Precipitation is a key source of freshwater; therefore, observing global patterns of precipitation and its intensity is important for science, society, and understanding our planet in a changing climate. In 2014, the National Aeronautics and Space Administration (NASA) and the Japan Aerospace Exploration Agency (JAXA) launched the Global Precipitation Measurement (GPM) Core Observatory (CO) spacecraft. The GPM CO carries the most advanced precipitation sensors currently in space including a dual-frequency precipitation radar provided by JAXA for measuring the three-dimensional structures of precipitation and a well-calibrated, multifrequency passive microwave radiometer that provides wide-swath precipitation data. The GPM CO was designed to measure rain rates from 0.2 to 110.0 mm h−1 and to detect moderate to intense snow events. The GPM CO serves as a reference for unifying the data from a constellation of partner satellites to provide next-generation, merged precipitation estimates globally and with high spatial and temporal resolutions. Through improved measurements of rain and snow, precipitation data from GPM provides new information such as details on precipitation structure and intensity; observations of hurricanes and typhoons as they transition from the tropics to the midlatitudes; data to advance near-real-time hazard assessment for floods, landslides, and droughts; inputs to improve weather and climate models; and insights into agricultural productivity, famine, and public health. Since launch, GPM teams have calibrated satellite instruments, refined precipitation retrieval algorithms, expanded science investigations, and processed and disseminated precipitation data for a range of applications. The current status of GPM, its ongoing science, and its future plans are presented.

Nepal's quake-driven landslide hazards Large earthquakes can trigger dangerous landslides across a wide geographic region. The 2015 M w 7.8 Gorhka earthquake near Kathmandu, Nepal, was no exception. Kargal et al. used remote observations to compile a massive catalog of triggered debris flows. The satellite-based observations came from a rapid response team assisting the disaster relief effort. Schwanghart et al. show that Kathmandu escaped the historically catastrophic landslides associated with earthquakes in 1100, 1255, and 1344 C.E. near Nepal's second largest city, Pokhara. These two studies underscore the importance of determining slope stability in mountainous, earthquake-prone regions. Science , this issue p. 10.1126/science.aac8353 ; see also p. 147

A systematic inventory of landslide events over the globe is valuable for estimating human and economic losses, quantifying the relationship between landslide occurrences and climate variations and for evaluating emerging global landslide prediction efforts based on remote sensing data. This study compiles a landslide catalog for rainfalltriggered events for several years, drawing upon news reports, scholarly articles, and other hazard databases to provide a landslide catalog at the global scale. While this database may only represent a subset of rainfall-triggered landslides globally, due to lack of reports, it presents a lower boundary on the number of events globally and provides initial insight into the spatiotemporal statistical trends in landslide distribution and impact. This article develops a methodology for landslide event compilation that can be used in evaluating global landslide forecasting initiatives and assessing patterns in landslide distribution and frequency worldwide.

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The measurement of global precipitation, both rainfall and snowfall, is critical to a wide range of users and applications. Rain gauges are indispensable in the measurement of precipitation, remaining the standard for precipitation information across the Earth's surface for hydro-meteorological purposes. However, their distribution across the globe is limited: over land their distribution and density is variable, while over oceans very few gauges exist and where measurements are made, they may not adequately reflect the rainfall amounts of the broader area. Critically, the number of gauges available, or appropriate for a particular study, varies greatly across the Earth due to temporal sampling resolutions, periods of operation, data latency and data access. Numbers of gauges range from a few thousand available in near real time, to about a hundred thousand for all 'official' gauges, and to possibly hundreds of thousands if all possible gauges are included. Gauges routinely used in the generation of global precipitation products cover an equivalent area of between about 250 m and 3,000 m. For comparison, the center circle of a soccer pitch or tennis court is about 260 m. Although each gauge should represent more than just the gauge orifice, auto-correlation distances of precipitation vary greatly with regime and the integration period. Assuming each Global Precipitation Climatology Centre (GPCC) -available gauge is independent and represents a surrounding area of 5 km radius, this represents only about 1% of the Earth's surface. The situation is further confounded for snowfall which has a greater measurement uncertainty.

Landslides can have significant and pervasive impacts to life and property around the world. Several attempts have been made to predict the geographic distribution of landslide activity at continental and global scales. These efforts shared common traits such as resolution, modeling approach, and explanatory variables. The lessons learned from prior research have been applied to build a new global susceptibility map from existing and previously unavailable data. Data on slope, faults, geology, forest loss, and road networks were combined using a heuristic fuzzy approach.The map was evaluated with a Global Landslide Catalog developed at the National Aeronautics and Space Administration, as well as several local landslide inventories. Comparisons to similar susceptibility maps suggest that the subjective methods commonly used at this scale are, for the most part, reproducible. However, comparisons of landslide susceptibility across spatial scales must take into account the susceptibility of the local subset relative to the larger study area. The new global landslide susceptibility map is intended for use in disaster planning, situational awareness, and for incorporation into global decision support systems.

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The increasing availability of remotely sensed data offers a new opportunity to address landslide hazard assessment at larger spatial scales. A prototype global satellite-based landslide hazard algorithm has been developed to identify areas that may experience landslide activity. This system combines a calculation of static landslide susceptibility with satellite-derived rainfall estimates and uses a threshold approach to generate a set of 'nowcasts' that classify potentially hazardous areas. A recent evaluation of this algorithm framework found that while this tool represents an important first step in larger-scale near real-time landslide hazard assessment efforts, it requires several modifications before it can be fully realized as an operational tool. This study draws upon a prior work's recommendations to develop a new approach for considering landslide susceptibility and hazard at the regional scale. This case study calculates a regional susceptibility map using remotely sensed and in situ information and a database of landslides triggered by Hurricane Mitch in 1998 over four countries in Central America. The susceptibility map is evaluated with a regional rainfall intensity-duration triggering threshold and results are compared with the global algorithm framework for the same event. Evaluation of this regional system suggests that this empirically based approach provides one plausible way to approach some of the data and resolution issues identified in the global assessment. The presented methodology is straightforward to implement, improves upon the global approach, and allows for results to be transferable between regions. The results also highlight several remaining challenges, including the empirical nature of the algorithm framework and adequate information for algorithm validation. Conclusions suggest that integrating additional triggering factors such as soil moisture may help to improve algorithm performance accuracy. The regional algorithm scenario represents an important step forward in advancing regional and global-scale landslide hazard assessment.