Validation of NOAA-Interactive Multisensor Snow and Ice Mapping System (IMS) by Comparison with Ground-Based Measurements over Continental United States

In this study, daily maps of snow cover distribution and sea ice extent produced by NOAA’s interactive multisensor snow and ice mapping system (IMS) were validated using in situ snow depth data from observing stations obtained from NOAA’s National Climatic Data Center (NCDC) for calendar years 2006 to 2010. IMS provides daily maps of snow and sea ice extent within the Northern Hemisphere using data from combination of geostationary and polar orbiting satellites in visible, infrared and microwave spectrums. Statistical correspondence between the IMS and in situ point measurements has been evaluated assuming that ground measurements are discrete and continuously distributed over a 4 km IMS snow cover maps. Advanced Very High Resolution Radiometer (AVHRR) land and snow classification data are supplemental datasets used in the further analysis of correspondence between the IMS product and in situ measurements. The comparison of IMS maps with in situ snow observations conducted over a period of four years has demonstrated a good correspondence of the data sets. The daily rate of agreement between the products mostly ranges between 80% and 90% during the Northern Hemisphere through the winter seasons when about a quarter to one third of the territory of continental US is covered with snow. Further, better agreement was observed for stations recording higher snow depth. The uncertainties in validation of IMS snow product with stationed NCDC data were discussed

A comparison of Normalised Difference Snow Index (NDSI) and Normalised Difference Principal Component Snow Index (NDPCSI) techniques in distinguishing snow from related land cover types

Snow is a common global meteorological phenomenon known to be a critical component of the hydrological cycle and an environmental hazard. In South Africa, snow is commonly limited to the country’s higher grounds and is considered one of the most destructive natural hazards. As a result, mapping of snow cover is an important process in catchment management and hazard mitigation. However, generating snow maps using survey techniques is often expensive, tedious and time consuming. Within the South African context, field surveys are therefore not ideal for the often highly dynamic snow covers. As an alternative, thematic cover–types based on remotely sensed data-sets are becoming popular. In this study we hypothesise that the reduced dimensionality using Principal Components Analysis (PCA) in concert Normalized Difference Snow Index (NDSI) is valuable for improving the accuracy of snow cover maps. Using the recently launched 11 spectral band Landsat 8 dataset, we propose a new technique that combines the principal component imager generated using PCA with commonly used NDSI, referred to as Normalised Difference Principal Component Snow Index (NDPCSI) to improve snow mapping accuracy. Results show that both NDPCSI and NDSI with high classification accuracies of 84.9% and 76.8% respectively, were effective in mapping snow. Results from the study also indicate that NDSI was sensitive to water bodies found on lower grounds within the study area while the PCA was able to de-correlate snow from water bodies and shadows. Although the NDSI and NDPCSI produced comparable results, the NDPCSI was capable of mapping snow from other related land covers with better accuracy. The superiority of the NDPCSI can particularly be attributed to the ability of principal component analysis to de-correlate snow from water bodies and shadows. The accuracy of both techniques was evaluated using a higher spatial resolution Landsat 8 panchromatic band and Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired on the same day. The findings suggest that NDPCSI is a viable alternative in mapping snow especially in heterogeneous landscape that includes water bodies

Assimilation of AMSR-E snow water equivalent data in a lumped hydrological model

Snow cover is a significant component of the hydrological cycle affecting stream discharge through snowmelt and soil moisture. Current operational streamflow forecasting is prone to error due to input data uncertainties and model biases, making it difficult to accurately forecast discharge during snow melt events. Data assimilation is a technique of weighting model estimates and observations based on uncertainties that allows optimal estimation of model states. In this study, we assimilate snow water equivalent (SWE) data from the Advance Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument into a conceptual temperature index snow model, the US National Weather Service (NWS) SNOW17 model. This model is coupled with the NWS Sacramento Soil Moisture Accounting (SAC-SMA) model, which ultimately produces stream discharge. The objective of this study is to improve the SNOW17 estimate of SWE by integrating SWE observations and uncertainties associated with meteorological forcing data within the model. For the purpose of this study, 25 km AMSR-E SWE data is used. An ensemble Kalman filter (EnKF) assimilation framework performs assimilation on a daily cycle for a 6 year period, water years 2006-2011. This method is tested on seven watersheds in the Upper Mississippi River basin that are under the forecasting jurisdiction of the NWS North Central River Forecasting Center (NCRFC). Prior to assimilation, AMSR-E data is bias corrected using data from the National Operational Hydrologic Remote Sensing Center (NOHRSC) airborne snow survey program. Discharge output from the SAC-SMA is verified using observed discharge from the outlet of each study site. Improvements in discharge are evident for five sites, in particular for high discharge magnitudes associated with snow melt runoff. Evidence points to the SNOW17 having a consistent SWE underestimation bias and error in snow melt rate. Overall results indicate that the EnKF is a viable and effective solution for integrating observations directly with operational models

A comparison of AMSR-E/Aqua snow products with in situ observations and MODIS snow cover products in the Mackenzie River Basin, Canada

Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions of topography and of land cover type because landscape characteristics have a strong influence on redistribution and physical properties of snow cover, and influence the microwave properties of the surface. Here we evaluate the AMSR-E SWE and derived snow map products in the Mackenzie River Basin (MRB), Canada, which is characterized by complex topography and varying land cover types from tundra to boreal forest. We compare in situ snow depth observations and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps from January 2003 to December 2007 with passive microwave remotely sensed SWE from AMSR-E and derived snow cover maps. In the MRB the mean absolute error ranges from 12 mm in the early winter season to 50 mm in the late winter season and overestimations of snow cover maps based on a 1 mm threshold of AMSR-E SWE varies from 4% to 8%. The optimal threshold for AMSR-E SWE to classify the pixels as snow ranges from 6 mm to 9 mm. The overall accuracy of new snow cover maps from AMSR-E varies from 91% to 94% in different sub-basins in the MRB. © 2010 by the authors

A comparison of AMSR-E/Aqua snow products with in situ observations and MODIS snow cover products in the Mackenzie River Basin, Canada

Since 2002, global snow water equivalent (SWE) estimates have been generated using Advanced Microwave Scanning Radiometer (AMSR-E)/Aqua data. Accurate estimates of SWE are important to improve monitoring and managing of water resources in specific regions. SWE and snow map product accuracy are functions of topography and of land cover type because landscape characteristics have a strong influence on redistribution and physical properties of snow cover, and influence the microwave properties of the surface. Here we evaluate the AMSR-E SWE and derived snow map products in the Mackenzie River Basin (MRB), Canada, which is characterized by complex topography and varying land cover types from tundra to boreal forest. We compare in situ snow depth observations and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover maps from January 2003 to December 2007 with passive microwave remotely sensed SWE from AMSR-E and derived snow cover maps. In the MRB the mean absolute error ranges from 12 mm in the early winter season to 50 mm in the late winter season and overestimations of snow cover maps based on a 1 mm threshold of AMSR-E SWE varies from 4% to 8%. The optimal threshold for AMSR-E SWE to classify the pixels as snow ranges from 6 mm to 9 mm. The overall accuracy of new snow cover maps from AMSR-E varies from 91% to 94% in different sub-basins in the MRB. © 2010 by the authors

DETECTION OF RAIN-ON-SNOW EVENTS AND ITS IMPACT ON PASSIVE MICROWAVE-BASED SNOW WATER EQUIVALENT RETRIEVAL

Rain-on-snow (ROS) events can impact snow stratigraphy via generation of wet snow and ice crust(s) within the snowpack. Considering the assumptions of most passive microwave-based snow water equivalent (SWE) retrievals, which include a dry and homogenous snowpack, ROS events could significantly impact SWE retrieval accuracy. This study explored the feasibility of various approaches to detect ROS events using multiple data types (i.e., satellite observations, model output, and in-situ measurements). Agreement in ROS events detected varied among the different data types. Only ~10% of suspected ROS events were flagged using the satellite-based algorithm. Alternatively, ~50% of suspected ROS events were flagged using the model-based algorithm, whereas ~40% of suspected ROS events were flagged using the in-situ measurements-based algorithm. Findings were unable to speak to the impact of ROS events on SWE retrieval accuracy due to the lack of in-situ SWE measurements; however, a slight pattern in local fluctuations was observed