Using CloudSat‐CPR Retrievals to Estimate Snow Accumulation in the Canadian Arctic

Abstract

Snow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability and flooding in cold regions. The vast size and remote nature of the Arctic present serious logistical and financial challenges to measuring snow over extended time periods. Satellite observations provided by the Cloud Profiling Radar instrument—installed on the National Aeronautics and Space Administration satellite CloudSat—allow the retrieval of snowfall rates in high‐latitude regions, which have been used to estimate surface snow accumulation. In this study, a validation of CloudSat‐derived terrestrial snow estimates is presented at four Environment and Climate Change Canada weather stations situated in the Arctic for the common period 2007–2015. Comparisons of monthly climatological snow accumulation show mean biases of less than 1.5‐mm snow water equivalent annually. Monthly time series exhibit correlations above 0.5 and root‐mean‐square error below 10‐mm snow water equivalent at the two highest‐latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70°N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat's accumulation estimates, or underestimation related to sampling. These results imply that CloudSat can provide reliable estimates of snow accumulation across similar high‐latitude regions above 70°N.

Using CloudSat‐Derived Snow Accumulation Estimates to Constrain Gridded Snow Water Equivalent Products

“…This latitudinal component of the CloudSat estimate error is directly related to Cloud-Sat's orbit and the reduction in its observational sample size as we move toward the equator (Figure 5c). This connection between CloudSat's estimate uncertainty and sample size with respect to latitude has also been examined in previous work by King and Fletcher (2020) and Duffy et al (2021b), which came to similar conclusions that high latitude regions generally exhibit improved accuracy and lower uncertainty from CloudSat as a result of the larger sample size. These findings, along with the spatial distribution of grid cells in Figure 4, provide valuable information into identifying where we have a sufficient sample to use Cloud-Sat as an observational constraint for comparisons at 1 E  resolution.…”

“…The new DO-Op energy requirements have been shown to substantially reduce the CloudSat sample in the southern hemisphere, along with smaller reductions in its sample at high latitudes in the NH (Kulie et al, 2020;Milani & Wood, 2021). An examination on the impact of repeated CloudSat battery failures on the CPR sample size across the Arctic was performed in our previous work (King & Fletcher, 2020), and the impacts of these battery failures were found to be negligible with respect to CloudSat's ability to accurately quantify snow accumulation in the region (additional details are discussed in Section 3.2).…”

Validation of CloudSat-CPR Derived Precipitation Occurrence and Phase Estimates across Canada

“…However, our results suggest that CloudSat's retrieval algorithm is highly accurate at detecting precipitation phase and we recommend increased use of this product, possibly to aid development and/or validation of rain-snow partitioning in atmospheric or land surface models [59]. Moreover, CloudSat precipitation products, together with their auxiliary meteorological information, represent a large quality-controlled data archive, which can be used to develop and train new data-driven phase partitioning schemes, perhaps using machine-learning methods that require large sample sizes [60].…”

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

“…Its −27 dBZ sensitivity makes it one of the most valuable satellite sensors for cloud observations, but it has also been widely used for light rain and snowfall detection and quantification [2]. CloudSat has helped scientists understand the vertical profiles of clouds and precipitation [3], improve precipitation estimates [4][5][6][7][8] as well as provide fundamental insights on cloud microphysics for cloud models [9,10] and radiation fluxes [11,12].…”

“…Since the beginning of DO-Op, the CPR has collected data primarily during only the sunlit portion of the orbit. While many studies have so far used data from the Full Operations (Full-Op) period prior to the battery anomaly [4,7,14,15], the need for more recent CloudSat observations for comparison against new sensors such as the Global Precipitation Measurement (GPM) Core Observatory [16,17] or for the recent validation and optimization of models in a changing climate have increasingly required the use of the DO-Op observations as well [5,9,18,19]. This study compares CloudSat snowfall measurements taken during the Full-Op period against those taken during the DO-Op period to assess the impact of this change in sampling on snowfall properties estimated from the CloudSat snowfall product.…”