Quantification of Spatially Distributed Errors of Precipitation Rates and Types from the TRMM Precipitation Radar 2A25 (the latest successive V6 and V7) using NOAA/NMQ over the Lower United States Sheng Chen 1,2, P.E. Kirstetter 1,2,3, Y. Hong 1,2, J.J. Gourley 3, J. Zhang 3, K. Howard 3, J.J. Hu 4 1 School of Civil Engineering and Environmental Science, University of Oklahoma, OK, USA 2 Atmosphere Radar Research Center, University of Oklahoma, Norman, OK, USA 3 NOAA/National Severe Storms Laboratory, National Weather Center, Norman, OK, USA 4 School of Computer Science, University of Oklahoma, Norman, OK, U.S.A Introduction Information on the spatial error characteristics of satellite-based quantitative precipitation estimates(QPEs) are important for application of satellite rainfall products including weather, hydrology and climate studies. The uncertainty of the QPEs will enable the QPE developers improve the QPE algorithms. In this study, the spatial error structure of surface rain rates and types from NASA/JAXA Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) was systematically studied by comparing them with NOAA/National Severe Storms Laboratory’s (NSSL) next generation, high-resolution (1km/5min) National Mosaic QPE (Q2) over the TRMM-covered Continental United States (CONUS). The difference between the latest successive version-6 PR(PRV6) and version-7 PR (PRV7) will be quantified and mapped around the southern CONUS Study Region and data Study region is southern conterminous United States (CONUS) in longitude of -124°N to -67°N and latitude 25°N to 37°N. The data are composed of NOAA/National Severe Storms Laboratory’s (NSSL) next generation, high-resolution (1km/5min) National Mosaic QPE (Q2), PRV6 and PRV7 level 2 products 2A25.Data time spans from Dec through Nov Q2 was considered as reference against which PR data were evaluated. Methodology Time and location matching technology is applied to obtain the instantaneous matching pairs of PR vs. Q2(Fig. 3) conditioning on time difference less than 2.5min and the range of field of view of PR with footprint resolution 5km. Results Fig. 8 gives the scatter plots, the cumulative density function by occurrence and volume, the probability of detection(POD), critical success index(CSI) and false alarm ratio(FAR) as a function of different thresholds for PRV6/7 vs. Q2. Fig. 9 and Fig 10 show the total precipitation from convective(stratiform) rainfall. Fig 11 shows the Spatial Bias, RMSE and CC for PRV6, PRV7 and their difference. The reference mean rainfall in the domain(A) of PR FOV can be computed as: In order to select robust pairs, two weighted standard errors are computed with the reference rainfall in the domain(A) of PR FOV, namely: Robust pairs selection conditioning on: Fig. 4 Robust pairs for PRV6 vs. Q2 Reference: Kirstetter, P. E., et.al (2012), Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar-based National Mosaic QPE, Journal of Hydrometeorology. Chen, S., et.al (2012), Rates and Types from NASA/JAXA Space-borne Radar against NOAA/NSSL High-resolution Ground-based National Mosaic Radar Network within TRMM-covered Continental of US, Journal of Hydrometeorology(submitted). Reference: Kirstetter, P. E., et.al (2012), Toward a Framework for Systematic Error Modeling of Spaceborne Precipitation Radar with NOAA/NSSL Ground Radar-based National Mosaic QPE, Journal of Hydrometeorology. Chen, S., et.al (2012), Rates and Types from NASA/JAXA Space-borne Radar against NOAA/NSSL High-resolution Ground-based National Mosaic Radar Network within TRMM-covered Continental of US, Journal of Hydrometeorology(submitted). Contact: Sheng Chen University of Oklahoma Fig.1 (a)TRMM PR satellite; (b)WSD88 Radar locations and TRMM PR coverage (a) (b) (a) PRV6 vs. Q2 Fig.2 Join available pairs for (a)PRV6 vs. Q2 and (b) PRV7 vs. Q2(right. (b) PRV7 vs. Q2 Fig.3 (a)PR and Q2 spatial overlapping. (b)Q2 and PR location matching. (b) (a) Q2 grid PR FOV Quantitative comparison was carried out with statistics metrics bias(Bias), relative bias(RB), root mean squared error (RMSE), and correlation coefficient(CC). Fig. 8 (a-c)scatter plots, CDF of occurrence(CDFc) and volume(CDFv), and contingency metrics as a function of different thresholds for PRV6 vs. Q2.(d-f) The same as (a-c) but for PRV7 vs. Q2. (b) (a) (c) (e) (d) (f) Fig. 9 (a-c)Total convective rainfall Q2 and PRV6 and scatter plot. (d-f)Total stratiform rainfall Q2 and PRV6 and scatter plots. Fig. 5 Robust pairs for PRV7 vs. Q2 Fig. 6 Total precipitation derived from PRV6. Fig. 7 Total precipitation derived from PRV7. Fig. 11 Spatial Bias(1 st row), RB(2 nd row),RMSE(3 rd row) and CC(4 th row) for PRV6, PRV7 and their difference. Fig. 10 (a-c)Total convective rainfall Q2 and PRV7 and scatter plot. (d-f)Total stratiform rainfall Q2 and PRV7 and scatter plot. Conclusion  PRV7 decreased the underestimation of rain rate from 22.09% to 18.38%.  PRV7(V6) is moderately correlated with Q2 with a mean CC of 0.58(0.56).  PRV7 has close CDFc, CDFv, POD,CSI and FAR with PRV6.  PRV7 and PRV6 shares similar spatial patters of Bias, RB, RMSE and CC with PRV6 over south CONUS.  PRV7 detected more stratiform precipitation than PRV6 and seen less convective rainfall than PRV6. AGU FALL MEETING San Francisco | 3-7 December 2012 (c) (a) (b) (c) (d) (e) (f) (g) (h) (i) (j) (k) (l) (a)PRV6 conv. (d)Q2 strat. (f) (e)PRV6 strat. (a)Q2 conv. (c) (a)PRV7 conv. (d)Q2 strat. (e)PRV7 strat. (a)Q2 conv. Paper No. H33C-1348