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Allyson Rugg, Julie Haggerty, and Alain Protat

Abstract

Conditions of high ice water content (HIWC; defined herein as at least 1.0 g m−3) are often found in the anvils of convective systems and can cause engine damage and/or failure in aircraft. We use ice water content (IWC) retrievals from satellite-borne radar and lidar (CloudSat and CALIOP) to provide the first analysis of global HIWC frequency using 11 years of data (2007–17). Results show that HIWC is generally present in 1%–2% of CloudSat and CALIOP IWC retrievals between flight level 270 (FL270; 27 000 ft or 8.230 km) and FL420 (42 000 ft or 12.801 km) in areas with frequent convection. Similar rates of HIWC are found over midlatitude oceans at relatively low altitudes (below FL270). Possible nonconvective mechanisms for the formation of this low-level HIWC are discussed, as are the uncertainties suggesting that the results at these low altitudes are an overestimation of the true threat of HIWC to aircraft engines. The satellite IWC retrievals are also used to validate an HIWC diagnostic tool that provides storm-scale statistics on HIWC over the contiguous United States (CONUS) during the summer convective season (May–August from 2012 to 2019). Results over the CONUS suggest that HIWC over the Great Plains is highest in June, when a point in the region is under HIWC conditions for approximately 25 h of 30 days on average. The mean area-equivalent diameters of HIWC conditions in some areas of the Great Plains exceed 350 km, and the conditions can persist for 4–5 h.

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Andreas Dörnbrack, Stephen D. Eckermann, Bifford P. Williams, and Julie Haggerty

Abstract

Stratospheric gravity waves observed during the DEEPWAVE research flight RF25 over the Southern Ocean are analyzed and compared with numerical weather prediction (NWP) model results. The quantitative agreement of the NWP model output and the tropospheric and lower-stratospheric observations is remarkable. The high-resolution NWP models are even able to reproduce qualitatively the observed upper-stratospheric gravity waves detected by an airborne Rayleigh lidar. The usage of high-resolution ERA5 data—partially capturing the long internal gravity waves—enabled a thorough interpretation of the particular event. Here, the observed and modeled gravity waves are excited by the stratospheric flow past a deep tropopause depression belonging to an eastward-propagating Rossby wave train. In the reference frame of the propagating Rossby wave, vertically propagating hydrostatic gravity waves appear stationary; in reality, of course, they are transient and propagate horizontally at the phase speed of the Rossby wave. The subsequent refraction of these transient gravity waves into the polar night jet explains their observed and modeled patchy stratospheric occurrence near 60°S. The combination of both unique airborne observations and high-resolution NWP output provides evidence for the one case investigated in this paper. As the excitation of such gravity waves persists during the quasi-linear propagation phase of the Rossby wave’s life cycle, a hypothesis is formulated that parts of the stratospheric gravity wave belt over the Southern Ocean might be generated by such Rossby wave trains propagating along the midlatitude waveguide.

Open access
Julie A. Haggerty, Stephen P. Carley, David B. Johnson, and Amy D. Michaelis

Following the onset of the Kuwait oil fires in early 1991, numerous efforts to monitor and estimate the environmental effects of the fires were initiated. These efforts produced a diverse set of atmospheric data from airborne, surface-based, and satellite platforms. Organizers of the experiments, including the World Meteorological Organization, quickly recognized the value of collecting all data into a central archive. This paper describes the development of that archive.

Basic requirements for the archive were that it contain all pertinent datasets, including detailed documentation about each, and provide easy access to all interested researchers. The requirements were met by developing a database management system that contains a catalog of the data inventory and a facility to order specific datasets. A graphical user interface provides access to the database. In addition to the basic capability of searching the data inventory, the system has a number of other features, including a visual catalog of satellite images, a bibliography of relevant publications, and an extensive metadata collection describing the datasets. The system is accessible from the Internet or telephone/modem from a variety of terminal types, making it available to virtually anyone with a computer. Researchers from around the world have successfully used the system.

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Jeffrey L. Stith, Julie A. Haggerty, Andrew Heymsfield, and Cedric A. Grainger

Abstract

The distributions of ice particles, precipitation embryos, and supercooled water are examined within updrafts in convective clouds in the Amazon and at Kwajalein, Marshall Islands, based on in situ measurements during two Tropical Rainfall Measuring Mission field campaigns. Composite vertical profiles of liquid water, small particle concentration, and updraft/downdraft magnitudes exhibit similar peak values for the two tropical regions. Updrafts were found to be favored locations for precipitation embryos in the form of liquid or frozen drizzle-sized droplets. Most updrafts glaciated rapidly, removing most of the liquid water between −5° and −17°C. However, occasional encounters with liquid water occurred in much colder updraft regions. The updraft magnitudes where liquid water was observed at cold (e.g., −16° to −19°C) temperatures do not appear to be stronger than updrafts without liquid water at similar temperatures, however. The concentrations of small spherical frozen particles in glaciated regions without liquid water are approximately one-half of the concentrations in regions containing liquid cloud droplets, suggesting that a substantial portion of the cloud droplets may be freezing at relatively warm temperatures. Further evidence for a possible new type of aggregate ice particle, a chain aggregate found at cloud midlevels, is given.

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Christopher A. Davis, David A. Ahijevych, Julie A. Haggerty, and Michael J. Mahoney

Abstract

Microwave temperature profiler (MTP) data are analyzed to document temperature signatures in the upper troposphere and lower stratosphere that accompany Atlantic tropical weather disturbances. The MTP was deployed on the National Science Foundation–National Center for Atmospheric Research Gulfstream V (GV) aircraft during the Pre-Depression Investigation of Cloud-Systems in the Tropics (PREDICT) in August and September 2010.

Temporal variations in cold-point temperature compared with infrared cloud-top temperature reveal that organized deep convection penetrated to near or beyond the cold point for each of the four disturbances that developed into a tropical cyclone. Relative to the lower-tropospheric circulation center, MTP and dropsonde data confirmed a stronger negative radial gradient of temperature in the upper troposphere (10–13 km) of developing disturbances prior to genesis compared with nondeveloping disturbances. The MTP data revealed a somewhat higher and shallower area of relative warmth near the center when compared with dropsonde data. MTP profiles through anvil cloud depicted cooling near 15 km and warming in the lower stratosphere near the time of maximum coverage of anvil clouds shortly after sunrise. Warming occurred through a deep layer of the upper troposphere toward local noon, presumably associated with radiative heating in cloud. The temperature signatures of anvil cloud above 10-km altitude contributed to the radial gradient of temperature because of the clustering of deep convection near the center of circulation. However, it is concluded that these signatures may be more a result of properties of convection than a direct distinguishing factor of genesis.

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Paquita Zuidema, Julie Haggerty, Maria Cadeddu, Jorgen Jensen, Emiliano Orlandi, Mario Mech, J. Vivekanandan, and Zhien Wang
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Julie A. Haggerty, Allyson Rugg, Rodney Potts, Alain Protat, J. Walter Strapp, Thomas Ratvasky, Kristopher Bedka, and Alice Grandin

Abstract

This paper describes development of a method for discriminating high ice water content (HIWC) conditions that can disrupt jet-engine performance in commuter and large transport aircraft. Using input data from satellites, numerical weather prediction models, and ground-based radar, this effort employs machine learning to determine optimal combinations of available information using fuzzy logic. Airborne in situ measurements of ice water content (IWC) from a series of field experiments that sampled HIWC conditions serve as training data in the machine-learning process. The resulting method, known as the Algorithm for Prediction of HIWC Areas (ALPHA), estimates the likelihood of HIWC conditions over a three-dimensional domain. Performance statistics calculated from an independent subset of data reserved for verification indicate that the ALPHA has skill for detecting HIWC conditions, albeit with significant false alarm rates. Probability of detection (POD), probability of false detection (POFD), and false alarm ratio (FAR) are 86%, 29% (60% when IWC below 0.1 g m−3 are omitted), and 51%, respectively, for one set of detection thresholds using in situ measurements. Corresponding receiver operating characteristic (ROC) curves give an area under the curve of 0.85 when considering all data and 0.69 for only points with IWC of at least 0.1 g m−3. Monte Carlo simulations suggest that aircraft sampling biases resulted in a positive POD bias and the actual probability of detection is between 78.5% and 83.1% (95% confidence interval). Analysis of individual case studies shows that the ALPHA output product generally tracks variation in the measured IWC.

Free access
Yaodeng Chen, Hongli Wang, Jinzhong Min, Xiang-Yu Huang, Patrick Minnis, Ruizhi Zhang, Julie Haggerty, and Rabindra Palikonda

Abstract

Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

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Sarah A. Tessendorf, Allyson Rugg, Alexei Korolev, Ivan Heckman, Courtney Weeks, Gregory Thompson, Darcy Jacobson, Dan Adriaansen, and Julie Haggerty

Abstract

Supercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (>500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

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David J. Serke, Scott M. Ellis, Sarah A. Tessendorf, David E. Albo, John C. Hubbert, and Julie A. Haggerty

Abstract

Detection of in-flight icing hazard is a priority of the aviation safety community. The “Radar Icing Algorithm” (RadIA) has been developed to indicate the presence, phase, and relative size of supercooled drops. This paper provides an evaluation of RadIA via comparison to in situ microphysical measurements collected with a research aircraft during the 2017 “Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment” (SNOWIE) field campaign. RadIA uses level-2 dual-polarization radar moments from operational National Weather Service WSR-88D and a numerical weather prediction model temperature profile as inputs. Moment membership functions are defined based on the results of previous studies, and fuzzy logic is used to combine the output of these functions to create a 0 to 1 interest for detecting small-drop, large-drop, and mixed-phase icing. Data from the two-dimensional stereo (2D-S) particle probe on board the University of Wyoming King Air aircraft were categorized as either liquid or solid phase water with a shape classification algorithm and binned by size. RadIA interest values from 17 cases were matched to statistical measures of the solid/liquid particle size distributions (such as maximum particle diameter) and values of LWC from research aircraft flights. Receiver operating characteristic area under the curve (AUC) values for RadIA algorithms were 0.75 for large-drop, 0.73 for small-drop, and 0.83 for mixed-phase cases. RadIA is proven to be a valuable new capability for detecting the presence of in-flight icing hazards from ground-based precipitation radar.

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