Snow Interception Research Articles

Snow process monitoring in montane forests with time‐lapse photography

AbstractA camera network with hourly resolution was used to monitor the complex snow processes in montane forest environments. We developed a semi‐automatic procedure to interpret snow depths from the digital images, which exhibited high consistency with manual measurements and station‐based recordings. To extract snow interception dynamics, six binary classification methods were compared. The MaxEntropy classifier demonstrated better performance than the other methods under conditions of varying illumination and was therefore selected as the method used for quantifying snow in tree canopies. Snow accumulation and ablation on the ground, as well as snow loading and unloading in the forest canopies, were investigated using snow parameters derived from the time‐lapse photography monitoring. The influences of meteorologic conditions, forest cover, and elevation on the snow processes were also considered. Time‐lapse photography proved to be an effective and low‐cost approach for collecting useful information on snow processes and facilitating the set‐up of hydrological models.

Evaluation of CLASS Snow Simulation over Eastern Canada

Abstract The Canadian Land Surface Scheme (CLASS), version 3.6.1, was run offline for the period 1990–2011 over a domain centered on eastern Canada, driven by atmospheric forcing data dynamically downscaled from ERA-Interim using the Canadian Regional Climate Model. The precipitation inputs were adjusted to replicate the monthly average precipitation reported in the CRU observational database. The simulated fractional snow cover and the surface albedo were evaluated using NOAA Interactive Multisensor Snow and Ice Mapping System and MODIS data, and the snow water equivalent was evaluated using CMC, Global Snow Monitoring for Climate Research (GlobSnow), and Hydro-Québec products. The modeled fractional snow cover agreed well with the observational estimates. The albedo of snow-covered areas showed a bias of up to −0.15 in boreal forest regions, owing to neglect of subgrid-scale lakes in the simulation. In June, conversely, there was a positive albedo bias in the remaining snow-covered areas, likely caused by neglect of impurities in the snow. The validation of the snow water equivalent was complicated by the fact that the three observation-based datasets differed widely. Also, the downward adjustment of the forcing precipitation clearly resulted in a low snow bias in some regions. However, where the density of the observations was high, the CLASS snow model was deemed to have performed well. Sensitivity tests confirmed the satisfactory behavior of the current parameterizations of snow thermal conductivity, snow albedo refreshment threshold, and limiting snow depth and underlined the importance of snow interception by vegetation. Overall, the study demonstrated the necessity of using a wide variety of observation-based datasets for model validation.

Snow disappearance timing is dominated by forest effects on snow accumulation in warm winter climates of the Pacific Northwest, United States

AbstractForests modify snow processes and affect snow water storage as well as snow disappearance timing. However, forest influences on snow accumulation and ablation vary with climate and topography and are therefore subject to temporal and spatial variability. We utilize multiple years of snow observations from across the Pacific Northwest, United States, to assess forest–snow interactions in the relatively warm winter conditions characteristic of maritime and transitional maritime–continental climates. We (a) quantify the difference in snow magnitude and disappearance timing between forests and open areas and (b) assess how forest modifications of snow accumulation and ablation combine to determine whether snow disappears later in the forest or in the open.We find that snow disappearance timing at 12 (out of 14) sites ranges from synchronous in the forest and open to snow persisting up to 13 weeks longer in the open relative to a forested area. By analyzing accumulation and ablation rates up to the day when snow first disappears from the forest, we find that the difference between accumulation rates in the open and forest is larger than the difference between ablation rates. Thus, canopy snow interception and subsequent loss, rather than ablation, set up longer snow duration in the open. However, at two relatively windy sites (hourly average wind speeds up to 8 and 17 m/s), differential snow disappearance timing is reversed: Snow persists 2–5 weeks longer in the forest. At the windiest sites, accumulation rates in the forest and open are similar. Ablation rates are higher in the open, but the difference between ablation rates in the forest and open at these sites is approximately equivalent to the difference at less windy sites. Thus, longer snow retention in the forest at the windiest sites is controlled by depositional differences rather than by reduced ablation rates. These findings suggest that improved quantification of forest effects on snow accumulation processes is needed to accurately predict the effect of forest management or natural disturbance on snow water resources.

東北地方の多雪地帯における 2013／2014 年冬季の落葉広葉樹林，スギ林の降雪遮断特性および融雪特性

東北地方の多雪地帯における 2013／2014 年冬季の落葉広葉樹林，スギ林の降雪遮断特性および融雪特性

Snow accumulation and ablation in different canopy structures at a plot scale: using degree-day approach and measured shortwave radiation

The knowledge of water volume stored in the snowpack and its spatial distribution is important to predict the snowmelt runoff. The objective of this study was to quantify the role of different forest types on the snowpack distribution at a plot scale during snow accumulation and snow ablation periods. Special interest was put in the role of the forest affected by the bark beetle (Ips typographus). We performed repeated detailed manual field survey at selected mountain plots with different canopy structure located at the same elevation and without influence of topography and wind on the snow distribution. A snow accumulation and ablation model was set up to simulate the snow water equivalent (SWE) in plots with different vegetation cover. The model was based on degree-day approach and accounts for snow interception in different forest types. The measured SWE in the plot with healthy forest was on average by 41% lower than in open area during snow accumulation period. The disturbed forest caused the SWE reduction by 22% compared to open area indicating increasing snow storage after forest defoliation. The snow ablation in healthy forest was by 32% slower compared to open area. On the contrary, the snow ablation in disturbed forest (due to the bark beetle) was on average only by 7% slower than in open area. The relative decrease in incoming solar radiation in the forest compared to open area was much bigger compared to the relative decrease in snowmelt rates. This indicated that the decrease in snowmelt rates cannot be explained only by the decrease in incoming solar radiation. The model simulated best in open area and slightly worse in healthy forest. The model showed faster snowmelt after forest defoliation which also resulted in earlier snow melt-out in the disturbed forest.

Behavioral plasticity in a variable environment: snow depth and habitat interactions drive deer movement in winter

In seasonally varying environments, animals should alter habitat selection through time to avoid the harshest conditions. Winter severity is limiting for many ungulates in high-latitude ecosystems, and quality of habitat is an important determinant of winter survival. Previous studies in Southeast Alaska indicated that Sitka black-tailed deer (Odocoileus hemionus sitkensis) selected old-growth forest that provides both snow interception and forage, but with great variability among studies, years, and geographic areas. Clearcut timber harvest has greatly reduced the extent and quality of old-growth forest. The value of 2nd-growth and old-growth forest types to deer likely depends on snow depth, which is highly variable in space and time. We measured selection for vegetation classes, landscape features, and forage biomass by monitoring 56 GPS-radiocollared adult female deer from 1 January to 1 April between 2011 and 2013. Simultaneously, we measured snow depth across deer home ranges daily. We determined that snow depth had a strong effect on selection for vegetation classes. During periods of low snow, deer selected young 2nd growth but avoided old 2nd growth and high-volume old growth. As snow depths increased, young 2nd growth was avoided and deer selected old 2nd-growth and productive old-growth forests. The composition of vegetation classes within the landscape influenced selection, with deer selecting locally abundant habitats. These behaviors suggest that the widespread distribution of forest patches that provide snow interception and forage biomass may be critical to fulfilling the energetic requirements of deer during winters with snow. Such context-dependent habitat selection is likely widespread among wildlife species in variable environments and should be incorporated into study design and analysis.

Mechanism and effects of snow accumulations and controls by lightweight snow fences

This paper investigates the snowdrifts caused by lightweight fences along the lines on the flatland through the computational fluid dynamics method. The characteristic ambient flows around the solid fences and the porous fences with varied heights and bottom wind gaps are simulated in the numerical model, and the working mechanism of “interception” and “scouring” of the lightweight fences are analyzed. Based on the friction velocities near the ground, two sets of criteria are proposed to evaluate the deposition and erosion effects of different fences. According to flow separation and reattachment, the simplified relationships between the most likely positions for snow accumulations and fence parameters are developed. The study indicates that the capabilities for snow interception by the solid fence without wind gap and the distance from which to the second snow coverage center both increase with the fence height. Furthermore, it is found that the scouring range for snow surface increases significantly with the size of wind gap, and the snow accumulation rate on the leeward side decreases with the increasing fence porosity.

Improving winter leaf area index estimation in coniferous forests and its significance in estimating the land surface albedo

Winter leaf area index (LAI) of evergreen coniferous forests exerts strong control on the interception of snow, snowmelt and energy balance. Simulation of winter LAI and associated winter processes in land surface models is challenging. Retrieving winter LAI from remote sensing data is difficult due to cloud contamination, poor illumination, lower solar elevation and higher radiation reflection by snow background. Underestimated winter LAI in evergreen coniferous forests is one of the major issues limiting the application of current remote sensing LAI products. It has not been fully addressed in past studies in the literature. In this study, we used needle lifespan to correct winter LAI in a remote sensing product developed by the University of Toronto. For the validation purpose, the corrected winter LAI was then used to calculate land surface albedo at five FLUXNET coniferous forests in Canada. The RMSE and bias values for estimated albedo were 0.05 and 0.011, respectively, for all sites. The albedo map over coniferous forests across Canada produced with corrected winter LAI showed much better agreement with the GLASS (Global LAnd Surface Satellites) albedo product than the one produced with uncorrected winter LAI. The results revealed that the corrected winter LAI yielded much greater accuracy in simulating land surface albedo, making the new LAI product an improvement over the original one. Our study will help to increase the usability of remote sensing LAI products in land surface energy budget modeling.

Investigations on physical properties and ablation processes of snow cover during the spring snowmelt period in the headwater region of the Irtysh River, Chinese Altai Mountains

To explore the effects of the underlying surface on snow properties and the “forest effects” on snow accumulation and ablation processes in the headwater area of the Irtysh River in the Chinese Altai Mountains, the physical properties of snow and certain snowmelt processes were investigated during the snowmelt period in 2014. Our observations showed that the diurnal and daily variations of air and snow temperatures, the liquid water contents (LWCs), and the densities of surface snow layers at 0–5 cm depths were in agreement, but differences in snow properties were obvious at the bottom snow layers. The LWCs were divided into three categories according to snow temperature (T s): LWC = 0, T s < −3 °C; LWC <1 %, −3 ≤ T s < −1 °C; LWC <7 %, T s ≥ −1 °C. The snow densities were greatest in the top layers above all underlying surfaces (grassland, concrete, and river ice) during the snowmelt period. The snowmelt experiment indicated that the mean air temperature was the highest on the grassland and lowest in the glade, but the diurnal temperature range was the lowest in the continuous forest and highest on the grassland. The canopy snow interception rate of Abies sibirica reached up to 70 % and the mean value of the continuous forest cover was about 30 %. The snowmelt duration periods were 20 days on the grassland, 35 days in the continuous forest, and 43 days in the forest glade, and the corresponding average snow melt rates (SMRs) were 2.1, 1.2, and 1.4 mm d−1. For one single A. sibirica tree, the SMR in the sub-canopy was only half that on the edge and outside of the forest canopy.

Sensitivities in Large-Eddy Simulations of Mixed-Phase Arctic Stratocumulus Clouds Using a Simple Microphysics Approach*

Abstract Arctic mixed-phase stratocumulus clouds are maintained by feedbacks between microphysical and dynamical phenomena, but the details of these interactions are incompletely understood. Although large-eddy simulations are a promising means of elucidating microphysics–turbulence relationships, the use of sophisticated microphysical schemes complicates analysis of their results. Here, the ability of a simplified one-moment scheme to capture basic features of this cloud type is investigated through simulations based on Mixed-Phase Arctic Cloud Experiment (MPACE), SHEBA/FIRE-ACE, and Indirect and Semi-Direct Aerosol Campaign (ISDAC) intercomparison studies. The results of the simple scheme show reasonable agreement with liquid and ice water path predictions reported by models using schemes of similar or greater complexity. Additional tests are performed to evaluate the sensitivity of the results to three main parameters of the scheme: the snow and ice size distribution intercept parameters and the exponent appearing in the temperature-dependent phase-partition function, which is used to diagnose cloud condensate amounts. Sensitivities of the SHEBA and ISDAC cases, both of which have low surface heat fluxes and low precipitation rates, tend to be similar, while the MPACE case, with higher surface fluxes and precipitation rates, shows somewhat different trends. Results of all three cases are found to be sensitive to the snow size distribution intercept parameter, but this quantity can be adequately estimated using a recently developed diagnostic expression based on observations of Arctic clouds.

The impacts of mountain pine beetle disturbance on the energy balance of snow during the melt period

AbstractMountain snowpacks provide most of the annual discharge of western US rivers, but the future of water resources in the western USA is tenuous, as climatic changes have resulted in earlier spring melts that have exacerbated summer droughts. Compounding changes to the physical environment are biotic disturbances including that of the mountain pine beetle (MPB), which has decimated millions of acres of western North American forests. At the watershed scale, MPB disturbance increases the peak hydrograph, and at the stand scale, the ‘grey’ phase of MPB canopy disturbance decreases canopy snow interception, increases snow albedo, increases net shortwave radiation, and decreases net longwave radiation versus the ‘red’ phase. Fewer studies have been conducted on the red phase of MPB disturbance and in the mixed coniferous stands that may follow MPB‐damaged forests. We measured the energy balance of four snowpacks representing different stages of MPB damage, management, and recovery: a lodgepole pine stand, an MPB‐infested stand in the red phase, a mixed coniferous stand (representing one successional trajectory), and a clear‐cut (representing reactive management) in the Tenderfoot Creek Experimental Forest in Montana, USA. Net longwave radiation was lower in the MPB‐infested stand despite higher basal area and plant area index of the other forests, suggesting that the desiccated needles serve as a less effective thermal buffer against longwave radiative losses. Eddy covariance observations of sensible and latent heat flux indicate that they are of similar but opposite magnitude, on the order of 20 MJ m−2 during the melt period. Further analyses reveal that net turbulent energy fluxes were near zero because of the temperature and atmospheric vapour pressure encountered during the melt period. Future research should place snow science in the context of forest succession and management and address important uncertainties regarding the timing and magnitude of needlefall events. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Multi-scale analysis of snow dynamics at the southern margin of the North American continental snow distribution

Abstract Snow provides a key water source for stream flow and agricultural production across western North America and drinking water for large populations in the Southwest. Accurate estimates of snow cover spatial distribution and temporal dynamics are important at regional and local scales as snow cover is projected to decrease due to global climate change. We examined regional-scale temporal trends in snow distribution across central and northern Arizona using two tiles of 2928 daily images of MOD10 snow product. The analysis included the entire MODIS archive time period, October 1, 2003–June 1, 2014, and a 245,041 km2 area of 51 HUC8 watersheds. We also examined the effects of a regional forest restoration effort, known as the Four Forest Restoration Initiative (4FRI), aimed at enhancing snow accumulation and retention for increased groundwater recharge through forest thinning and burning treatments. We analyzed 66 Landsat TM/ETM + images spanning 26 years between 1988 and 2014 at five sites and one hyperspectral image from 2014 at two sites. The MOD10 snow product performs well in estimating Arizona's thin and discontinuous snow distribution. Mann-Kendall time-series analysis indicate significantly increasing trends in the annual number of snow cover days (SCD) over the 12-year period in 1.6% of the region at elevation transitions such as the Mogollon Rim in central Arizona, while significantly decreasing trends are observed at a few locations of lower elevations leading to the desert margins in eastern Arizona. The observed temporal trends are mostly consistent with ground-based SNOTEL snow measurements. An Arizona specific, Landsat sensor-derived binary classification model, similar to the MOD10 product, was developed at a local scale. It performs better than commonly-used simple threshold-based approaches, but demonstrates the continued challenges associated with Landsat sensor-derived snow classification in Arizona likely due to its coarse temporal resolution. Landsat-derived multi-temporal Normalized Difference Snow Index (NDSI) analysis indicate that treated (thinned and thinned-and-burned) forest sites had significantly greater NDSI values than untreated control sites. Snowpack at treated sites also appeared to persist longer into the spring season with potentially greater contributions to groundwater recharge in this semi-arid region. The high-resolution hyperspectral data analysis indicate that sites treated to approximately 24% forest canopy cover appear to have an optimum threshold for accumulating and maintaining snowpack. It balances canopy cover versus canopy gap, which reduces snow interception and sublimation by canopy, while providing enough shade. These results are encouraging for the 4FRI, the first and largest forest restoration effort in the US history, aimed at improving watershed health and function in the face of changing climate.

A simple algorithm for identifying periods of snow accumulation on a radiometer

Downwelling solar, Qsi, and longwave, Qli, irradiances at the earth's surface are the primary energy inputs for many hydrologic processes, and uncertainties in measurements of these two terms confound evaluations of estimated irradiances and negatively impact hydrologic modeling. Observations of Qsi and Qli in cold environments are subject to conditions that create additional uncertainties not encountered in other climates, specifically the accumulation of snow on uplooking radiometers. To address this issue, we present an automated method for estimating these periods of snow accumulation. Our method is based on forest interception of snow and uses common meteorological observations. In this algorithm, snow accumulation must exceed a threshold to obscure the sensor and is only removed through scouring by wind or melting. The algorithm is evaluated at two sites representing different mountain climates: (1) Snoqualmie Pass, Washington (maritime) and (2) the Senator Beck Basin Study Area, Colorado (continental). The algorithm agrees well with time-lapse camera observations at the Washington site and with multiple measurements at the Colorado site, with 70–80% of observed snow accumulation events correctly identified. We suggest using the method for quality controlling irradiance observations in snow-dominated climates where regular, daily maintenance is not possible.

Improved snow interception modeling using canopy parameters derived from airborne LiDAR data

AbstractForest snow interception can account for large snow storage differences between open and forested areas. The effect of interception can also lead to significant variations in sublimation, with estimates varying from 5 to 60% of total snowfall. Most current interception models utilize canopy closure and LAI to partition interception from snowfall and calculate interception efficiency as an exponential decrease of interception efficiency with increasing precipitation. However, as demonstrated, these models can show specific deficiencies within heterogeneous canopy. Seven field areas were equipped with 1932 surveyed points within various canopy density regimes in three elevation bands surrounding Davos, Switzerland. Snow interception measurements were taken from 2012 to 2014 (∼9000 samples) and compared with measurements at two open sites. The measured data indicated the presence of snow bridging from a demonstrated increase in interception efficiency as precipitation increased until a maximum was reached. As precipitation increased beyond this maximum, the data then exhibited a decrease in interception efficiency. Standard and novel canopy parameters were developed using aerial LiDAR data. These included estimates of LAI, canopy closure, distance to canopy, gap fraction, and various tree size parameters. These canopy metrics and the underlying efficiency distribution were then integrated to formulate a conceptual model based upon the snow interception measurements. This model gave a ∼27% increase in the r2 (from 0.39 to 0.66) and a ∼40% reduction in RMSE (from 5.19 to 3.39) for both calibration and validation data sets when compared to previous models at the point scale. When upscaled to larger grid sizes, the model demonstrated further increases in performance.

Novel forest structure metrics from airborne LiDAR data for improved snow interception estimation

A set of 57 novel canopy metrics of potential value for snow modeling were created from airborne LiDAR data. The metrics were meant to estimate size and relative location of gap openings around a point within forested areas, allowing for measures of the spatial arrangement of surrounding canopy elements. These new metrics were correlated with snow interception measured in the field (8488 manually measured snow interception points). The results were further compared to the correlation values between effective interception and traditionally used forest parameters (CC and LAI). The correspondence between all metrics was also analyzed in order to understand the potential cross correlation between each variable. LAI (average R: 0.57) demonstrated low correlations when directly compared to snow interception and further showed a large cross correlation with canopy closure (average R: 0.72). A new metric, ‘mean distance to canopy’ had the highest correlation (average R: 0.78) over all storm events to the effective interception. But in contrast to LAI, this metric did not show any cross correlation with canopy closure (CC). Likewise, ‘total gap area,’ an indirect measurement of apparent gap fraction (another new metric), also showed a high correlation to effective interception (average R: 0.72) without demonstrating a significant cross correlation to CC or to mean distance to canopy. These findings suggest that modeling forest snow processes with both CC and LAI may not be the best option due to both the low correlation of LAI as well as high cross correlation between these parameters. However, the pairing of mean distance to canopy and/or total gap area with canopy closure could give more robust estimations of snow interception within heterogeneous terrain.

The relationship between canopy openness and snow interception at Cryptomeria japonica stand in Tohoku Research Center Forestry and Forest Products Research Institute

The relationship between canopy openness and snow interception at <i> Cryptomeria japonica</i> stand in Tohoku Research Center Forestry and Forest Products Research Institute

Seasonal snow accumulation in the mid-latitude forested catchment

The study deals with the snow cover characteristics (snow depth — SD and snow water equivalent — SWE) concerning the mid-latitude forested catchment. Namely, the influence of the forest canopy (Picea abies (L.) Karst. and Fagus sylvatica L.) and altitude (ranging from 835 m a.s.l. to 1118 m a.s.l.) was investigated. Forest cover was proved to have a significant influence on the snow cover accumulation, reducing SWE by 50 % on average, compared to open sites. The elevation gradient concerning SWE ranged from 30 to 40 mm and from 5 to 20 mm per 100 m in open and forested sites, respectively. Its magnitude was found to be temporarily variable and positively related to the total seasonal snowfall amount. The SWE/SD variability among measurement sites (with different altitude) was higher in open sites compared to forested ones. The catchment SWE/SD variability increases significantly in the snowmelt period (March–April) both in open and forested locations. The differences among snow interception losses, concerning various elevations and the forest canopy, were not statistically significant.

Representation of canopy snow interception, unloading and melt in a parsimonious snowmelt model

Recent improvements in the Utah Energy Balance (UEB) snowmelt model are focused on snow–vegetation–atmosphere interactions to understand how different types of vegetation affect snow processes in the mountains of Western USA. This work presents field work carried out in the Rocky Mountains of Northern Utah to evaluate new UEB model algorithms that represent the processes of canopy snow interception, sublimation, mass unloading and melt. Four years' continuous field observations showed generally smaller accumulations of snow beneath the forest canopies in comparison with open (sage and grass) areas, a difference that is attributed to interception and subsequent sublimation and redistribution of intercepted snow by wind, much of it into surrounding open areas. Accumulations beneath the denser forest (conifer) canopies were found to be less than the accumulation beneath the less dense forest (deciduous) canopies. The model was able to represent the accumulation of snow water equivalent in the open and beneath the deciduous forest quite well but without accounting for redistribution tended to overestimate the snow water equivalent beneath the conifer forest. Evidence of redistribution of the intercepted snow from the dense forest (i.e. conifer forest) to the adjacent area was inferred from observations. Including a simple representation of redistribution in the model gave satisfactory prediction of snow water equivalent beneath the coniferous forest. The simulated values of interception, sublimation and unloading were also compared with previous studies and found in agreement. Copyright © 2013 John Wiley & Sons, Ltd.

Development and testing of a snow interceptometer to quantify canopy water storage and interception processes in the rain/snow transition zone of the North Cascades, Washington, USA

[1] Tree canopy snow interception is a significant hydrological process, capable of removing up to 60% of snow from the ground snowpack. Our understanding of canopy interception has been limited by our ability to measure whole canopy water storage in an undisturbed forest setting. This study presents a relatively inexpensive technique for directly measuring snow canopy water storage using an interceptometer, adapted from Friesen et al. (2008). The interceptometer is composed of four linear motion position sensors distributed evenly around the tree trunk. We incorporate a trunk laser-mapping installation method for precise sensor placement to reduce signal error due to sensor misalignment. Through calibration techniques, the amount of canopy snow required to produce the measured displacements can be calculated. We demonstrate instrument performance on a western hemlock (Tsuga heterophylla) for a snow interception event in November 2011. We find a snow capture efficiency of 83 ± 15% of accumulated ground snowfall with a maximum storage capacity of 50 ± 8 mm snow water equivalent (SWE). The observed interception event is compared to simulated interception, represented by the variable infiltration capacity (VIC) hydrologic model. The model generally underreported interception magnitude by 33% using a leaf area index (LAI) of 5 and 16% using an LAI of 10. The interceptometer captured intrastorm accumulation and melt rates up to 3 and 0.75 mm SWE h−1, respectively, which the model failed to represent. While further implementation and validation is necessary, our preliminary results indicate that forest interception magnitude may be underestimated in maritime areas.

Evaluation of cloud microphysics schemes in simulations of a winter storm using radar and radiometer measurements

AbstractUsing observations from a space‐borne radiometer and a ground‐based precipitation profiling radar, the impact of cloud microphysics schemes in the WRF model on the simulation of microwave brightness temperature (Tb), radar reflectivity, and Doppler velocity (Vdop) is studied for a winter storm in California. The unique assumptions of particles size distributions, number concentrations, shapes, and fall speeds in different microphysics schemes are implemented into a satellite simulator and customized calculations for the radar are performed to ensure consistent representation of precipitation properties between the microphysics schemes and the radiative transfer models.Simulations with four different schemes in the WRF model, including the Goddard scheme (GSFC), the WRF single‐moment 6‐class scheme (WSM6), the Thompson scheme (THOM), and the Morrison double‐moment scheme (MORR), are compared directly with measurements from the sensors. Results show large variations in the simulated radiative properties. General biases of ~20 K or larger are found in (polarization‐corrected) Tb, which is linked to an overestimate of the precipitating ice aloft. The simulated reflectivity with THOM appears to agree well with the observations, while high biases of ~5−10 dBZ are found in GSFC, WSM6 and MORR. Peak reflectivity in MORR exceeds other schemes. These biases are attributable to the snow intercept parameters or the snow number concentrations. Simulated Vdop values based on GSFC agree with the observations well, while other schemes appear to have a ~1 m s‐1 high bias in the ice layer. In the rain layer, the model representations of Doppler velocity vary at different sites.