Spatial Representativeness Analysis for Snow Depth Measurements of Meteorological Stations in Northeast China

2020 ◽  
Vol 21 (4) ◽  
pp. 791-805
Author(s):  
Yuanyuan Wang ◽  
Zhaojun Zheng
Keyword(s):  
Snow Depth ◽  
Northeast China ◽  
Ground Truth ◽  
Snow Accumulation ◽  
Point Scale ◽  
Coarse Scale ◽  
Study Region ◽  
Performance Metric

AbstractTriple collocation (TC) is a popular technique for determining the data quality of three products that estimate the same geophysical variable using mutually independent methods. When TC is applied to a triplet of one point-scale in situ and two coarse-scale datasets that have the similar spatial resolution, the TC-derived performance metric for the point-scale dataset can be used to assess its spatial representativeness. In this study, the spatial representativeness of in situ snow depth measurements from the meteorological stations in northeast China was assessed using an unbiased correlation metric estimated with TC. Stations are considered representative if ; that is, in situ measurements explain no less than 50% of the variations in the “ground truth” of the snow depth averaged at the coarse scale (0.25°). The results confirmed that TC can be used to reliably exploit existing sparse snow depth networks. The main findings are as follows. 1) Among all the 98 stations in the study region, 86 stations have valid values, of which 57 stations are representative for the entire snow season (October–December, January–April). 2) Seasonal variations in are large: 63 stations are representative during the snow accumulation period (December–February), whereas only 25 stations are representative during the snow ablation period (October–November, March–April). 3) The is positively correlated with mean snow depth, which largely determines the global decreasing trend in from north to south. After removing this trend, residuals in can be explained by heterogeneity features concerning elevation and conditional probability of snow presence near the stations.

scholarly journals Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

2021 ◽  
Vol 15 (1) ◽  
pp. 345-367
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu ◽  
Alek Petty ◽  
Rachel Tilling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Spatial And Temporal Scales ◽  
Airborne Measurements ◽  
Arctic Sea ◽  
Danish Meteorological Institute

Abstract. In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

scholarly journals The ESA Permanent Facility for Altimetry Calibration: Monitoring Performance of Radar Altimeters for Sentinel-3A, Sentinel-3B and Jason-3 Using Transponder and Sea-Surface Calibrations with FRM Standards

2020 ◽  
Vol 12 (16) ◽  
pp. 2642
Author(s):  
Stelios Mertikas ◽  
Achilleas Tripolitsiotis ◽  
Craig Donlon ◽  
Constantin Mavrocordatos ◽  
Pierre Féménias ◽  
...  
Keyword(s):  
Objective Assessment ◽  
Ground Truth ◽  
Sea Surface ◽  
Crossover Analysis ◽  
Operational Phase ◽  
The Stability ◽  
Independent Calibration ◽  
Monitoring Performance

This work presents the latest calibration results for the Copernicus Sentinel-3A and -3B and the Jason-3 radar altimeters as determined by the Permanent Facility for Altimetry Calibration (PFAC) in west Crete, Greece. Radar altimeters are used to provide operational measurements for sea surface height, significant wave height and wind speed over oceans. To maintain Fiducial Reference Measurement (FRM) status, the stability and quality of altimetry products need to be continuously monitored throughout the operational phase of each altimeter. External and independent calibration and validation facilities provide an objective assessment of the altimeter’s performance by comparing satellite observations with ground-truth and in-situ measurements and infrastructures. Three independent methods are employed in the PFAC: Range calibration using a transponder, sea-surface calibration relying upon sea-surface Cal/Val sites, and crossover analysis. Procedures to determine FRM uncertainties for Cal/Val results have been demonstrated for each calibration. Biases for Sentinel-3A Passes No. 14, 278 and 335, Sentinel-3B Passes No. 14, 71 and 335, as well as for Jason-3 Passes No. 18 and No. 109 are given. Diverse calibration results by various techniques, infrastructure and settings are presented. Finally, upgrades to the PFAC in support of the Copernicus Sentinel-6 ‘Michael Freilich’, due to launch in November 2020, are summarized.

scholarly journals Snow depth uncertainty and its implications on satellite derived Antarctic sea ice thickness

2018 ◽  
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice ◽  
Fast Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel falls within 0.02 m snow water equivalent (swe) of in situ measurements across the entire study area, but exhibits deviations of 0.05 m swe from these measurements in the east where large topographic features appear to have caused a positive bias in snow depth. AMSR-E provides swe values half that of SnowModel for the majority of the sea ice growth season. The coarser resolution ERA-Interim, not segmented for sea ice freeze up area reveals a mean swe value 0.01 m higher than in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on the assumptions involved in separating snow and ice freeboard. With various assumptions about the radar penetration into the snow cover, the sea ice thickness estimates vary by up to 2 m. However, we find the best agreement between CS-2 derived and in situ thickness when a radar penetration of 0.05-0.10 m into the snow cover is assumed.

scholarly journals Snow-driven uncertainty in CryoSat-2-derived Antarctic sea ice thickness – insights from McMurdo Sound

2019 ◽  
Vol 13 (4) ◽  
pp. 1409-1422
Author(s):  
Daniel Price ◽  
Iman Soltanzadeh ◽  
Wolfgang Rack ◽  
Ethan Dale
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Ice Thickness ◽  
Growth Season ◽  
Mcmurdo Sound ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

Abstract. Knowledge of the snow depth distribution on Antarctic sea ice is poor but is critical to obtaining sea ice thickness from satellite altimetry measurements of the freeboard. We examine the usefulness of various snow products to provide snow depth information over Antarctic fast ice in McMurdo Sound with a focus on a novel approach using a high-resolution numerical snow accumulation model (SnowModel). We compare this model to results from ECMWF ERA-Interim precipitation, EOS Aqua AMSR-E passive microwave snow depths and in situ measurements at the end of the sea ice growth season in 2011. The fast ice was segmented into three areas by fastening date and the onset of snow accumulation was calibrated to these dates. SnowModel captures the spatial snow distribution gradient in McMurdo Sound and falls within 2 cm snow water equivalent (s.w.e) of in situ measurements across the entire study area. However, it exhibits deviations of 5 cm s.w.e. from these measurements in the east where the effect of local topographic features has caused an overestimate of snow depth in the model. AMSR-E provides s.w.e. values half that of SnowModel for the majority of the sea ice growth season. The coarser-resolution ERA-Interim produces a very high mean s.w.e. value 20 cm higher than the in situ measurements. These various snow datasets and in situ information are used to infer sea ice thickness in combination with CryoSat-2 (CS-2) freeboard data. CS-2 is capable of capturing the seasonal trend of sea ice freeboard growth but thickness results are highly dependent on what interface the retracked CS-2 height is assumed to represent. Because of this ambiguity we vary the proportion of ice and snow that represents the freeboard – a mathematical alteration of the radar penetration into the snow cover – and assess this uncertainty in McMurdo Sound. The ranges in sea ice thickness uncertainty within these bounds, as means of the entire growth season, are 1.08, 4.94 and 1.03 m for SnowModel, ERA-Interim and AMSR-E respectively. Using an interpolated in situ snow dataset we find the best agreement between CS-2-derived and in situ thickness when this interface is assumed to be 0.07 m below the snow surface.

scholarly journals Inter-comparison of snow depth over sea ice from multiple methods

2020 ◽  
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu ◽  
Alek Petty ◽  
Rachel Tilling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Significant Trend ◽  
Data Sets ◽  
Snow Density ◽  
Spatial And Temporal Scales ◽  
Danish Meteorological Institute ◽  
The University

Abstract. In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

Inter-comparison of snow depth over sea ice from multiple methods

2020 ◽  
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Significant Trend ◽  
Data Sets ◽  
Snow Density ◽  
Spatial And Temporal Scales ◽  
Danish Meteorological Institute ◽  
The University

<p>In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations.</p><p>Large snow depth differences between data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest Spring snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest Spring snow depths. There is no significant trend for mean snow depth among all snow products since the 2000s, however, those in regional varies larhely. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model: NESOSIM, also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. Compared to climatology, snow density from SnowModel-LG is generally denser, whereas that from NESOSIM is less. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology.</p><p>Inconsistencies in the reconstructed snow parameters among the products, as well as differences and with in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.</p>

scholarly journals Methodological approaches to infer end-of-winter snow distribution on alpine glaciers

2013 ◽  
Vol 59 (218) ◽  
pp. 1047-1059 ◽  
Author(s):  
Leo Sold ◽  
Matthias Huss ◽  
Martin Hoelzle ◽  
Hubert Andereggen ◽  
Philip C. Joerg ◽  
...  
Keyword(s):  
Mass Balance ◽  
Snow Depth ◽  
Multiple Linear Regression Model ◽  
Field Measurements ◽  
Liquid Water Content ◽  
Snow Accumulation ◽  
Small Scale ◽  
Alpine Glaciers ◽  
Mass Balance Models

AbstractSnow accumulation is an important component of the mass balance of alpine glaciers. To improve our understanding of the processes related to accumulation and their representation in state-of-the-art mass-balance models, extensive field measurements are required. We present measurements of snow accumulation distribution on Findelengletscher, Switzerland, for April 2010 using (1) in situ snow probings, (2) airborne ground-penetrating radar (GPR) and (3) differencing of two airborne light detection and ranging (lidar) digital elevation models (DEMs). Calculating high-resolution snow depth from DEM-differencing requires careful correction for vertical ice-flow velocity and densification in the accumulation area. All three methods reveal a general increase in snow depth with elevation, but also a significant small-scale spatial variability. Lidar-differencing and in situ snow probings show good agreement for the mean specific winter balance (0.72 and 0.78 m w.e., respectively). The lidar-derived distributed snow depth reveals significant zonal correlations with elevation, slope and curvature in a multiple linear regression model. Unlike lidar-differencing, GPR-derived snow depth is not affected by glacier dynamics or firn compaction, but to a smaller degree by snow density and liquid water content. It is thus a valuable independent data source for validation. The simultaneous availability of the three datasets facilitates the comparison of the methods and contributes to a better understanding of processes that govern winter accumulation distribution on alpine glaciers.

scholarly journals Snow Cover and Spring Flood Flow in the Northern Part of Western Siberia (the Poluy, Nadym, Pur, and Taz Rivers)

2011 ◽  
Vol 12 (6) ◽  
pp. 1498-1511 ◽  
Author(s):  
E. A. Zakharova ◽  
A. V. Kouraev ◽  
S. Biancamaria ◽  
M. V. Kolmakova ◽  
N. M. Mognard ◽  
...  
Keyword(s):  
Snow Depth ◽  
Meteorological Station ◽  
Spring Flood ◽  
Study Region ◽  
Snow Water ◽  
In Situ Observations ◽  
Microwave Imager ◽  
Flood Flow

Abstract The paper aims to quantitatively estimate the role of snowmelt in the spring flood flow and the redistribution of river runoff for the northern (Arctic) part of the western Siberian Plain (the rivers Poluy, Nadym, Pur, and Taz). In this region, the presence of wetlands and thermokarst lakes significantly influences the seasonal redistribution of river discharge. First the study region is described, and the snow regime from in situ observations at the Tarko-Sale meteorological station is analyzed. As Special Sensor Microwave Imager (SSM/I) estimates of snow depth for this region are lower than in situ observations, a correction of the SSM/I snow depth estimates is done using snow parameters measured on the snow transect near the meteorological station Tarko-Sale for 1991–96. This reestimated snow depth is then used to assess the volume of water stored every winter on the watersheds for 1989–2006. This snow product is compared with the spring flood streamflow estimated from in situ observations, and the regional relationship between the snow water storage and flood flow is constructed. The proportion of meltwater that does not reach the main rivers and is thus evaporated or stored by the wetlands is estimated to be on average 30% (varying from 0% to 74%). We observe an increasing trend of this value from 20%–30% in the early 1990s to 50%–60% in the mid-2000s. This increase could be attributed to several factors such as increased air temperature (leading to increased evaporation, changes in vegetation cover, and active layer depth) and also to human activity.

scholarly journals Surface Pressure Tide Climatologies Deduced from a Quality-Controlled Network of Barometric Observations*

2014 ◽  
Vol 142 (12) ◽  
pp. 4872-4889 ◽  
Author(s):  
Michael Schindelegger ◽  
Richard D. Ray
Keyword(s):  
Surface Pressure ◽  
Global Analysis ◽  
Ground Truth ◽  
Fine Mesh ◽  
Control Framework ◽  
Spatial Coverage ◽  
Analysis Models

Abstract Global “ground truth” knowledge of solar diurnal S1 and semidiurnal S2 surface pressure tides as furnished by barometric in situ observations represents a valuable standard for wide-ranging geophysical and meteorological applications. This study attempts to aid validations of the air pressure tide signature in current climate or atmospheric analysis models by developing a new global assembly of nearly 6900 mean annual S1 and S2 estimates on the basis of station and marine barometric reports from the International Surface Pressure Databank, version 2 (ISPDv2), for a principal time span of 1990–2010. Previously published tidal compilations have been limited by inadequate spatial coverage or by internal inconsistencies and outliers from suspect tidal analyses; here, these problems are mostly overcome through 1) automated data filtering under ISPDv2’s quality-control framework and 2) a meticulously conducted visual inspection of station harmonic decompositions. The quality of the resulting compilation is sufficient to support global interpolation onto a reasonably fine mesh of 1° horizontal spacing. A multiquadric interpolation algorithm, with parameters fine-tuned by frequency and for land or ocean regions, is employed. Global charts of the gridded surface pressure climatologies are presented, and these are mapped to a wavenumber versus latitude spectrum for comparison with long-term means of S1 and S2 from four present-day atmospheric analysis systems. This cross verification, shown to be feasible even for the minor stationary modes of the tides, reveals a small but probably significant overestimation of up to 18% for peak semidiurnal amplitudes as predicted by global analysis models.

scholarly journals A new snow depth data set over northern China by developing a comprehensive framework using the complex GNSS station networks (GSnow-CHINA v1.0, 2013–2020)

2021 ◽  
Author(s):  
Wei Wan ◽  
Jie Zhang ◽  
Liyun Dai ◽  
Hong Liang ◽  
Baojian Liu ◽  
...  
Keyword(s):  
Snow Depth ◽  
Northern China ◽  
Data Sets ◽  
Point Scale ◽  
Data Set ◽  
Raw Data ◽  
Depth Data ◽  
Comprehensive Framework

Abstract. 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 model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal 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 to 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/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). 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. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring 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 the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

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