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

1. Introduction Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the joint NASA/JAXA Global Precipitation Measurement Mission (GPM) ( Hou et al. 2014 ; Skofronick-Jackson et al. 2017 ). This is a challenging problem for the passive microwave constellation component of GPM, as the hydrometeor signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used in retrievals are more indirect than over

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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

-CO overpass time in shallow cloud features located over the central lakes due to possible emission signatures observed in lower-frequency channels (not shown). Fig . 4. (a) MRMS-GV snowfall rate over Lakes Erie and Ontario on 20 Nov 2014 at 1820 UTC (orbit 4140), (b) GPROF precipitation rate (using the SurfPrecip parameter from the operational product), (c) GPROF PRT precipitation rate (≥0.08 mm h −1 ), and (d) surface classification as from the GPROF SurfType parameter: Sea ice edge (SIE), land/ocean or

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

; McCabe and Dettinger 1999 ; Dai 2013 ], also exhibit limited predictive skill. The main reason is that the complex and nonstationary interactions between large-scale dynamics and regional hydroclimate cannot be captured sufficiently well with a limited number of prespecified climate indices (regions used for computing sea surface temperature anomalies) as predictors, even when sophisticated statistical schemes are used (nonlinear statistical schemes, Bayesian techniques, etc.). Recognizing the

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Clément Guilloteau, Antonios Mamalakis, Lawrence Vulis, Phong V. V. Le, Tryphon T. Georgiou, and Efi Foufoula-Georgiou

interest, exhibiting a plethora of modes caused by different physical processes (e.g., solar forcing, oceanic/atmospheric circulations, land–atmosphere interactions, etc.), and imprinting themselves at various spatial and temporal scales. The accurate identification and modeling of the modes of the climate system is necessary for many key problems in geosciences, such as weather/climate prediction, attribution of extreme events and hazards, and assessment of climate change impacts. The comprehensive

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Stephen E. Lang and Wei-Kuo Tao

pairs of rain-normalized convective and stratiform diabatic heating profiles [i.e., Q 1 or the apparent heat source; Yanai et al. (1973) ], one pair for land and one for ocean, obtained from composites of both GCE model ( Tao and Simpson 1993 ) simulations and sounding budget calculations; a single additional pair was later added for shallow heating. Using surface rainfall rates and the proportion of stratiform rain, cloud heating profiles could then be retrieved remotely from satellite or other

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

closer inspection of active- and passive-estimated precipitation distributions confirms that PMW biases exist over the majority of their characteristic precipitation rates. Results over ocean, not shown here, yield the same general conclusions. Fig . 1. Comparison of global over land pixel-level distribution of precipitation rate estimates of GPM’s DPR-combined (gray) and GMI (colored bars) products. (top) Convective and (bottom) stratiform systems are delineated using a 50% threshold for DPR

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Clément Guilloteau and Efi Foufoula-Georgiou

profiles associated with collocated GMI radiometric measurements. The first database contains only profiles over vegetated land surfaces, excluding, in particular, coastal areas and snow-covered areas. For this, we rely on the surface type classification used in the current operational implementation (V05) of the GPROF algorithm ( Aires et al. 2011 ). The vegetated surface classes account for 70% of all land surfaces at the latitudes covered by the GPM Core Observatory . The second database contains

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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

global land areas. It is used here as precipitation climatology to adjust the T b – R relationships over global land. 4) PERSIANN-CDR PERSIANN–Climate Data Record (CDR) ( Ashouri et al. 2015 ) belongs to the PERSIANN family of precipitation datasets ( Nguyen et al. 2019 ). It provides daily precipitation for the period (1983–delayed present) over land and oceans at (60°S–60°N); precipitation estimates from PERSIANN-CDR are bias-adjusted using GPCP V2.3 monthly 2.5° × 2.5°. Here, PERSIANN-CDR is

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Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, and Vincenzo Levizzani

, snow- and ice-covered regions, at high latitudes and along land margins (coast lines and lakes), and in estimating heavy precipitation from convective weather systems [e.g., Decadal Survey; National Academies of Science, Engineering, and Medicine (NASEM); NASEM 2018 , chapter 6]. The past and future successes of precipitation observation from space rely on the synergy and complementarity with ground and airborne measurements (for calibration and validation in particular, see Kirstetter et al

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Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

1. Introduction Landslides result in thousands of fatalities, property loss, and infrastructure damage around the world every year ( Dilley et al. 2005 ; Froude and Petley 2018 ; Petley 2012 ). They occur across a broad range of geographic, climatic, and land use settings and can range from minor slope failures to kilometers-long debris flows. Factors that determine landslide hazard can be sorted into two categories: 1) static factors that determine an area’s preexisting susceptibility to

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