Traditionally, the nowcasting of precipitation was conducted to a large extent by means of extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of traditional extrapolation-based techniques with high-resolution numerical weather prediction (NWP) is gaining popularity in the nowcasting community. The increased need of NWP products in nowcasting applications poses great challenges to the NWP community because the nowcasting application of high-resolution NWP has higher requirements on the quality and content of the initial conditions compared to longer-range NWP. Considerable progress has been made in the use of NWP for nowcasting thanks to the increase in computational resources, advancement of high-resolution data assimilation techniques, and improvement of convective-permitting numerical modeling. This paper summarizes the recent progress and discusses some of the challenges for future advancement.

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.

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The purpose of the International Global Precipitation Measurement (GPM) Program is to develop a next-generation space-based measuring system which can fulfill the requirements for frequent, global, and accurate precipitation measurements. The associated GPM Mission is being developed as an international collaboration of space agencies, weather and hydrometeorological forecast services, research institutions, and individual scientists. The design and development of the GPM Mission is an outgrowth of valuable knowledge and published findings enabled by the Tropical Rainfall Measurement Mission (TRMM). From the TRMM experience, it was recognized that the GPM Mission must consist of a mixed nonsunsynchronous and sunsynchronous orbiting satellite constellation in order to have the capability to provide physically based retrievals on a global basis, with ~3-h sampling assured at any given Earth coordinate ~90% of the time. The heart of the GPM constellation is the Core satellite, under joint development by NASA and the Japan Aerospace Exploration Agency (JAXA), which will carry a dual frequency Ku/Kaband precipitation radar (PR) and a high-resolution, multichannel passive microwave (PMW) rain radiometer. The core is required to serve as the calibration reference system and the fundamental microphysics probe to enable an integrated measuring system made up of additional constellationsupport satellites, each carrying at a minimum some type of PMW radiometer. In this article the background, planning, design, and implementation of the GPM is described.

Ground-based microwave radiometer profilers in the 20-60-GHz range operate continuously at numerous sites in different climate regions. Recent work suggests that a 1-D variational (1-DVAR) technique, coupling radiometric observations with outputs from a numerical weather prediction model, may outperform traditional retrieval methods for temperature and humidity profiling. The 1-DVAR technique is applied here to observations from a commercially available microwave radiometer deployed at Whistler, British Columbia, which was operated by Environment Canada to support nowcasting and short-term weather forecast-ing during the Vancouver 2010 Winter Olympic and Paralympic Winter Games. The analysis period included rain, sleet, and snow events (∼235-mm total accumulation and rates up to 18 mm/h). The 1-DVAR method is applied "quasi-operationally," i.e., as it could have been applied in real time, as no data were culled. The 1-DVAR-achieved accuracy has been evaluated by using simultaneous radiosonde and ceilometer observations as reference. For atmospheric profiling from the surface to 10 km, we obtain retrieval errors within 1.5 K for temperature and 0.5 g/m 3 for water vapor density. The retrieval accuracy for column-integrated water vapor is 0.8 kg/m 2 , with small bias (−0.1 kg/m 2 ) and excellent correlation (0.96). The retrieval of cloud properties shows a high probability of detection of cloud/no cloud (0.8/0.9, respectively), low false-alarm ratio (0.1), and cloud-base height estimate error within ∼0.60 km.

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As a component of Earth’s hydrologic cycle, and especially at higher latitudes, falling snow creates snowpack accumulation that in turn provides a large proportion of the freshwater resources required by many communities throughout the world. To assess the relationships between remotely sensed snow measurements with in situ measurements, a winter field project, termed the Global Precipitation Measurement (GPM) Cold Season Precipitation Experiment (GCPEx), was carried out in the winter of 2011/12 in Ontario, Canada. Its goal was to provide information on the precipitation microphysics and processes associated with cold season precipitation to support GPM snowfall retrieval algorithms that make use of a dual-frequency precipitation radar and a passive microwave imager on board the GPM core satellite and radiometers on constellation member satellites. Multiparameter methods are required to be able to relate changes in the microphysical character of the snow to measureable parameters from which precipitation detection and estimation can be based. The data collection strategy was coordinated, stacked, high-altitude, and in situ cloud aircraft missions with three research aircraft sampling within a broader surface network of five ground sites that in turn were taking in situ and volumetric observations. During the field campaign 25 events were identified and classified according to their varied precipitation type, synoptic context, and precipitation amount. Herein, the GCPEx field campaign is described and three illustrative cases detailed.

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