RainCube (Radar in a CubeSat) is a technology demonstration mission to enable Ka-band precipitation radar technologies on a low-cost, quick-turnaround platform. The 6U CubeSat, features a Ka-band nadir pointing precipitation radar with a half-meter parabolic antenna. RainCube first observed rainfall over Mexico in August 2018 and in the following months captured the distinct structures of a variety of storms as well as characteristic signatures of Earth’s surface essential to diagnose pointing and calibration. In this presentation we will focus on the characteristics of the observed scenes, specifically to convey the potential, as well as the limitations, of a radar of this class in addressing the goal of observing weather processes from space.
Even though vertical motion is resolved within convection-permitting models, recent studies have demonstrated significant departures in predicted storm updrafts and downdrafts when compared with Doppler observations of the same events. Several previous studies have attributed these departures to shortfalls in the representation of microphysical processes, in particular those pertaining to ice processes. Others have suggested that our inabilities to properly represent processes such as entrainment are responsible. Wrapped up in these issues are aspects such as the model grid resolution, as well as accuracy of models to correctly simulate the environmental conditions. Four primary terms comprise the vertical momentum equation: advection, pressure gradient forcing, thermodynamics and turbulence. Microphysical processes including their impacts on latent heating and their contributions to condensate loading strongly impact the thermodynamic term. The focus of this study is on the thermodynamic contributions to vertical motion, the shortfalls that arise when modeling this term, and the observations that might be made to improve the representation of those thermodynamical processes driving convective updrafts and downdrafts.
Recent technological advances have enabled the miniaturization of microwave instruments (radars and radiometers) so they can fit on very small satellites, with enough capability to measure atmospheric temperature, water vapor and clouds. The miniaturization makes these systems inexpensive enough to allow scientists to contemplate placing several examples in low-Earth orbit concurrently, to observe atmospheric dynamics in clouds and storms. To identify the most important weather and climate problems that can be addressed with these new observations, and to develop corresponding observation strategies using these "distributed" systems, specific analyses were conducted and used to justify "distributed" measurement requirements and quantify their expected performance. This presentation will describe the types of convoys, the expected observations, and their applications.
Numerical climate and weather models depend on measurements from space-borne satellites to complete model validation and improvements. Precipitation profiling capabilities are currently limited to a few instruments deployed in Low Earth Orbit (LEO), which cannot provide the temporal resolution necessary to observe the evo- lution of short time-scale weather phenomena and improve numerical weather prediction models. A constellation of cloud- and precipitation-profiling instruments in LEO would provide this essential capability, but the cost and timeframe of typical satellite platforms and instruments constitute a possibly prohibitive challenge. A new radar instrument architecture that is compatible with low-cost satellite platforms, such as CubeSats and SmallSats, has been designed at JPL. Its small size, moderate mass and low power requirement enable constellation missions, which will vastly expand our ability to observe weather systems and their dynamics and thermodynamics at sub-diurnal time scales down to the temporal resolutions required to observe developing convection. In turn, this expanded observational ability can revolutionize weather now-casting and medium-range forecasting, and enable crucial model improvements to improve climate predictions.
Few systematic attempts to interpret the measurements of mm-wave radiometers over clouds and precipitation have been made to date because the scattering signatures of hydrometeors at these frequencies are very difficult to model. The few algorithms that have been developed try to retrieve surface precipitation, to which the observations are partially correlated but not directly sensitive. In fact, over deep clouds, mm-wave radiometers are most sensitive to the scattering from solid hydrometeors within the upper levels of the cloud. In addition, mm-wave radiometers have a definite advantage over the lower-frequency window-channel radiometers in that they have finer resolution and can therefore explicitly resolve deep convection. Preliminary analyses (in particular of NOAA's MHS brightness temperatures, as well as Megha-Tropiques's SAPHIR observations) indicate that the measurements are indeed very sensitive to the depth and intensity of convection. The challenge is to derive a robust approach to make quantitative estimates of the convection, for example the height and depth of the condensed water, directly from the mm-wave observations, as a function of horizontal location. To avoid having to rely on a specific set of microphysical assumptions, this analysis exploits the substantial amount of nearly- simultaneous coincident observations by mm-wave radiometers and orbiting atmospheric profiling radars in order to enforce unbiased consistency between the calculated brightness temperatures and the radar and radiometer observations.
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