Accurate monitoring of the large-scale dimensions of global snow cover is essential for understanding details of climate dynamics and climate change. Presently, such information is gathered individually from ground station networks and satellite platforms. Efforts are in progress to consolidate and analyze long-term station records from a number of countries. To gain truly global coverage, however, satellite-based monitoring techniques must be employed. A 27-year record of Northern Hemisphere continental snow cover produced by the National Oceanic and Atmospheric Administration (NOAA) is the longest such environmental record available. Records of Southern Hemisphere continental cover and snow on top of Arctic sea ice have been produced by similar means for a portion of this interval. The visible imagery charting technique used to generate these data provides information on snow extent but not on snow volume. Satellite microwave analyses over Northern Hemisphere lands show some promise in this regard, howe...

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Global Snow Cover Monitoring: An Update

The operational analysis of snow depth at the Canadian Meteorological Centre is described. The analysis makes use of forecasts of precipitation and analyses of screen-level temperature to estimate snowfall and snowmelt for a global domain, and assumes persistence of the mass of the snowpack between melting and/or snowfall events. In addition, wherever snow depth observations are available, these are incorporated using the method of statistical interpolation, performed every 6 h on a ⅓° grid. Correlations between two observations or between observations and grid points are taken as functions of spatial separation in both the horizontal and vertical with an e-folding distance of 120 km in the horizontal and 800 m in the vertical. Observations undergo three separate tests designed to eliminate 1) false reports of snow when none is present, 2) systematically understated snow depths, and 3) reports that violate temporal continuity. Verification of the analysis is presented using three sources of indep...

A Global Analysis of Snow Depth for Numerical Weather Prediction

Mapping dry/wet snow cover in the Indian Himalayas using IRS multispectral imagery

SMOS-IC data record of soil moisture and L-VOD: Historical development, applications and perspectives

Recent estimates indicate that the Antarctic sea ice cover is expanding at a statistically significant rate with a magnitude one-third as large as the rapid rate of sea ice retreat in the Arctic. However, during the mid-2000s, with several fewer years in the observational record, the trend in Antarctic sea ice extent was reported to be considerably smaller and statistically indistinguishable from zero. Here, we show that much of the increase in the reported trend oc- curred due to the previously undocumented effect of a change in the way the satellite sea ice observations are processed for the widely used Bootstrap algorithm data set, rather than a physical increase in the rate of ice advance. Specifically, we find that a change in the intercalibration across a 1991 sensor transition when the data set was reprocessed in 2007 caused a substantial change in the long-term trend. Although our anal- ysis does not definitively identify whether this change intro- duced an error or removed one, the resulting difference in the trends suggests that a substantial error exists in either the current data set or the version that was used prior to the mid- 2000s, and numerous studies that have relied on these ob- servations should be reexamined to determine the sensitivity of their results to this change in the data set. Furthermore, a number of recent studies have investigated physical mech- anisms for the observed expansion of the Antarctic sea ice cover. The results of this analysis raise the possibility that much of this expansion may be a spurious artifact of an error in the processing of the satellite observations.

A Spurious Jump in the Satellite Record: Has Antarctic Sea Ice Expansion Been Overestimated?

The NASA-sponsored Calibrated Passive Micro- wave Daily Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record Project team has generated a multisensor, multidecadal time series of high-resolution radiometer products designed to support climate studies. This project uses image reconstruction techniques to generate conventional and enhanced-resolution daily brightness temperature images on a standard set of map projections. Sensors included in CETB are the Aqua Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E), Scanning Multichannel Microwave Radiometer, and all Special Sensor Microwave/Imager and Special Sensor Microwave Imager/Sounder radiometers. These span frequencies between 6 and 89 GHz. This paper considers the issues of adding the L-band (1.6 GHz) Soil Moisture Active Passive (SMAP) radiometer measurements to the CETB climate record, with emphasis on optimizing the reconstruction to provide the highest possible spatial resolution at the lowest noise level. SMAP radiometer reconstruction on SMAP-standard grids is also considered. Simulation is used to optimize the reconstruction, and the results confirmed using actual data. A comparison of the performance of the Backus–Gilbert approach and the radiometer form of the Scatterometer Image Reconstruction algorithm is provided. These are compared to the conventional drop-in-the-bucket gridded imaging.

Enhanced-Resolution SMAP Brightness Temperature Image Products

Since the late 1970s, satellite passive-microwave brightness temperatures have been a mainstay in remote sensing of the cryosphere. Polar snow and ice-covered ocean and land surfaces are especially sensitive to climate change and are observed to fluctuate on interannual to decadal timescales. In regions of limited sunlight and cloudy conditions, microwave measurements are particularly valuable for monitoring snow- and ice-covered ocean and land surfaces, due to microwave sensitivity to phase changes of water. Historically available at relatively low resolutions (25 km) compared to optical techniques (less than 1 km), passive-microwave sensors have provided short-timescale, large-area spatial coverage, and high temporal repeat observations for monitoring hemispheric-wide changes. However, historically available gridded passive microwave products have fallen short of modern requirements for climate data records, notably by using inconsistently-calibrated input data, including only limited periods of sensor overlaps, employing image-reconstruction methods that tuned for reduced noise rather than enhanced resolution, and using projection and grid definitions that were not easily interpreted by geolocation software. Using a recently completed Fundamental Climate Data Record of the swath format passive-microwave record that incorporated new, cross-sensor calibrations, we have produced an improved, gridded data record. Defined on the EASE-Grid 2.0 map projections and derived with numerically efficient image-reconstruction techniques, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) increases spatial resolution up to 3.125 km for the highest frequency channels, and satisfies modern Climate Data Record (CDR) requirements as defined by the National Research Council. We describe the best practices and development approaches that we used to ensure algorithmic integrity and to define and satisfy metadata, content and structural requirements for this high-quality, reliable, consistently gridded microwave radiometer climate data record.

Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness Temperature Earth System Data Record

A snow model that supports the daily, operational analysis of global snow depth and age has been developed. It provides improved spatial interpolation of surface reports by incorporating digital elevation data, and by the application of regionalized variables (kriging) through the use of a global snow depth climatology. Where surface observations are inadequate, the model applies satellite remote sensing. Techniques for extrapolation into data-void mountain areas and a procedure to compute snow melt are also contained in the model.

Monitoring Global Snow Cover

Mapping the spatial extent of recently identified englacial hydrological features (i.e., ice slabs and perennial firn aquifers) formed by meters-thick water-saturated firn layers over the percolation facies of the Greenland Ice Sheet using L-band microwave radiometry has recently been demonstrated. However, these initial maps are binary, and do not provide a parameter to estimate the spatial variability in the thickness and volumetric fraction of meltwater stored within the firn pore space. Here, we exploit enhanced-resolution vertical-polarization L-band brightness temperature ($T_{V}^{B}$) imagery (2015–2019) generated using observations collected over Greenland by NASA’s Soil Moisture Active Passive (SMAP) satellite and a simple two-layer L-band brightness temperature model. We map water-saturated firn layers via a “firn saturation” parameter, and interpret our results together with ice slab and perennial firn aquifer spatial extents, estimates of snow accumulation simulated via the Regional Atmospheric Climate Model (RACMOp2.3), and airborne radar surveys collected via NASA’s Operation IceBridge (OIB) campaigns. We find that variable firn saturation parameter values are mapped in lower snow accumulation ice slab areas in western, northern, and northeastern Greenland, where firn is colder and water-saturated firn layers seasonally refreeze as solid-ice. Higher firn saturation parameter values are mapped in higher snow accumulation perennial firn aquifer areas in southeastern, southern, and northwestern Greenland, where firn is near the melting point, and meters-thick water-saturated firn layers exist. Our results have implications for identifying expansive englacial reservoirs that store significant volumes of meltwater in locations that are vulnerable to meltwater-induced hydrofracturing and accelerated outlet glacier flow year-round.

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Mapping Firn Saturation Over Greenland Using NASA’s Soil Moisture Active Passive Satellite

Since 1978, a series of Earth-observing, satellite-borne passive microwave sensors has produced a rich record of microwave brightness temperatures. Passive microwave sensors can see through most clouds and collect measurements both day and night, which is especially useful in high latitudes during polar night. Passive microwave measurements are used to derive significant and meaningful climate records of many parameters, including the dramatic decline in Arctic sea ice. Earlier revisions of this significant climate record have been produced as flat, binary, gridded arrays with minimal or no file-level metadata and no machine-readable geolocation. Developed for many applications in polar regions, data were projected to polar azimuthal and global cylindrial projections that users found difficult to handle in standard mapping software packages. Funded by the NASA MEaSUREs program, we are using state-of-the-art image reconstruction techniques to produce a Calibrated Enhanced-Resolution Passive Microwave Equal-Area Scalable Earth Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) that leverages the improved EASE-Grid 2.0 projection definitions and netCDF-CF metadata conventions to improve usability of the data products. We describe our approach to defining file-level metadata that is intelligible to standard software packages, including open source netCDF Operators (NCO) and Geospatial Data Abstraction Library (gdal), and the commercial ESRI ArcMap geospatial mapping tool.

M. Hardman

Papers

Enhanced-Resolution SMAP Brightness Temperature Image Products

Best Practices in Crafting the Calibrated, Enhanced-Resolution Passive-Microwave EASE-Grid 2.0 Brightness Temperature Earth System Data Record

Monitoring Global Snow Cover

Mapping Firn Saturation Over Greenland Using NASA’s Soil Moisture Active Passive Satellite

Leveraging metadata conventions to improve usability of an ease-grid 2.0 passive microwave data product