Validation of precipitation phase estimates from CloudSat-CPR across Canada

Snow is an important component of the global climate system with significant impacts on local weather, fresh water resources, and energy balance in high latitude cold countries. Therefore precise snowfall monitoring is essential for cold countries such as Canada. Apart from the sampling issues related to access and climate in cold regions, a further significant issue that impacts snowfall monitoring is the accurate detection of precipitation phase. The CloudSat Cloud Profiling Radar (CPR) instrument is highly useful because it provides an estimate of precipitation phase along with retrievals of solid precipitation intensity. Furthermore, the sun-synchronous orbit of CloudSat allows it to have enhanced coverage over the Canadian Arctic. In this study, we validate the precipitation phase retrievals from CloudSat using the present weather information recorded on the ground by human observers (ECCC hourly weather data) from 27 stations across Canada and Precipitation Occurrence Sensor System (POSS) radar at Eureka, both maintained by Environment and Climate Change Canada (ECCC). Probability of Detection (POD), defined as the percentage of coincident CloudSat and ground observations that agree on the precipitation phase (solid, liquid or no precipitation), is used as the metric for validation. Mean POD values of CloudSat in classifying solid, liquid and non-precipitating weather at the 27 stations are 80.8%±1.5, 83.2%±1.9 and 69.8%±0.8 respectively. Binning the collocated CloudSat-ECCC hourly weather observations across Canada by the snowfall rate information available from CloudSat, we find that the accuracy of CloudSat in classifying precipitation phase increases with snowfall rate with a maximum accuracy of 85% for snowfall rates >1 mm/hr. We find that the POD varies with precipitation type, and is inversely proportional to cloud cover, with the lowest POD obtained under the heaviest cloud cover. Also, using binomial and multinomial logistic regression analysis of different physical factors, it is seen that POD of CloudSat is influenced by near-surface reflectivity, near-surface temperature and altitude of the lowest cloud layer. The results from this study imply that CloudSat has high accuracy in classifying precipitation phase and can be used to improve snowfall monitoring in cold regions

Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos

Differencing of digital surface models derived from structure from motion (SfM) processing of airborne imagery has been used to produce snow depth (SD) maps with between  ∼ 2 and  ∼ 15&thinsp;cm horizontal resolution and accuracies of ±10&thinsp;cm over relatively flat surfaces with little or no vegetation and over alpine regions. This study builds on these findings by testing two hypotheses across a broader range of conditions: (i) that the vertical accuracy of SfM processing of imagery acquired by commercial low-cost unmanned aerial vehicle (UAV) systems can be adequately modelled using conventional photogrammetric theory and (ii) that SD change can be more accurately estimated by differencing snow-covered elevation surfaces rather than differencing a snow-covered and snow-free surface. A total of 71 UAV missions were flown over five sites, ranging from short grass to a regenerating forest, with ephemeral snowpacks. Point cloud geolocation performance agreed with photogrammetric theory that predicts uncertainty is proportional to UAV altitude and linearly related to horizontal uncertainty. The root-mean-square difference (RMSD) over the observation period, in comparison to the average of in situ measurements along  ∼ 50&thinsp;m transects, ranged from 1.58 to 10.56&thinsp;cm for weekly SD and from 2.54 to 8.68&thinsp;cm for weekly SD change. RMSD was not related to microtopography as quantified by the snow-free surface roughness. SD change uncertainty was unrelated to vegetation cover but was dominated by outliers corresponding to rapid in situ melt or onset; the median absolute difference of SD change ranged from 0.65 to 2.71&thinsp;cm. These results indicate that the accuracy of UAV-based estimates of weekly snow depth change was, excepting conditions with deep fresh snow, substantially better than for snow depth and was comparable to in situ methods.</p

Estimating Snow Depth Using Multi-Source Data Fusion Based on the D-InSAR Method and 3DVAR Fusion Algorithm

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R2 = 0.31 vs. R2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R2 = 0.27 vs. R2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential