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Lisa Milani, Mark S. Kulie, Daniele Casella, Pierre E. Kirstetter, Giulia Panegrossi, Veljko Petkovic, Sarah E. Ringerud, Jean-François Rysman, Paolo Sanò, Nai-Yu Wang, Yalei You, and Gail Skofronick-Jackson

precipitation retrievals up to the Arctic and Antarctic Circles. Detecting snowfall is one of the critical GPM mission requirements. Both DPR and GMI have shown snowfall detection capabilities ( Adhikari et al. 2018 ; Casella et al. 2017 ; Ebtehaj and Kummerow 2017 ; Panegrossi et al. 2017 ; Rysman et al. 2018 ; You et al. 2017 ; Petersen et al. 2020 ). However, different sampling strategies, sensor sensitivities, phase classification, and other algorithm assumptions strongly influence global snowfall

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Sarah Ringerud, Christa Peters-Lidard, Joe Munchak, and Yalei You

minimization of false detection as both are decreasing. The HSS is not at a maximum here, however, and this is not necessarily a final answer. Future implementation of this technique should explore the possibility that the cutoff need not be static and may vary by a yet to be determined location or regime. Fig . 9. Ratio of retrieval false alarms (not detected by the active radar) to all observations for the 1-yr period September 2015–August 2016. (bottom) Results using the GPROF classification scheme

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Veljko Petković, Marko Orescanin, Pierre Kirstetter, Christian Kummerow, and Ralph Ferraro

of constructing an accurate precipitation regime prediction model if provided with a high-quality training dataset consisting of brightness temperatures and the relevant convective/stratiform classification. The GPM instrument suite is seen as an ideal data source for this demanding task. Being directly affected by the challenges in linking storms structures and their PMW signatures, the problem is approached from a surface precipitation rate bias perspective (as depicted in Fig. 1 ). The paper

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

1. Introduction Providing consistent and accurate precipitation measurements remains one of the most challenging tasks facing the weather and climate community. Rain gauge, radar, and satellite are the primary instruments for measuring precipitation. Rain gauge observations represent the most direct method for precipitation measurement and provide the longest historical records ( Kidd and Levizzani 2011 ; Mahmoud et al. 2018 ); however, the variability of precipitation across all spatial and

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Stephen E. Lang and Wei-Kuo Tao

fairly consistent range of gradient values that could be adopted as part of a metric for the LUTs and that the model, for the most part, at least spans the range of observed values. However, the reflectivity gradient metric is initially introduced here with just a simple positive/negative classification with zero gradient values grouped as positive. So, in addition to the convective/stratiform and land/ocean separation, whether or not low-level reflectivities increase or decrease toward the surface

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Clément Guilloteau and Efi Foufoula-Georgiou

correlation between the 37 and 89V TBs is negative for the coarse scale gradients and positive for the finescale gradients. The correlation coefficients are computed from 70 000 randomly sampled independent data points over global ocean and are all statistically significant at the 99% level. All TBs are corrected for surface temperature variations before applying the filtering [see appendix B and Guilloteau et al. (2018) ]. While inferring a precipitation regime and precipitation rate from the pixel

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