Guosheng Liu - US grants

Research supported by this grant is under the auspices of the Arctic Systems Science (ARCSS) Global Change Research Program and is jointly sponsored by the Division of Ocean Sciences and the Office of Polar Programs. Work to be performed represents preliminary steps towards a major 5-year research project named SHEBA, which is envisioned to study the heat budget of the Arctic Ocean and its impact on global change. The primary goals of SHEBA are: (1) to develop, test and implement models of arctic ocean- atmosphere-ice processes that demonstrably improve simulations of the present day arctic climate, including its variability, using General Circulation Models (GCMs), and (2) to improve the interpre- tation of satellite remote sensing data in the Arctic for analysis of the arctic climate system and provide reliable data for model input, model validation and climate monitoring. Researchers at the University of Colorado will use data taken from aircraft in previous experiments similar to SHEBA to analyze the status cloud data so that the SHEBA field experiment may be planned more efficiently. The aircraft-derived data will be used to validate the results from remote sensing data taken from satellites so that those results may be extended beyond the specific region of the proposed field experiment. ??

This project supports the Indian Ocean Experiment (INDOEX) to be conducted during the period February-March 1999. The principal goal of INDOEX is to measure the direct and indirect effects of aerosol on the tropospheric radiative energy budget. To support INDOEX, we propose to deploy the Advanced Microwave Imaging Radiometer (AIMR) on the NCAR C-130 research aircraft during its flights for INDOEX. We plan to analyze the AIMR data for cloud liquid water path (vertically integrated amount of cloud liquid water) determined over the swath of the radiometer. This dataset can also be used to determine rainfall rate and to estimate integrated water vapor path (precipitable water). This project supports INDOEX primary scientific objectives by providing information on gradients in the liquid water path of low-level clouds, to aid in the interpretation of the indirect effect of aerosols on the radiative fluxes.

This project is an extension of the PI's earlier NSF INDOEX project (Curry and Liu: Application of airborne passive microwave measurements for INDOEX, ATM-9910640). Because Airborne Imaging Microwave Radiometer (AIMR) was originally designed for sea ice observations in the polar region, their first task was to assess the suitability of using AIMR for cloud retrievals in the "hot" environment of INDOEX. Their study showed that the data are of good quality and retrieved values of cloud liquid water path are within the expected range. Therefore, they concluded that these data can be used to conduct research on INDOEX related science issues. The purpose of the present study is to analyze the cloud water characteristics in relation to anthropogenic aerosols using AIMR and other ancillary data collected during 1999 INDOEX field experiment. This project supports INDOEX primary scientific objectives by providing information on horizontal distributions of liquid water path and characteristic cloud particle size (together with using airborne visible measurements) for low-level clouds, to aid in the interpretation of the indirect effect of aerosols on the radiative fluxes. This work is important because it will contribute to enhanced understanding of the influence of aerosols on climate change.

The focus of this project is the use of state-of-the-art techniques for retrieving cloud optical properties, utilizing both in-situ and satellite data from the Indian Ocean Experiment (INDOEX) to quantify the first and second indirect effects of aerosols on clouds, that is the effects on cloud albedo and on cloud lifetime. The principal research objectives are to: (a) retrieve and collocate liquid water path, effective radius, cloud number concentration, and cloud thickness data; (b) develop and validate a technique for drizzle/rain detection using the ratio of the liquid water path derived from microwave brightness temperatures to the liquid water path derived from solar reflectances; (c) estimate the interrelated terms that determine cloud particle size, and use them to assess aerosol indirect radiative effects, and (d) extend the methodology to satellite measurements to gain better temporal and spatial coverage.

The modeling community will benefit from a better quantification of aerosol indirect effects. Further, the synergetic use of different airborne instruments is a necessary step before combining data from similar spaceborne instruments and developing cloud products.

One post-doc and one graduate student will also receive scientific guidance and training during their contribution to this study. The project also promotes collaboration between Florida State University, the Georgia Institute of Technology, and the National Center for Atmospheric Research.

Significant and steady progress has been made in understanding and predicting tropical cyclone (TC) motion over the past 15-20 years. In contrast, only modest improvements in intensity prediction have been made over the same period. The primary objective of this study is to improve understanding and prediction of intensity change of tropical cyclones by assimilating satellite microwave observations into atmospheric prediction models. Unlike visible and infrared channel observations, microwave satellite data convey especially rich information originated from deep in the cloud/precipitation layers, therefore, reflect the distributions of hydrometeors within TCs. This makes microwave observations particularly advantageous in overcast and rainy TC areas.

Many meteorological satellites that are currently in operation carry sensors operating at microwave frequencies. Passive radiance observations at frequencies lower than 80 GHz are more sensitive to cloud liquid and raindrops, while those at higher frequencies are more sensitive to cloud ice, snowflake, and graupel. In this project, we investigate how these satellite microwave observations can effectively be assimilated into hurricane forecasting models (WRF and MM5) to provide improve analyses of cloud ice, snow, cloud liquid, and raindrop distributions within TCs, which may, in turn, pose a positive impact on hurricane intensity forecasts.

Intellectual merits. New and effective ways of applying these hydrometeor-sensitive data to TC analysis and forecast at different resolutions will be developed. In particular, we will: (i) develop and refine forward observation operators of microwave radiances through model verification with observations; (ii) design and test an effective quality control algorithm for these data; (iii) incorporate the new observation operators and the satellite microwave data into a hurricane data assimilation system; and (iv) assess the role of accurate analysis of ice, snow, liquid water, and raindrops in TC rapid intensification forecasts.

Broader impacts. The research activity will impose a significant scientific, technological, and societal impact. Improvement of forecasts of hurricane intensity and convective processes are important and challenging. The assessment of the impact of the satellite hydrometeor data on these forecasts and the development of new techniques to use these data will advance the science of hurricane research and prediction. The work will also provide benefit to the society by minimizing damages caused by hurricanes. The research activity will train two graduate students.

Hurricanes are among the most deadly and destructive storms impacting the United States, causing losses of life, devastating damages to buildings and infrastructures, and enormous financial losses. Both hurricanes? intensity and size are the key factors determining their severity and destructive capability. Therefore, accurate prediction of hurricanes? intensity and size is essential to general public and government officials. Observed hurricanes exhibit very rich and complex intensity-size relations. Even after taking the differences in the radius of the maximum wind into consideration, hurricanes with the same maximum wind can still have various sizes or hurricanes with the same size can have a large range of intensity. The existing empirical and theoretical models, however, tend to predict a nearly one-to-one relation between hurricanes? intensity and size after taking the differences in the radius of the maximum wind into consideration. The goal of this project is to develop a data-driven physical model whose solutions can reproduce the rich and complex intensity-size relations of observed hurricanes. A successful project will lead to a better understanding of the physics governing hurricanes? intensity-size relations. This project will provide a powerful tool to identify the major deficiencies in reproducing the rich and complex intensity-size relation by operational forecast models, leading to an improvement in forecasts and public safety and economic benefits. This project will train a postdoc in fields of atmospheric dynamics and data science and support STEM education by working with two undergraduate students on their honor theses research. The PIs will actively engage with North Florida Chapter of the American Meteorological Society and provide our experimental real time assessment of hurricanes? intensity to the Florida State University/Tallahassee communities.

This project will take a novel approach that combines theories and data-driven techniques to build a new model for the hurricanes? intensity-size relation. Specifically, the PIs will utilize data analysis techniques to explore what are the factors controlling the variation of inward radial velocity among different hurricanes and then link these factors to the variation of hurricanes? angular momentum loss. Another novelty of this project is that the developed model will be validated by examining its ability to predict the radial profile of azimuthal wind both inwardly from the boundary condition at outer radii and outwardly from the boundary condition at inner radii. Such exchangeability of the predictends and predictors allows ones to test whether the data-driven model possesses the quality of physical laws.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Tropical cyclones (TCs) are the most destructive natural phenomena, causing many casualties and enormous economic loss every year. Their intensity and size are the two most important matrics for assessing TC severity and potential hazards but their relation is not well understood and challenging to be predicted. The improved understanding of TC intensity-size relation will shed insights into how operational forecasts for TC intensity and size could be further improved. These improvements will in turn help coastal communities to prepare for the maximum potential damage and lead to saving lives and minimizing the losses of properties. This project will train two Ph.D. students and a postdoc in fields of atmospheric dynamics, modeling, and data science, and support STEM education by working with an undergraduate student to launch experimental forecasts for TC intensity and size based on the knowledge gained from the project. The research findings and experimental forecasts will be communicated to the science community via peer-reviewed publications, to college students via classroom teaching and curriculum development, and to general public via website and twitter accounts.

This project is built on the knowledge and insights gained from a previously NSF-funded EAGER project, which put forward a radial invariant model of “effective absolute angular momentum” (eAAM) for radial profiles of TC surface winds by combining the absolute angular momentum (AAM) and the loss of AAM due to surface drags. The main goal of this project is to explore the dependencies of radial loss of AAM and eAAM values on environment factors so that the eAAM model would better explain the observed complexity and rich diversity of TC intensity-size relation. This research is guided by the central hypothesis substantiated by three process-based working hypotheses. Under this framework, different eAAM values result from differences in the inward radial loss of AAM that depends environment conditions. The central question of how the inward radial loss rate of AAM and eAAM are related to environment conditions is investigated through 6 sets of A-B series numerical experiments using the Cloud Model Version 1 (CM1) model for the A-series and cloud-permitting version of the Weather Research and Forecast (WRF) model for the B-series. The resultant environment-dependent eAAM model will be validated by requiring that both the eAAM-predicted TC winds using observed size information and the eAAM-predicted TC sizes using observed wind information are highly in agreement (within observation uncertainties) with their observational counterparts

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.