AS1.9

Microwave radiometers and radars in low Earth orbit (LEO) are sensitive to the amount of condensed water in clouds. However, their temporal sampling is quite limited – a single LEO instrument will very rarely observe a weather system more than once during the lifetime of the system. Recent technological advances have enabled the design of miniaturized microwave instruments that are quite capable and, at the same time, inexpensive enough to consider the formation of a convoy of identical radars or radiometers in low-Earth orbit, separated in time by a very short interval, on the order of a minute, the temporal scale required to observe the highly nonlinear cloud dynamics. The time sequences of observations are conceptually similar to the loops that are currently obtained from ground weather radar, as well as geostationary imagery, which readily show the evolution of precipitation (in the radar case) or cloud tops (in the imagery case) over minutes. The satellite convoys overcome the limitations of geostationary images (which are sensitive only to the very top of the clouds), and those of ground radar (with its very limited spatial coverage and its insufficiently short interval between consecutive scans). Because each satellite instrument is sensitive to the 3-dimensional distribution of condensed water within its field of view, the convoy is sensitive to the change in this condensed water over the minute(s) separating the convoy members. NASA’s recently selected INCUS mission will be the first project to demonstrate this new concept, with a convoy of three identical Ka-band reflectivity profiling radars, along with a five-channel microwave radiometer.

We have conducted analyses based on simulations – as well as observations from ground-based zenith-pointing profilers – to quantify the ability of a convoy made up of a pair of small Ka-band radars that measures reflectivity only, or a pair of mm-wave radiometers that measures microwave radiances in several mm-wavelength channels, to detect convective updrafts above the freezing level and to quantify their intensity. In the case of a pair of radars, one can retrieve the vertical profile of the vertical transport in the portion of the column where the condensed water concentration is above a minimum threshold (of about 0.05 g/m³ in our analyses) and the vertical velocity exceeds a minimum threshold (of about 2 m/s) with quite low uncertainty (whose characteristics depend on the coarse shape of the vertical velocity as a function of height). These new observation strategies are not only useful to evaluate and improve the model representation of vertical transport in convective storms, they are also uniquely useful to quantify a currently “missing link” in the Hadley circulation, in establishing the potential-energy contribution by an individual convective system to the Upper Troposphere / Lower Stratosphere bubble of high-entropy air mass.

Acknowledgement: This research was carried out at Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration

How to cite: Haddad, Z., Sawaya, R., Prasanth, S., van den Heever, M., Sy, O., van den Heever, S., Grant, L., Rao, T. N., Stephens, G., Hristova-Veleva, S., Posselt, D., and Storer, R.: Observation strategy of the INCUS mission: retrieving vertical mass flux in convective storms from low-earth-orbit convoys of miniaturized microwave instruments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2124, https://doi.org/10.5194/egusphere-egu22-2124, 2022.

It has long been known that model physics uncertainty can contribute as much or more to errors in forecasting and data assimilation as errors in initial conditions. Many studies have attempted to include the effects of model physics uncertainty in data assimilation by introducing static perturbations to model parameters. In such studies, parameter values are modified at the beginning of a simulation and remain unchanged throughout the duration of the forecast. Uncertainty is spanned by generating an ensemble of forecasts, each member having a different set of parameter values. Other studies have implemented dynamic perturbations to parameters, introducing methods that modify parameter values online in a stochastic fashion.

We present here the results of a study that investigates the sensitivity of convective cloud structures to static and stochastic cloud microphysical parameter perturbations. Static parameter values are drawn from a database produced by a Markov chain Monte Carlo algorithm, while stochastic perturbations are applied via a stochastically perturbed parameterization (SPP) scheme. Both static parameter perturbations and SPP are applied to multiple microphysical parameters within a Lagrangian column model, used in several prior published studies. The 1D column microphysics model is forced with prescribed time-varying profiles of temperature, humidity and vertical velocity in such a way as to emulate the environment inside of a convective storm. This modeling framework allows for investigation of the effect of changes in model physics parameters on the model output in isolation from any feedback to the cloud-scale dynamics. 

The results are evaluated in terms of changes to the ensemble mean and variance of time evolving profiles of hydrometeor mass quantities, the microphysics processes within the model as well as in terms of the simulated column integral microphysics-sensitive satellite-based  observables. The outcomes of our experiments indicate a high degree of sensitivity of the to the way in which the SPP scheme is implemented. In particular, the distributions from which parameters are drawn, as well as the decorrelation time scale, have a large effect on the simulation outcomes. We discuss the results of SPP, compare with our static perturbation experiments, and note the implications for convective scale data assimilation. 

How to cite: Posselt, D. J., Vukicevic, T., and Stankovich, A.: Including Cloud Microphysics Uncertainty in Convective Data Assimilation: Stochastic vs Static Parameter Perturbations, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-850, https://doi.org/10.5194/egusphere-egu22-850, 2022.

Prediction of rapidly intensifying tropical cyclones (TCs) have been a challenging topic. Because most TCs are born and develop over tropical oceans with limited in-situ observation networks and infrequent low Earth orbiting satellite overpasses, geostationary satellite observations are often the only available information to capture the lifecycle of TCs.

In this study, the impacts of assimilating all-sky satellite radiances from GOES-16, together with the set of conventional observations, on the prediction of the rapid intensification process of TCs are examined using convection permitting ensemble Kalman filter data assimilation system originally developed at Penn State University with WRF and CRTM. We have conducted assimilation experiments for 2017 Atlantic hurricane season. The assimilation of all-sky satellite radiances contributed to better constraining the dynamic and thermodynamic state variables, which helped to capture the developing convective activity within the inner-core region of TCs. The TC intensity forecast error was reduced by roughly 20 % at the peak time. We found that the all-sky satellite radiances contributed to more than 90 % of error reduction. This study will provide implications about what are the sources of uncertainty in predicting rapidly intensifying TCs, as well about the design of future observation networks tasked with better initializing and predicting developing TCs.

How to cite: Minamide, M. and Posselt, D.: Convection-Permitting Ensemble All-sky Satellite Radiance Assimilation for the Prediction of Rapidly Intensifying Tropical Cyclones, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8448, https://doi.org/10.5194/egusphere-egu22-8448, 2022.

The NASA Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission will provide nearly all-weather observations of 3-D temperature and humidity, as well as cloud ice and precipitation horizontal structure, at high temporal resolution to conduct high-value science investigations of tropical cyclones. TROPICS will provide rapid-refresh microwave measurements (median refresh rate of approximately 50 minutes for the baseline mission) over the tropics that can be used to observe the thermodynamics of the troposphere and precipitation structure for storm systems at the mesoscale and synoptic scale over the entire storm lifecycle. The TROPICS constellation mission comprises six 3U CubeSats (5.4 kg each) in three low-Earth orbital planes. Each CubeSat will host a high performance radiometer to provide temperature profiles using seven channels near the 118.75 GHz oxygen absorption line, water vapor profiles using three channels near the 183 GHz water vapor absorption line, imagery in a single channel near 90 GHz for precipitation measurements (when combined with higher resolution water vapor channels), and a single channel at 205 GHz that is more sensitive to precipitation-sized ice particles. TROPICS spatial resolution and measurement sensitivity is comparable with current state-of-the-art observing platforms. Launches for the TROPICS constellation mission are planned in the first half of 2022. NASA’s Earth System Science Pathfinder (ESSP) Program Office approved the separate TROPICS Pathfinder mission, which launched on June 30, 2021, in advance of the TROPICS constellation mission as a technology demonstration and risk reduction effort. The TROPICS Pathfinder mission has provided an opportunity to checkout and optimize all mission elements prior to the primary constellation mission. This presentation will describe the on-orbit results for the successful TROPICS Pathfinder precursor mission and will highlight numerous technical innovations that have made the TROPICS mission possible and enabled new capabilities for future earth observing missions.

How to cite: Blackwell, W.: On-orbit Results from the NASA TROPICS Pathfinder Constellation Precursor Mission, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8835, https://doi.org/10.5194/egusphere-egu22-8835, 2022.