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C55B-0591 Building Trusted Satellite Passive Microwave Data Sets: New Advancements in Calibrated, Enhanced-Resolution Brightness Temperatures for Cryospheric Applications

Molly A. Hardman¹², Mary J. Brodzik¹², David G. Long³, Julie Z. Miller², Joan M. Ramage⁴

¹National Snow & Ice Data Center, ²CIRES, University of Colorado at Boulder, ³MERS Laboratory, Brigham Young University, ⁴Lehigh University

About the CETB Data

Calibrated, Enhanced-Resolution Daily EASE-Grid 2.0 Passive Microwave Brightness Temperatures (CETB) montage of cylindrical and Northern and Southern Hemisphere azimuthal projections.

Collected from passive microwave sensors observing the Earth since 1978, brightness temperatures are used to study many important geophysical variables, including the dramatic recent decline in sea ice concentrations. The Calibrated, Enhanced-Resolution Brightness Temperatures (CETB) data sets (Brodzik et al., 2016; 2019) provide gridded passive microwave images at conventional and enhanced spatial resolutions up to 3.125 km, now with daily, near real-time updates for currently operating SSMIS and SMAP observations.

Adhering to FAIR (Findable, Accessible, Interoperable and Reusable) Data Principles (Wilkinson et al., 2016), the CETB data comprise over 60 TB and more than 4 million files. Using FAIR principles improves the ability of humans and our machines to find and understand the data we produce, and to trust in the science derived from this ongoing climate record.

EASE-Grid 2.0 AMSR-E 36 GHz vertically-polarized, evening pass brightness temperatures, 31 Mar 2011. Images showing conventional, coarse resolution data at 25 km (left) and enhanced-resolution data at 3.125 km (right). GSHSS coastlines in green (Wessel and Smith, 2015).

Originally funded by NASA MEaSUREs, our project reprocessed the long record of satellite passive microwave gridded brightness temperatures:

With the latest Level 2 cross-sensor calibrations (Berg et al., 2018) in EASE-Grid 2.0 projections (Brodzik et al., 2012; 2014)
Using image reconstruction to enhance spatial resolutions (Long and Brodzik, 2016)
As a high quality Earth System Data Record (Brodzik et al., 2018)

With additional funding from NASA, NOAA and the Department of Defense, we have extended processing to include SMAP; we now produce daily, near real-time SSMIS and SMAP images. We will be including AMSR2 CETB images in early 2022.

AMSR-E 36 GHz horizontally-polarized CETB conventional-resolution, low-noise, 25 km GRD (center) vs. enhanced-resolution, 3.125 km rSIR (right) near Vancouver Island, North America, 20 Feb 2003, with elevation from SRTM DEM (left). GSHSS coastlines in green (Wessel and Smith, 2015). Note that rSIR image reconstruction is resolving elevation-related variability.

The radiometer version of Scatterometer Image Reconstruction (rSIR) transforms radiometer data from swath to gridded format (Long and Brodzik, 2016). Using rSIR, we produce the EASE-Grid 2.0 CETB product at enhanced resolutions up to 3.125 km in addition to conventional, low-noise, low-resolution gridded images (denoted GRD) at 25 km. For GRD and rSIR images, the effective gridded image resolution depends on the number of input measurements for each pixel and the precise details of measurement overlap, orientation and spatial location.

Gridding techniques with the lowest noise factors take the average of all measurements whose locations fall inside the gridded pixel area, producing smooth but relatively coarse-resolution output (left). Compare to image reconstruction for resolution enhancement (right), which takes advantage of oversampled information in the overlapping brightness temperatures footprints to deduce higher-resolution gridded images.

In this poster, we discuss methods to estimate the effective resolution enhancement of CETB data. We include selected cryospheric applications of the enhanced-resolution data: to map the extent of ice sheet firn aquifers and to estimate melt onset timing. To demonstrate CETB data interoperability, we include a movie of CETB data, comparing conventional spatial resolution with the enhanced-resolution improvements now possible.

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Effective Resolution Enhancement

20-day average of daily SMAP V-pol TB images over the study area spanning day-of-year 91-110, 2015 with a coastline overlay, GRD (25 km, left) and rSIR (3.125 km, right). Note the apparent offset of the island in the GRD, which results from the coarse resolution. Thick horizontal lines indicate data transect locations extracted for analysis, designated "island crossing" (black) and "coastline crossing"(red) cases.

Numerical simulation can be used (Long and Brodzik, 2016; Long et al., 2019) to determine actual resolution enhancement. An alternative method (Long et al., 2021) defines effective image resolution as the 3 dB width of the pixel spatial response function (PSRF) as it is derived from actual CETB data, rather than from numerical modeling.

Data transects are selected that span large brightness temperature gradients from open ocean to land, including areas of sea ice and small islands. Actual resolution enhancement varies by sensor, frequency and latitude. Sensor variations are a function of frequency and the size of effective field of view. Latitude variations are a function of swath overlap patterns, with predictable improvements in resolution enhancement at higher latitudes, where data from multple swaths are available.

Plots of TB along the two transect lines, for coast-crossing (left) and island-crossing cases. Daily conventional-resolution GRD and enhanced-resolution rSIR TBs (thin lines) and 20-day averages (thick lines) are plotted, with modeled step (cyan) and Gaussian values (green).

Derived single-pass rSIR and GRD pixel spatial response functions (PSRFs) from the coast-crossing (left) and island-crossing (right) cases. Shown for comparison are a 36-km wide Gaussian window and a 36-km wide sinc function, which represents the ideal GRD pixel PSRF. The horizontal dashed line corresponds to the -3 dB line. The effective 3-dB linear resolution is the width of the PSRF at this line.

We compared expected daily modeled and measured signal samples along the two transects. As expected, we see a steeper transition from enhanced-resolution rSIR compared to conventional-resolution GRD at the land-ocean coastlines. The corresponding modelled and measured PSRFs can be derived, with the effective resolution estimated as the distance along the -3 dB threshold.

Similar analyses with CETB images from SMMR, SSM/I, SSMIS, AMSR-E and SMAP (Long et al., 2021) demonstrate that the effective resolution of the images is coarser than the posting (pixel spacing) resolution for both conventionally processed (GRD) and enhanced resolution (rSIR) images. The rSIR PSRF demonstrates a 30-60% improvement in effective resolution for rSIR compared with GRD, depending on sensor and frequency.

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Mapping Ice Sheet Firn Aquifers

Perennial firn aquifer (blue shading), ice slab (cyan shading), and percolation facies (purple shading) extents (2015-2019) derived from SMAP CETB images, overlaid (left) on the 2015 MODIS Mosaic of Greenland (MOG) image map (Haran et al., 2018). SMAP-derived extents are overlaid (right) with airborne radar-derived perennial firn aquifer, ice slab and spatially coherent melt layer detections along OIB flight lines (Miller et al., 2021).

Recent research has demonstrated that perennial firn aquifers and ice slabs significantly affect the mass balance and overall stability of the Greenland Ice Sheet. Until now, maps of these areas have been limited to locations of airborne ice-penetrating radar. Using SMAP CETB enhanced-resolution L-band brightness temperature imagery and an empirical algorithm calibrated using NASA’s Operation IceBridge campaigns, Miller et al. (2021) identify perennial firn aquifer and ice slab areas, demonstrating the first mapping of these areas from space.

As the Greenland climate continues to warm, we will need to quantify the possible rapid expansion of perennial firn aquifers and ice slabs. This research improves our understanding of ice sheet-wide variability in englacial firn hydrology that contributes to meltwater-induced hydrofracturing and accelerated ice flow, as well as high-elevation meltwater runoff.

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Deriving Melt Onset

Hunza subbasin (green) of Upper Indus, AMSR-E 37 GHz vertically-polarized brightness temperatures (TBs), 2004 day 164, gridded conventionally at 25 km (left) compared to image reconstruction at 3.125 km, to enhance spatial resolution (right). Middle image shows elevation for same region for comparison. Image reconstruction technique clearly produces reasonable TB variation due to elevation and surface types, with colder (darker) TBs in higher-elevation (lighter) ice-covered areas. All images include RGI v6 glacier outlines, (blue, RGI Consortium, 2017) and met station locations (yellow) at Naltar (elevation 2898 m), Ziarat (3020 m) and Khunjerab (4440 m) and Hunza River stream gauge at Dainyor Bridge (1370 m).

Passive microwave detection of melt onset is based on time series evaluation of 18/19 and/or 36/37 GHz TBs. The signal is sensitive to changes in near-surface liquid water that characterize melt onset, melt intensification and refreezing. Conventional-resolution (25 km) TB grids have successfully detected snow melt states in seasonal snow and glacial environments at high latitudes (Semmens and Ramage, 2014). However, due to mixed pixels at such coarse resolutions, analysis has been impractical in glacier-marginal zones and in regions near water bodies.

We use AMSR-E CETB data and meteorological station data to assess glacier and snow melt time series in the Hunza River (basin area 13,733 km², 36N, 74E), a tributary to the Upper Indus River, Pakistan. In this region, the enhanced resolution rSIR data are still coarser than the topography, but can differentiate melt and refreeze timing for different altitudes and land cover in this remote area with snow and glacier melt as significant hydologic variables. While retaining twice daily observations, the improved spatial resolution CETB images, enhanced to ~3-6 km, are key to better analysis of snowpack melt characteristics in remote mountainous regions.

AMSR-E TB, XPGR (Abdalati and Steffen, 1997) and DAV (Semmens and Ramage, 2014) time series at glacier pixels compared to nearby station surface temperatures at Ziarat, and measured daily discharge at Dainyor Bridge gauge, 2003. For this basin, 36V GHz DAVs demonstrate larger amplitude fluctuations than at 18H GHz. At this latitude, there are more temporal gaps in DAV compared to XPGR, resulting in greater uncertainty in melt onset timing.

Analysis using the enhanced-resolution rSIR images is proving successful at evaluating melt timing across boundaries and transition zones that had previously been impossible.

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Conclusions & Data Updates

CETB passive microwave sensor timelines. Sensors indicated in color are currently included in CETB (AMSR2 is scheduled to be included in early 2022). Labels ending with ">>" indicate that sensor operation is ongoing, with daily, near real-time CETB processing ongoing.

Enhanced-resolution CETB images are being used to derive new insights about cryospheric phenomena than would have been possible with conventional-resolution images. We continue to extend the original record of CETB data with:

SMAP CETB Version 2 (Brodzik et al., 2021), using the latest calibrated SMAP L1B data from the SMAP Science Team, data through December 2020 available from NSIDC DAAC (nsidc-0738)
Daily, near real-time SMAP CETB V2 is produced at NSIDC, available by request from [email protected] (annual batches will be migrated to the NSIDC DAAC each year in January)
Daily, near real-time CETB images are now produced for ongoing measurements from DMSP-F16, -F17 and -F18 and will be distributed by the NSIDC DAAC beginning early 2022 (nsidc-0630)
We are adding AMSR2 measurements to the CETB sensors, planned for near real-time distribution in early 2022

In late 2022, we are planning a complete reprocessing of CETB data, using newly cross-calibrated JPSS L1C inputs from AMSR-E, AMSR2 and all SSM/I-SSMIS sensors (Berg et al., 2018). CETB V2 data is expected to be distributed in 2023.

Effective resolution enhancement for the rSIR CETB images is demonstrated to be 30-60% better than conventional gridding methods. The rSIR technique leverages oversampled measurement information to produce passive microwave images that are providing new insights in regions and times with large temperature gradients.

The user community for the original CETB and SMAP CETB data is growing. In addition to the examples included here for firn aquifer mapping and melt onset, new research has recently been funded at NSIDC to use the CETB data to improve operational sea ice motion algorithms.

For more information on our project, including schedules for new data production, and current scientific applications of the data, please refer to our PMESDR project site. If you make use of our data in your research, please cite the data set completely, including the DOI, to ensure that our citation statistics are accurate and complete (Vannan et al., 2010). Notify us of your publications ([email protected]) so that we can maintain an active bibliography of our user community.

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CETB Data Interoperability

Movie of CETB images, zoomed to Alaska. DMSP-F17 SSMIS 37 GHz, horizontally-polarized, evening-pass brightness temperatures, calendar year 2015, gridded conventionally at 25 km (left) compared to enhanced-resolution rSIR image reconstruction at 3.125 km (right).

Movies of CETB Image Time Series

Using best practices for CETB file format and CF metadata standards allows many standard software packages to work with the data to overlay useful geolocation. The movie shown here was produced with the following steps:

Use python wrappers for netCDF Command-Line Operators (NCO) tools to convert daily netCDF files to 3-D netCDF cubes, with spatial subsets of the Alaska region, concatenated along the time axis for a morning-evening series of brightness temperatures for the calendar year 2015; one cube file is created for 25 km series, and another is created for 3.125 km series
Using python netCDF tools, read the 25 km and 3.125 km cubes, get geolocation and rotate the data 180 degrees to orient North roughly "up" for the viewer
Using python cartopy package and metadata from the cubefile, define the EASE-Grid 2.0 Northern Hemisphere projection coordinate system, and the subset extent for Alaska
Read each set of images in turn, and overlay the gridlines and default cartopy coastlines
Save each frame as a .png image
Use ImageMagick convert utility to combine the set of images as .m4v video movie format

Easy Geolocation with Elevation Rasters and Watershed Boundaries

Sample CETB images, zoomed to area of the Hunza subbasin (green) of the Upper Indus basin, Pakistan. AMSR-E 37 GHz, vertically-polarized brightness temperatures (TBs), 2004 day 165, gridded conventionally at 25 km (left) compared to enhanced-resolution rSIR image reconstruction at 3.125 km (right). Middle image shows elevation (CHARIS DEM from SRTMGL3, courtesy S. J. S. Khalsa, NSIDC) for same region for comparison. Image reconstruction technique is clearly producing reasonable TB variation due to elevation differences, with colder (darker) TBs in higher-elevation (lighter) snow- and ice-covered areas (glacier outlines, blue, RGI Consortium, 2017).

The images shown here are produced using the following steps:

Use gdal_translate to convert the TB image data from NetCDF (that includes complete CF-convention map projection metadata) to GeoTIFF
Open the GeoTIFFs in ArcMap (which recognizes the geolocation for the EASE-Grid 2.0 Northern Hemisphere projection without any special steps)
Add shapefile for 25 km grid "fishnet" overlay
Add shapefile for Hunza basin (which ArcMap correctly reprojects to EASE-Grid 2.0, because GeoTIFFs contain complete projection metadata)
Zoom to the area of the Hunza subbasin of Upper Indus
Add raster layer for SRTMGL3 DEM (DEM data are correctly reprojected)
Add shapefile for RGI glacier outlines
Set up multiple Data Frames and add legends, scale and North Arrow

OPEN

Disclosures

Acknowledgements

Background Image: Color montage of AMSR-E brightness temperatures, 29-30 June 2003.

This research has been supported by:

NASA MEaSUREs grant NNX13AI23A at CU and NASA MEaSUREs grant NNX13AI26A at BYU
NASA Science Utilization of SMAP grant NNX16AN02G at CU and NNX16AN01G at BYU
NASA SMAP Science Team grant 80NSSC20K1806 at CU and BYU
US Army CRREL award W913E518C0014 at CU and BYU
NASA Cryospheric Science grant 80NSSC18K1055 at CU and BYU
NASA Cryospheric Science grant 80NSSC21K0749 at CU and BYU
NOAA 2021 JPSS Proving Ground/Risk Reduction Research Federal Award ID Number NA20NES4320003
NASA Cryospheric Science grant 80NSSC21K0763 at CU

Data archiving and distribution supported by the NASA NSIDC Distributed Active Archive Center (DAAC).

The original CETB product was produced on the Janus supercomputer, which was supported by the National Science Foundation (award CNS-0821794) and the University of Colorado Boulder. The Janus supercomputer was a joint effort of the University of Colorado Boulder, the University of Colorado Denver, and the National Center for Atmospheric Research.

Current research uses the RMACC Summit supercomputer, which is supported by the National Science Foundation (awards ACI-1532235 and ACI-1532236), the University of Colorado Boulder, and Colorado State University. The Summit supercomputer is a joint effort of the University of Colorado Boulder and Colorado State University.

Data storage is supported by the University of Colorado Boulder "PetaLibrary."

Acknowledgements

Background Image: Color montage of AMSR-E brightness temperatures, 29-30 June 2003.

This research has been supported by:

NASA MEaSUREs grant NNX13AI23A at CU and NASA MEaSUREs grant NNX13AI26A at BYU
NASA Science Utilization of SMAP grant NNX16AN02G at CU and NNX16AN01G at BYU
NASA SMAP Science Team grant 80NSSC20K1806 at CU and BYU
US Army CRREL award W913E518C0014 at CU and BYU
NASA Cryospheric Science grant 80NSSC18K1055 at CU and BYU
NASA Cryospheric Science grant 80NSSC21K0749 at CU and BYU
NOAA 2021 JPSS Proving Ground/Risk Reduction Research Federal Award ID Number NA20NES4320003
NASA Cryospheric Science grant 80NSSC21K0763 at CU

Data archiving and distribution supported by the NASA NSIDC Distributed Active Archive Center (DAAC).

Data storage is supported by the University of Colorado Boulder "PetaLibrary."

Author Information

Molly Hardman graduated from the University of Natal, South Africa in 1977 with a BSc Hons in Physics and from the University of Colorado, Boulder in 1991 with an MS Aerospace Engineering. She has a diverse background working in academia, research and private industry. She has worked on software projects ranging from Fortran components of an early VLBI system on an HP-1000 at NASA GSFC, to embedded software for high power amplifiers to processing the dataset depicted on this poster on the supercomputer at the University of Colorado. She is particularly interested in cryospheric applications for passive microwave data.

Mary J. Brodzik received the B.A. (summa cum laude) degree in mathematics from Fordham University, Bronx, NY, USA, in 1987.

Since 1993, she has been with the National Snow and Ice Data Center (NSIDC) and with the Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA, where she is a Senior Associate Scientist. At NSIDC, she has implemented software systems to design, produce, and analyze snow and ice data products from satellite-based visible and passive microwave imagery. She has served on NSIDC data management and software development teams for the NASA Cold Lands Processes Experiment and Operation IceBridge.

As a Principal Investigator on the NASA Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Brightness Temperature (CETB) project, she managed the operational production of the EASE-Grid 2.0 Earth Science Data Record of satellite passive microwave data, and related enhanced-resolution SMAP images. She used MODIS data products to derive the first systematically-derived global map of the world’s glaciers. For the U.S. Agency for International Development, she developed snow and glacier ice melt models to better understand the contribution of glacier ice melt to major rivers with headwaters in High Asia. She is currently leading a team that is extending the CETB with near real-time input data. Her experience includes software development, validation and verification on Defense Department satellite command and control, and satellite tracking systems. Her research interests include optical and passive microwave sensing of snow, remote sensing data gridding schemes, and effective ways to visualize science data.

Prof. David G. Long received the Ph.D. in Electrical Engineering from the University of Southern California in 1989 and B.S. and M.S. degrees in Electrical Engineering from Brigham Young University in 1982 and 1983, respectively. From 1983 through 1990 he worked at NASA's Jet Propulsion Laboratory (JPL) where he was responsible for the design and development of the NASA Scatterometer (NSCAT) system. He was the Experiment Manager for SCANSCAT (which became SeaWinds). SeaWinds flew multiple times: 1999-2017 on QuikSCAT, 2005-2006 on ADEOS-II, and 2014-2016 as RapidSCAT on the International Space Station.

Since 1990 David has been on the faculty of the Electrical and Computer Engineering department at Brigham Young University where he teaches classes in radar, remote sensing, communications and signal processing. He is the Director of the BYU Center for Remote Sensing and Head of the Microwave Earth Remote Sensing Laboratory. David has been a principal investigator (PI) on a number of NASA projects in scatterometry, climate studies, rain observation, soil moisture and SAR. He is the PI for the Scatterometer Climate Record Pathfinder (www.scp.byu.edu) and is a long-term member of the Ocean Vector Winds Science Team (OVWST), among others. His publication record includes over 140 journal papers, one book, and over 300 conference papers. He has received several NASA Award of Achievement and Team Recognition awards. He is a founding Associate Editor for IEEE Geoscience and Remote Sensing Letters, and Editor-in-Chief for the IEEE GRS Remote Sensing Code Library. His research interests include microwave remote sensing, spaceborne scatterometry, synthetic aperture radar, signal processing, estimation theory, resolution enhancement and polar ice.

A research scientist with 12 years’ experience specializing in microwave remote sensing of polar ice sheets, Dr. Julie Miller focuses on combining satellite, airborne, and field observations with simple electromagnetic modeling to advance the current state of knowledge of mapping englacial firn hydrology from space. She completed her graduate work at the University of Utah, where she developed the first algorithms to map firn aquifers from space in both Greenland and Antarctica using C-band satellite radar scatterometry. During her fellowship at Byrd Polar Climate and Research Center at Ohio State University (OSU), she extended her algorithms to include satellite L-band satellite microwave radiometry. As part of the experimental Ultra-Wideband RADiometer (UWBRAD) project and airborne field campaign over Greenland and northern Canada, she planned and executed flight lines over several firn aquifer areas, and served as science advisor on the field team. She was Co-I on a NASA Instrument Incubator/Cryospheric Sciences Program-funded project to analyze the multi-frequency airborne UWBRAD data collected over the firn aquifer on the Maniitsoq Ice Cap together with L-band satellite data. As Co-I on a NSF Glaciology funded project, she first identified, then confirmed via a field expedition, the first known firn aquifer in Antarctica using satellite C-band scatterometry and L-band microwave radiometry, and further linked firn aquifers to ice shelf disintegration on the Antarctic Peninsula. She is currently Co-I on two NASA Cryospheric Sciences Program projects to develop sophisticated algorithms to map firn aquifers in Greenland and Antarctica using multi-frequency satellite microwave radiometer and radar scatterometer data, and serves on the SMAP Science Team. She is a reviewer for IEEE Geoscience and Remote Sensing, Remote Sensing of Environment, and Journal of Glaciology.

Dr. Joan M. Ramage is Associate Professor in Earth and Environmental Sciences at Lehigh University with a joint appointment in the Environmental Initiative. Ramage has a Ph.D. (2001) from Cornell University’s Department of Geological Sciences (now Earth and Atmospheric Sciences), a M.S. (1995) From the Pennsylvania State University Dept. of Geosciences, and a B.A. from Carleton College (1993) in Geology and Archaeology. Ramage’s research expertise is on satellite remote sensing of the cryosphere. She has worked extensively with passive and active microwave satellite data and applications to snow and glacier hydrology processes in remote high latitude and mountain environments. She has a particular interest in novel methods of applying passive microwave data to measuring seasonal changes in dynamic environments. Her current research is on expanding the use of calibrated enhanced resolution brightness temperatures (CETB) from the suite of passive microwave sensors. By using pixel-level statistics to enhance retrievals from legacy passive microwave data there is increased potential to contribute new snowmelt monitoring techniques and lake ice melt/freeze status. Ramage recently became an FAA-licensed UAS remote pilot to enable her to do research with visible, multispectral and thermal imaging drones. Her research is related to glacier change and snow hydrology in tropical, mid-latitude and high arctic environments. She serves on Lehigh’s ADVANCE Center for Women in STEM advisory board. She is on the Executive Committee of the Eastern Snow Conference, an international organization dedicated to research about basic and applied snow science. She actively brings her academic expertise to the public and teaches adult programs on watershed hydrology and is an officer on a multi-township groundwater management committee in eastern Pennsylvania.

Mary J. Brodzik received the B.A. (summa cum laude) degree in mathematics from Fordham University, Bronx, NY, USA, in 1987.

Abstract

Since 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, all-weather, near-global spatial coverage for monitoring changes in cryospheric parameters. This global, gridded data set is a fundamental tool in studying surprisingly rapid cryospheric change in the satellite era. Used to analyze geophysical parameters including sea ice concentration and motion, snow water equivalent, snow- and ice-surface melt onset, and most recently, ice sheet firn aquifer extent and volume, this satellite record is a fundamental community resource for the international cryospheric research community. Due to computational and data volume limits, historical versions of the gridded passive microwave data sets were produced at coarse (25 km) spatial resolutions, using relatively simple averaging or interpolation methods. In the spirit of FAIR data concepts, the Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) leverages the EASE-Grid 2.0 definition, self-describing data content and provenance conventions, machine-readable geolocation, and transparency in data processing parameters. Using the radiometer version of Scatterometer Image Reconstruction (rSIR), CETB data are enhancing spatial resolutions up to 3 km. CETB yields greatest improvements to geophysical algorithms where brightness temperature gradients are large over short distances, including snow- and ice-covered areas in complex terrain, in transition zones from forested to open areas, along land-water coastline boundaries, and in regions of small lakes typical in the boreal regions. We have recently added daily, near real-time processing of currently operating sensors, (SSMIS and SMAP). We are adding AMSR2 measurements, and planning a major CETB reprocessing with newly recalibrated inputs. To supplement prior work using numerical simulations, we present recent work to quantify effective resolution enhancement using actual measurements, which are demonstrating spatial resolution enhancements of 30-60% over conventional gridding techniques. This reliable, high-quality, enhanced-resolution data set is advancing our understanding of both spatial and temporal variability in cryospheric phenomena.

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C55B-0591 Building Trusted Satellite Passive Microwave Data Sets: New Advancements in Calibrated, Enhanced-Resolution Brightness Temperatures for Cryospheric Applications

Molly A. Hardman¹², Mary J. Brodzik¹², David G. Long³, Julie Z. Miller², Joan M. Ramage⁴

¹National Snow & Ice Data Center, ²CIRES, University of Colorado at Boulder, ³MERS Laboratory, Brigham Young University, ⁴Lehigh University

About the CETB Data

Effective Resolution Enhancement

Mapping Ice Sheet Firn Aquifers

Deriving Melt Onset

Conclusions & Data Updates

CETB Data Interoperability

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About the CETB Data

Effective Resolution Enhancement

Mapping Ice Sheet Firn Aquifers

Deriving Melt Onset

Conclusions & Data Updates

CETB Data Interoperability

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