Inclusion of Non-spherical Ice Particles Improves GPM Precipitation Retrievals

1. Inclusion of Non-spherical Ice Particles Improves GPM Precipitation Retrievals Sarah Ringerud (Code 612 NASA/GSFC and UMD); Mark S. Kulie (MTU); David L. Randel and Christian D. Kummerow (CSU); Gail S. Skofronick-Jackson (NASA/HQ) Global Tb improvement from use of spheres (left) to non-spherical ice particles (right) Precipitation differences resulting from the change are on the order of several mm/day, The Goddard Profiling Algorithm, used operationally for the Global Precipitation Measurement (GPM) constellation retrievals, depends heavily on simulated brightness temperatures (Tbs) for consistent retrievals across all platforms. Substituting more realistic non-spherical ice particles for the previously used spherical approximations significantly improves Tb agreement with observations and consequently improves rain rate retrieval bias, RMSE, and correlation.

2. Name: Sarah Ringerud, NASA/GSFC, Code 612 and UMD E-mail: sarah.e.ringerud@nasa.gov Phone: 301-614-5496 • References: • S. Ringerud, M. S. Kulie, D. L. Randel, G. M. Skofronick-Jackson and C. D. Kummerow, (2019), "Effects of Ice Particle Representation on Passive Microwave Precipitation Retrieval in a Bayesian Scheme," IEEE Transactions on Geoscience and Remote Sensing.doi: 10.1109/TGRS.2018.2886063 • Data Sources: NASA Global Precipitation Measurement (GPM) mission core satellite data from both the GPM Microwave Radiometer (GMI) and Dual Frequency Precipitation Radar (DPR) are used in this analysis. • Technical Description of Figures: • Graphic 1 (top left): 166 GHz H-pol brightness temperature differences between Goddard Profiling (GPROF) algorithm database simulations performed with spherical ice particle approximations and the observed GMI Tb values. The database covers the one-year period September 1, 2014 – August 31, 2015; and only precipitating pixels are included. There is a general high bias, particularly in convective area such as the intertropical convergence zone and Western Pacific, where the scattering signal, i.e., brightness temperature (Tb) depression, is clearly not simulated correctly because of the spherical particle approximations. • Graphic 2 (top right): Same as graphic 1, but for database simulations using an ensemble of nonspherical ice particles. Agreement with observations is significantly improved, including a bias reduction of 0.5 K over the oceans and an RMSE improvement of 2 K. • Graphic 3 (bottom): Change in retrieved GPROF precipitation rate for the period June, July, August 2016, using the non-spherical particle database in place of the previously used spheres. There are differences of up to several mm/day, Over land, where ice scattering is the dominant signal in the retrieval, more precipitation is retrieved in areas where simulated Tbs have been lowered. Global correlations with DPR are improved from 0.77 to 0.81. • Scientific significance, societal relevance, and relationships to future missions: Improving estimates of global precipitation is of fundamental importance for countless applications, ranging from real-time hazard monitoring to numerical weather prediction and global energy budgets. This has once again been highlighted in the 2017 Decadal Survey and there is much to be learned from GPM for designing the next generation of observing systems that will begin looking into precipitation processes. GPM’s use of physically-based methods is fundamental to linking retrievals to physical processes. A physically-based Bayesian passive microwave precipitation retrieval requires an accurate forward radiative transfer model along with realistic database representation of hydrometeors, atmospheric properties, and surface emission. NASA’s GPM mission provides an unprecedented opportunity for development of such databases, matching a well-calibrated radiometer with dual-frequency radar. Early versions of passive microwave products from GPM utilized a physically-constructed database in a Bayesian retrieval scheme and approximated ice particles using spheres. A large body of recent work demonstrates that this is insufficient for retrievals utilizing the GPM radiometer frequencies. In this study, the retrieval is updated to use nonspherical particles. Simulated brightness temperature agreement with observations is shown to be significantly improved across the high frequencies, appreciably decreasing biases and increasing correlations to observed Tb. This is compared with a second identical retrieval performed with the assumption of spherical ice particles, and retrieval results are compared globally, seasonally, and instantaneously for a case study at the rain rate level. While not at the high level of improvement shown in Tb space, the precipitation retrieval is improved as compared to one using observed Tb in correlation, bias, and RMSE. Reported improvements, while modest in magnitude, advance the retrieval to more physical consistency which allows for deeper insight into ice particle properties associated with precipitation. Earth Sciences Division - Atmospheres

3. How much extra smoke can be retrieved from MODIS during the 2015 Indonesia fire event?Yingxi Shi (USRA/613), Robert Levy (GSFC/613), Shana Mattoo (SSAI/613), Thomas Eck (USRA/618), Brad Fisher (SSAI/614), Ilya Slutsker (SSAI/618), Lorraine Remer (UMBC-JCET), Jianglong Zhang (UND) The extreme Indonesian fire and smoke event of 2015(panel a) caused severe public health, economic, and environmental damage. The standard MODIS Dark Target aerosol algorithm significantly underestimated smoke levels during this episode, as the most intense parts of the plume were filtered out (black areas in panel b).  This motivated the development of a research algorithm that was able to recover these erroneously-filtered areas, while still correctly removing cloud-contaminated pixels (panel c). In the research product, the number of pixels with a very high aerosol optical depth (AOD) above 1, indicating extreme smoke, doubles during this event.

4. Name: Yingxi Shi, NASA/GSFC, Code 613, USRA-GESTAR E-mail: yingxi.shi@nasa.gov Phone: 4-5835 References: Shi, Y. R., Levy, R. C., Eck, T. F., Fisher, B., Mattoo, S., Remer, L. A., Slutsker, I., and Zhang, J.: Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals, Atmos. Chem. Phys., 19, 259-274, https://doi.org/10.5194/acp-19-259-2019, 2019. Data Sources: We analyzed the MODIS Aqua C6.1 aerosol products (MYD04_L2) and the OMI OMAERUV product. The research product is generated based on MODIS Aqua level1B data, the MODIS Aqua C6.1 cloud product (MYD06_L2), and the Collocated MODIS Aqua and OMI level 2 product (OMMYDAGEO). The regional aerosol model was generated using the AERONET Version 3 inversion product. We validated the research product against the AERONET Version 3 AOD product. Technical Description of Figures: Graphic 1: Case study of a fire in Kalimantan on Indonesia’s Borneo island on 22 September 2015. A smoke plume of extreme optical thickness can be seen at the center of the image with clouds within and around the smoke plumes. (a) MODIS Aqua RGB image, (b) MODIS Aqua DT operational image AOD, DT misses the thickest part of the plume near the emission source, and (c) Research AOD at 0.55 μm retrieved using altered thresholds in the NDVI test, cloud mask, upper bound limits of the retrieval and a new regional aerosol model. The research product has greater areal coverage, especially over the center of the smoke plumes and near the source regions. At the center of the plume, the research AOD at 0.55 μm can be higher than 5. Areas with no AOD retrievals within the plume are identified as clouds. Scientific significance, societal relevance, and relationships to future missions:  Extreme aerosol events, resulting from severe biomass burning, have large regional and global impacts. Satellite aerosol products, both from passive and active sensors have trouble identifying and retrieving these events, particularly over very thick smoke plumes. Our study shows that the modified algorithm increases AOD by as much as 3.0 in 0.5° grid boxes with severe burning compared to the operational algorithm. This amount of missing AOD can skew the perception of the severity of the event by researchers and decision-makers who rely on the satellite aerosol products. The missing AOD can also significantly affect estimates of observationally based regional aerosol forcing and improperly influence assimilation systems that rely on the MODIS DT product.  These results are expected to influence the future development of the global DT aerosol algorithm. Earth Sciences Division - Atmospheres