Snow Depth Estimates Research Articles

Estimating snow depth by combining satellite data and ground-based observations over Alaska: A deep learning approach

Snow cover plays a vital role in the climate system because it is related to climate, hydrological cycle, and ecosystem. On this basis, deriving a long-term and large-scale snow depth (SD) time series and monitoring its temporal and spatial variations are crucial. Passive microwave remote sensing data in combination with in-situ SD data have long been used to retrieve SD. However, the retrieval accuracy is limited in case of sparse meteorological stations, and the high-quality applications of retrieval results are hindered in specific areas. The ground-based global navigation satellite system reflectometry (GNSS-R) method is currently a potential way to monitor SD variations with a high degree of accuracy but has a limited spatial coverage. In this study, a deep learning-based approach, which displays a stronger nonlinear expressiveness capability than conventional neural networks, was applied to estimate SD by combining satellite observations, in-situ data, and GNSS-R estimates. The model was trained and tested with data obtained in Alaska between 2008 and 2017. Results show that the proposed deep belief network model performs better than linear methods and conventional neural network models and demonstrate the effectiveness of combining GNSS-R estimation with increased cross-validation R of 0.85 and decreased RMSE of 15.40 cm. The predicted SD distribution indicates that the variations in mean SD in Alaska for March and April between 2008 and 2017 were associated with the climate anomalies and air temperature. Overall, the proposed deep learning-based method is a promising approach in the satellite-retrieved SD field.

Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination

AbstractWe present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

Spatially Extensive Ground‐Penetrating Radar Snow Depth Observations During NASA's 2017 SnowEx Campaign: Comparison With In Situ, Airborne, and Satellite Observations

AbstractSeasonal snow is an important component of Earth's hydrologic cycle and climate system, yet it remains challenging to consistently and accurately measure snow depth and snow water equivalent (SWE) across the range of diverse snowpack conditions that exist on Earth. The NASA SnowEx campaign is focused on addressing the primary gaps in snow remote sensing in order to gain an improved spatiotemporal understanding of this important resource and to further efforts toward a future satellite‐based snow remote sensing mission. Ground‐penetrating radar (GPR) is an efficient and mature approach for measuring snow depth and SWE. We collected ~1.3 million GPR snow depth observations during the NASA SnowEx 2017 campaign, yielding a spatially extensive (~133‐km total length) and high‐resolution (~10‐cm lateral spacing) validation data set to assess various remote sensing and modeling approaches. We found high correlation between the GPR and manual snow probe derived snow depths (r = 0.89, p < 0.0001, root‐mean‐square error (RMSE) = 18 cm), but a median difference of −10 cm, which we attribute, in part, to probe penetration into the unfrozen subsurface. We also compared GPR‐derived snow depths to two other independent estimates of snow depth, as an example of how this data set can be used for validation of remote sensing techniques: Airborne Snow Observatory lidar‐derived snow depths (r = 0.90, p < 0.0001, median difference = −1 cm, RMSE = 14 cm) and preliminary DigitalGlobe WorldView‐3 satellite‐derived snow depths (r = 0.70, p < 0.0001, median difference = −3 cm, RMSE = 24 cm).

Estimation of snow depth using pseudorange and carrier phase observations of GNSS single-frequency signal

A new method is proposed to estimate snow depth by using observations of the GNSS single-frequency signal collected by a ground-based receiver. The proposed method utilizes the pseudorange and carrier phase observations to form the geometry-free combination. Based on mathematical formulas of the amplitude attenuation factor, the pseudorange multipath error, and the carrier phase multipath error, a function is derived serving as the theoretical model that describes the relationship between the antenna height and the peak frequency of a series of function values associated with the range of satellite elevation angles. In the observation data processing stage, the moving average filtering method is used to remove the ionospheric delay from the combined observation series, followed by spectrum analysis to obtain the peak frequency, which is used to determine the antenna height and hence snow depth based on the theoretical model. A weighting method is proposed to combine individual snow depth estimates related to the use of signals of individual satellites to enhance the estimation accuracy. Each weighting coefficient is proportional to the maximum of the power spectral density of the combined observation series. The proposed method is substantiated by simulations and observations from geodetic-grade receivers, which can process multi-constellations and multi-frequency GNSS signals. Two field GNSS data sets collected in Heilongjing, China, and Colorado, USA, were used to evaluate the method. The results show that the root-mean-square error of GPS, BDS, and Galileo-based snow depth estimations is in the range of 2–6 cm when the topography around the GNSS receiver is flat.

Optimal design of snow stake networks to estimate snow depth in an alpine mountain range

AbstractMonitoring and estimation of snow depth in alpine catchments is needed for a proper assessment of management alternatives for water supply in these water resources systems. The distribution of snowpack thickness is usually approached by using field data that come from snow samples collected at a given number of locations that constitute the monitoring network. Optimal design of this network is required to obtain the best possible estimates. Assuming that there is an existing monitoring network, its optimization may imply the selection of an optimal network as a subset of the existing one (if there are no funds to maintain them) or enlarging the existing network by one or more stations (optimal augmentation problem). We propose an optimization procedure that minimizes the total variance in the estimate of snowpack thickness. The novelty of this work is to treat, for the first time, the problem of snow observation network optimization for an entire mountain range rather than for small catchments as done in the previous studies. Taking into account the reduced data available, which is a common problem in many mountain ranges, the importance of a proper design of these observation networks is even larger. Snowpack thickness is estimated by combining regression models to approach the effect of the explanatory variables and kriging techniques to consider the influence of the stakes location. We solve the optimization problems under different hypotheses, studying the impacts of augmentation and reduction, both, one by one and in pairs. We also analyse the sensitivity of results to nonsnow measurements deduced from satellite information. Finally, we design a new optimal network by combining the reduction and augmentation methods. The methodology has been applied to the Sierra Nevada mountain range (southern Spain), where very limited resources are employed to monitor snowfall and where an optimal snow network design could prove critical. An optimal snow observation network is defined by relocating some observation points. It would reduce the estimation variance by around 600 cm2 (15%).

Snow Depth Estimation with GNSS-R Dual Receiver Observation

Two estimation methods using a dual GNSS (Global Navigation Satellite System) receiver system are proposed. The dual-frequency combination method combines the carrier phase observations of dual-frequency signals, whereas the single-frequency combination method combines the pseudorange and carrier phase observations of a single-frequency signal, both of which are geometry-free strictly combination and free of the effect of ionospheric delay. Theoretical models are established in the offline phase to describe the relationship between the spectral peak frequency of the combined sequence and the antenna height. A field experiment was conducted recently and the data processing results show that the root mean squared error (RMSE) of the dual-frequency combination method is 5.04 cm with GPS signals and 6.26 cm with BDS signals, which are slightly greater than the RMSE of 4.16 cm produced by the single-frequency combination method of L1 band with GPS signals. The results also demonstrate that the proposed two combination methods and the SNR method achieve similar performance. A dual receiver system enables the better use of GNSS signal carrier phase observations for snow depth estimation, achieving increased data utilization.

The Consistency of SSM/I vs. SSMIS and the Influence on Snow Cover Detection and Snow Depth Estimation over China

The long-term variations in snow depth are important in hydrological, meteorological, and ecological implications and climatological studies. The series of Special Sensor Microwave/Imager (SSM/I) and Special Sensor Microwave Imager Sounder (SSMIS) instruments onboard the Defense Meteorological Satellite Program (DMSP) platforms has provided a consistent 30+ year data record of global observations that is well-suited for the estimation of snow cover, snow depth, and snow water equivalent (SWE). To maximize the use of this continuous microwave observation dataset in long-term snow analysis and obtain an objective result, consistency among the SSM/I and SSMIS sensors is required. In this paper, we evaluated the consistency between the SSM/I and SSMIS concerning the observed brightness temperature (Tb) and the retrieved snow cover area and snow depth from January 2007 to December 2008, where the F13 SSM/I and the F17 SSMIS overlapped. Results showed that Tb bias at 19 GHz spans from −2 to −3 K in snow winter seasons, and from −4 to −5 K in non-snow seasons. There is a slight Tb bias at 37 GHz from −2 to 2 K, regardless of season. For 85 (91) GHz, the bias presents some uncertainty from the scattering effect of the snowpack and atmospheric emission. The overall consistency between SSM/I and SSMIS with respect to snow cover detection is between 80% and 100%, which will result in a maximum snow cover area difference of 25 × 104 km2 in China. The inconsistency in Tb between SSM/I and SSMIS can result in a −2 and −0.67 cm snow depth bias for the dual-channel and multichannel algorithms, respectively. SSMIS tends to yield lower snow depth estimates than SSM/I. Moreover, there are notable bias differences between SSM/I- and SSMIS-estimated snow depths in the tundra and taiga snow classes. Our results indicate the importance of considering the Tb bias in microwave snow cover detection and snow depth retrieval and point out that, due to the sensitivity of bias to seasons, it is better to do the intercalibration with a focus on snow-covered winter seasons. Otherwise, the bias in summer will disturb the calibration coefficients and introduce more error into the snow retrievals if the seasonal difference is not carefully evaluated and separated.

Accounting for permafrost creep in high-resolution snow depth mapping by modelling sub-snow ground deformation

Snow depth estimation derived from high-resolution digital elevation models (DEMs) can lead to improved understanding of the spatially highly heterogeneous nature of snow distribution, as well as help us improve our knowledge of how snow patterns influence local geomorphic processes. Slope deformation processes such as permafrost creep can make it challenging to acquire a snow-free DEM that matches the sub-snow topography at the time of the associated snow-covered DEM, which can cause errors in the computed snow depths. In this study, we illustrate how modelling changes in the sub-snow topography can reduce errors in snow depths derived from DEM differencing in an area of permafrost creep. To model the sub-snow topography, a surface deformation model was constructed by performing non-rigid registration based on B-splines of two snow-free DEMs. Seasonal variations in creep were accounted for by using an optimization approach to find a suitable value to scale the deformation model based on in-situ snow depth measurements or the presence of snow-free areas corresponding to the date of the snow-covered DEM. This scaled deformation model was used to transform one of the snow-free DEMs to estimate the sub-snow topography corresponding to the date of the snow-covered DEM. The performance of this method was tested on an active rock glacier in the southern French Alps for two surveys dates, which were conducted in the winter and spring of 2017.By accounting for surface displacements caused by permafrost creep, we found that our method was able to reduce the errors in the estimated snow depths by up to 33% (an interquartile range reduction of 11 cm) compared to using the untransformed snow-free DEM. The accuracy of the snow depths only slightly improved (root-mean-square error decrease of up to 3 cm). Greater reductions in error were observed for the snow depths calculated for the date that was furthest (i.e., the winter survey) in time from the snow-free DEM. Additionally, we found that our approach to scaling the deformation model has promising potential to be adapted for monitoring seasonal variations in permafrost creep by combining in-situ snow depth measurements with high-resolution surface deformation models.

A New GPS SNR-based Combination Approach for Land Surface Snow Depth Monitoring

Snow is not only a critical storage component in the hydrologic cycle but also an important data for climate research; however, snowfall observations are only sparsely available. Signal-to-noise ratio (SNR) has recently been applied for sensing snow depths. Most studies only consider either global positioning system (GPS) L1 or L2 SNR data. In the current study, a new snow depth estimation approach is proposed using multipath reflectometry and SNR combination of GPS triple frequency (i.e. L1, L2 and L5) signals. The SNR combination method describes the relationship between antenna height variation and spectral peak frequency. Snow depths are retrieved from the SNR combination data at YEL2 and KIRU sites and validated by comparing it with in situ observations. The elevation angle ranges from 5° to 25°. The correlations for the two sites are 0.99 and 0.97. The performance of the new approach is assessed by comparing it with existing models. The proposed approach presents a high correlation of 0.95 and an accuracy (in terms of Root Mean Square Error) improvement of over 30%. Findings indicate that the new approach could potentially be applied to monitor snow depths and may serve as a reference for building multi-system and multi-frequency global navigation satellite system reflectometry models.

Snow Depth Estimation Based on Combination of Pseudorange and Carrier Phase of GNSS Dual-Frequency Signals

Global navigation satellite system reflectometry (GNSS-R) is a new remote sensing technique, which can be used to measure a wide range of geophysical parameters. GNSS-R makes use of the simultaneous reception of the direct transmission and the coherent surface reflections of the GNSS signal with either a single antenna or multiple separate antennas. This paper presents a new snow depth estimation method using a combination of pseudorange and carrier phase of GNSS dual-frequency signals. The proposed method is geometry-free and is not affected by ionospheric delays. The formulas of the amplitude attenuation factor of reflected signals, multipath-induced carrier-phase error, and pesudorange error for ground-based GNSS receivers are used to describe the combined signals. Using theoretical formulas instead of in situ measurement data, analytical linear models are established in advance to describe the relationship between snow depth and main frequency of combined signal time series. When the main frequency of the combined measurements is obtained by spectrum analysis, the model is used to determine snow depth. Two experimental data sets recorded in two different environments were used to test the proposed method. The results demonstrate that there exists good agreement between the proposed method and the ground-truth measurements.

Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry.

This study presents a novel approach in the application of Unmanned Aerial Vehicle (UAV) imaging for the conjoint assessment of the snow depth and winter leaf area index (LAI), a structural property of vegetation, affecting the snow accumulation and snowmelt. The snow depth estimation, based on a multi-temporal set of high-resolution digital surface models (DSMs) of snow-free and of snow-covered conditions, taken in a partially healthy to insect-induced Norway spruce forest and meadow coverage area within the Šumava National Park (Šumava NP) in the Czech Republic, was assessed over a winter season. The UAV-derived DSMs featured a resolution of 0.73–1.98 cm/pix. By subtracting the DSMs, the snow depth was determined and compared with manual snow probes taken at ground control point (GCP) positions, the root mean square error (RMSE) ranged between 0.08 m and 0.15 m. A comparative analysis of UAV-based snow depth with a denser network of arranged manual snow depth measurements yielded an RMSE between 0.16 m and 0.32 m. LAI assessment, crucial for correct interpretation of the snow depth distribution in forested areas, was based on downward-looking UAV images taken in the forest regime. To identify the canopy characteristics from downward-looking UAV images, the snow background was used instead of the sky fraction. Two conventional methods for the effective winter LAI retrieval, the LAI-2200 plant canopy analyzer, and digital hemispherical photography (DHP) were used as a reference. Apparent was the effect of canopy density and ground properties on the accuracy of DSMs assessment based on UAV imaging when compared to the field survey. The results of UAV-based LAI values provided estimates were comparable to values derived from the LAI-2200 plant canopy analyzer and DHP. Comparison with the conventional survey indicated that spring snow depth was overestimated, and spring LAI was underestimated by using UAV photogrammetry method. Since the snow depth and the LAI parameters are essential for snowpack studies, this combined method here will be of great value in the future to simplify snow depth and LAI assessment of snow dynamics.

The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

Abstract. The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currently configured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean through the accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection–divergence, blowing snow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis-derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim, and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus median daily snowfall dataset from these reanalyses. The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate–strong correlations and root mean squared errors of ∼ 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards

Abstract. Snow depth on sea ice remains one of the largest uncertainties in sea ice thickness retrievals from satellite altimetry. Here we outline an approach for deriving snow depth that can be applied to any coincident freeboard measurements after calibration with independent observations of snow and ice freeboard. Freeboard estimates from CryoSat-2 (Ku band) and AltiKa (Ka band) are calibrated against data from NASA's Operation IceBridge (OIB) to align AltiKa with the snow surface and CryoSat-2 with the ice–snow interface. Snow depth is found as the difference between the two calibrated freeboards, with a correction added for the slower speed of light propagation through snow. We perform an initial evaluation of our derived snow depth product against OIB snow depth data by excluding successive years of OIB data from the analysis. We find a root-mean-square deviation of 7.7, 5.3, 5.9, and 6.7 cm between our snow thickness product and OIB data from the springs of 2013, 2014, 2015, and 2016 respectively. We further demonstrate the applicability of the method to ICESat and Envisat, offering promising potential for the application to CryoSat-2 and ICESat-2, which launched in September 2018.

Using a Linear Unmixing Method to Improve Passive Microwave Snow Depth Retrievals

Satellite microwave radiometer measurements have been used to extract snow depth information for approximately three to four decades. However, the coarse spatial resolution of satellite microwave radiobrightness observations affects the snow depth retrieval accuracy and hinders the application of snow depth data in studies on water, energy, and carbon cycles. Therefore, snow depth measurements require better spatial resolution to improve the retrieval accuracy and enhance the resolution of the data. In this paper, an improved linear unmixing method is proposed to improve the accuracy of existing methods and downscale brightness temperatures from satellite observations to improve snow depth estimates. Tests conducted on a dataset consisting of both simulated and satellite data show the effectiveness of the proposed method. The unmixing method is then applied to FengYun-3B satellite/microwave radiation imager measurements for snow depth retrievals, and the resulting snow depths are compared with weather station observations from January to February 2011. The results show that the snow depth estimated from downscaled brightness temperatures performs better than the original data and presents higher correlations and lower root mean square errors.

The calm during the storm: Snowfall events decrease the movement rates of grey wolves (Canis lupus).

Mammalian predators encounter unique hunting challenges during the winter as snow increases the cost of locomotion and influences predator-prey interactions. Winter precipitation may also affect predators’ ability to detect and pursue prey. We investigated the effects of snowfall events on grey wolves (Canis lupus) in a boreal forest ecosystem in northeastern Alberta, Canada. We predicted that wolves would respond to snowfall events by reducing their travel speed and the time they spent travelling. Over the course of two winters, we used remote cameras to identify localized snowfall events and estimate snow depth. We used telemetry data from 17 wolves to calculate travel speed and time spent travelling versus resting. Data were categorized by time of day (night versus day) and time since snowfall events, and analyzed using linear and logistic regression mixed-effects models. We found that wolves were less likely to travel on dates of snowfall events than any date prior to or after an event. Wolves also travelled slower during snowfall events, but only when compared to their travel speed 24 hours before. Effects were most pronounced at night, when movements appeared to be consistent with hunting behavior, and activity levels resumed within 24 hours of a snowfall event. Including snow depth as a variable did not improve model fit. Collectively, our findings suggest that wolves’ response is not driven by increased hunting success or by energetic considerations resulting from increased snow depth. Instead, we propose that wolves reduce their activity levels because precipitation dampens hunting success. Snowfall events may impact wolves’ ability to detect prey and changes in prey behavior could also lead to decreased encounter rates. We encourage scientists to further investigate the effects of short-term weather events on movement rates and predator-prey interactions.

Three years of sea ice freeboard, snow depth, and ice thickness of the Weddell Sea from Operation IceBridge and CryoSat-2

Abstract. We examine the variability of sea ice freeboard, snow depth, and ice thickness in three years (2011, 2014, and 2016) of repeat surveys of an IceBridge (OIB) transect across the Weddell Sea. Averaged over this transect, ice thickness ranges from 2.40±1.07 (2011) to 2.60±1.15 m (2014) and snow depth from 35.8±11.5 (2016) to 43.6±10.2 cm (2014), suggesting a highly variable but broadly thicker ice cover compared to that inferred from drilling and ship-based measurements. Spatially, snow depth and ice thickness are higher in the more deformed ice of the western Weddell. The impact of undersampling the thin end of the snow depth distribution on the regional statistics, due to the resolution of the snow radar, is assessed. Radar freeboards (uncompensated for snow thickness) from CryoSat-2 (CS-2) sampled along the same transect are consistently higher (by up to 8 cm) than those computed using OIB data. This suggests radar scattering that originates above the snow–ice interface, possibly due to salinity in the basal layer of the snow column. Consequently, sea ice thicknesses computed using snow depth estimates solely from differencing OIB and CS-2 freeboards (without snow radar) are therefore generally higher; mean differences in sea ice thickness along a transect are up to ∼0.6 m higher (in 2014). This analysis is relevant to the use of differences between ICESat-2 and CS-2 freeboards to estimate snow depth for ice thickness calculations. Our analysis also suggests that, even with these expected biases, this is an improvement over the assumption that snow depth is equal to the total freeboard, with which the underestimation of thickness could be up to a meter. Importantly, better characterization of the source of these biases is critical for obtaining improved estimates and understanding the limits of retrievals of Weddell Sea ice thickness from satellite altimeters.

Multi-sensor land data assimilation: Toward a robust global soil moisture and snow estimation

Global monitoring of soil moisture and snow is now available through various satellite observations from optical, microwave, and gravitational sensors. However, very few modeling frameworks exist that conjointly use the above sensors to produce mutually and physically consistent earth system records. To this goal, a prototype of multi-sensor land data assimilation system is developed by linking the Community Land Model version 4 (CLM4) and a series of forward models with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global soil moisture and snow by assimilating brightness temperature, snow cover fraction, and daily total water storage observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Moderate Resolution Imaging Spectroradiometer (MODIS), and Gravity Recovery and Climate Experiment (GRACE), respectively. A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to introduce ensemble spread in CLM4 land states and some methods are used to reduce the computational load. Data assimilation with different combinations of sensors is implemented for 2003–2009 to investigate individual contributions from different satellite observations. Evaluation results and cross-comparison of open-loop and data assimilation cases suggest that 1) assimilation of MODIS snow cover fraction slightly improves snow estimation in mid and high latitudes; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system.

Investigating Arctic Sea Ice Survivability in the Beaufort Sea

Arctic sea ice extent has continued to decline in recent years, and the fractional coverage of multi-year sea ice has decreased significantly during this period. The Beaufort Sea region has been the site of much of the loss of multi-year sea ice, and it continues to play a large role in the extinction of ice during the melt season. We present an analysis of the influence of satellite-derived ice surface temperature, ice thickness, albedo, and downwelling longwave/shortwave radiation as well as latitude and airborne snow depth estimates on the change in sea ice concentration in the Beaufort Sea from 2009 to 2016 using a Lagrangian tracking database. Results from this analysis indicate that parcels that melt during summer in the Beaufort Sea reside at lower latitudes and have lower ice thickness at the beginning of the melt season in most cases. The influence of sea ice thickness and snow depth observed by IceBridge offers less conclusive results, with some years exhibiting higher thicknesses/depths for melted parcels. Parcels that melted along IceBridge tracks do exhibit lower latitudes and ice thicknesses, however, which indicates that earlier melt and breakup of ice may contribute to a greater likelihood of extinction of parcels in the summer.

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model.

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from −0.017 m for OL to −0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from −0.111 m for OL to −0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.