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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

. 2017 ; You et al. 2017 ) showed that low-frequency channels (10–37 GHz) contain limited snow detection information compared to higher frequencies (89–183 GHz). In contrast, the channels at 183.31 ± 3, 183.31 ± 7, and 166 GHz are respectively more important because of their sensitivity to frozen hydrometeors and increasing ability to travel through the atmosphere to sense shallow events, especially for low total precipitable water (TPW) conditions. Numerous previous studies have demonstrated the

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

, straight implementation of this in the algorithm requires more reliance on ancillary model data. Relying heavily on coarse model data decouples the algorithm from the observations and may potentially lead to problems near strong gradients, such as cold fronts, where the retrieval may be assuming conditions associated with different boundary layer and freezing level heights, with different scattering signal–rain rate relationships as a result. While the Bayesian technique is adopted for operational NASA

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Abby Stevens, Rebecca Willett, Antonios Mamalakis, Efi Foufoula-Georgiou, Alejandro Tejedor, James T. Randerson, Padhraic Smyth, and Stephen Wright

1. Introduction Seasonal prediction of regional hydroclimate is typically based on deterministic physical models or statistical techniques, yet both approaches exhibit limited predictive ability ( Wang et al. 2009 ; National Academies of Sciences, Engineering, and Medicine 2016 ). Precipitation predictions based on deterministic physical models (regional climate models) exhibit high uncertainty due to imperfect physical conceptualizations, sensitivity to initial and boundary conditions, and

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Giuseppe Mascaro

orographic control is more significant for larger time accumulations. Fig . 1. (a) Study area within the county boundaries in Arizona. (b) Percent occurrence of annual rainfall maxima, averaged across the 223 gauges, for different time accumulations τ in summer (July–September), winter (November–March), and other months. (c) Digital elevation model (derived from the U.S. Geological Survey National Elevation Dataset) of the study region in Maricopa, La Paz, Yavapai, and Pinal Counties along with

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Alberto Ortolani, Francesca Caparrini, Samantha Melani, Luca Baldini, and Filippo Giannetti

Abstract

Measuring rainfall is complex, due to the high temporal and spatial variability of precipitation, especially in a changing climate, but it is of great importance for all the scientific and operational disciplines dealing with rainfall effects on the environment, human activities, and economy.

Microwave (MW) telecommunication links carry information on rainfall rates along their path, through signal attenuation caused by raindrops, and can become measurements of opportunity, offering inexpensive chances to augment information without deploying additional infrastructures, at the cost of some smart processing. Processing satellite telecom signals bring some specific complexities related to the effects of rainfall boundaries, melting layer, and non-weather attenuations, but with the potential to provide worldwide precipitation data with high temporal and spatial samplings. These measurements have to be processed according to the probabilistic nature of the information they carry. An EnKF-based (Ensemble Kalman Filter) method has been developed to dynamically retrieve rainfall fields in gridded domains, which manages such probabilistic information and exploits the high sampling rate of measurements. The paper presents the EnKF method with some representative tests from synthetic 3D experiments. Ancillary data are assumed as from worldwide-available operational meteorological satellites and models, for advection, initial and boundary conditions, rain height. The method reproduces rainfall structures and quantities in a correct way, and also manages possible link outages. It results computationally viable also for operational implementation and applicable to different link observation geometries and characteristics.

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Zhe Li, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke

to get gridded estimates of a wide range of atmospheric fields, including precipitation. A growing body of work has argued that NWP model simulations can, at least under specific conditions, yield precipitation estimates of comparable or better accuracy than data-driven satellite precipitation datasets (e.g., Lee et al. 2017 ; Nikolopoulos et al. 2015 ; X. Zhang et al. 2013 , 2016 ; J. Zhang 2018 ; X. Zhang 2018 ; Lundquist et al. 2019 ). It is becoming increasingly feasible to run

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Stephen E. Lang and Wei-Kuo Tao

Global Precipitation Measurement (GPM) mission ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ), to higher latitudes. However, given the continued coverage as well as the immense importance of LH over the tropics, it is essential to continue and to improve heating retrievals for this region. As such, this study is focused on improving heating retrievals from the Goddard CSH algorithm in the tropics and for warm-season conditions using precipitation products from GPM. The paper is organized as

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Allison E. Goodwell

. Climate conditions, topography, and regional land–atmosphere feedbacks drive these aspects of temporal persistence and spatial synchronicity of precipitation, which in turn influence soil moisture, flows, and vegetation. For example, the direction, speed, and size of a storm event moving across a basin can impact downstream flows and ecohydrologic processes. Goodwell and Kumar (2019) explored temporal precipitation persistence and predictability, addressing the extent to which the knowledge of past

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Nobuyuki Utsumi, F. Joseph Turk, Ziad S. Haddad, Pierre-Emmanuel Kirstetter, and Hyungjun Kim

algorithm product The EPC ( Turk et al. 2018 ) algorithm is designed around the collection of the GPM Microwave Imager (GMI) observations coincident with DPR for precipitation-free conditions ( Fig. 1 ). The precipitation-free condition is declared for each GMI field-of-view (FOV) pixel when any range bin of the DPR’s normal scan (NS; Ku band) and matched scan (MS; Ka band) 3 × 3 radar reflectivity profiles surrounding the center of the GMI FOV do not exceed a sufficiently small threshold (15 dB). From

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