Volume II CICS Annual Report 2017 Figure. 1. Validation of the Blended Rain Rate product relative to MRMS over CONUS. The bottom panels show data latency, observing satellite, scatterplot, and validation statistics. AMSR2/GCOM-W was recently incorporated into the bRR product. Correlation coefficient, root mean squared difference (RMSD), probability of detection (POD), and false alarm rate (FAR) are calculated for each time step. The most recent analyses are available online (http://cics.umd.edu/pmeyers/brr/). In addition to the bRR monitoring, routine monitoring of the stand- alone AMSR2 rain rate product is available through the CICS-MD International Precipitation Working Group website (http://cics.umd.edu/ipwg/NPPAMSR2.html). The effects of field of view (FOV) size on validation metrics were examined to create a more equitable evaluation of AMSR2 versus the Advanced Technology Microwave Sounder (ATMS). The ATMS FOVs range from 15km to 60km in diameter, relative to 5km for AMSR2, so direct comparisons of RMSD favor ATMS because lower rain rates, spatial smooth- ing, and reduced variance. Resampling AMSR2 to similar ATMS FOV sizes reduces RMSD by 50%, and performance metrics for both sensors are comparable. Efforts were underway to reduce high false alarm rates over radiometrically cold land surfaces. The leg- acy GPROF2010 empirical screening procedures are incapable of discriminating between rain and cold semi-arid surfaces, so methods to reduce the false alarm rate have been explored. Initial results suggest applying a probabilistic cloud-free determination scheme (Turk et al. 2016) dramatically improves the Critical Success Index (CSI) and Heidke Skill Score (HSS), metrics of overall detection skill. This linear dis- criminant analysis method identifies scenes with a high probability that no clouds exist, which in turn can reduce false alarms by eliminating precipitation where clouds are not expected (Fig. 2). Using a vari- able threshold dependent on the surface vegetation and temperature, CSI improved from 0.37 to 0.46 and HSS improved from 0.52 to 0.62 in cold-season cases. This methodology will be further explored, developed, and implemented in the next version of GPROF2010 for AMSR2. 242 Volume II CICS Annual Report 2017 Figure 2. Example of the improved rain detection by using LDA to identify likely cloud-free scenes. The GPROF2010 AMSR2 retrieval (left) shows light rain throughout much of Nevada and California. Applying the LDA filtering (cen- ter) removes much of these false alarms relative to the MRMS radar-based ground truth (right). Collaborations with the GPM Science Team aimed to evaluate NASA’s next generation rain rate algo- rithm. GPROF2014 applies a Bayesian approach over all surface types, and largely eliminates the need for empirical surface screening. From one year of observations, CSI is improved by 15% relative to GPROF2010. GPROF2014 rain rates are reasonable, however the algorithm struggles to identify strong convection because of the searching method of the Bayesian database. Future versions of GPROF2014 developed by the GPM Science Team will aim to improve detection of deep convection. Planned work • Reduce cold scene false alarms by incorporating surface identification techniques • Evaluate relative performance of GPROF2010V2 and GPROF2014 • Collaborate with related NASA/GPM and GOES-R projects • Continue monitoring efforts of operational AMSR2 EDRs • Continue product validation and identify algorithm deficiencies • Recommend algorithm improvements for next version release of AMSR2 EDRs Publications Ferraro, R., P. Meyers, P. S. Chang, Z. Jelenak, C. Grassotti, and S. Liu: Application of GCOM-W AMSR2 and S-NPP ATMS Hydrological Products to a flooding event in the United States, J. of Sel. Top. Earth Obs. Remote Sens., under minor revision. Products NOAA/NESDIS/OSPO Operation GCOM-W1 AMSR2 Rain Rate EDR. Presentations Meyers, P. et al.: GCOM-W/AMSR2 precipitation EDR, JPSS Science Team Meeting, August 2016, College Park, MD. 243 Volume II CICS Annual Report 2017 Other Advising a senior in the Atmospheric Science program at the University of Maryland for his senior re- search project related to satellite meteorology. Prepared and presented Validated Maturity Review ma- terials. Performance Metrics # of new or improved products developed that became operational 1 (please identify below the table) # of products or techniques submitted to NOAA for consideration in operations use # of peer reviewed papers 1 # of NOAA technical reports 1 # of presentations 1 # of graduate students supported by your CICS task # of graduate students formally advised # of undergraduate students mentored during the year 1 Continued monitoring and development of the AMSR2 rain rate products. The product has reached the validated maturity level, which requires extensive documentation and exceeding performance metrics. A paper related to the performance of NOAA/STAR hydrological satellite products is under minor revi- sion. An update was given at the JPSS science team meeting. One undergraduate is partially supported on a satellite meteorology project. 244 Volume II CICS Annual Report 2017 Scientific Support to JPSS ATMS Calibration Task Leader: Hu Yang Task Code: HYHY_ICVS_16 NOAA Sponsor: Quanhua Liu NOAA Office: NESDIS/STAR/SMCD Contribution to CICS Research Themes (%): Theme 2: 100% Main CICS Research Topic: Scientific Support for JPSS Mission Contribution to NOAA goals (%) Goal 1: 100% Strategic Research Guidance Memorandum: 2. Environmental Observations Background In 2015, ground TVAC test results shown several major issues of J1 ATMS instrument performance, which include receiver anomaly in channel 17, strong correlation between channel 18 and 19, speckles in all channels and strong stripping noise. These issues lead to rework for some of J1 ATMS channels. After the rework has been done in March, 2016, another TVAC test was carried out by Vendor, and re- sults shown several major improvements regarding the previously identified issue. During the TVAC tests in 2015 and 2016, the CICS ATMS SDR team has been worked on the test data and provided the real- time technique support for Vendor and NOAA management team to make better understanding of the test results and decision making. ATMS SDR team in CICS also generated the key calibration parameters from TVAC and ground test datasets, such as nonlinearity correction parameters, instrument mounting matrix, antenna side lobe correction parameters, and band correction parameters for J1 block 2.0 IDPS PCT table updates. For ATMS on-orbit geolocation, based on the coastline inflection point geolocation evaluation/correction software package we developed last year, we further improved the geolocation correction algorithm by incorporating lunar observation in the algorithm. Since J1 ATMS will be launched on September, 2017, the ATMS SDR team in CICS is working on the read- iness of J1 ATMS calibration algorithm and preparing for intensified post-launch cal/val work. Accomplishments 1. J1 ATMS ground test data analysis and PCT table updates for IDPS block2.0 software During 2015 TVAC test we found several major issues of J1 ATMS performance, which include receiver anomaly in channel 17, strong correlation between channel 18 and 19, speckles in all channels and strong stripping noise. After rework in 2016 and 2017, most of these issues has been solved or get im- provements. Compare to NPP ATMS, Stripping noise in J1 ATMS W and G bands was largely reduced, correction between channel 18 and 19 is still exist but with weaker magnitude. But the maximum non- linearity of J1 ATMS is much larger than NPP for most of the channels. Based on the TVAC test data and other ground test data delivered by Vendor and flight team, such as antenna pattern data and instrument mounting matrix measurements, we calculated the several key calibration and geolocation related parameters and delivered to J1 operational ground processing sys- tem for PCT updates. Table.1 is beam efficiency calculated for J1 and NPP ATMS. Results show that beam efficiency of J1 ATMS G band is much higher than NPP ATMS. 245 Volume II CICS Annual Report 2017 Figure 1. Comparison between NPP and J1 ATMS maximum nonlinearity across all 22 channels 2. ATMS long time gain stability monitoring by using Lunar observations We also did some new study of assessing microwave radiometer long-term on-orbit calibration stability by taking the lunar observation as a reference. This method was applied to 5 years of NPP ATMS lunar observations, results show that ATMS calibration accuracy and stability can be well assessed by using the Moon as a reference target. Figure 2 shows the long-term stability assessment results for ATMS ob- servations at channel 16 (89GHz center frequency) from Dec.03, 2011 to Jun. 13, 2016. Panels from top to bottom are Moon phase angle, Lunar observation angle, daily maximum observed lunar brightness temperature, daily maximum reference lunar brightness temperature, and daily average of the differ- ence between observed and reference lunar Tb. It shows that for all lunar observation, the lunar phase varies between 100 to 120 degree, and minimum lunar observation angle varies between 0 to 2 degree. The observed lunar Tb is highly consistent with reference lunar Tb, with a mean bias of 0.05K and less than 1x10-4K /year trend. Similar results can be derived for other channels, which show that current cali- bration status of NPP ATMS is kept very stable after 5 years on-orbit operations. 246 Volume II CICS Annual