Comparison of bulk snow density measurements using different methods

Snow density is one of the basic properties used to describe snow cover characteristics,and it is critical for remote sensing retrieval,water resources assessment and modeling inputs.There are many instruments available to measure snow density in situ.However,there are mea-surement errors of snow density for bulk and layers or gravimetric and electronic instruments,which may affect the accuracy of remote sensing retrieval and model simulation.Especially in China,due to the noticeable heterogeneity of snowpacks,it is necessary to evaluate in detail the performance and applicability of snow density instruments in different snowpack conditions.This study evaluated the performance of different snow density instruments:the Federal Sampler,the model VS-43 snow density cylinder(VS-43),the wedge snow density cutter(WC1000 and WC25O),and the Snow Fork.The average bulk snow density of all instrument measurements was set as the reference value for evaluation.The results showed that as compared with the reference,the VS-43 cylinder presented the best performance for bulk snow density measurement in the measured range with the lowest RMSE(11 kg m^(-3)),BIAS(3 kg m^(-3)),and MRE(1.6%).For layer observation,bulk snow density was overestimated by 8.1%with WC1000 and underestimated by 11.4%with Snow Fork which was the worst performance compared with the reference value,and there were greater measurement errors of snow density in the depth hoar than other snow layers.Compared with grassland,the uncertainty of snow density measurements was slightly lower in forests.Overall,the Federal Sampler and VS-43 cylinder are more suitable for bulk snow density measurement in deep snowpack regions across China,and it is recommended to use WC1000,WC250 and Snow Fork to measure the snow density of snow layers in the snow stratigraphy.

Performance of climate reanalyses in the determination of pan-Arctic terrestrial rain-on-snow events

Rain-on-snow(ROS)events can cause rapid snowmelt,leading to flooding and avalanches in the pan-Arctic and can also lead to starvation and the death of massive ungulates.Reanalysis products(e.g.,ERA-I,ERA5-land,JRA55,MERRA2)are the primary source data for the research about ROS events in the large-scale region.However,the accuracy and reliability of reanalyses have never been evaluated with respect to the determination of terrestrial ROS events.The present study aims to statistically evaluate the performance of reanalysis datasets in identifying ROS events with different criteria based on in-situ rainfall data and MODIS snow cover product.The results show that all reanalysis datasets exhibit poor performance(Recall≤0.16,Kappa coefficient≤0.26,F-score≤0.42,MCC≤0.33)in all criteria in the pan-Arctic,mainly due to the low accuracy of rainfall data(r≤0.56).Nevertheless,the spatial distribution pattern and hot spots of ROS from all reanalysis datasets are essentially close.The hot spots of ROS are mainly located on the coast of Alaska,Norway,and Greenland.All reanalyses demonstrate an increase in rainy days,but there is little overall change in ROS events due to the reduction in snow cover days.This work suggests that none of the current reanalyses are reliable in the determination of ROS events due to the poor representation of the rainfall parameterization scheme.The development of alternative strategies that can investigate ROS events at large-scale is urgently needed in a changing Arctic under rapid warming.