PI: Dorothea Ivanova
Ice and mixed phase clouds have an important impact on aviation, but they are often poorly represented in the models.
This proposal seeks to help improve our understanding of aircraft icing occurrence through better parameterizations of the ice microphysical cloud properties. The goal of this proposal is to create a new Global Climate Model (GCM) parameterization for Arctic ice and mixed-phase clouds, and explore possible relationship between different type size distributions (SDs), and airplane icing.
The study will utilize data for different ice crystal size spectra in arctic cold clouds, and data for the corresponding airplane icing occurrences. The PI has already developed and published parameterizations for mid-latitude and tropical ice clouds (Ivanova 2001, Ivanova 2004, Mitchell and Ivanova 2006, Mitchell et al. 2008). The tropical and mid-latitude schemes predict different behavior of the SDs for the same ice water content (IWC) and temperatures.
As temperature decreases beyond -35C, the concentration of the small crystals is enhanced with the tropical scheme, but the opposite occurs with the mid-latitude scheme. This finding indicates that the microphysics properties of tropical and mid-latitude cold clouds are considerably different for the same IWC. It may also point to the different mechanisms by which convective and non-convective cold clouds are generated.
Clearly, there is a need for Arctic and polar ice cloud parameterization, and for a study to explore the possibility of a relationship between the environmental conditions (temperature, IWC, supercooled liquid water content), different predicted size spectra, and aircraft icing. Cold cloud interactions with aircrafts that fly through them require knowledge of cloud microphysics. Aircrafts must be designed to fly into supercooled clouds, or they must avoid those clouds in order to prevent problems associated with airframe and engine icing. De-icing or anti-icing systems must be engineered to withstand reasonable extremes in terms of ice water content (IWC), supercooled liquid water content (LWC), ice particle size distributions (SDs), and temperature.
The aircraft design or certification envelopes (FAR 25, Appendix C; Federal Aviation Administration, 1999) were developed before the advent of modern cloud physics instrumentation. In the case of ice and mixed-phase clouds, data from the new arctic field campaigns suggest that cloud temperature is one of the main parameters governing cloud microstructure, the size distributions, and ice water content affecting aircraft icing. Korolev et al. (2001) showed that the cold cloud size distributions may depend on the value of the ice particle size assumed.
Parameterizations of ice particle sizes for mid-latitude and tropical ice clouds (Ivanova et al., 2001, Boudala et al., 2002; Ivanova 2004; Mitchell et al., 2008) appear in recent literature, and were implemented in the U. S. Community Climate model 3 (CCM3) Global Climate Model (GCM), and U.K. MetOffice GCM, but little is done to study high latitude cold clouds size distributions and how they may be related to the aircraft icing.
We are working to train a neural network to forecast the initiation time, location, and intensity of thunderstorms. These results will support operations during the proposed CONVECT project and could ultimately aid operational forecasting during the North American Monsoon (NAM).
This research, funded through an NSF EAGER grant, seeks to improve forecast accuracy of monsoon thunderstorm activity and precipitation amounts in the Southwest. The project creates an innovative machine learning tool trained using regional numerical weather model output and satellite remote sensing data (the predictors) with respect to known thunderstorm cell locations and intensities detected by radar (the targets). The tool will be designed to extract important fundamental relationships between the predictors and targets that help explain the development and evolution of thunderstorms. After an intense training, validation and testing phase, the relationships will then be leveraged to generate better forecasts of the timing, severity and location of future thunderstorm events in the Southwest. The tool will be shared with the National Weather Service tto help forecasters predict thunderstorm-related hazards such as large hail, flash flooding or wildfire ignition. This innovative approach will also provide a framework for improving operational meteorological and geophysical prediction systems and for guiding scientific field studies.
The project develops a probabilistic model to predict convective initiation, rain rates, and convective cell tracks during the wet phase of the North American Monsoon (NAM). Predictors of convection (e.g., relative humidity, convective available potential energy, precipitable water) will be collected from dynamic mesoscale model (High Resolution Rapid Refresh, University of Arizona-Weather Research Forecast model) analyses and forecasts and combined with new satellite-derived observations of soil moisture and surface temperature to produce a unique prediction tool. A novel machine learning approach – causality informed learning – will be applied to identify the most suitable predictors for further training in a neural network and to gain insight into the processes governing convective initiation and evolution. Hourly forecasts of precipitation occurrence, nature, and categorical rain rates will be produced operationally to guide forecasters and field research.
Research Dates: 05/01/2023 to 04/30/2024
CONVECT is a major meteorological field research project being proposed for July - August 2025 in north-central Arizona. The project is aimed at improving our understanding and ability to predict the convective development and organization of boundary layer thermals, thunderstorms, and mesoscale convective systems during the North American Monsoon (NAM).
The proposed field project is the Convective Organization aNd Venting Experiment in Complex Terrain (CONVECT), focused in north-central Arizona near the city of Prescott. This targeted region, encompassing the Black Hills, Verde and Prescott Valleys, and Mogollon Rim, provides an ideal laboratory for investigating processes connecting complex terrain to boundary-layer and convective processes. During the summer monsoon season, this region experiences frequent deep, precipitating convection. These storms typically initiate over the most prominent terrain features in this region and then may propagate into the populated lower lying areas or send out density currents or buoyancy bores that subsequently initiate new convection. The thunderstorms are generally spatially localized, forming over a deep convective boundary layer, but are often associated with pulse severe conditions (damaging wind gusts or large hail). Some cells may become terrain-locked or exhibit back-building behavior, leading to intense rainfall and flash flooding.
The multi-scale approach proposed for CONVECT will, for the first time, capture the complete physical chain of land-atmosphere processes that drive water vapor transport and monsoonal precipitation over complex terrain at meso- to micro-scales. This diurnally cycling chain includes energy and moisture exchange over a heterogeneous, sloping surface, thermally-driven planetary boundary layer (PBL) circulations, the venting of PBL air into the free troposphere, and the initiation, upscale growth, and propagation of deep convection. The proposed deployment includes a dense network of surface flux and energy balance probes, lower-tropospheric thermodynamic and kinematic profiling systems, mobile radars, and crewed and uncrewed aircraft with in-situ and remote sensors. The campaign will be carefully guided by multi-scale modeling, and in turn, experimental observations will be assimilated to evaluate their impact on multi-scale predictability and the validity of surface layer and PBL parameterizations in complex terrain. The CONVECT science team of instrument scientists and numerical modelers contains the necessary, complementary expertise in the surface layer, the boundary layer, and deep convection to substantially advance understanding of mountain exchange between the surface and free troposphere, as well as extreme precipitation, through a multi-scale lens.
Research Dates: 09/01/2024 to 08/31/2027