AMPR ORACLES Calibrated and Quality Controlled Brightness Temperatures - Level 2B, R4 Data were acquired by the Advanced Microwave Precipitation Radiometer (AMPR) during the Observations of Aerosols above Clouds and their Interactions (ORACLES) field campaign in August-September of 2016. These files include the Level 2B calibrated, corrected, 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 and quality-controlled brightness temperatures in addition to P-3 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 These data have been determined to be science quality. They have been corrected for significant impact from the radome. However, significant interference from the APR radar is seen on the 37 GHz channels. AMPR is not expected to provide useful data during significant aircraft maneuvers. Further corrections may be provided in future revisions. As of R3, in addition to the mixed polarization channels (A and B), full H and V polarizations have been deconvolved and are provided as separate fields for each frequency. ******** 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). 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 250-m 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. ######################## AMPR is a significant project at MSFC. If you plan to use the final version of 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) TB10H, TB10V - 10 GHz deconvolved brightness temperatures (K) TB19H, TB19V - 19 GHz deconvolved brightness temperatures (K) TB37H, TB37V - 37 GHz deconvolved brightness temperatures (K) TB85H, TB85V - 85 GHz deconvolved brightness temperatures (K) qctb10a, qctb10b, qctb19a, qctb19b, qctb37a, qctb37b, qctb85a, qctb85b - QC flags for all frequencies and channels. Latitude, Longitude - Geolocation for the AMPR beam (degrees) 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 03.03.2017, Release R4 - Fixed bad geolocations accidentally introduced in R3 03.03.2017, Release R3 01.05.2017, Release R2 12.05.2016, Release R1 09.02.2016, Release R0 Contacts: Timothy Lang, timothy.j.lang@nasa.gov, AMPR PI