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Space-based precipitation radar data have been underused in data assimilation studies and operations despite their valuable information on vertically resolved hydrometeor profiles around the globe. The authors developed direct assimilation of reflectivities (Ze) from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) Core Observatory to improve mesoscale predictions. Based on comparisons with Ze observations, this cloud resolving model mostly reproduced Ze but produced overestimations of Ze induced by excessive snow with large diameter particles. With an ensemble-based variational scheme and preprocessing steps to properly treat reflectivity observations including conservative quality control and superobbing procedures, the authors assimilated DPR Ze and/or rain-affected radiances of GPM Microwave Imager (GMI) for the case of Typhoon Halong in July 2014. With the vertically resolving capability of DPR, the authors effectively selected Ze observations most suited to data assimilation, for example, by removing Ze above the melting layer to avoid contamination due to model bias. While the GMI radiance had large impacts on various control variables, the DPR made a fine delicate analysis of the rain mixing ratio and updraft. This difference arose from the observation characteristics (coverage width and spatial resolution), sensitivities represented in the observation operators, and structures of the background error covariance. Because the DPR assimilation corrected excessive increases in rain and clouds due to the radiance assimilation, the combined use of DPR and GMI generated more accurate analysis and forecast than separate use of them with respect to the agreement of observations and tropical cyclone position errors.

Developing and least developed countries are particularly vulnerable to the impact of climate change and climate extremes, including drought. In Papua New Guinea (PNG), severe drought caused by the strong El Niño in 2015-2016 affected about 40% of the population, with almost half a million people impacted by food shortages. Recognizing the urgency of enhancing early warning systems to assist vulnerable countries with climate change adaptation, the Climate Risk and Early Warning Systems (CREWS) international initiative has been established. In this chapter, the CREWS-PNG project is described. The CREWS-PNG project aims to develop an improved drought monitoring and early warning system, running operationally through a collaboration between PNG National Weather Services (NWS), the Australian Bureau of Meteorology and the World Meteorological Organization that will enable better strategic decision-making for agriculture, water management, health and other climate-sensitive sectors. It is shown that current dynamical climate models can provide skillful predictions of regional rainfall at least 3 months in advance. Dynamical climate model-based forecast products are disseminated through a range of Web-based information tools. It is demonstrated that seasonal climate prediction is an effective solution to assist governments and local communities with informed decision-making in adaptation to climate variability and change.

To improve monitoring of extreme weather and climate events from space, the World Meteorological Organization (WMO) initiated the space-based weather and climate extremes monitoring demonstration project (SEMDP). Presently, SEMDP is focused on drought and heavy precipitation monitoring over Southeast Asia and the Pacific. Space-based data and derived products form critical part of meteorological services' operations for weather monitoring; however, satellite products are still not fully utilized for climate applications. Using SEMDP satellitederived precipitation products, it would be possible to monitor extreme precipitation events with uniform spatial coverage and over various time periods -pentad, weekly, 10 days, monthly and longer time-scales. In this chapter, SEMDP satellitederived precipitation products over the Asia-

A need to monitor precipitation extremes from space is widely recognized, especially for regions where ground-based observations are limited or unavailable. This paper examines the usefulness of precipitation extremes monitoring using the Global Satellite Mapping of Precipitation (GSMaP) near-real-time product in the East Asia and Western Pacific region-one of the world's most disaster-prone regions. With case studies and statistical analysis, heavy rainfall and drought detected using the GSMaP Near-real-time Gauge-adjusted Rainfall Product (GNRT6) were validated. Heavy rainfall for daily and weekly precipitation and short-term drought from one month up to three months were defined by a 90th percentile threshold or more and the Standardized Precipitation Index over periods from April 2000 to March 2019, respectively. The results of analyses suggested that the detectability varied depending on the region, such as good detection in dry areas and poor detection in rainy island nations. While the accuracy of GNRT6 is confirmed as being generally better than that of the satellite-only uncorrected product, low detectability can be caused by coarse resolutions of parameters used in the gauge-adjustment technique of the GNRT6 and is regarded as a future task.