Latent and sensible ocean surface heat fluxes (LHF and SHF, respectively) can significantly impact the genesis and evolution of climate and weather systems. As heat fluxes increase within the boundary layer, static stability decreases, leading to a lower lifted condensation level, which could lead to dry and moist convection development in various weather systems [1] - [2] . The energy transported through LHF and SHF between the ocean surface and boundary layer can influence weather systems at the microscale, mesoscale, and synoptic scale, including, but not limited to, tropical and extratropical cyclones, convection, and large-scale waves such as the Madden-Julian Oscillation [3] - [6] .

Atmospheric motion vector (AMV) retrieval from water vapor measurements is important in climate research and weather forecasting. However, conventional feature tracking methods for AMV retrievals generate velocity fields with gaps and large errors. In this work, we test the optical flow algorithm by generating a nature run of a convective weather phenomenon, which provides water vapor variables and wind vector fields at various pressure levels. We show that our optical flow algorithm generates superior performance when compared with traditional feature tracking algorithms used in operational centers, generating dense AMVs with no gaps and significantly improving AMV accuracy. The optical flow algorithm performs well down to very low wind speeds and does not require a low-wind cutoff threshold. In our studies, we considered various measurement configurations, including water vapor retrievals at different temporal resolutions and found that the optical flow algorithm is not sensitive to the time interval between images.

NASA recently chose the Investigation into Convective Updrafts (InCUs) proposal as the next Earth Ventures program mission. INCUS will use a convoy of three identical Ka-band radars measuring radar reflectivity within their common swath to infer the characteristics of any convective updrafts that they observe. We summarize the theoretical basis for this approach, with justification from ground-based zenith profiler data as well as sensitivity analyses of convection-permitting simulations. We then describe and quantify the performance of the approach to detect updrafts from the radar observations. Finally, we illustrate the expected performance of retrievals of vertical transport, and evaluate their ability to meet the objectives of the INCUS mission. How this observation strategy can be adapted to miniaturized passive mm-wave radiometers is also discussed.

NASA's Cyclone Global Navigation Satellite System (CYGNSS) constellation of 8 small satellites was launched into low Earth orbit in 2016. The objectives of its initial two year mission were to study how well GPS signals that are reflected from the ocean surface can measure the winds in hurricanes and how well those measurements can improve our ability to forecast them. In the 5+ years it has been in orbit, CYGNSS has accomplished those objectives. It has also significantly expanded the scope of its scientific investigations. GPS signals reflected from the storm-fres parts of the ocean as well as the signals reflected from land have also been found to contain valuable information about surface conditions. An overview of the scientific impacts of CYGNSS observations, for hurricane prediction studies and many other applications, are presented.

We introduce temporal multimodal multivariate learning, a new family of decision making models that can indirectly learn and transfer online information from simultaneous observations of a probability distribution with more than one peak or more than one outcome variable from one time stage to another. We approximate the posterior by sequentially removing additional uncertainties across different variables and time, based on data-physics driven correlation, to address a broader class of challenging time-dependent decision-making problems under uncertainty. Extensive experiments on real-world datasets ( i.e., urban traffic data and hurricane ensemble forecasting data) demonstrate the superior performance of the proposed targeted decision-making over the state-of-the-art baseline prediction methods across various settings.

After exploring numerous observing system designs, the NASA aerosols, clouds, convection, and precipitation (ACCP) study team arrived at the top candidate architecture referred to as the atmosphere observing system (AOS) that is composed of a suite of spaceborne instruments in two orbital inclinations to characterize the complexity of hydrometeors and aerosols in the Earth’s atmosphere. This study proposes hybrid Bayesian retrieval algorithms that combine the Monte Carlo integration (MCI) and cost function optimization approaches to quantitatively evaluate the AOS architecture for skill in constraining the ice cloud microphysical properties. The remote sensor candidates under evaluation include multiple-frequency radars with W-, Ka-, and Ku-band channels and a submillimeter-wave radiometer. Two optimization techniques, the optimal estimation method (OEM) and Markov chain Monte Carlo (MCMC), are developed to maximize the posterior distribution function to retrieve ice cloud microphysical quantities with uncertainty estimates. Observing system simulation experiments are conducted using simulated synergistic radar and radiometer observations to determine the pixel-level retrieval accuracies by comparing the retrieved parameters to the true values. Results demonstrate that the low-frequency Ka-/Ku-band radar observations are complementary to the W-band channel since they provide more constraints on the condensed cloud scenes that are composed of large particles. The brightness temperature measurements exhibit sensitivities to the ice cloud layers with large water content (WC), and the synergistic active and passive observations improve the ice water path retrieval accuracies significantly. The scores measuring how well the AOS architecture satisfies the desired retrieval uncertainties for different ice cloud geophysical variables are also derived.

Estimates of cloud droplet effective radius (re) and optical thickness (τ) can be derived using reflected sunlight in a visible non-absorbing channel combined with reflectances from a near IR channel that is absorbing (e.g., The bi-spectral method or BSM). Discrepancies between BSM-estimatedreand collocated in situ measurements are commonly attributed to a violation of the assumptions used in the BSM algorithm such as plane parallel geometry, and a single mode droplet size distribution. This research uses Markov Chain Monte Carlo experiments to examine the impact of precipitation on BSM-retrievedrenear optical cloud top by comparing the retrievals and associated uncertainties obtained from two types of experiments assuming a unimodal or bimodal drop size distribution. Where rain is present, BSM-retrievedreoverestimates the true cloud modere. Moreover, there is no longer a unique measure ofrewithin the precipitating liquid-phased clouds, resulting in a substantial increase in retrieval uncertainties. This leads to a corresponding loss of information on total number concentration and liquid water content near cloud top. It is found thatrebiases are not strongly correlated with properties exclusively pertaining to rain, such as rain water content or precipitation rates, but tend to be a function of the ratio between rain and cloud water content and the cloud total number concentration. These results highlight the need for additional independent information such as from an active or passive microwave sensor that can identify the presence of precipitation and constrain additional aspects of bimodal droplet distributions.

The recent successful space-borne demonstration of a miniaturized CubeSat precipitation radar is highlighted. The low cost of such a radar, together with the availability of small satellite, platforms to carry it, now make it feasible to consider employing a more distributed approach to observe important atmospheric processes that relate to precipitation. An approach to quantify the transport of water and air by deep convection is described based on a clustering of small radar satellites providing measurements seconds apart. This strategy now adds time as a new dimension for observing such processes. A mission concept, referred to as D-train, comprised of a train of three satellites 30, 90, and 120 s apart is described, and the expected performance of it for providing measures of convective transport is examined based on a large ensemble of simulations of convection with an advanced cloud-resolving model.

The CYGNSS constellation was successfully launched on 15 Dec 2016 and has been operating continuously in science data-taking mode since March 2017. Updates will be presented on the mission status, calibration and validation activities for its science data products, and recent scientific applications of the measurements. Those applications include the use of ocean wind measurements to estimate air-sea latent heat flux, the assimilation of wind measurements made near tropical cyclones into hurricane numerical prediction models, the retrieval of soil moisture from the scattering measurements made over land, and the imaging of inland flooding using overland measurements.