AMPR OLYMPEX Calibrated Brightness Temperatures - Level 2B Data within this directory were acquired by the Advanced Microwave Precipitation Radiometer (AMPR) during the Olympic Mountain Experiment (OLYMPEX) field campaign in November-December of 2015. These files include the Level 2B calibrated and geo-referenced brightness temperature for the four AMPR-observed frequencies (10, 19, 37, 85 GHz). These data are archived in a netCDF-4 format that contains the calibrated brightness temperatures in addition to ER-2 aircraft navigation and instrument scene geo-rectification variables. Python software has been developed for reading, plotting, and providing some additional analysis capabilities. This software is available from: https://github.com/nasa/PyAMPR In addition to the calibrated brightness temperature, an objectively determined quality control metric is provided. The quality control metric is estimated based on the brightness temperature difference of a pixel within a 9x9 kernel of neighboring brightness temperatures. The QC metric is a discretized indicator of the difference within 5 Kelvin increments. Typical scene values fall in the QC 1 & 2 bins. However, very noisy scenes -- generally indicative of instrument issues or potential scene contamination or interference from another instrument --- are isolated to values >= 4. As with any objective measure based on thresholding, however, there is a gray area in the higher bins where some of the data is of high quality but physical phenomena are generating sharp, local features that are flagged as suspect. This, in and of itself, could be useful for those wanting to isolate features (e.g. the edges of a strong convective cell). When over clear ocean, the HIWRAP instrument often produces a surface reflection near nadir in the AMPR 37 GHz (B) channel. This has been flagged with a high QC index (8). An incidence angle flag has also been included for quickly identifying pixels associated with large incidence angles typically encountered during aircraft roll maneuvers. During a roll, often the edge pixels began to see very large incidence angles or may even contain off-earth sidelobe contamination. But, non-edge pixels may still be receiving observations from a typical/moderate (say -45 to 45 degree) incidence angle. Thus, we have opted for use of incidence angle flagging directly versus simply eliminated entire scans when the abs(roll angle) is greater than a threshold. Pixel field of view (FOV) water fractions are included. A 1-km gridded land/water fraction dataset -- constructed from 250m MODIS Land/Water Mask (https://lpdaac.usgs.gov/products/modis_products_table/mod44w) -- has been used, together with the instrument FOV beamwidths to estimate the percent FOV that contains surface-water features. These data can be used to quickly identify (and eliminate if desired) those pixels originating from a mixed-surface (land and water) scene. ** Note ** The 250-m MODIS Land/Water Mask includes water flagging for inland water bodies. However, no land-water mask is perfect, and it is possible that some smaller inland water bodies are missed. If so, then our FOV estimates will also be missing the water fraction contributions in such cases. ******** As an EXAMPLE to quickly identify typical good data, a series of flagging based on the following conditions may be used: QC Incidence Angle = 1 Pixel FOV < 0.1 or Pixel FOV > 0.9 (i.e., mostly land or mostly water) QC Flag Value <= 4 It is possible that sharp but valid contrasts near precipitation/clouds edges will be flagged by this, so recommended usage of these criteria is only as a guide and not an objective mask. ******** A set of quick-look imagery (in .png) is included - both the brightness temperatures and the QC flags for each channel for all flights. Imagery is located in the imagery/ directories. The notebooks/ directory contains PDFs of Jupyter notebooks used to preliminarily analyze the data from each flight. Also included is a Word document that discusses any significant QC issues from each flight. Finally, for those with Python, a working Jupyter notebook is provided demonstrating how to use PyAMPR to ingest and display the data. AMPR is a significant project at MSFC. If you plan to use these data in a publication, please contact the PI (Timothy Lang, timothy.j.lang@nasa.gov) to discuss potential co-authorship. ######################## Other info: Notable variables in AMPR data files (Note - Order in documentation does not necessarily match order in data files) --------------------------------------- nscans = Number of scans (depends on file) swath_size = 50 (hard coded) nav_size = 18 (hard coded) shape = (nscans) ***** Scan - Individual scan record number (usually thousands of scans per flight) Year, Month, Day, Hour, Minute, Second, Day_of_Year, Second _of_Day - Scan timing info (UTC) shape = (nscans, swath_size) ***** TB10A, TB10B - 10 GHz brightness temperatures (A: Left V -> Right H, B: Left H -> Right V, units: K) TB19A, TB19B - 19 GHz brightness temperatures (V->H, H->V, K) TB37A, TB37B - 37 GHz brightness temperatures (V->H, H->V, K) TB85A, TB85B - 85 GHz brightness temperatures (V->H, H->V, K) Latitude, Longitude - Geolocation for the AMPR beam (degrees) Land_Fraction10 - Estimated fraction of land in 10 GHz pixel Land_Fraction19 - Estimated fraction of land in 19 GHz pixel Land_Fraction37 - Estimated fraction of land in 37 GHz pixel Land_Fraction85 - Estimated fraction of land in 85 GHz pixel (0 = All Ocean, 1 = All Land) qctb##? (## is frequency, ? is channel) - QC flags for each frequency/channel shape = (nscans, nav_size) ***** Aircraft_Nav - Python dict of Aircraft navigation info: key (units) GPS Latitude (deg) GPS Longitude (deg) GPS Altitude (m MSL) Pitch (deg, + is nose up) Roll (deg, + is right wing down) Yaw (deg from N) Heading (deg from N) Ground Speed (m/s) Air Speed (m/s) Static Pressure (hPa) Total Pressure (hPa) Total Temperature (C) Static Temperature (C) Wind Speed (m/s) Wind Direction (deg from N) INS Latitude (deg) INS Longitude (deg) INS Altitude (m MSL) ------------------ HISTORY 03.01.2016, Release V1 Contacts: Timothy Lang, timothy.j.lang@nasa.gov, AMPR PI