived from GNSS, in situ, and PMW data sets, respectively.
(a1) The spatial distribution of the sites marked by their corresponding
values of the 5-year average annual maximum snow depth; (b1) same
as (a1) but the annual mean. (a2) The site-by-site comparison of the
5-year average annual maximum snow depth; (b2) same as (a2) but
the annual mean. (a3) The correlation between the GNSS and in situ/PMW data sets for
the 5-year average annual maximum; (b3) Same as (a3) but the
annual mean. Sixteen sites with the least missing daily snow depth values
from 2016 to 2022 are used to draw this figure. The site names are shown in
(b2).Root mean
square difference is abbreviated as RMSD.
The interannual variability of the multiyear average annual maximum
(mean) snow depth using the same data in Fig. 14 is further shown in
Fig. 15. The snow depth values in this figure are the mean values of all
16 sites. The maximum and mean achieve consistent interannual variabilities
for all three data sets, with the absolute maximums of PMW being
relatively higher than the other two. This result generally indicates that
the GNSS data set in this study can be used as a new data source to monitor
the interannual variability of snow depth.
Interannual variability of the multiyear average annual
maximum (mean) snow depth derived from GNSS, in situ, and PMW data sets. Sixteen sites
with the least missing daily snow depth values from 2016 to 2022 are used to
draw this figure. The site names are shown in Fig. 14b2. The PMW data
were only available for the period 2016–2020.
Reflection on extreme snow event
Real-time and accurate monitoring of extreme snow events is of vital
practical value. To test whether the new GNSS data set can provide supportive
information for this application, we use the extreme snow event that
happened on 21–22 February in the year 2015 to analyze the
performance of the GNSS, in situ, and PMW data sets. This event is selected
because we have overlapped GNSS data from two GPS/GLONASS compatible sites,
i.e., bfqe and bttl, which can provide finer resolution snow depth
observations. Figure 16a shows the daily snow depth variations before and
after the snow event. As expected, the GNSS and in situ data have similar
responses to the event, while the PMW data have a weak response. As indicated
previously, these two sites are located in the evergreen coniferous forest
region, which prevents the PMW data from acquiring reliable snow depth
values due to its much larger footprint of 25 km. Figure 16b further
shows the response of the 6 h GNSS snow depth data during the week of the
event. It captures the evolution of the event in a more detailed way from
DOY 51 than that of the other two data sets. However, due to the lack of
reference data at the same rate, it is impossible to evaluate the quality of
the 6 h GNSS data set. There are several discontinuities in the
GNSS-derived snow depth (i.e., sharp decrease or increase) that are
typically not seen in snowstorm data. The common feature of these abnormal
values is that they all have high SEs (as shown in the bottom panel of Fig. 16b). As shown in the top panel of Fig. 16b, it is possibly due to the
relatively low number of tracks used for producing the data set. The 2
and 3 h data are not shown in the figure due to severe data missing for
some periods. Regardless of the limitations mentioned above, the GNSS data
provide the potential to increase the monitoring frequency of extreme
weather in a cheap and effective way in the future, even with a higher
resolution of 6 h or better, particularly for those sites that have
compatible observations from more GNSS satellite systems such as GPS,
GLONASS, BDS and Galileo.
Performance of the GNSS snow depth on a snow event. (a) Daily
data; (b) 2 h data. Two GPS/GLONASS compatible sites, i.e., bfqe (in
red) and bttl (in blue), are used to draw this figure. The error bar of each
point in the figure is the standard error (SE) of the snow depths for all
the available tracks of this point.
Data set descriptions
The GSnow-CHINA v1.0 data set is developed using observations from
the two GNSS networks constructed by CMA and CEA. The data set is
available at National Tibetan Plateau/Third Pole Environment Data Center via
10.11888/Cryos.tpdc.271839 (Wan et al., 2021). It is called
version 1.0 because we produce the data set using historical observations
till the year 2022, and there is room for improvement of the algorithm
(e.g., how to properly consider the effects of vegetation and terrain). We
will continue to maintain and update the algorithm and the data set as more
years of data become available in the future. The data set includes snow
depths of 24, 12, and 6/3/2 h temporal resolutions if possible,
for 80 sites from 2013–2022 over northern China
(25–55∘ N, 70–140∘ E). The sites over southern China are not
included because there is most probably no snow in that region. The high and
medium sites are all preserved in the data set with multiple quality flags
for users to apply to the data.
There are two folders in the data set, i.e., the SITE_INFO
and the SNOW_DEPTH. The SITE_INFO folder
includes the general information of the 80 GNSS sites, with four separate
sheets in one .XLS file corresponding to CMA high-quality, CMA
medium-quality, CEA high-quality, and CEA medium-quality, respectively. The
items in the file are listed as SITE_NAME, LAT (latitude),
LON (longitude), ALT (altitude), RECEIVER_TYPE,
GNSS_TYPE, ANTENNA_HEIGHT (in meter), and
MEAN_VSM (volumetric soil moisture in cm3 cm-3; mean
value derived using SMAP soil moisture data of 2015–2020). The
SNOW_DEPTH folder includes the snow depth values for all
available sites. The folder is structured by ∼/site/. For
example, ∼/hltl/ stores the snow depth data of Site hltl.
There are four sub-folders in the folder of each site, i.e., raw0,
filtered0, raw, and filtered. The “raw0” and “filtered0” folders store
raw data and raw-but-filtered data for individual
satellite/quadrant/frequency/time. The “raw” and “filtered” folders store
24 h/12 h data produced using raw data in the corresponding “raw0”
and “filtered0” folders. The file names including *_24h.csv, *_12h.csv, and *_02h.csv represent
the 24, 12, and 2 h resolution data. Each CSV file gathers this
specific snow season (e.g., the 2019 file stores values from 1 October 2019, to 30 April 2020). We recommend using the snow depth data in the
“filtered” folder for validation/application purposes while using the snow
depth data in the “raw” folder for algorithm testing purposes.
Three quality flags are included in each snow depth file, i.e., the SE,
NUM_OF_PRNs, and NDVI, denoting the SE of
snow estimations, the number of GNSS sites, and the MODIS NDVI value, respectively. These
flags should be used to filter the data to balance the data volume and the
snow depth accuracy. In addition, we do not recommend using the snow depth
values of less than 5 cm in the data set, which is beyond the accuracy of
the current GNSS-IR technology.
Figure 17 shows an example of the snow sensing footprint for a specific
satellite track. For a 3 m antenna height under regular 10–30∘ elevation angles, the footprint of a specific
satellite track is defined as ellipses characterized by the First Fresnel
Zone (Larson and Nievinski, 2013), with the maximum length of ∼ 30 m for one direction. The GNSS footprint can be recognized as a
∼ 30 m × 30 m circle for all orientations. This footprint is
between the point-scale of the in situ measurements and the coarse 25 km
resolution of PMW, which makes it an effective supplement data source for
research, validation and application purposes.
The footprint of the GNSS snow depth observation for a specific
satellite track with different satellite elevation angles.
Extended analysis of the data set and method
Although this study releases a data set using the current GNSS sites, which
are suitable for snow depth retrieval, those sites that are not suitable for
this purpose still deserve an extended analysis to promote this research
domain's development further. Furthermore, although the method to retrieve snow
depth used in this data set is determined as the SNR model due to data
availability, it deserves an extended discussion of the selection of the
method for interested readers who are dedicated to developing their own data
set. Section 6.1 and 6.2 give an extended analysis of the two
issues mentioned above.
Simulations of the effects of terrain slopes on snow depth
retrievals for a 2 m antenna height of GPS L1 (wavelength = 19 cm).
Examples showing the vegetation effects on snow depth retrieval.
The site presented here is bfxc (2015–2020). The error bar of each point in
the figure is the standard error (SE) of the snow depths for all the
available tracks of this point.
Correlation between the GNSS snow depth and the in-situ
measurement colored by NDVI. The top panel shows the statistics of the GNSS
snow depth when the corresponding in situ = 0. Three-month data from 74
high- and medium-quality CMA sites are used to draw this figure. For each
point in the figure, the number of valid observations is more than five, and
the SE of snow depths is less than 2 cm.
Factors that affect the site quality for snow depth retrieval
Natural surroundings. The natural environment within the footprint of
the observations is the most significant factor that determines whether a
specific GNSS site is suitable for snow depth retrieval or not. Open and
flat ground with no vegetation is the ideal environment to set up a snow
site. In other words, terrain and vegetation are the two significant issues
that affect snow depth retrieval.
In practical applications, none of the planar surfaces is entirely
horizontal. Small ground tilting angles translate into several tens of
centimeters of bias due to the large horizontal distances involved (Larson and Nievinski, 2013). Figure 18 shows simulations for a
2 m antenna height with a variety of snow depth levels and positive terrain
slopes using the open-source GPS multipath simulator provided by
Nievinski and Larson (2014b). For slopes of 5∘ and less,
the error in snow depth retrieval is below 10 cm, while for larger slopes
(e.g., 8∘ in the figure), the residual effects are ∼ 15 cm and higher. Fortunately, for GPS satellites with repeatable ground
tracks, such a topographic bias remains stable over time. It thus could be
canceled out when using Eq. (1) to estimate snow depth, most of which is the
case in this study. While the ground tracks are non-repeatable for GNSS satellites like GLONASS and BDS, the terrain effect should be considered.
Some previous studies investigated methods to eliminate the influence of
terrain (Zhang et al., 2017, 2020). We are also developing
a new approach to consider the terrain effects, which will be demonstrated
in a future study.
Vegetation is another factor that needs to be considered for accurate
retrieval of snow depth. Figure 19 shows an example of Site bfxc, which has
vegetation effects on snow depth retrieval before DOY 300 for 2015
∼ 2019. The vegetation information is presented by the MODIS
1 km 8 d NDVI data. The period of
the vegetation effects for different years are different, e.g., the years
2016 and 2017 have the most extended period of ∼ 30 d from
DOYs 270 to 300, while the years 2018 and 2019 only have ∼ 10 d around DOY 270. The effect of vegetation is not strictly consistent
with the variation of NDVI, which makes it impossible to build a model to
qualify the vegetation effect using NDVI data.
Figure 20 shows a correlation between the GNSS snow depth and the in situ
measurement colored by NDVI. Note that for those points on the x axis with
in situ values equal to 0, but with various GNSS snow depth values, the NDVI
values are generally higher than other data points. It illustrates that GNSS
measures vegetation rather than snow for these data points. A previous study
suggested that it is practical to use the amplitude of the GNSS SNR data to
retrieve vegetation height for observations of 1 s sampling (Wan et al., 2015). Therefore, for GNSS observations at the
sampling intervals, it may be possible to use the SNR amplitude to build a
model to qualify the vegetation effect on snow depth retrieval. However,
this is not practical for the CMA or CEA sites used in this study because
the sampling interval is 30 s, making it impossible to model the SNR
data series to derive the amplitude. Future research will consider using
other vegetation indicators to identify this issue.
Quality of the observation data. The data quality is another critical
factor that determines whether a site is suitable for snow depth retrieval or
not. Firstly, the minimum elevation angle of GNSS satellites should be set to
a single number like 5∘ or 10∘ to preserve the multipath
effect as much as possible, because only data with low elevation angles can
show the surface reflection. Secondly, the observables used as inputs for the
corresponding snow depth models should be stored in the raw RINEX file. If
the stored observables satisfy conditions for multiple models, one can
choose the model according to its accuracy or combine them to use all the
models during the calculation. This issue will be discussed further in Sect. 6.2. Thirdly, the GNSS tracks may miss data in some epochs during
the ascending or descending sequences, although they satisfy the condition
of minimum to maximum elevation angles. These data are removed in this study
to ensure the accurate acquisition of the reflector heights. Finally, random
errors, e.g., human activities at some point, may exist during the
observations.
The strategy of model selection for using GNSS data to retrieve
snow depth. Carrier phase is abbreviated as CP. Different solutions are represented as Plan
A, B and C.
Selection of snow depth models
Although there are many models to retrieve snow depth, as illustrated in
Table 1, considering the availability of the observables and the accuracy of
the models, not all models are applicable or optimal in practical
application. Figure 21 shows an overall strategy of model determination for
using GNSS data to retrieve snow depth. One should first consider whether the SNR
observable exists in the RINEX file since CP and pseudorange
are observations that generally exist for positioning. If the observables
satisfy all the snow depth models, the optimal model is selected according
to the number of frequencies in the RINEX file. If the frequencies received
by the receiver are less than 3, the SNR model is the best choice since it
is simple and has reliable accuracy (Plan A in the figure). If the received
frequencies are equal to or are greater than 3, the SNR_COM
and F3 models can be used (Plan B in the figure). However, one can still use
Plan A to replace Plan B in practical applications. If the SNR observable
does not exist (Plan C), the F3 model is preferred when the number of CP is
greater than 3, while the L4 or F2C model is selected when the number of
CP is less than 3. Nevertheless, the effects of the ionosphere delay on the
L4 and F2C models are difficult to remove, which leads to the relatively low
accuracy of these two models (Liu et al., 2022).
Data availability
The GSnow-CHINA v1.0 data set is archived and available at the National Tibetan
Plateau/Third Pole Environment Data Center (Li et al., 2020; Pan et al.,
2021) via 10.11888/Cryos.tpdc.271839 (Wan et al., 2021).
Conclusions
This study proposes a comprehensive framework using raw data of the complex
GNSS station networks to automatically retrieve snow depth and control its
quality. Based on this, the study further produces a long-term snow depth
data set over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022)
using the proposed framework and historical data from 80 stations.
The data set has high internal consistency with regards to different GNSS
constellations (mean r= 0.98, RMSD = 0.99 cm, and nRMSD (snow depth > 5 cm) = 0.11), different frequency bands (mean r= 0.97,
RMSD = 1.46 cm, and nRMSD (snow depth > 5 cm) = 0.16), and
different GNSS receivers (mean r= 0.62). The data set also has high
external consistency with the in situ measurements and the PMW products,
with a consistent illustration of the interannual snow depth variability.
Results from the 17 GNSS sites with the most extended temporal coverage
(i.e., from 2013 to 2022) show better performance between GNSS and in situ
that between GNSS and PMW, with RMSD = 2.37 cm and nRMSD = 0.23 for the
former, and RMSD = 3.55 cm and nRMSD = 0.35 for the latter. The results
also show the good potential of GNSS to derive hourly snow depth
observations for better monitoring of snow disasters. The proposed framework to
develop the data set provides comprehensive and supportive information for
users to process raw data of ground GNSS stations with complex environmental
conditions and various observation conditions. The resulting GSnow-CHINA
v1.0 data set is distinguished from the current point-scale in situ data or
coarse-gridded data, and can be used as an independent data source for
validation purposes. The data set is also useful for regional and global
climate research and other meteorological and hydrological applications.
Finally, it should be noted that, although we tried our best to reuse the
data from the current GNSS networks, there are still limitations concerning
the raw data (e.g., limited site numbers and GNSS data types). We look
forward to having more sites and data from more GNSS systems (such as from
China's Beidou) from CMA or other organizations to use in the future.
Both the algorithm and data set will be maintained and updated as more
years of data become available.
Author contributions
WW designed the study and wrote the manuscript. HL
provided the GNSS raw data for the production of this data set and
co-designed the study. LD provided supportive information for the validation
using the PMW snow depth product. LZ provided supportive information for the
data filtering. JZ, TY, BL, ZG and HH contributed to the data/codes
accumulation. All authors contributed to the writing and editing of this
paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Extreme environment datasets for the three poles”. It is not associated with a conference.
Acknowledgements
The first author would like to thank team members from the
Meteorological Observation Center, China Meteorological Administration (CMA) for
producing, maintaining and providing the raw GNSS RINEX data and the
in situ data. The first author would also like to thank team members Lei
Xiao and Yuan Gao from Peking University for their contributions to data
preparation. The authors would like to thank the SMAP team, the MODIS team,
and the PMW team for archiving and providing the data used in this study.
The first author would like to give special thanks to Waner Zhao for
her collaboration during the preparation and writing of this manuscript.
Financial support
This study is jointly supported by the National Key
Research and Development Program of China (grant no. 2019YFE0126600), the National Natural Science Foundation of China (NSFC) projects (grant no. 41971377 and no. 41501360), The open fund of the National Earth Observation
Data Center (no. NODAOP2021002), the observing experiment project of
Meteorological Observation Center of China Meteorological Administration
(no. SY2020005), and the ESA-MOST China Dragon 5 Programme (ID.58070).
Review statement
This paper was edited by Xin Li and reviewed by Kristine Larson, Achille Capelli and one anonymous referee.
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