Procedure for measuring liquid-water content and droplet sizes in supercooled clouds by rotating multicylinder method
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The process by which liquid cloud droplets homogeneously crystallize into ice is still not well-understood. The ice nucleation process based on the standard and classical theory of homogeneous freezing, initiates within the interior volume of a cloud droplet. Current experimental data on homogeneous freezing rates of ice in droplets of supercooled water, both in air and emulsion oil samples, show considerable scatter. For example, at -33 C, the reported volume-based freezing rates of ice in supercooled water vary by as much as 5 orders of magnitude, which is well outside the range of measurement uncertainties. Here, we show that the process of ice nucleus formation at the air (or oil)-liquid water interface may help to explain why experimental results on ice nucleation rates yield different results in different ambient phases. Our results also suggest that surface crystallization of ice in cloud droplets can explain why low amounts of supercooled water have been observed in the atmosphere near -40 C.
The Bayesian passive microwave retrievals of snowfall often rely on mathematical matching of the observed vectors of brightness temperature with an a priori database of precipitation profiles and their corresponding brightness temperatures. Mathematical proximity does not necessarily lead to consistent retrievals due to limited information content of passive microwave observations. This paper defines imposter (genuine) vectors of brightness temperature as those that are mathematically close but physically inconsistent (consistent) and characterizes them through the Silhouette Coefficient (SC) analysis. The Neyman–Pearson (NP) hypothesis testing is used to separate the imposter and genuine brightness temperatures based on their associated values of cloud ice (IWP) and liquid water path (LWP), given by coincidences of CloudSat Profiling Radar (CPR) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The study determines thresholds for IWP and LWP that allow optimal identification of imposter brightness temperatures of non-snowing and snowing clouds, which can mislead the passive microwave retrieval algorithms to falsely detect or miss the snowfall events. It is demonstrated that emission signal of supercooled liquid water in snowing clouds can lead to improved passive microwave retrieval of snowfall and conditioning the retrievals to the cloud IWP and LWP can result in marginal correction of the snowfall detection probability; however, reduce the probability of false alarm by 6%–8% over sea ice and open oceans.
The particle size characterization portion of the 2017 Propulsion Systems Laboratory (PSL) Cloud Calibration is described. The work focuses on characterizing the particle size distribution of the icing cloud as a function of simulated atmospheric conditions.These results will aid in upcoming ice crystal and supercooled liquid icing tests in PSL. Measurements acquired with the Phase Doppler Interferometer and High Speed Imaging instruments are presented. Experimental results indicate that the particle size distribution is primarily a function nozzle air and water pressures, and that air speed is not a significant effect for ice crystal clouds in PSL and both thermodynamic conditions and air speed are not significant effects for supercooled liquid water clouds in PSL.
The particle size characterization portion of the 2017 Propulsion Systems Laboratory (PSL) Cloud Calibration is described. The work focuses on characterizing the particle size distribution of the icing cloud as a function of simulated atmospheric conditions.These results will aid in upcoming ice crystal and supercooled liquid icing tests in PSL. Measurements acquired with the Phase Doppler Interferometer and High Speed Imaging instruments are presented. Experimental results indicate that the particle size distribution is primarily a function nozzle air and water pressures, and that air speed is not a significant effect for ice crystal clouds in PSL and both thermodynamic conditions and air speed are not significant effects for supercooled liquid water clouds in PSL.
Supercooled clouds substantially impact polar surface energy budgets but large-scale models often underestimate their occurrence, which motivates accurately establishing metrics of basic processes. An analysis of long-term measurements at Utqiaġvik, Alaska, and McMurdo Station, Antarctica, combines lidar-validated use of soundings to identify supercooled cloud layers and colocated ground-based profiling radar measurements to quantify cloud base precipitation. We find that more than 85 % (75 %) of sampled supercooled layers are precipitating over the Arctic (Antarctic) site, with more than 75 % (50 %) precipitating continuously to the surface. Such high frequencies can be reconciled with substantially lesser spaceborne estimates by considering differences in radar hydrometeor detection sensitivity. While ice precipitation into supercooled clouds from aloft is common, we also find that the great majority of supercooled cloud layers without ice falling into them are themselves continuously generating precipitation. Such sustained primary ice formation is consistent with continuous activation of immersion-mode ice nucleating particles (INPs), suggesting that supercooled cloud formation is a principal gateway to ice formation at temperatures greater than ~ −38 °C over polar regions. The prevalence of weak precipitation fluxes is also consistent with supercooled cloud longevity, and with well-observed and widely simulated case studies. An analysis of colocated microwave radiometer retrievals suggests that weak precipitation fluxes can be nonetheless consequential to moisture budgets for supercooled clouds owing to small liquid water paths. Finally, we suggest that these ground-based precipitation rate statistics offer valuable guidance for improving the representation of polar cloud processes in large-scale models.
Supercooled clouds substantially impact polar surface energy budgets, but large-scale models often underestimate their occurrence, which motivates accurately establishing metrics of basic processes. An analysis of long-term measurements at Utqiaġvik, Alaska, and McMurdo Station, Antarctica, combines lidar-validated use of soundings to identify supercooled cloud layers and colocated ground-based profiling radar measurements to quantify cloud base precipitation. We find that more than 85 % (75 %) of sampled supercooled layers are precipitating over the Arctic (Antarctic) site, with more than 75 % (50 %) precipitating continuously to the surface. Such high frequencies can be reconciled with substantially lesser spaceborne estimates by considering differences in radar hydrometeor detection sensitivity. While ice precipitation into supercooled clouds from aloft is common, we also find that the great majority of supercooled cloud layers without ice falling into them are themselves continuously generating precipitation. Such sustained primary ice formation is consistent with continuous activation of immersion-mode ice-nucleating particles (INPs), suggesting that supercooled cloud formation is a principal gateway to ice formation at temperatures greater than ∼−38 ∘C over polar regions. The prevalence of weak precipitation fluxes is also consistent with supercooled cloud longevity and with well-observed and widely simulated case studies. An analysis of colocated microwave radiometer retrievals suggests that weak precipitation fluxes can be nonetheless consequential to moisture budgets for supercooled clouds owing to small liquid water paths. The results here also demonstrate that the observed abundance of mixed-phase clouds can vary substantially with instrument sensitivity and methodology. Finally, we suggest that these ground-based precipitation rate statistics offer valuable guidance for improving the representation of polar cloud processes in large-scale models.
The accurate modeling and prediction of cloud base heights is critical for energy balance calculations and aviation operations, alike. Low-level (i.e., boundary-layer) Arctic clouds can be difficult to model, making prediction of formation and dissipation challenging. Primarily mixed-phase, these clouds typically contain low quantities of supercooled liquid water and often slowly precipitate relatively small amounts of moderately and heavily rimed snow particles. While this appears to be the predominant cloudy state on the North Slope of Alaska (NSA), the delicate balance of microphysical, dynamical, radiative, surface coupling, and advective processes can rapidly shift to heavy snow (with various degrees of riming) or to a complete dissipation of the cloud layer without any precipitation, depending on the dominant processes. Here we strive to disentangle these various processes. First, we compare the predictive performances of four different numerical weather models in forecasting the presence and base-heights of low-level clouds: the High-Resolution Rapid Refresh - Alaska (HRRR-AK) model, the Polar Weather Research and Forecasting (Polar WRF) model, the Unified Model (UM), and the European Centre for Medium-range Weather Forecasting (ECMWF) model. Initial results comparing model output at two U.S. Department of Energy Atmospheric Radiation Measurement (AMT) NSA sites, during the fall season in 2019 and 2022, show that the UM slightly outperforms the HRRR-AK in terms of accurately forecasting the presence of a low-level cloud layer (89% of the time). All models have a significant bias of 300 to 800 meters in forecasting cloud base height (lower than is observed); however, the UM and ECMWF models have the lowest biases. Finally, a case study for a particularly challenging April 2017 thin-cloud event is presented, wherein we compare the performance of four different bulk microphysical parameterization schemes using a higher-resolution large eddy simulation (LES) model, the WRF-LES. Initial results show that the Thompson scheme was the only one able to reproduce and sustain a substantial supercooled liquid layer, but it was unable to reproduce the transition from a deep, liquid-rich cloud to a thin layer with moderately and heavily rimed precipitation. This is the first step in linking simulated LES-scale riming processes with those parameterized at a coarser mesoscale model scale. This has important implications for forecasting low-level clouds in an operational environment, given the efficiency of the riming process.
The NASA Icing Remote Sensing System (NIRSS) has been under definition and development at NASA Glenn Research Center since 1997. The goal of this development activity is to produce and demonstrate the required sensing and data processing technologies required to accurately remotely detect and measure icing conditions aloft. As part of that effort NASA has teamed with NCAR to develop software to fuse data from multiple instruments into a single detected icing condition product. The multiple instrument approach utilizes a X-band vertical staring radar, a multifrequency microwave, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data is post-processed with a LabVIEW program with a resultant supercooled liquid water profile and aircraft hazard depiction. Ground-based, remotely-sensed measurements and in-situ measurements from research aircraft were gathered during the international 2003-2004 Alliance Icing Research Study (AIRS II). Comparisons between the remote sensing system s fused icing product and the aircraft measurements are reviewed here. While there are areas where improvement can be made, the cases examined suggest that the fused sensor remote sensing technique appears to be a valid approach.
In ice-crystal icing, ice erosion can occur simultaneously with ice accretion. This paper presents a proof-of-concept experimental study that attempts to isolate and quantify ice erosion. The experiments took place at the NASA Icing Research Tunnel and Propulsion Systems Laboratory in 2017 and 2018. The test procedure involved first generating an ice accretion on various NACA 0012 airfoil models using a supercooled liquid water or mixed-phase icing cloud and, subsequently, exposing that ice accretion to an ice crystal cloud. The resulting ice mass decay rates were analyzed in terms of an erosion term, defined as the ratio of the eroded mass flux to impinging mass flux. The erosion term generally increased with airspeed with a value of 1.8% at air velocities of 270 knots and increasing to just over 6% at 360 knots. A preliminary correlation for ice erosion is developed for use in ice accretion models subject to the conditions tested. The study revealed several challenges, both experimental and analytical, including spray bar nozzle freeze-out as well as measurement of the cloud melt ratio to ensure freeze-out.
NASA and the U.S. Army Cold Regions Research and Engineering Laboratory (CRREL) have an on-going activity to develop remote sensing technologies for the detection and measurement of icing conditions aloft. A multiple instrument approach is the current emphasis of this activity. Utilizing radar, radiometry, and lidar, a region of supercooled liquid is identified. If the liquid water content (LWC) is sufficiently high, then the region of supercooled liquid cloud is flagged as being an aviation hazard. The instruments utilized for the current effort are an X-band vertical staring radar, a radiometer that measures twelve frequencies between 22 and 59 GHz, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data are post-processed with a LabVIEW program with a resultant supercooled LWC profile and aircraft hazard identification. Remotely sensed measurements gathered during the 2003-2004 Alliance Icing Research Study (AIRS II) were compared to aircraft in-situ measurements. Although the comparison data set is quite small, the cases examined indicate that the remote sensing technique appears to be an acceptable approach.
NASA has an on-going activity to develop remote sensing technologies for the detection and measurement of icing conditions aloft. A multiple instrument approach is the current emphasis of this activity. Utilizing radar, radiometry, and lidar, a region of supercooled liquid is identified. If the liquid water content (LWC) is sufficiently high, then the region of supercooled liquid cloud is flagged as being an aviation hazard. The instruments utilized for the current effort are an X-band vertical staring radar, a radiometer that measures twelve frequencies between 22 and 59 GHz, and a lidar ceilometer. The radar data determine cloud boundaries, the radiometer determines the sub-freezing temperature heights and total liquid water content, and the ceilometer refines the lower cloud boundary. Data is post-processed with a LabVIEW program with a resultant supercooled LWC profile and aircraft hazard identification. Individual remotely sensed measurements gathered during the 2003-2004 Alliance Icing Research Study (AIRS II) were compared to aircraft in-situ measurements. Comparisons between the remote sensing system s fused icing product and in-situ measurements from the research aircraft are reviewed here. While there are areas where improvement can be made, the cases examined indicate that the fused sensor remote sensing technique appears to be a valid approach.
The process of supercooled liquid water crystallization into ice is still not well understood. Current experimental data on homogeneous freezing rates of ice nucleation in supercooled water droplets show considerable scatter. For example, at -33 C, the reported freezing nucleation rates vary by as much as 5 orders of magnitude, which is well outside the range of measurement uncertainties. Until now, experimental data on the freezing of supercooled water has been analyzed under the assumption that nucleation of ice took place in the interior volume of a water droplet. Here, the same data is reanalyzed assuming that the nucleation occurred "pseudoheterogeneously" at the air (or oil)-liquid water interface of the droplet. Our analysis suggest that the scatter in the nucleation data can be explained by two main factors. First, the current assumption that nucleation occurs solely inside the volume of a water droplet is incorrect. Second, because the nucleation process most likely occurs on the surface, the rates of nuclei formation could differ vastly when oil or air interfaces are involved. Our results suggest that ice freezing in clouds may initiate on droplet surfaces and such a process can allow for low amounts of liquid water (approx. 0.002 g per cubic meters) to remain supercooled down to -40 C as observed in the atmosphere.
The simulation of clouds containing Supercooled Large Droplets has received increasing attention due to the introduction of Appendix O and future associated requirements for means of compliance. Supercooled Large Droplet conditions can cover four orders of magnitude in drop sizes, imposing a larger instrument measurement challenge than for Appendix C conditions. Wind tunnel facilities have adopted different instrumentation for liquid water content measurement, with fundamentally different principles of operation. In order to explore the comparability of the different instruments used for Appendix C and SLD measurement, and its impact on confidence in measurements used for means of compliance, a project was established to conduct a series of dedicated tests at three wind tunnel facilities. To date, liquid water content measurements have been completed using a Multi-Element sensor as the common instrument at two of the facilities. The data have provided preliminary information suggesting that substantial inter-facility differences likely exist in liquid water content estimates in Supercooled Large Droplet conditions that appear to be largely attributable to the choice of calibration instruments. These results are dependent on the assumption that the Multi-Element probe would produce equivalent measurements at the two facilities if the liquid water content were the same, regardless of other environmental differences that may exist between the two facilities. Planned further testing with other liquid water content measurement techniques may provide further information to confirm or refute the results of this study.
Operational products used by the U.S. Federal Aviation Administration to alert pilots of hazardous icing provide nowcast and short-term forecast estimates of the potential for the presence of supercooled liquid water and supercooled large droplets. The Current Icing Product (CIP) system employs basic satellite-derived information, including a cloud mask and cloud top temperature estimates, together with multiple other data sources to produce a gridded, three-dimensional, hourly depiction of icing probability and severity. Advanced satellite-derived cloud products developed at the NASA Langley Research Center (LaRC) provide a more detailed description of cloud properties (primarily at cloud top) compared to the basic satellite-derived information used currently in CIP. Cloud hydrometeor phase, liquid water path, cloud effective temperature, and cloud top height as estimated by the LaRC algorithms are into the CIP fuzzy logic scheme and a confidence value is determined. Examples of CIP products before and after the integration of the LaRC satellite-derived products will be presented at the conference.
A method to distinguish cloud thermodynamic phase from polarized Micro Pulse Lidar (MPL) measurements is described. The method employs a simple enumerative approach to classify cloud layers as either liquid water, ice water, or mixed-phase clouds based on the linear volume depolarization ratio and cloud top temperatures derived from Goddard Earth Observing System, version 5 (GEOS-5) assimilated data. Two years of cloud retrievals from the Micro Pulse Lidar Network (MPLNET) site in Greenbelt, MD are used to evaluate the performance of the algorithm. The fraction of supercooled liquid water in the mixed-phase temperature regime (-37 °C – 0 °C) calculated using MPLNET data is compared to similar calculations made using the spaceborne Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument on board the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite, with reasonable consistency.
The NASA Glenn Research Center 2019 Icing Research Tunnel cloud calibration results are presented in this paper. The current status of the cloud uniformity, liquid water content, and drop-size calibration results will be discussed. The goals achieved in this calibration were to establish a uniform cloud and to generate transfer functions from the inputs of airspeed, spray bar atomizing air pressure, and spray bar water pressure to the outputs of median volumetric diameter (drop size distributions) and liquid water content. This was completed for both 14 CFR Parts 25 and 29, Appendix C (“typical” icing) and Appendix O (supercooled large drop) conditions. The cloud uniformity from the Standard nozzles has remained the same as what was reported for the 2014 full calibration. Changes were made to the Mod1 nozzle cloud, resulting in higher liquid water content values associated with the Mod1 cloud.
Experimental data on ice nucleation, presented in an earlier paper, are analyzed to yield information about the homogeneous nucleation rate of ice from supercooled liquid and the heights of energy barriers to that nucleation. The experiment consisted of using an expansion cloud chamber to nucleate from the vapor a cloud of supercooled pure water drops and the observation of the fraction of drops which subsequently froze. The analysis employed standard classical homogeneous nucleation theory. The data are used to extract the first experimental measurement (albeit indirect) of the activation energy for the transfer of a water molecule across the liquid-ice interface at temperatures near -40 C. The results provide further evidence that the local liquid structure becomes more icelike as the temperature is lowered.