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Jian Zhang and Youcun Qi

small improvement in the RMAE for KFWS 20080527 event, and in RMB scores for KLBB20090209, KMAF, and KFWS20090311 events were due to the imperfect convective–stratiform segregation as mentioned earlier in this paper (see section 2 ). 4. Summary and future work A real-time algorithm for the correction of brightband (BB) effects in radar precipitation estimation was developed. The correction was based on the radar observed (“apparent”) vertical profiles of reflectivity (AVPRs) from volumetric

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Youcun Qi, Jian Zhang, Brian Kaney, Carrie Langston, and Kenneth Howard

1. Introduction In the National Mosaic & Multi-Sensor Quantitative Precipitation Estimation (QPE) system (NMQ; ; Zhang et al. 2011 ), an apparent vertical profile of reflectivity (AVPR) correction algorithm ( Zhang and Qi 2010 , hereafter ZQ10 ; Zhang et al. 2012 , hereafter ZQKH12 ; Qi et al. 2013a , b , c ; Qi and Zhang 2013 ) is used to derive surface precipitation from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network. The AVPRs, one for each tilt, are

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H. Leijnse, R. Uijlenhoet, C. Z. van de Beek, A. Overeem, T. Otto, C. M. H. Unal, Y. Dufournet, H. W. J. Russchenberg, J. Figueras i Ventura, H. Klein Baltink, and I. Holleman

precipitation measurements at the CESAR site. This is primarily achieved through validation, but the data collected at CESAR could also contribute to improvement of retrieval algorithms. Numerical weather prediction (NWP) models rely heavily on proper representation of microphysics of clouds and precipitation. Again, datasets collected at CESAR can serve to validate and improve these representations (e.g., Lin and Colle 2009 ) but also as input to data assimilation schemes for NWP (e.g., Jung et al. 2008

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Jonathan J. Gourley, Scott E. Giangrande, Yang Hong, Zachary L. Flamig, Terry Schuur, and Jasper A. Vrugt

-correlation coefficient ρ HV in rain. This smoothing was meant to reduce the noisiness in Z DR measurements stemming from the short dwell time to within 0.1–0.2 dB. The quality controlled polarimetric variables collected at 0.5° elevation angle were used to compute rainfall rates. In this study, we investigated the skill of seven different rainfall algorithms. Prior to the computation of rainfall rates, a fuzzy logic hydrometeor classification algorithm (HCA) described in Giangrande and Ryzhkov (2008) and Park

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Ali Behrangi, Bisher Imam, Kuolin Hsu, Soroosh Sorooshian, Timothy J. Bellerby, and George J. Huffman

longer calibration time scale allows for better retention of spatial details but at the expense of short-term variation in the IR–rainfall relationship. Conversely, when calibration uses coincident MW–IR images, the algorithm can better capture short-term variability of IR–rainfall relationships, but at the expense of information regarding their spatial variability because of the limited number or coincident samples. A second combination strategy, which may be used in concert with the first, focuses

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Xiaogu Zheng and Craig S. Thompson

possible station pairs. In the present paper, we propose an alternative algorithm for the estimation of the spatial dependence of precipitation occurrence and intensity. This algorithm is faster and easier to implement. The paper is arranged as follows. First the proposed estimation method for the spatial dependence of daily precipitation is introduced. Then the results of a simulation study of long-term daily precipitation in the upper Waitaki catchment, New Zealand, using the proposed multisite

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Dusanka Zupanski, Sara Q. Zhang, Milija Zupanski, Arthur Y. Hou, and Samson H. Cheung

-Var) algorithm for the assimilation of precipitation-affected microwave radiances. The two-step approach, where satellite radiances are assimilated by the nonlinear 1D-Var step to produce increments of total column water vapor, and then these increments are assimilated by the linear (so-called incremental) 4D-Var step, has proven better in handling nonlinearities than the incremental 4D-Var approach alone. In Vukicevic et al. (2004 , 2006) , a different approach was taken to use a full blown 4D-Var with

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Yudong Tian, Christa D. Peters-Lidard, and John B. Eylander

, but are short in determining the correct rate rates of the events. Therefore, we developed a Bayesian approach to “train” an algorithm with the coincidental satellite and gauge data within a recent historical period. This algorithm essentially establishes a statistical relationship between coincidental gauge measurements and satellite estimates. Then we apply this “learned” relationship to real-time satellite estimates, when gauge data are not available, to derive the mostly likely values of gauge

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Timothy J. Lang, Steven A. Rutledge, and Robert Cifelli

custom tuning of the well-known Yuter and Houze (1998) algorithm to better match NAME precipitating system structure; see Rowe et al. for more details. Because the 2D and 3D grids matched horizontally, and because the 2D regional grid data in the vicinity of S-Pol were mostly from S-Pol ( Lang et al. 2007a ), this provided accurate partitioning and isolation of convection. All the data in the vertical column associated with a 2D convective pixel were treated as convection. For more information

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F. M. Ralph, T. Coleman, P. J. Neiman, R. J. Zamora, and M. D. Dettinger

Neiman P. J. , 2010 : Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements . Geophys. Res. Lett. , 37 , L20401 , doi:10.1029/2010GL044696 . Jankov, I. , Bao J.-W. , Neiman P. J. , Schultz P. J. , Huiling Y. , and White A. B. , 2009 : Evaluation and comparison of microphysical algorithms in ARW-WRF model simulations of atmospheric river events affecting the California coast . J. Hydrometeor. , 10 , 847 – 870 . Lavers, D. A

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