Snow Depth Measurements Research Articles

Using GNSS-IR Snow Depth Estimation to Monitor the 2022 Early February Snowstorm over Southern China

Snow depth is an essential meteorological indicator for monitoring snow disasters. The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has been proven to be a practical approach to retrieving snow depth. This study presents a case study to explore utilizing the GNSS-IR-derived snow depth to monitor the 2022 early February snowstorm over southern China. A snow depth retrieval framework considering data quality control and specific ground surface substances was developed using 8-day data from 13 operational GNSS/Meteorology stations. The daily snow depths retrieved from different ground surfaces, i.e., dry grass, wet grass, and concrete, agreed well with the measured snow depth, with Mean Absolute Error (MAE) of 2.79 cm, 3.36 cm, and 2.53 cm, respectively. The percentage MAE when snow depths > 5 cm for the three ground surface substances was 26.8%, 53.7%, and 35.0%, respectively. The 6 h snow depth results also showed a swift and significant response to the snowfall event. This study proves the potential of GNSS-IR, used as a new operational tool in the automatic meteorological system, to monitor snow disasters over southern China, particularly as an efficient and cost-effective framework for real-time and accurate monitoring.

A Meteorology and Snow Data Set From Adjacent Forested and Meadow Sites at Crested Butte, CO, USA

AbstractWe present meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains (USA) over three consecutive water years with different amounts of snow water equivalent (SWE) accumulation: A year with above average SWE (2019), a year with average SWE (2020), and a year with below average SWE (2021). This data set is distinguished by its emphasis on paired open‐forest sites in a continental snow climate. Approximately once a month during February–May, we collected data from 15 to 20 snow pits and took 8 to 19 snow depth transects. Our sampling sites were in open and adjacent forested areas at 3,100 m and in a lower elevation aspen (3,035 m) and higher elevation conifer stand (3,395 m). In total, we recorded 270 individual snow pit density and temperature profiles and over 4,000 snow depth measurements. These data are complimented by continuous meteorological measurements from two weather stations: One in the open and one in the adjacent forest. Meteorology data—including incoming shortwave and longwave radiation, outgoing shortwave radiation, relative humidity, wind speed, snow depth, and air and infrared surface temperature—were quality controlled and the forcing data were gap‐filled. These data are available to download from Bonner, Smyth, et al. (2022) at https://doi.org/10.5281/zenodo.6618553, at three levels of processing, including a level with downscaled, adjusted precipitation based on data assimilation using observed snow depth and a process‐based snow model. We demonstrate the utility of these data with a modeling experiment that explores open‐forest differences and identifies opportunities for improvements in model representation.

Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles

Abstract. Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the forecasting phase. The goal is to ensure that the forecasts are initialized from state variables that are as representative of reality as possible, and also to estimate the uncertainty of the state variables. There are several data assimilation methods, and particle filters are increasingly popular because of their minimal assumptions. The baseline idea is to produce an ensemble of scenarios (i.e. the particles) using perturbations of the forcing variables and/or state variables of the model. The different particles are weighted using the observations when they become available. However, implementing a particle filter over a domain with large spatial dimensions remains challenging, as the number of required particles rises exponentially as the domain size increases. Such a situation is referred to as the “curse of dimensionality”, or a “dimensionality limit”. A common solution to overcome this curse is to localize the particle filter. This consists in dividing the large spatial domain into smaller portions, or “blocks”, and applying the particle filter separately for each block. This can solve the above-mentioned dimensionality problem because it reduces the spatial scale at which each particle filter must be applied. However, it can also cause spatial discontinuities when the blocks are reassembled to form the whole domain. This issue can become even more problematic when additional data are assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering them. We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local SWE observations from manual snow surveys. We apply two reordering strategies based on (1) a simple ascending-order sorting and (2) the Schaake shuffle and evaluate their ability to maintain the spatial structure of the particles. To increase the amount of assimilated data, we investigate the inclusion of a second data set (SR50), in which the SWE is indirectly estimated from automatic measurements of snow depth using sonic sensors. The two reordering solutions maintain the spatial structure of the individual particles throughout the winter season, which significantly reduces the spatial random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. The Schaake shuffle proves to be a better tool for maintaining a realistic spatial structure for all particles, although we also found that sorting provides a simpler and satisfactory solution. The assimilation of the secondary data set improved SWE estimates in ungauged sites when compared with the deterministic model, but we noted no significant improvement when both snow courses and the SR50 data were assimilated.

Machine learning model for snow depth estimation using a multisensory ubiquitous platform

Snow depth estimation is an important parameter that guides several hydrological applications and climate change prediction. Despite advances in remote sensing technology and enhanced satellite observations, the estimation of snow depth at local scale still requires improved accuracy and flexibility. The advances in ubiquitous and wearable technology promote new prospects in tackling this challenge. In this paper, a wearable IoT platform that exploits pressure and acoustic sensor readings to estimate and classify snow depth classes using some machine-learning models have been put forward. Significantly, the results of Random Forest classifier showed an accuracy of 94%, indicating a promising alternative in snow depth measurement compared to in situ, LiDAR, or expensive large-scale wireless sensor network, which may foster the development of further affordable ecological monitoring systems based on cheap ubiquitous sensors.

Modelling Debris-Covered Glacier Ablation Using the Simultaneous Heat and Water Transport Model. Part 1: Model Development and Application to North Changri Nup

Modelling ablation of glacier ice under a layer of mineral debris is increasingly important, because the extent of supraglacial debris is expanding worldwide due to glacier recession. Physically based models have been developed, but the uncertainty in predictions is not yet well constrained. A new one-dimensional model of debris-covered ice ablation that is based on the Simultaneous Heat and Water transfer model is introduced here. SHAW-Glacier is a physically based, vertically integrated, fully coupled, water and energy balance model, which includes the advection of heat by rainwater and lateral flow. SHAW-Glacier was applied to North Changri Nup, a high elevation alpine glacier in the monsoon-dominated Central Himalaya. Simulations were compared with observed debris temperature profiles, snow depth, and ablation stake measurements for debris 0.03–0.41 m thick, in a 2500 m2 study area. Prediction uncertainty was estimated in a Monte Carlo analysis. SHAW-Glacier simulated the characteristic pattern of decreasing ablation with increasing debris thickness. However, the observations of ablation did not follow the characteristic pattern; annual ablation was highest where the debris was thickest. Recursive partitioning revealed a substantial, non-linear sensitivity to the snow threshold air temperature, suggesting a sensitivity to the duration of snow cover. Photographs showed patches of snow persisting through the ablation season, and the observational data were consistent with uneven persistence of snow patches. The analyses indicate that patchy snow cover in the ablation season can overwhelm the sensitivity of sub-debris ablation to debris thickness. Patchy snow cover may be an unquantified source of uncertainty in predictions of sub-debris ablation.

A D-InSAR method to improve snow depth estimation accuracy

<p indent="0mm">Snow depth is an important parameter that reflects the law of spatiotemporal variation in snow cover and is an indispensable observation variable for studying global and regional climate change and the hydrological cycle. Traditional manual field measurements and ground station observations have limited scopes and cannot fully reflect regional-scale snow conditions. Satellite remote sensing can simultaneously observe surface information over a large area and has been widely used in snow monitoring since the 1970s. Optical remote sensing has difficulty obtaining snow depth information directly through the visible light band and is limited by atmospheric conditions. Microwave remote sensing can penetrate clouds and fog and can effectively capture changes in snow depth. Differential interferometric synthetic aperture radar (D-InSAR) technology uses the differential interferometric phase formed by microwave penetration through the snow layer before and after snowfall to establish a geometric function relationship with the snow depth; it is widely used in regional small-scale snow depth estimation research. However, its estimation accuracy is affected by many factors, such as interferometric image pair coherence, local topography, and snow dielect capability at microwave wavelengths. Based on high-resolution Sentinel-1 SAR data, this study optimizes the snow depth differential interferometric phase unwrapping accuracy by introducing Sentinel-2 optical images and high coherence coefficient regions to select ground control points, which are phase correction benchmarks. Furthermore, introducing field-measured snow parameters such as snow density and satellite local terrain incidence angles based on digital elevation model (DEM) data reduces the error of the differential interferometric phase-slope distance relationship model, thereby enabling estimations of the spatiotemporal distribution of snow depth in the Babao River Basin in the northeastern Qinghai-Tibet Plateau during the 2021 ablation period. Simultaneously, the accuracy of the snow depth estimation ability is evaluated based on the synchronous field measurement data of snow cover satellites. The main factors affecting the accuracy of snow depth estimation are discussed and analysed. Data from 122 ground snow depth measurements (meteorological stations + field measurements) are used to verify the results. The results show that the optimized D-InSAR differential interferometry can improve the snow depth estimation accuracy. The RMSE is <sc>3.9 cm,</sc> the<italic> </italic>MAPE is 20.03%, and the <italic>R</italic>² is 0.92. However, the estimated snow depth is generally underestimated (MBE%=−16.8%), and the maximum underestimation error of the snow depth is <sc>9.1 cm.</sc> In addition to the influencing factors of D-InSAR system interferometric decorrelation, the estimations are affected by the penetration ability of microwaves in the snow and snow parameters such as stratigraphy structure, temperature and humidity. This limits the ability of differential interferometry to estimate snow depth and is more suitable for dry and homogeneous snow cover. This method allows for more precise and faster monitoring of centimetre-level snow depth changes; at the same time, the underlying surface of the study is relatively simple, and the influence of forest and other vegetation coverage is not considered. In follow-up research, snow cover should be optimized with multi-property and multi-environment interferometric models. An InSAR scattering model with a layered factor is introduced to improve the propagation information inside the snow layer then combined with the multiangle radar measurement of the ascending and descending orbits; experiments are carried out using the penetration ability of more bands in microwaves to reduce interferometric error sources and improve the accuracy of snow depth estimation. This study can provide a scientific reference for D-InSAR snow depth estimation research.

Impact of Forest Canopy Closure on Snow Processes in the Changbai Mountains, Northeast China

Forest canopy closure affects snow processes by changing the redistribution of snowfall, snow interception, accumulation, sublimation, and melt. However, how the forest closure impacts snow processes at different periods has not been well explored. We conducted 3-year measurements of snow density and depth and carried out snow process calculations (i.e., interception, sublimation, and snowmelt) from 2018 to 2021 in four mixed forests with different canopy closures and an open site in the Changbai Mountains, northeast China. We found that the snow density of the five study sites varied greatly (0.14–0.45 g/cm3). The snow depth (SD) at four mixed forests sites was smaller than that of the nearby open site. The SD decreased as the forest canopy closure increased. Additionally, the forest interception effect increased with the canopy closure and decreased as the snowfall intensity increased. The total interception efficiency of the four mixed forests in normal snow years changed from 34% to 73% and increased with forest canopy closure. The averaged sublimation rate (Ss) and snowmelt rate (Sr) of the four mixed forests varied during different periods of snow process. The Ss was 0.1–0.4 mm/day during the accumulation period and 0.2–1.0 mm/day during the ablation period, and the Sr was 1.5–10.5 mm/day during the ablation period. There was a good correlation between Ss, or Sr, and canopy closure, but interannual variation was observed in the correlation. The mean values of the effect of the four mixed forests on understory SWE (snow water equivalent) over the 3 years ranged from −45% to −65%. Moreover, the impact effect was correlated with the forest canopy closure and enhanced with the canopy closure. This study provided more scientific information for studies of snow cover response to forest management.

Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone

Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.

Snow Depth Measurements by GNSS-IR at an Automatic Weather Station, NUK-K

Studies have shown that geodetic Global Navigation Satellite System (GNSS) stations can be used to measure snow depths using GNSS interferometric reflectometry (GNSS-IR). Here, we study the results from a customized GNSS setup installed in March through August 2020 at the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather station NUK-K located on a small glacier outside Nuuk, Greenland. The setup is not optimized for reflectometry purposes. The site is obstructed between 85 and 215 degrees, and as the power supply is limited due to the remote location, the logging time is limited to 3 h per day. We estimate reflector heights using GNSS-IR and compare the results to a sonic ranger also placed on the weather station. We find that the snow melt measured by GNSS-IR is comparable to the melt measured by the sonic ranger. We expect that a period of up to 45 cm difference between the two is likely related to the much larger footprint GNSS-IR and the topography of the area. The uncertainty on the GNSS-IR reflector heights increase from approximately 2 cm for a snow surface to approximately 5 cm for an ice surface. If reflector height during snow free periods are part of the objective of a similar setup, we suggest increasing the logging time to reduce the uncertainty on the daily estimates.

A Time Series of Snow Density and Snow Water Equivalent Observations Derived From the Integration of GPR and UAV SfM Observations

Snow depth can be mapped from airborne platforms and measured in situ rapidly, but manual snow density and snow water equivalent (SWE) measurements are time consuming to obtain using traditional survey methods. As a result, the limited number of point observations are likely insufficient to capture the true spatial complexity of snow density and SWE in many settings, highlighting the value of distributed observations. Here, we combine measured two-way travel time from repeat ground-penetrating radar (GPR) surveys along a ∼150 m transect with snow depth estimates from UAV-based Structure from Motion Multi-View Stereo (SfM-MVS) surveys to estimate snow density and SWE. These estimates were successfully calculated on eleven dates between January and May during the NASA SnowEx21 campaign at Cameron Pass, CO. GPR measurements were made with a surface-coupled Sensors and Software PulseEkko Pro 1 GHz system, while UAV flights were completed using a DJI Mavic 2 Pro platform and consisted of two orthogonal flights at ∼60 m elevation above ground level. SfM-MVS derived dense point clouds (DPCs) were georeferenced using eight ground control points and evaluated using three checkpoints, which were distributed across the ∼3.5 ha study plot containing the GPR transect. The DPCs were classified to identify the snow surface and then rasterized to produce snow-on digital surface models (DSMs) at 1 m resolution. Snow depths on each survey date were calculated by differencing these snow-on DSMs from a nearly snow-off DSM collected near the end of the melt season. SfM-derived snow depths were evaluated with independent snow depth measurements from manual probing (mean r2 = 0.67, NMAD = 0.11 m and RMSE = 0.12 m). The GPR-SfM derived snow densities were compared to snow density measurements made in snowpits (r2 = 0.42, NMAD = 39 kg m−3 and RMSE = 68 kg m−3). The integration of SfM and GPR observations provides an accurate, efficient, and a relatively non-destructive approach for measuring snow density and SWE at intermediate spatial scales and over seasonal timescales. Ongoing developments in snow depth retrieval technologies could be leveraged in the future to extend the spatial extent of this method.

An improved snow depth retrieval method with adaptive noise reduction for GPS/GLONASS/Galileo/BDS multi-frequency signals

The Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique is an effective method of monitoring snow depth. The detrended signal-to-noise ratio (dSNR) series is analyzed by a Lomb–Scargle periodogram (LSP) to extract the characteristic frequency, which can be converted to the snow depth. However, the dSNR data are greatly affected by noise in the observation environment, which leads to an abnormal characteristic frequency and low accuracy of snow depth retrieval. In order to reduce the influence of noise and to ensure the correct extraction of the characteristic frequency, we present an improved adaptive retrieval method for the multi-constellation retrieval scenario. First, the dSNR sequences are decomposed adaptively into several singular spectrum components (SSCs) with different frequency scales by singular spectrum decomposition. Then, the corresponding SSCs are selected, according to the empirical scope of snow depth, to reconstruct the ‘pure’ dSNR series. Finally, the reconstructed signals are analyzed by LSP to derive the characteristic frequency, in order to obtain the snow depth. Multi-GNSS observations of site SG27 and site P351 from the plate boundary observation network in a representative period from winter 2019 to spring 2020 were used to validate the proposed method. The snow depths were estimated from individual signals, individual constellations and multi-GNSS combination using both the traditional and improved methods. The experimental results show that compared with the traditional method, the snow depth trend of the improved method is more consistent with the measured snow depth trend, especially in the early stage of snowfall. Furthermore, the proposed method shows a universal applicability to various signals of GPS, GLONASS, Galileo and BDS and the retrieval accuracy of all signals is improved to different degrees. When using multi-GNSS combination signals, the mean bias and root mean square error (RMSE) of multi-GNSS snow depth retrieval at site SG27 are improved from 4.6 and 6.2 cm to 4.2 and 5.4 cm, respectively. The mean bias and RMSE at site P351 are improved from 10.5 and 12.4 cm to 9.5 and 11.5 cm, respectively.

Snow Depth on Sea Ice and on Land in the Canadian Arctic from Long-Term Observations

ABSTRACT Intra-annual and decadal observations of snow depth on sea ice and on terrestrial land are examined within the Canadian Arctic. In situ snow depth measurements at 11 study sites spanning 1955–2019 form the basis of the analysis. Ice chart data acquired via the Canadian Ice Service are used to establish sea ice break-up and freeze-up dates and assess their impact on snow depth evolution. We find that on-ice and on-land snow accumulation in autumn differ due to the lag between the freeze-up and the first snow of the season. Once sea ice consolidates, on-ice and on-land snow depth become positively correlated in winter (p < 0.05). The mean seasonal rate of snow accumulation on sea ice from September to April is 3.2 ± 0.6 cm month−1 across the Canadian Arctic. Snow depth on terrestrial land is generally higher than on sea ice in the southern Canadian Arctic by up to 20–30 cm; but snow depth on sea ice tends to exceed that on land in the northern Canadian Arctic from winter to spring. Four sites (Eureka, Resolute, Cambridge Bay and Hall Beach) with continuous long-term records are selected for interannual analysis. Decadal trends in on-ice snow depth are mostly negative from autumn to spring. Autumn and spring snowfall have increased at three of the four sites. The Canadian Arctic experiences warming on a decadal scale, especially in autumn, by 0.5 to 0.8°C decade−1. Sea ice freeze-up is delayed by up to 2.5 days decade−1 in the southern Canadian Arctic, whereas break-up occurs earlier by about 3 days decade−1 in the northern Canadian Arctic.

Drone-Borne Ground-Penetrating Radar for Snow Cover Mapping

Ground-penetrating radar (GPR) is one of the most commonly used instruments to map the Snow Water Equivalent (SWE) in mountainous regions. However, some areas may be difficult or dangerous to access; besides, some surveys can be quite time-consuming. We test a new system to fulfill the need to speed up the acquisition process for the analysis of the SWE and to access remote or dangerous areas. A GPR antenna (900 MHz) is mounted on a drone prototype designed to carry heavy instruments, fly safely at high altitudes, and avoid interference of the GPR signal. A survey of two test sites of the Alpine region during winter 2020–2021 is presented, to check the prototype performance for mapping the snow thickness at the catchment scale. We process the data according to a standard flow-chart of radar processing and we pick both the travel times of the air–snow interface and the snow–ground interface to compute the travel time difference and to estimate the snow depth. The calibration of the radar snow depth is performed by comparing the radar travel times with snow depth measurements at preselected stations. The main results show fairly good reliability and performance in terms of data quality, accuracy, and spatial resolution in snow depth monitoring. We tested the device in the condition of low snow density (&lt;200 kg/m3) and this limits the detectability of the air–snow interface. This is mainly caused by low values of the electrical permittivity of the dry soft snow, providing a weak reflectivity of the snow surface. To overcome this critical aspect, we use the data of the rangefinder to properly detect the travel time of the snow–air interface. This sensor is already installed in our prototype and in most commercial drones for flight purposes. Based on our experience with the prototype, various improvement strategies and limitations of drone-borne GPR acquisition are discussed. In conclusion, the drone technology is found to be ready to support GPR-based snow depth mapping applications at high altitudes, provided that the operators acquire adequate knowledge of the devices, in order to effectively build, tune, use and maintain a reliable acquisition system.

Sub-kilometre scale distribution of snow depth on Arctic sea ice from Soviet drifting stations

AbstractThe sub-kilometre scale distribution of snow depth on Arctic sea ice impacts atmosphere-ice fluxes of energy and mass, and is of importance for satellite estimates of sea-ice thickness from both radar and lidar altimeters. While information about the mean of this distribution is increasingly available from modelling and remote sensing, the full distribution cannot yet be resolved. We analyse 33 539 snow depth measurements from 499 transects taken at Soviet drifting stations between 1955 and 1991 and derive a simple statistical distribution for snow depth over multi-year ice as a function of only the mean snow depth. We then evaluate this snow depth distribution against snow depth transects that span first-year ice to multiyear ice from the MOSAiC, SHEBA and AMSR-Ice field campaigns. Because the distribution can be generated using only the mean snow depth, it can be used in the downscaling of several existing snow depth products for use in flux modelling and altimetry studies.

An Accuracy Assessment of Snow Depth Measurements in Agro-Forested Environments by UAV Lidar

This study assesses the performance of UAV lidar system in measuring high-resolution snow depths in agro-forested landscapes in southern Québec, Canada. We used manmade, mobile ground control points in summer and winter surveys to assess the absolute vertical accuracy of the point cloud. Relative accuracy was determined by a repeat flight over one survey block. Estimated absolute and relative errors were within the expected accuracy of the lidar (~5 and ~7 cm, respectively). The validation of lidar-derived snow depths with ground-based measurements showed a good agreement, however with higher uncertainties observed in forested areas compared with open areas. A strip alignment procedure was used to attempt the correction of misalignment between overlapping flight strips. However, the significant improvement of inter-strip relative accuracy brought by this technique was at the cost of the absolute accuracy of the entire point cloud. This phenomenon was further confirmed by the degraded performance of the strip-aligned snow depths compared with ground-based measurements. This study shows that boresight calibrated point clouds without strip alignment are deemed to be adequate to provide centimeter-level accurate snow depth maps with UAV lidar. Moreover, this study provides some of the earliest snow depth mapping results in agro-forested landscapes based on UAV lidar.

Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product

The European Space Agency (ESA) Snow Climate Change Initiative (CCI+) provides long-term, global time series of daily snow cover fraction and snow water equivalent (SWE). The Snow CCI+ SWE Version 1 (CCIv1) product is built on the GlobSnow algorithm, which combines passive microwave (PMW) data with in situ snow depth (SD) measurements to estimate SWE. While CCIv1 remains algorithmically similar to the most recent GlobSnow product (GlobSnow Version 3), Snow CCI+ SWE Version 2 (CCIv2) incorporates two notable differences. CCIv2 uses updated PMW data from the NASA MEaSUREs Calibrated Passive Microwave Daily EASE-Grid 2.0 Earth Science Data Record and is generated in EASE-Grid 2.0 with 12.5 km grid spacing. It also adjusts SWE retrievals in post-processing by incorporating spatially and temporally varying snow density information. Due to the phased product development framework CCI+ employs, proposed changes between CCIv1 and CCIv2 were implemented in a series of step-wise developmental datasets. Using these developmental datasets, we analyze how changes to input PMW and SD data and the snow density parameterization affect the resulting SWE product. Using in situ snow courses as reference data, we demonstrate that the correlation and RMSE of the CCIv2 developmental product improved 18% (0.10) and 12% (5 mm), respectively, relative to CCIv1. The timing of peak snow mass is shifted two weeks later and a temporal discontinuity in the monthly northern hemisphere snow mass time series associated with the shift from the Special Sensor Microwave/Imager (SSM/I) to the Special Sensor Microwave Imager/Sounder (SSMIS) in 2009 is also removed.

Snow depth product over Antarctic sea ice from 2002 to 2020 using multisource passive microwave radiometers

Abstract. Snow over sea ice controls energy budgets and affects sea ice growth and melting and thus has essential effects on the climate. Passive microwave radiometers can be used for basin-scale snow depth estimation at a daily scale; however, previously published methods applied to the Antarctic clearly underestimated snow depth, limiting their further application. Here, we estimated snow depth using passive microwave radiometers and a newly constructed, robust method by incorporating lower frequencies, which have been available from AMSR-E and AMSR-2 since 2002. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements showed that the gradient ratio (GR) calculated using brightness temperatures in vertically polarized 37 and 7 GHz, i.e. GR(37/7), was optimal for deriving Antarctic snow depth, with a correlation coefficient of −0.64. We hence derived new coefficients based on GR(37/7) to improve the current snow depth estimation from passive microwave radiometers. Comparing the new retrieval with in situ measurements from the Australian Antarctic Data Centre showed that this method outperformed the previously available method (i.e. linear regression model based on GR(37/19)), with a mean difference of 5.64 cm and an RMSD of 13.79 cm, compared to values of −14.47 and 19.49 cm, respectively. A comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicated that in thin-ice regions, the proposed method performed slightly better than the previous method (with RMSDs of 16.85 and 17.61 cm, respectively). We generated a complete snow depth product over Antarctic sea ice from 2002 to 2020 on a daily scale, and negative trends could be found in all sea sectors and seasons. This dataset (including both snow depth and snow depth uncertainty) can be downloaded from the National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences at http://data.tpdc.ac.cn/en/disallow/61ea8177-7177-4507-aeeb-0c7b653d6fc3/ (last access: 7 February 2022) (Shen and Ke, 2021, https://doi.org/10.11888/Snow.tpdc.271653).

Assimilation of sea ice thickness derived from CryoSat-2 along-track freeboard measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

Abstract. The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Improving Fractional Snow Cover Retrieval From Passive Microwave Data Using a Radiative Transfer Model and Machine Learning Method

Optical sensors are subject to cloud obscuration and sunlight dependence, resulting in large proportions of missing snow cover information. Microwave sensors are a good alternative to snow cover monitoring in all weather conditions. Thus far, few studies in the literature have directly derived the fractional snow cover (FSC) from passive microwave data, and none have considered the relationship between FSC and brightness temperature (TB). This study first explores the FSC–TB relationship with a radiation transfer model, exhibiting that no generic function can properly describe the nonlinear and complex FSC–TB relationship. Therefore, a new algorithm based on machine learning method was designed to improve FSC retrieval from TB data, considering other auxiliary information, including soil property, land surface, and geography information. Benchmarked against the Moderate Resolution Imaging Spectroradiometer (MODIS) reference FSC, our FSC retrieval model performed well with an average correlation coefficient of 0.70, the mean absolute error ranging from 0.15 to 0.17, and the root-mean-square error ranging from 0.19 to 0.21. The generated FSC maps reasonably characterized the seasonal dynamics and spatial distribution patterns of snow cover; time series analysis with three AmeriFlux stations observation indicated effective capture of snowpack evolution process by the generated FSC. In addition, the verification of snow mapping capability using snow depth measurements from 13 521 stations indicates that it was relatively stable with overall accuracy greater than 0.88. For precise monitoring of snow cover extent in all weather conditions, particularly for subpixel snow cover areas, the development of the FSC estimation scheme with TB data should be extensively encouraged and implemented.