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Yi Huang
,
Steven T. Siems
,
Michael J. Manton
,
Daniel Rosenfeld
,
Roger Marchand
,
Greg M. McFarquhar
, and
Alain Protat

Abstract

This study employs four years of spatiotemporally collocated A-Train satellite observations to investigate cloud and precipitation characteristics in relation to the underlying properties of the Southern Ocean (SO). Results show that liquid-phase cloud properties strongly correlate with the sea surface temperature (SST). In summer, ubiquitous supercooled liquid water (SLW) is observed over SSTs less than about 4°C. Cloud-top temperature (CTT) and effective radius of liquid-phase clouds generally decrease for colder SSTs, whereas the opposite trend is observed for cloud-top height, cloud optical thickness, and liquid water path. The deduced cloud depth is larger over the colder oceans. Notable differences are observed between “precipitating” and “nonprecipitating” clouds and between different ocean sectors. Using a novel joint SST–CTT histogram, two distinct liquid-phase cloud types are identified, where the retrieved particle size appears to increase with decreasing CTT over warmer water (SSTs >~7°C), while the opposite is true over colder water. A comparison with the Northern Hemisphere (NH) storm-track regions suggests that the ubiquitous SLW with markedly smaller droplet size is a unique feature for the cold SO (occurring where SSTs <~4°C), while the presence of this cloud type is much less frequent over the NH counterparts, where the SSTs are rarely colder than about 4°C at any time of the year. This study also suggests that precipitation, which has a profound influence on cloud properties, remains poorly observed over the SO with the current spaceborne sensors. Large uncertainties in precipitation properties are associated with the ubiquitous boundary layer clouds within the lowest kilometer of the atmosphere.

Full access
Luis Ackermann
,
Yi Huang
,
Steven Siems
,
Michael Manton
,
Francisco Lang
,
Thomas Chubb
,
Andrew Peace
,
Johanna Speirs
,
Kenyon Suzanne
,
Alain Protat
, and
Simon P. Alexander

Abstract

Understanding the key dynamical and microphysical mechanisms driving precipitation in the Snowy Mountains region of southeast Australia, including the role of orography, can help improve precipitation forecasts, which is of great value for efficient water management. An intensive observation campaign was carried out during the 2018 austral winter, providing a comprehensive range of ground-based observations across the Snowy Mountains. We used data from three vertically pointing rain radars, cloud radar, a PARSIVEL disdrometer, and a network of 76 pluviometers. The observations reveal that all of the precipitation events were associated with cold front passages. About half accumulated during the frontal passage associated with deep, fully glaciated cloud tops, while the rest occurred in the postfrontal environment and were associated with clouds with supercooled liquid water (SLW) tops. About three-quarters of the accumulated precipitation was observed under blocked conditions, likely associated with blocked stratiform orographic enhancement. Specifically, more than a third of the precipitation resulted from moist cloudless air being lifted over stagnant air, upwind from the barrier, creating SLW-top clouds. These SLW clouds then produced stratiform precipitation mostly over the upwind slopes and mountain tops, with hydrometeors reaching the mountain tops mostly as rimed snow. Two precipitation events were studied in detail, which showed that during unblocked conditions, orographic convection invigoration and unblocked stratiform enhancement were the two main mechanisms driving the precipitation, with the latter being more prevalent after the frontal passage. During these events, ice particle growth was likely dominated by vapor deposition and aggregation during the frontal periods, while riming dominated during the postfrontal periods.

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Tzu-Ying Yang
,
Cho-Ying Huang
,
Jehn-Yih Juang
,
Yi-Ying Chen
,
Chao-Tzuen Cheng
, and
Min-Hui Lo

Abstract

Fog plays a vital role in maintaining ecosystems in montane cloud forests. In these forests, a large amount of water on the surface of leaves and canopy (hereafter canopy water) evaporates during the morning. This biophysical process plays a critical factor in regulating afternoon fog formation. Recent studies have found that alterations in precipitation, temperature, humidity, and CO2 concentrations associated with future climate changes may affect terrestrial hydroclimatology, but the responses in cloud forests remain unclear. Utilizing numerical experiments with the Community Land Model, we explored changes in surface evaporative fluxes in Chi-Lan Mountain cloud forests in northeastern Taiwan under the RCP8.5 scenario with changes in the aforementioned various atmospheric variables. The results showed that increased rainfall intensity in climate change runs decreased the accumulation of canopy water, while larger water vapor concentrations led to more nighttime condensation on leaves. Elevated CO2 concentrations did not greatly impact canopy water amounts, but photosynthesis was enhanced, while transpiration was reduced and contributed to decreased latent heat fluxes, implying the importance of forest plant physiology in modulating land evaporative fluxes. Evapotranspiration decreased in Chi-Lan due to multiple combined factors, in contrast to the expected intensification in the global water cycle under global warming. The study, however, is restricted to an offline land surface model without land–atmosphere interactions and the interactions with adjacent grids, which deserves further analyses for the water cycle changes in the montane cloud forest regions.

Open access
Sisi Chen
,
Lulin Xue
,
Sarah Tessendorf
,
Thomas Chubb
,
Andrew Peace
,
Luis Ackermann
,
Artur Gevorgyan
,
Yi Huang
,
Steven Siems
,
Roy Rasmussen
,
Suzanne Kenyon
, and
Johanna Speirs

Abstract

This study presents the first numerical simulations of seeded clouds over the Snowy Mountains of Australia. WRF-WxMod, a novel glaciogenic cloud-seeding model, was utilized to simulate the cloud response to winter orographic seeding under various meteorological conditions. Three cases during the 2018 seeding periods were selected for model evaluation, coinciding with an intensive ground-based measurement campaign. The campaign data were used for model validation and evaluation. Comparisons between simulations and observations demonstrate that the model realistically represents cloud structures, liquid water path, and precipitation. Sensitivity tests were performed to pinpoint key uncertainties in simulating natural and seeded clouds and precipitation processes. They also shed light on the complex interplay between various physical parameters/processes and their interaction with large-scale meteorology. Our study found that in unseeded scenarios, the warm and cold biases in different initialization datasets can heavily influence the intensity and phase of natural precipitation. Secondary ice production via Hallett–Mossop processes exerts a secondary influence. On the other hand, the seeding impacts are primarily sensitive to aerosol conditions and the natural ice nucleation process. Both factors alter the supercooled liquid water availability and the precipitation phase, consequently impacting the silver iodide (AgI) nucleation rate. Furthermore, model sensitivities were inconsistent across cases, indicating that no single model configuration optimally represents all three cases. This highlights the necessity of employing an ensemble approach for a more comprehensive and accurate assessment of the seeding impact.

Significance Statement

Winter orographic cloud seeding has been conducted for decades over the Snowy Mountains of Australia for securing water resources. However, this study is the first to perform cloud-seeding simulation for a robust, event-based seeding impact evaluation. A state-of-the-art cloud-seeding model (WRF-WxMod) was used to simulate the cloud seeding and quantified its impact on the region. The Southern Hemisphere, due to low aerosol emissions and highly pristine cloud conditions, has distinctly different cloud microphysical characteristics than the Northern Hemisphere, where WRF-WxMod has been successfully applied in a few regions over the United States. The results showed that WRF-WxMod could accurately capture the clouds and precipitation in both the natural and seeded conditions.

Restricted access
Jun Li
,
Hung-Lung Huang
,
Chian-Yi Liu
,
Ping Yang
,
Timothy J. Schmit
,
Heli Wei
,
Elisabeth Weisz
,
Li Guan
, and
W. Paul Menzel

Abstract

The Moderate Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the NASA Earth Observing System Aqua satellite enable global monitoring of the distribution of clouds during day and night. The MODIS is able to provide a high-spatial-resolution (1–5 km) cloud mask, cloud classification mask, cloud-phase mask, cloud-top pressure (CTP), and effective cloud amount during both the daytime and the nighttime, as well as cloud particle size (CPS) and cloud optical thickness (COT) at 0.55 μm during the daytime. The AIRS high-spectral-resolution measurements reveal cloud properties with coarser spatial resolution (13.5 km at nadir). Combined, MODIS and AIRS provide cloud microphysical properties during both the daytime and nighttime. A fast cloudy radiative transfer model for AIRS that accounts for cloud scattering and absorption is described in this paper. One-dimensional variational (1DVAR) and minimum-residual (MR) methods are used to retrieve the CPS and COT from AIRS longwave window region (790–970 cm−1 or 10.31–12.66 μm, and 1050–1130 cm−1 or 8.85–9.52 μm) cloudy radiance measurements. In both 1DVAR and MR procedures, the CTP is derived from the AIRS radiances of carbon dioxide channels while the cloud-phase information is derived from the collocated MODIS 1-km phase mask for AIRS CPS and COT retrievals. In addition, the collocated 1-km MODIS cloud mask refines the AIRS cloud detection in both 1DVAR and MR procedures. The atmospheric temperature profile, moisture profile, and surface skin temperature used in the AIRS cloud retrieval processing are from the European Centre for Medium-Range Weather Forecasts forecast analysis. The results from 1DVAR are compared with the operational MODIS products and MR cloud microphysical property retrieval. A Hurricane Isabel case study shows that 1DVAR retrievals have a high correlation with either the operational MODIS cloud products or MR cloud property retrievals. 1DVAR provides an efficient way for cloud microphysical property retrieval during the daytime, and MR provides the cloud microphysical property retrievals during both the daytime and nighttime.

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Ming Cai
,
Yueyue Yu
,
Yi Deng
,
Huug M. van den Dool
,
Rongcai Ren
,
Suru Saha
,
Xingren Wu
, and
Jin Huang

Abstract

Extreme weather events such as cold-air outbreaks (CAOs) pose great threats to human life and the socioeconomic well-being of modern society. In the past, our capability to predict their occurrences has been constrained by the 2-week predictability limit for weather. We demonstrate here for the first time that a rapid increase of air mass transported into the polar stratosphere, referred to as the pulse of the stratosphere (PULSE), can often be predicted with a useful degree of skill 4–6 weeks in advance by operational forecast models. We further show that the probability of the occurrence of continental-scale CAOs in midlatitudes increases substantially above normal conditions within a short time period from 1 week before to 1–2 weeks after the peak day of a PULSE event. In particular, we reveal that the three massive CAOs over North America in January and February of 2014 were preceded by three episodes of extreme mass transport into the polar stratosphere with peak intensities reaching a trillion tons per day, twice that on an average winter day. Therefore, our capability to predict the PULSEs with operational forecast models, in conjunction with its linkage to continental-scale CAOs, opens up a new opportunity for 30-day forecasts of continental-scale CAOs, such as those occurring over North America during the 2013/14 winter. A real-time forecast experiment inaugurated in the winter of 2014/15 has given support to the idea that it is feasible to forecast CAOs 1 month in advance.

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Rong-Yu Gu
,
Min-Hui Lo
,
Chi-Ya Liao
,
Yi-Shin Jang
,
Jehn-Yih Juang
,
Cho-Ying Huang
,
Shih-Chieh Chang
,
Cheng-I Hsieh
,
Yi-Ying Chen
,
Housen Chu
, and
Kuang-Yu Chang

Abstract

Hydroclimate in the montane cloud forest (MCF) regions is unique for its frequent fog occurrence and abundant water interception by tree canopies. Latent heat (LH) flux, the energy flux associated with evapotranspiration (ET), plays an essential role in modulating energy and hydrological cycles. However, how LH flux is partitioned between transpiration (stomatal evaporation) and evaporation (nonstomatal evaporation) and how it impacts local hydroclimate remain unclear. In this study, we investigated how fog modulates the energy and hydrological cycles of MCF by using a combination of in situ observations and model simulations. We compared LH flux and associated micrometeorological conditions at two eddy-covariance sites—Chi-Lan (CL), an MCF, and Lien-Hua-Chih (LHC), a noncloud forest in Taiwan. The comparison between the two sites reveals an asymmetric LH flux with an early peak at 0900 local time in CL as opposed to LHC, where LH flux peaks at noon. The early peak of LH flux and its evaporative cooling dampen the increase in near-surface temperature during the morning hours in CL. The relatively small diurnal temperature range, abundant moisture brought by the valley wind, and local ET result in frequent afternoon fog formation. Fog water is then intercepted by the canopy, sustaining moist conditions throughout the night. To further illustrate this hydrological feedback, we used a land surface model to simulate how varying canopy water interception can affect surface energy and moisture budgets. Our study highlights the unique hydroclimatological cycle in the MCF and, specifically, the inseparable relationship between the canopy and near-surface meteorology during the diurnal cycle.

Open access
I.-I. Lin
,
Robert F. Rogers
,
Hsiao-Ching Huang
,
Yi-Chun Liao
,
Derrick Herndon
,
Jin-Yi Yu
,
Ya-Ting Chang
,
Jun A. Zhang
,
Christina M. Patricola
,
Iam-Fei Pun
, and
Chun-Chi Lien

Abstract

Devastating Japan in October 2019, Supertyphoon (STY) Hagibis was an important typhoon in the history of the Pacific. A striking feature of Hagibis was its explosive rapid intensification (RI). In 24 h, Hagibis intensified by 100 knots (kt; 1 kt ≈ 0.51 m s−1), making it one of the fastest-intensifying typhoons ever observed. After RI, Hagibis’s intensification stalled. Using the current typhoon intensity record holder, i.e., STY Haiyan (2013), as a benchmark, this work explores the intensity evolution differences of these two high-impact STYs. We found that the extremely high prestorm sea surface temperature reaching 30.5°C, deep/warm prestorm ocean heat content reaching 160 kJ cm−2, fast forward storm motion of ∼8 m s−1, small during-storm ocean cooling effect of ∼0.5°C, significant thunderstorm activity at its center, and rapid eyewall contraction were all important contributors to Hagibis’s impressive intensification. There was 36% more air–sea flux for Hagibis’s RI than for Haiyan’s. After its spectacular RI, Hagibis’s intensification stopped, despite favorable environments. Haiyan, by contrast, continued to intensify, reaching its record-breaking intensity of 170 kt. A key finding here is the multiple pathways that storm size affected the intensity evolution for both typhoons. After RI, Hagibis experienced a major size expansion, becoming the largest typhoon on record in the Pacific. This size enlargement, combined with a reduction in storm translational speed, induced stronger ocean cooling that reduced ocean flux and hindered intensification. The large storm size also contributed to slower eyewall replacement cycles (ERCs), which prolonged the negative impact of the ERC on intensification.

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Chu-Chun Huang
,
Shu-Hua Chen
,
Yi-Chiu Lin
,
Kenneth Earl
,
Toshihisa Matsui
,
Hsiang-He Lee
,
I-Chun Tsai
,
Jen-Ping Chen
, and
Chao-Tzuen Cheng

Abstract

This study evaluates the impact of dust–radiation–cloud interactions on the development of a mesoscale convective system (MCS) by comparing numerical experiments run with and without dust–radiation and/or dust–cloud interactions. An MCS that developed over North Africa on 4–6 July 2010 is used as a case study. The CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellites passed over the center of the MCS after it reached maturity, providing valuable profiles of aerosol backscatter and cloud information for model verification. The model best reproduces the MCS’s observed cloud structure and morphology when both dust–radiation and dust–cloud interactions are included. Our results indicate that the dust–radiation effect has a far greater influence on the MCS’s development than the dust-cloud effect. Results show that the dust-radiative effect, both with and without the dust–cloud interaction, briefly delays the MCS’s formation but ultimately produces a stronger storm with a more extensive anvil cloud. This is caused by dust–radiation-induced changes to the MCS’s environment. The impact of the dust–cloud effect on the MCS, on the other hand, is greatly affected by the presence of the dust–radiation interaction. The dust–cloud effect alone slows initial cloud development but enhances heterogeneous ice nucleation and extends cloud lifetime. When the dust–radiation interaction is added, increased transport of dust into the upper portions of the storm—due to a dust–radiation-driven increase in convective intensity—allows dust–cloud processes to more significantly enhance heterogeneous freezing activity earlier in the storm’s development, increasing updraft strength, hydrometeor growth (particularly for ice particles), and rainfall.

Open access
Ayrton Zadra
,
Keith Williams
,
Ariane Frassoni
,
Michel Rixen
,
Ángel F. Adames
,
Judith Berner
,
François Bouyssel
,
Barbara Casati
,
Hannah Christensen
,
Michael B. Ek
,
Greg Flato
,
Yi Huang
,
Falko Judt
,
Hai Lin
,
Eric Maloney
,
William Merryfield
,
Annelize Van Niekerk
,
Thomas Rackow
,
Kazuo Saito
,
Nils Wedi
, and
Priyanka Yadav
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