scholarly journals Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2020 ◽  
Vol 14 (1) ◽  
pp. 331-347
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Balance Model ◽  
High Mountain ◽  
Wide Range ◽  
Spatial Estimates ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus River and Amu Darya in western High Mountain Asia, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem. However, recently we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). To validate SWE reconstruction, at each station, accumulated precipitation and SWE were derived from snow depth using the numerical snow cover model SNOWPACK. High-resolution (500 m) reconstructed SWE estimates from the Parallel Energy Balance Model (ParBal) were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km resolution to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provides stability information. The median number of critical layers and percentage of faceted layers across all of the pixels containing the AKAH stations were computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at peak SWE. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. A heavily faceted snowpack was observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

scholarly journals Low Cost and Compact FMCW 24 GHz Radar Applications for Snowpack and Ice Thickness Measurements

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3909
Author(s):  
Patrick Pomerleau ◽  
Alain Royer ◽  
Alexandre Langlois ◽  
Patrick Cliche ◽  
Bruno Courtemanche ◽  
...  
Keyword(s):  
Snow Depth ◽  
Continuous Wave ◽  
Snow Water Equivalent ◽  
Low Cost ◽  
Ice Thickness ◽  
Lake Ice ◽  
Wide Range ◽  
Thickness Measurements ◽  
24 Ghz

Monitoring the evolution of snow on the ground and lake ice—two of the most important components of the changing northern environment—is essential. In this paper, we describe a lightweight, compact and autonomous 24 GHz frequency-modulated continuous-wave (FMCW) radar system for freshwater ice thickness and snow mass (snow water equivalent, SWE) measurements. Although FMCW radars have a long-established history, the novelty of this research lies in that we take advantage the availability of a new generation of low cost and low power requirement units that facilitates the monitoring of snow and ice at remote locations. Test performance (accuracy and limitations) is presented for five different applications, all using an automatic operating mode with improved signal processing: (1) In situ lake ice thickness measurements giving 2 cm accuracy up to ≈1 m ice thickness and a radar resolution of 4 cm; (2) remotely piloted aircraft-based lake ice thickness from low-altitude flight at 5 m; (3) in situ dry SWE measurements based on known snow depth, giving 13% accuracy (RMSE 20%) over boreal forest, subarctic taiga and Arctic tundra, with a measurement capability of up to 3 m in snowpack thickness; (4) continuous monitoring of surface snow density under particular Antarctic conditions; (5) continuous SWE monitoring through the winter with a synchronized and collocated snow depth sensor (ultrasonic or LiDAR sensor), giving 13.5% bias and 25 mm root mean square difference (RMSD) (10%) for dry snow. The need for detection processing for wet snow, which strongly absorbs radar signals, is discussed. An appendix provides 24 GHz simulated effective refractive index and penetration depth as a function of a wide range of density, temperature and wetness for ice and snow.

scholarly journals Variations of the Snow Water Equivalent in the Ourika Catchment (Morocco) over 2000–2018 Using Downscaled MERRA-2 Data

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1120 ◽  
Author(s):  
Mohamed Baba ◽  
Simon Gascoin ◽  
Lionel Jarlan ◽  
Vincent Simonneaux ◽  
Lahoucine Hanich
Keyword(s):  
Snow Cover ◽  
River Flow ◽  
Snow Water Equivalent ◽  
Temporal Variations ◽  
In Situ Measurements ◽  
High Mountain ◽  
Snow Cover Area ◽  
Mountain Areas ◽  
Snow Water

The Ourika River is an important tributary of the Tensift River in the water-stressed region of Marrakesh (Morocco). The Ourika river flow is dominated by the snow melt contribution from the High Atlas mountains. Despite its importance in terms of water resources, the snow water equivalent (SWE) is poorly monitored in the Ourika catchment. Here, we used MERRA-2 data to run a distributed energy-balance snowpack model (SnowModel) over 2000–2018. MERRA-2 data were downscaled to 250-m spatial resolution using a digital elevation model. The model outputs were compared to in situ measurements of snow depth, precipitation, river flow and remote sensing observations of the snow cover area from MODIS. The results indicate that the model provides an overall acceptable representation of the snow cover dynamics given the coarse resolution of the MERRA-2 forcing. Then, we used the model output to analyze the spatio-temporal variations of the SWE in the Ourika catchment for the first time. We suggest that MERRA-2 data, which are routinely available with a delay of a few weeks, can provide valuable information to monitor the snow resource in high mountain areas without in situ measurements.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

2021 ◽  
Author(s):  
Colleen Mortimer ◽  
Lawrence Mudryk ◽  
Chris Derksen ◽  
Kari Luojus ◽  
Pinja Venalainen ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Microwave Remote Sensing ◽  
Passive Microwave ◽  
Snow Density ◽  
Snow Season ◽  
Snow Water ◽  
Snow Mass

<p>The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.</p><p>Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m<sup>-3</sup> early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.</p>

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 Permafrost Variability over the Northern Hemisphere Based on the MERRA-2 Reanalysis

2018 ◽  
Author(s):  
Jing Tao ◽  
Randal D. Koster ◽  
Rolf H. Reichle ◽  
Barton A. Forman ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
In Situ Measurements ◽  
Active Layer Thickness ◽  
Permafrost Region ◽  
Meteorological Forcing ◽  
Northern Alaska

Abstract. This study introduces and evaluates a comprehensive, model-generated dataset of Northern Hemisphere permafrost conditions. Surface meteorological forcing fields from the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis data were used to drive an improved version of the land component of MERRA-2 in middle-to-high northern latitudes from 1980 to 2017. The resulting simulated permafrost distribution across the Northern Hemisphere captures well the observed extent of continuous permafrost except in Western Siberia, which is permafrost-free in the simulation. Noticeable discrepancies also appear along the southern edge of the permafrost region where sporadic and isolated permafrost types dominate. The evaluation of the simulated active layer thickness (ALT) climatology against in-situ measurements demonstrates reasonable skill except in Mongolia. In northern Alaska, both ALT retrievals from airborne remote sensing for 2015 and the corresponding simulated ALT exhibit reasonable accuracy vs. in-situ measurements. However, the remotely sensed ALT retrievals generally demonstrate lower levels of spatial variability than both the observed and simulated ALT. Controls on the spatial variability of ALT are examined with idealized numerical experiments focusing on northern Alaska; meteorological forcing and soil type are found to have dominant impacts on the spatial variability of ALT, with vegetation also playing a role through its modulation of snow accumulation. A correlation analysis further reveals that accumulated air temperature and maximum snow water equivalent (SWE) explain most of the year-to-year variability of ALT nearly everywhere over the model-simulated permafrost regions. Simulated ALT trends from 1980 to 2017 indicate that some permafrost areas are experiencing significant degradation, with ALT increasing up to 0.5 cm/year.

scholarly journals Brief Communication: Evaluation and comparisons of permafrost map over Qinghai-Tibet Plateau based on inventory of in-situ evidence

2018 ◽  
Author(s):  
Bin Cao ◽  
Tingjun Zhang ◽  
Qinghai Wu ◽  
Yu Sheng ◽  
Lin Zhao ◽  
...  
Keyword(s):  
Controlling Factors ◽  
In Situ Measurements ◽  
Tibet Plateau ◽  
Permafrost Region ◽  
Improved Method ◽  
Limited Evidence ◽  
Wide Range ◽  
Communication Evaluation ◽  
Qinghai Tibet Plateau

Abstract. Many maps have been produced to estimate permafrost distribution over the Qinghai-Tibet Plateau, however, the evaluation and comparisons of them are poorly understood due to limited evidence. Using a large number data from various sources, we present the inventory of permafrost presence/absence with 1475 sites/plots over the QTP. Based on the in-situ measurements, our evaluation results showed a wide range of map performance with the overall accuracy of about 59–82 %, and the estimated permafrost region (1.42–1.84 × 106 km2) and area (0.76–1.25 × 106 km2) are extremely large. The low agreement in areas near permafrost boundary and fragile landscapes require improved method considering more controlling factors at both medium-large and local scales.

scholarly journals Comparison of ambient aerosol extinction coefficients obtained from in-situ, MAX-DOAS and LIDAR measurements at Cabauw

2010 ◽  
Vol 10 (12) ◽  
pp. 29683-29734 ◽  
Author(s):  
P. Zieger ◽  
E. Weingartner ◽  
J. Henzing ◽  
M. Moerman ◽  
G. de Leeuw ◽  
...  
Keyword(s):  
Optical Properties ◽  
Extinction Coefficient ◽  
In Situ Measurements ◽  
Optical Absorption Spectroscopy ◽  
Aerosol Composition ◽  
Large Variability ◽  
Extinction Coefficients ◽  
Ambient Aerosol ◽  
Wide Range

Abstract. In the field, aerosol in-situ measurements are often performed under dry conditions (relative humidity RH<30–40%). Since ambient aerosol particles experience hygroscopic growth at enhanced RH, also their microphysical and optical properties – especially the aerosol light scattering – are strongly dependent on RH. The knowledge of this RH effect is of crucial importance for climate forcing calculations or for the comparison of remote sensing with in-situ measurements. Here, we will present results from a four-month campaign which took place in summer 2009 in Cabauw, The Netherlands. The aerosol scattering coefficient σsp(λ) was measured dry and at various, predefined RH conditions between 20 and 95% with a humidified nephelometer. The scattering enhancement factor f(RH,λ) is the key parameter to describe the effect of RH on σsp(λ) and is defined as σsp(RH,λ) measured at a certain RH divided by the dry σsp(dry,λ). The measurement of f(RH,λ) together with the dry absorption measurement (assumed not to change with RH) allows the determination of the actual extinction coefficient σep(RH,λ) at ambient RH. In addition, a wide range of other aerosol properties were measured in parallel. The measurements were used to characterize the effects of RH on the aerosol optical properties. A closure study showed the consistency of the aerosol in-situ measurements. Due to the large variability of air mass origin (and thus aerosol composition) a simple parameterization of f(RH,λ) could not be established. If f(RH,λ) needs to be predicted, the chemical composition and size distribution needs to be known. Measurements of four MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments were used to retrieve vertical profiles of σep(λ). The values of the lowest layer were compared to the in-situ values after conversion of the latter to ambient RH. The comparison showed a good correlation of R2=0.62–0.78, but the extinction coefficients were a factor of 1.5–3.4 larger than the in-situ values. Best agreement is achieved for a few cases characterized by low aerosol optical depths and low planetary boundary layer heights. Differences showed to be dependent on the applied MAX-DOAS retrieval algorithm. The comparison of the in-situ data to a Raman lidar (light detection and ranging) showed a good correlation and higher values measured by the lidar (R2=0.79, slope of 1.81) if the Raman retrieved profile was used to extrapolate the directly measured extinction coefficient to the ground. The comparison improved if only nighttime measurements were used in the comparison (R2=0.93, slope of 1.19).

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