publications

2025

1. JGR: ML&C

   

   Leveraging Sparse Autoencoders to Reveal Interpretable Features in Geophysical Models

   Fraser King, Claire Pettersen, Derek Posselt, and 2 more authors

   Journal of Geophysical Research: Machine Learning and Computation, 2025

   Abs DOI

   Machine learning is an increasingly popular tool in the geosciences, offering new approaches to numerical weather prediction and complex dataset analysis. However, as reliance on these techniques grows, pressing questions about model transparency, internal biases, and trust emerge. While post-hoc explainability analyses can provide insights on how neural network (NN) outputs are generated, a robust framework for interpreting internal decision-making remains underdeveloped. We address this challenge by exploring a framework to better understand the inner structure of NNs using sparse autoencoders (SAEs). With simplified multilayer perceptrons (MLPs), we demonstrate that hidden layer neurons often exhibit polysemantic behavior where each feature is mapped to a linear combination of neurons, creating an overcomplete representation. This phenomenon, known as superposition, arises when networks encode more features than available neurons, causing neurons to respond to multiple, seemingly unrelated inputs. By introducing a regularized SAE that learns from the original MLP’s activations, we can disentangle these representations, resulting in a 33% reduction in the average number of sensitive inputs per neuron. Applied to a precipitation classification model, this framework reveals evidence of monosemantic behavior in which neurons respond to a single meaningful concept tied to specific physical phenomena such as temperature and fallspeed thresholds for precipitation phase partitioning. We observe similar monosemantic behavior in SAE activations from a snowfall rate regressor related to particle concentration intensity, and vertical radar structures. This framework supports the development of more physically consistent interpretations of hidden neuron activations and improved trust in operational ML models across the geosciences.

2. Sci. Adv.

   

   Decoding global precipitation processes and particle evolution using unsupervised learning

   Fraser King, Claire Pettersen, Brenda Dolan, and 2 more authors

   Science Advances, 2025

   Abs DOI

   High-quality hydrometeor microphysical observations are essential for accurate precipitation estimates and for evaluating weather and climate models. However, analyzing these properties is challenging due to their high variability, complex interactions, and large data volumes. In this study, we examine more than 1.5 million minute-scale rain and snow particle attributes and collocated meteorological variables from seven global measurement sites over 9 years. Applying Uniform Manifold Approximation and Projection (UMAP) for nonlinear dimensionality reduction, we reduce the dataset’s dimensionality by 75%, identifying nine distinct precipitation groups and associated particle evolution pathways. UMAP effectively captures the global structure of precipitation phases such as rain, snow, and mixed-phase types, revealing clear patterns that linear methods struggle to resolve. The resulting UMAP manifold offers a unique perspective on precipitation phase and intensity, advancing our understanding of particle evolutionary processes and offering valuable insights for improving weather and climate models and remote sensing precipitation estimates. Analyzing rain and snow particles reveals hidden patterns, improving precipitation classification for better model predictions.

3. JAMC

   

   Macro- and Microphysical Properties of Atmospheric River Snowfall: Results from Two Instrument Sites

   Jack Richter, Claire Pettersen, Dmitri Moisseev, and 2 more authors

   Journal of Applied Meteorology and Climatology, 2025

   Abs DOI

   Atmospheric rivers (ARs) can produce intense snowfall events with different characteristics compared to snowfall events not associated with ARs. This study uses remotely sensed and in situ observations at multi-year instrument sites in Marquette, Michigan and Hyytiälä, Finland to comprehensively evaluate snowfall properties during ARs and compare to snowfall properties during non-AR conditions (NoAR). A snowfall classification scheme is applied at each site to find deep (large-scale) snowfall events and classify them as either AR or NoAR snowfall. To identify differences between the two regimes, reanalysis data products and ground-based observations are utilized to examine the large-scale properties, environmental conditions, and macro- and microphysical characteristics of AR and NoAR snowfall. During AR snowfall, there is a preferential large-scale setup at both instrument sites that result in enhanced water vapor transport. The AR snowfall events produce higher snowfall rates, as well as a higher number of denser and faster falling snow particles compared to NoAR snowfall.

4. JGR: ML&C

   

   Machine Learning Detection of Melting Layers From Radar Observations

   Yan Xie, Fraser King, Claire Pettersen, and 1 more author

   Journal of Geophysical Research: Machine Learning and Computation, 2025

   Abs DOI

   Melting layers in the atmosphere signify where falling ice hydrometeors melt into raindrops, and can be identified by discernible radar signatures. Accurate detection of melting layers is crucial to improving quantitative precipitation estimation, weather forecasts, microwave communication, and aviation risk assessments in a changing climate. Traditional detection algorithms based on fixed thresholds or a priori assumptions lack general robustness across diverse weather conditions, which can be addressed by leveraging machine learning techniques. This study presents a binary semantic segmentation U-Net model for automatic detection of melting layers, using Ka-band vertical profiling ground radar observations collected at the North Slope of Alaska between March 2015 and February 2016. An interactive data extraction tool, ClickCollect, has been developed to generate a labeled data set of melting layer boundaries from radar observations during all seasons. Results show that the U-Net effectively detects 96% of the melting layer cases, and is applicable to complex weather conditions including heavy precipitation with velocity folding, multiple layer melting, and near-surface melt layers. Compared to a traditional detection method, the U-Net model increases the Probability of Detection by 57% and improves the mean Dice-Sørensen coefficient from 0.69 to 0.91. Furthermore, the U-Net model provides additional information of detection uncertainty based on ensemble predictions. The U-Net model and the data extraction tool can be applied to similar profiling radar instruments in different regions of the world, contributing to an enhanced understanding of the distribution and variations of melting layers.

2024

1. AIES

   

   Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

   Fraser King, Claire Pettersen, Christopher G. Fletcher, and 1 more author

   Artificial Intelligence for the Earth Systems, Jun 2024

   Publisher: American Meteorological Society Section: Artificial Intelligence for the Earth Systems

   Abs DOI

   CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state. Significance Statement Snowfall is a critical contributor to the global water–energy budget, with important connections to water resource management, flood mitigation, and ecosystem sustainability. However, traditional spaceborne remote monitoring of snowfall faces challenges due to a near-surface radar blind zone, which masks a portion of the atmosphere. In this study, a deep learning model was developed to fill in missing data across these regions using surface radar and atmospheric state variables. The model accurately predicts reflectivity, with significant improvements over conventional methods. This innovative approach enhances our understanding of reflectivity patterns and atmospheric interactions, bolstering advances in remote snowfall prediction.

2. ESS

   

   A Comprehensive Northern Hemisphere Particle Microphysics Data Set From the Precipitation Imaging Package

   Fraser King, Claire Pettersen, Larry F. Bliven, and 19 more authors

   Earth and Space Science, Jun 2024

   Abs DOI

   Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher-order products including an approximation of volume-weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self-describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase-changes at high temporal resolution. Bulk precipitation characteristics from a multi-site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high-quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics.

3. JAS

   

   Primary Modes of Northern Hemisphere Snowfall Particle Size Distributions

   Fraser King, Claire Pettersen, Brenda Dolan, and 2 more authors

   Journal of the Atmospheric Sciences, Dec 2024

   Publisher: American Meteorological Society Section: Journal of the Atmospheric Sciences

   Abs DOI

   A comprehensive understanding of snowfall microphysics is crucial for enhancing the accuracy of remote sensing snowfall retrievals. However, variations in regional and seasonal snow particle size distributions (PSDs) contribute substantial uncertainty. Here, we examine snowfall PSDs from across the Northern Hemisphere, applying principal component analysis (PCA) to disdrometer observations with the aim of identifying dominant modes of variability across varying regional climates. The PCA revealed three empirical orthogonal functions (EOFs) that account for a combined 95% of the variability across the dataset, which are attributed to latent linear embeddings of snowfall intensity (EOF1), snowfall character (EOF2), and snowfall regime (EOF3). Examining point clusters with the highest combined EOF values reveals six distinct modes of variability [i.e., principal component (PC) groups] with unique PSD traits. These groups are then correlated with environmental factors using data from collocated vertically pointing radar, surface meteorology, and reanalysis to assist in assigning physical attributes. The first and second PC groups, linked to EOF1’s intensity embedding, are described by their PSD intercepts, snowfall rates, and reflectivity and Doppler velocity values, representing low- and high-intensity snowfall modes, respectively. The third and fourth PC groups, associated with EOF2’s character embedding, are defined by temperature, fall speed, and density, indicative of cold, fluffy snowfall and warm, dense snowfall, respectively. The fifth and sixth PC groups, related to EOF3’s regime embedding, are distinguished by their PSD slope, snowfall rate, and reflectivity profiles, signifying shallow, weak convective systems with small particles and deep, stratiform snowfall events with large aggregates, respectively. Significance Statement This research enhances our understanding of varying snow particle size distributions in the Northern Hemisphere, offering valuable new insights for improving future remote sensing–based snowfall retrieval algorithms. Using a statistical technique called principal component analysis, we found that 95% of the variability in observed snowfall could be explained by three primary features: how intense the snowfall is, how dense the particles are, and the depth of the storm. We identified six unique snowfall groups, each with its own set of traits, such as the snow being light and fluffy, or heavy and packed. By linking these traits to external environmental observations, we can better understand the driving physical mechanisms within each group.

2023

1. CRST

   

   New opportunities for low-cost LiDAR-derived snow depth estimates from a consumer drone-mounted smartphone

   Fraser King, Richard Kelly, and Christopher G. Fletcher

   Cold Regions Science and Technology, Mar 2023

   Abs DOI

   Snow has significant impacts on springtime flooding, water resource management practices and the regional water-energy budget. In situ observations are considered some of the highest quality measurements of snow depth available, and are useful constraints for numerical weather prediction models and reanalysis system estimates of snow water equivalent. The application of laser altimetry (LiDAR-Light Detection and Ranging) for measuring snow depth has proven an effective method for quickly observing large areas, however this technique is expensive to perform due to the high cost of the necessary equipment coupled with required operator training. In this work, we examine the capabilities of the iPhone 12 Pro LiDAR (iLiDAR) when attached to a consumer-grade DJI Phantom 4 quadcopter in estimating snow depth at three study sites in southern Ontario, Canada. Initial comparisons between drone iLiDAR depth estimates and collocated snow ruler measurements demonstrate good agreement, with a root-mean-square error of 3 cm and absolute mean error of 2.5 cm. The intersection of these two technologies defines a novel, low-cost alternative to traditional LiDAR-based snow depth measurement systems, while maintaining a high level of observational accuracy and precision.

2022

1. GRSL

   

   Evaluation of LiDAR-Derived Snow Depth Estimates From the iPhone 12 Pro

   Fraser King, Richard Kelly, and Christopher G. Fletcher

   IEEE Geoscience and Remote Sensing Letters, Mar 2022

   Abs DOI

   Snow is a critical contributor to the global water-energy budget with impacts on springtime flooding and water resource management practices. Laser altimetry [light detection and ranging (LiDAR)] is a remote-sensing technique that has demonstrated skill in monitoring snow depth, but the expense of purchasing and transporting traditional LiDAR equipment limits their operational use. In this work, we demonstrate that the LiDAR sensor installed on the Apple iPhone 12 Pro consumer smartphone is a real-time, handheld measurement instrument for accurately observing changes in snow depth. Two independent field experiments in Southern Ontario, Canada, found that the iPhone LiDAR was able to accurately capture daily changes in snow depth when compared to in situ snow ruler measurements. In situ and LiDAR comparisons of xs n=75 days at measurement site A exhibit a correlation of r \textgreater 0.99 , mean absolute bias less than 1 mm, and a root mean squared error (RMSE) of approximately 6 mm. A similar positive agreement was also noted at the second field study site for n=16 measurements over the same period. The high accuracy of the LiDAR sensor suggests that a mobile application could be developed which allows users to quickly scan a snow-covered area before and after a snowfall event and consequently use this data to aid in filling current observational gaps through a citizen-science-based approach to measuring changes in snow depth.

2. JAMES

   

   Toward Efficient Calibration of Higher-Resolution Earth System Models

   Christopher G. Fletcher, William McNally, John G. Virgin, and 1 more author

   Journal of Advances in Modeling Earth Systems, Mar 2022

   Abs DOI

   Projections of future climate change to support decision-making require Earth system models (ESMs) running at high spatial resolution, but at present this is computationally prohibitive. A major challenge is the calibration (parameter tuning) during the development of ESMs, which requires running large numbers of simulations to identify optimal values for parameters that are poorly constrained by observations. Here, we train a convolutional neural network (CNN) to emulate perturbed parameter ensembles from two lower-resolution (and thus much less expensive) versions of the same ESM, and a smaller number of higher-resolution simulations. Cross-validated results show that the CNN’s skill exceeds that of a climatological baseline for most variables with as few as 5–10 examples of the higher-resolution ESM, and for all variables (including precipitation) with at least 20 examples. This proof-of-concept study demonstrates a machine learning based approach that makes the process of constructing a higher-resolution emulator 20%–40% more computationally efficient, and thus offers the prospect of significantly more efficient calibration of ESMs.

3. AMT

   

   DeepPrecip: a deep neural network for precipitation retrievals

   Fraser King, George Duffy, Lisa Milani, and 3 more authors

   Atmospheric Measurement Techniques, Oct 2022

   Publisher: Copernicus GmbH

   Abs DOI

   Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval algorithm from a deep convolutional neural network (DeepPrecip) to estimate 20 min average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays a high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 160 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5–2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.

4. JAMC

   

   A Centimeter-Wavelength Snowfall Retrieval Algorithm Using Machine Learning

   Fraser King, George Duffy, and Christopher G. Fletcher

   Journal of Applied Meteorology and Climatology, Aug 2022

   Publisher: American Meteorological Society Section: Journal of Applied Meteorology and Climatology

   Abs DOI

   Remote sensing snowfall retrievals are powerful tools for advancing our understanding of global snow accumulation patterns. However, current satellite-based snowfall retrievals rely on assumptions about snowfall particle shape, size, and distribution that contribute to uncertainty and biases in their estimates. Vertical radar reflectivity profiles provided by the vertically pointing X-band radar (VertiX) instrument in Egbert, Ontario, Canada, are compared with in situ surface snow accumulation measurements from January to March 2012 as a part of the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx). In this work, we train a random forest (RF) machine learning model on VertiX radar profiles and ERA5 atmospheric temperature estimates to derive a surface snow accumulation regression model. Using event-based training–testing sets, the RF model demonstrates high predictive skill in estimating surface snow accumulation at 5-min intervals with a low mean-square error of approximately 1.8 × 10−3 mm2 when compared with collocated in situ measurements. The machine learning model outperformed other common radar-based snowfall retrievals (Ze–S relationships) that were unable to accurately capture the magnitudes of peaks and troughs in observed snow accumulation. The RF model also displayed consistent skill when applied to unseen data at a separate experimental site in South Korea. An estimate of predictor importance from the RF model reveals that combinations of multiple reflectivity measurement bins in the boundary layer below 2 km were the most significant features in predicting snow accumulation. Nonlinear machine learning–based retrievals like those explored in this work can offer new, important insights into global snow accumulation patterns and overcome traditional challenges resulting from sparse in situ observational networks.

2021

1. ESS

   

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

   Fraser King and Christopher G. Fletcher

   Earth and Space Science, Aug 2021

   Abs DOI

   Changes in the quantity of terrestrial Arctic snow have far-reaching implications for the global water-energy budget, ecosystem development, and cold region flooding. Snow water equivalent (SWE) is a useful metric for monitoring these changes, however only a sparse observing network is available in the Arctic. Space-based remote sensing offers the potential to fill these measurement gaps, and the CloudSat cloud profiling radar (CPR) has been shown to provide high-quality estimates of surface snowfall rates across the Arctic. We propose a novel method to leverage monthly information from CloudSat to identify data quality issues in the Blended-4 (B4) gridded SWE product. A regression of estimated monthly mean snow accumulation between CloudSat and B4 provides a confidence interval to objectively flag individual months in B4 with data quality issues. Applying this method to a case study in the Canadian high Arctic identifies an unphysical January melt event in B4 which is traced back to a measurement error in the assimilated station observations. We generalize this technique to a one-degree grid and find a total of 4,885 cases in B4 with low data quality. We find that the low-quality SWE product values are not random errors, and that they introduce a systematic bias of −3.1 mm on B4’s estimates of mean Arctic SWE. CloudSat is uniquely positioned as one of the only observational data sets for observing snowfall at high latitudes, and when combined with the methodology developed here, its estimates can be used to further enhance the accuracy of current gridded SWE products.

2. Atmosphere

   

   Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

   George Duffy, Fraser King, Ralf Bennartz, and 1 more author

   Atmosphere, Mar 2021

   Number: 3 Publisher: Multidisciplinary Digital Publishing Institute

   Abs DOI

   CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

2020

1. HESS

   

   Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

   Fraser King, Andre R. Erler, Steven K. Frey, and 1 more author

   Hydrology and Earth System Sciences, Oct 2020

   Publisher: Copernicus GmbH

   Abs DOI

   Snow is a critical contributor to Ontario’s water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

2. ESS

   

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

   Fraser King and Christopher G. Fletcher

   Earth and Space Science, Oct 2020

   Abs DOI

   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.

2019

1. Waterloo

   

   Validating CloudSat-CPR retrievals for the estimation of snow accumulation in the Canadian Arctic

   Fraser King

   May 2019

   Publisher: University of Waterloo

   Abs DOI

   Snow is a critical contributor to our global water and energy budget, with profound impacts for water resource availability, snow albedo feedback 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 (CPR) instrument-installed on the NASA 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 (ECCC) 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 SWE annually. Monthly time series exhibit correlations above 0.5 and RMSE below 10 mm SWE at the two highest latitude stations (Eureka and Resolute Bay) with correlations falling below 0.5 south of 70 degrees N. CloudSat was also found to underestimate annual mean snow accumulation at the majority of sites, suggesting a potential negative bias in CloudSat’s snowfall 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 degrees N. Accurate space-based snowfall measurements provide new important observational perspectives of Arctic snow accumulation, which is a critical region for environmental monitoring in an era of global change.