Microwave Radiometer Processing for Carbon in the Arctic Reservoirs Experiment (CARVE) Nick Steiner1 1 - Advanced Science Research Center, City University of New York, New York, NY NSIDC DAAC UWG Meeting August 9-10 Boulder, CO

Overview Study Area: CARVE Domain Data Types: Radiometer Observations (AMSR-E,AMSR-2, SSM/I) Data Workflow Challenges/friction points Recommendations

CARVE -Carbon in Arctic Reservoirs Vulnerability Experiment The carbon budget of Arctic ecosystems are not well known Study how greenhouse gas exchange is linked to fundamental elements across the complex Arctic biological-climatologic- hydrologic systems Aircraft measurements of important greenhouse gases on local to regional scales in the Alaskan Arctic Related remote sensing products (hydrologic/climatologic surface controls) Surface hydrology and inundation Freeze/thaw cycles

Data Types - Combined Passive Microwave Radiometers Satellite data from multiple instruments to create continuous product Gaps in coverage overlapping experiment Combined to create continuous observational record Multi-frequency observations needed for processing Many channels to coordinate Swath to grid processing Account for the physical characteristics of instrument Antennae footprint size. shape

Data Access - Overview Question Driven Data Access – Multi-frequency observations to focus on hydrologic change in different terrain components Multiple passive microwave radiometer channels and pass timing (6 final product) Combined with observations from aircraft and station measurements of CO2, CH4 Continuous data pull during daily experiment flights Data product browse used in experimental design – flight lines Near real-time for observations capturing freeze/thaw and snowmelt transitions.

Data Workflow – Friction Points/Challenges Three Different Satellite instruments – AMSR-E (validation), SSM/I (gaps in data record - 2012) and AMSR-2 High Data Volume - 3 x 120 swath level products daily/satellite Basic Data Access – pulled from FTP/Filesystem (no API) Need way to obtain swaths programmatically Solutions – Strip metadata and ingest locally Upload into local databases with appropriate fields (versioning, time, location in file/system) Object-Relational Mapping (Python-SQLAlchemy) Query return object with read/write functions for associated data (HDF5)

Data Workflow – Friction Points/Challenges Preprocessing Filter bad scan-lines RFI Correction Water-body and coastline filtering Swath to grid aggregation Subset raw observations by location and time (ascending/descending) Gridding based on physical parameters of the instrument Footprint matching - channel specific antennae Overlap - by design Grid-spacing Solution: Multidimensional Database (SciDB)

Recommendations/Ideas Python notebook – Recreate / stage figure with graphing scripts Allow user to manipulate data analysis Libraries for Common Tasks (Community Based) Programmatic access to data (Download  Archive) Selecting and Filtering Read/Write and Visualize Reprojection and gridding Packaged collections of Remote Sensing libraries Numpy, Sci-Kit Learn, Pandas, GDAL Anaconda (python) Virtual machines Data/Software kits