Aerosol effects on the timing of warm rain processes

2015 ◽  
Vol 42 (11) ◽  
pp. 4590-4598 ◽  
Author(s):  
Guy Dagan ◽  
Ilan Koren ◽  
Orit Altaratz
Keyword(s):  
Warm Rain ◽  
Aerosol Effects

A comparison study of aerosol impacts on idealized supercell between two bulk microphysics parameterizations

2020 ◽  
Author(s):  
Wanchen Wu ◽  
Wei Huang ◽  
Baode Chen
Keyword(s):  
Weather Prediction ◽  
Cloud Droplet ◽  
Forecast Model ◽  
Cloud Microphysics ◽  
Microphysics Scheme ◽  
Freezing Level ◽  
Cold Pools ◽  
Warm Rain ◽  
Warm Rain Process ◽  
Aerosol Effects

<p>Considering aerosol effects via microphysics parameterization is an imperative work in high-resolution numerical weather prediction. This paper uses two bulk microphysics parameterizations, Aerosol-Aware Thompson and CLR schemes, with the Weather and Research Forecast model to study the impacts of aerosols and microphysics scheme on an idealized supercell storm. Our results show that the implementation of aerosols can successfully modify the cloud droplet size and influence the subsequent warm-rain, mixed-phase, and accumulated precipitation. It implies that aerosols can make numerous differences to cloud microphysics properties and processes but the uncertainty in the magnitude of aerosol effects is huge because the two schemes are different from each other since the warm-rain process including CCN activation and rainwater formation. On the other hand, it is also found that the two schemes make tremendous differences in the rainfall pattern and storm dynamics due to the presence of graupel below the freezing level. The Thompson scheme has hail-like graupel which can fall below the freezing level to chill the air temperature effectively, intensify the downdraft, and enhance the uplifting on the front of cold pools. The mean graupel size represented by the two schemes plays a much more important role than the fall-speed formula for the dynamical feedbacks. Our results suggest that particle size is the core of a myriad of microphysics processes and highly associated with key cloud and dynamical signatures.</p>

scholarly journals Simulation of hurricane response to suppression of warm rain by sub-micron aerosols

2007 ◽  
Vol 7 (2) ◽  
pp. 5647-5674 ◽  
Author(s):  
D. Rosenfeld ◽  
A. Khain ◽  
B. Lynn ◽  
W. L. Woodley
Keyword(s):  
Hurricane Katrina ◽  
Cloud Condensation Nuclei ◽  
Evaporative Cooling ◽  
Central Area ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Wind Speeds ◽  
Warm Rain ◽  
Aerosol Effects ◽  
Rain Formation

Abstract. The feasibility of hurricane modification was investigated for hurricane Katrina using the Weather Research and Forecasting Model (WRF). The possible impact of seeding of clouds with submicron cloud condensation nuclei (CCN) on hurricane structure and intensity as measured by nearly halving of the area covered by hurricane force winds was simulated by "turning–off" warm rain formation in the clouds at Katrina's periphery (where wind speeds were less than 22 m s−1). This simplification of the simulation of aerosol effects is aimed at evaluating the largest possible response. This resulted in the weakening of the hurricane surface winds compared to the "non-seeded" simulated storm during the first 24 h within the entire tropical cyclone (TC) area compared to a control simulation without warm rain suppression. Later, the seeding-induced evaporative cooling at the TC periphery led to a shrinking of the eye and hence to some increase in the wind within the small central area of the TC. Yet, the overall strength of the hurricane decreased in response to the suppressed warm rain at the periphery, as measured by a 25% reduction in the radius of hurricane force winds. In a simulation with warm rain suppression throughout the hurricane, the relative weakening compared to the control continued throughout the simulations and the eye shrunk even further. This shows that the main mechanism by which suppressing warm rain weakens the TC is the low level evaporative cooling of the un-precipitated cloud drops and the added cooling due to melting of precipitation that falls from above.

scholarly journals Aerosol Effects on Simulated Storm Electrification and Precipitation in a Two-Moment Bulk Microphysics Model

2013 ◽  
Vol 70 (7) ◽  
pp. 2032-2050 ◽  
Author(s):  
Edward R. Mansell ◽  
Conrad L. Ziegler
Keyword(s):  
Cloud Condensation Nuclei ◽  
Lightning Activity ◽  
Cloud Droplets ◽  
Precipitation Mechanism ◽  
Warm Rain ◽  
Droplet Sizes ◽  
Ice Multiplication ◽  
Aerosol Effects ◽  
Precipitation Formation ◽  
Very High

Abstract The effects of cloud condensation nuclei (CCN) concentrations are found to strongly affect the microphysical and electrical evolution of a numerically simulated small multicell storm. The simulations reproduce the well-known effects of updraft invigoration and delay of precipitation formation as increasing CCN from low to intermediate concentrations causes droplet sizes to decrease. Peak updrafts increased from 16 m s−1 at the lowest CCN to a maximum of 21–22 m s−1 at moderate CCN, where condensation latent heating is maximized. The transition from low to high CCN first maximizes warm-rain production before switching over to the ice process as the dominant precipitation mechanism. Average graupel density stays fairly high and constant at lower CCN, but then drops monotonically at higher CCN concentration, although high CCN also foster the appearance of small regions of larger, high-density graupel with high simulated radar reflectivity. Graupel production increases monotonically as CCN concentration rises from 50 to about 2000 cm−3. The lightning response is relatively weak until the Hallett–Mossop rime-splintering ice multiplication becomes more active at CCN > 700 cm−3. At very high CCN concentrations (>2000 cm−3), graupel production decreases slowly, but lightning activity drops dramatically when the parameterization of Hallett–Mossop rime-splintering ice multiplication is based on the number of large cloud droplets collected by graupel. Conversely, lightning activity remains steady at extremely high CCN concentration when the Hallett–Mossop parameterization is based simply on the rate of rime mass accumulation. The results lend support to the aerosol hypothesis as applied to lightning production, whereby greater CCN concentration tends to lead to greater lightning activity, but with a large sensitivity to ice multiplication.

scholarly journals Simulation of hurricane response to suppression of warm rain by sub-micron aerosols

2007 ◽  
Vol 7 (13) ◽  
pp. 3411-3424 ◽  
Author(s):  
D. Rosenfeld ◽  
A. Khain ◽  
B. Lynn ◽  
W. L. Woodley
Keyword(s):  
Hurricane Katrina ◽  
Cloud Condensation Nuclei ◽  
Evaporative Cooling ◽  
Central Area ◽  
Weather Research And Forecasting ◽  
Forecasting Model ◽  
Wind Speeds ◽  
Warm Rain ◽  
Aerosol Effects ◽  
Rain Formation

Abstract. The feasibility of hurricane modification was investigated for hurricane Katrina using the Weather Research and Forecasting Model (WRF). The possible impact of seeding of clouds with submicron cloud condensation nuclei (CCN) on hurricane structure and intensity as measured by nearly halving of the area covered by hurricane force winds was simulated by "turning–off" warm rain formation in the clouds at Katrina's periphery (where wind speeds were less than 22 m s−1). This simplification of the simulation of aerosol effects is aimed at evaluating the largest possible response. This resulted in the weakening of the hurricane surface winds compared to the "non-seeded" simulated storm during the first 24 h within the entire tropical cyclone (TC) area compared to a control simulation without warm rain suppression. Later, the seeding-induced evaporative cooling at the TC periphery led to a shrinking of the eye and hence to some increase in the wind within the small central area of the TC. Yet, the overall strength of the hurricane, as defined by the area covered by hurricane force winds, decreased in response to the suppressed warm rain at the periphery, as measured by a 25% reduction in the radius of hurricane force winds. In a simulation with warm rain suppression throughout the hurricane, the radius of the hurricane force winds was reduced by more than 42%, and although the diameter of the eye shrunk even further the maximum winds weakened. This shows that the main mechanism by which suppressing warm rain weakens the TC is the low level evaporative cooling of the un-precipitated cloud drops and the added cooling due to melting of precipitation that falls from above.

scholarly journals A study of the fraction of warm rain in a pre-summer rainfall event over South China

2021 ◽  
pp. 105792
Author(s):  
Wenhua Gao ◽  
Lulin Xue ◽  
Liping Liu ◽  
Chunsong Lu ◽  
Yuxing Yun ◽  
...  
Keyword(s):  
South China ◽  
Summer Rainfall ◽  
Rainfall Event ◽  
Warm Rain

scholarly journals IAP-AACM v1.0： Global to regional evaluation of the atmospheric chemistry model in CAS-ESM

2018 ◽  
Author(s):  
Ying Wei ◽  
Xueshun Chen ◽  
Huansheng Chen ◽  
Jie Li ◽  
Zifa Wang ◽  
...  
Keyword(s):  
East Asia ◽  
Spatial Variation ◽  
Atmospheric Chemistry ◽  
Comprehensive Evaluation ◽  
Correlation Coefficients ◽  
Full Description ◽  
Atmospheric Physics ◽  
Temporal And Spatial Variability ◽  
Fine Mode ◽  
Aerosol Effects

Abstract. In this study, a full description and comprehensive evaluation of a global-regional nested model, the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics (IAP-AACM), is presented for the first time. Not only the global budgets and distribution, but also a comparison of nested simulation over China against multi-datasets are investigated, benefiting from the access of air quality monitoring data in China since 2013 and the Model Inter-Comparison Study for Asia project. The model results and analysis can greatly help reduce uncertainties and understand model diversity in assessing global and regional aerosol effects, especially over East Asia and areas affected by East Asia. The 1-year simulation for 2014 shows that the IAP-AACM is within the range of other models, and well reproduces both spatial distribution and seasonal variation of trace gases and aerosols over major continents and oceans (mostly within the factor of two). The model nicely captures spatial variation for carbon monoxide except an underestimation over the ocean that also shown in other models, which suggests the need for more accurate emission rate of ocean source. For aerosols, the simulation of fine-mode particulate matter (PM2.5) matches observation well and it has a better simulating ability on primary aerosols than secondary aerosols. This calls for more investigation on aerosol chemistry. Furthermore, IAP-AACM shows the superiority of global model, compared with regional model, on performing regional transportation for the nested simulation over East Asia. For the city evaluation over China, the model reproduces variation of sulfur dioxide (SO2), nitrogen dioxide (NO2) and PM2.5 accurately in most cities, with correlation coefficients above 0.5. Compared to the global simulation, the nested simulation exhibits an improved ability to capture the high temporal and spatial variability over China. In particular, the correlation coefficients for PM2.5, SO2 and NO2 are raised by ~ 0.25, ~ 0.15 and ~ 0.2 respectively in the nested grid. The summary provides constructive information for the application of chemical transport models. In future, we recommend the model's ability to capture high spatial variation of PM2.5 is yet to be improved.

scholarly journals The warm rain project in Hawaii

Tellus ◽  
1967 ◽  
Vol 19 (3) ◽  
pp. 1-1
Author(s):  
R. L. Lavoie
Keyword(s):  
Warm Rain

scholarly journals Estimate of the Cloud and Aerosol Effects on the Surface Radiative Flux Based on the Measurements and the Transfer Model Calculations

1999 ◽  
Vol 77 (5) ◽  
pp. 1007-1021 ◽  
Author(s):  
Yukari N. Takayabu ◽  
T. Ueno ◽  
T. Nakajima ◽  
I. Matsui ◽  
Y. Tsushima ◽  
...  
Keyword(s):  
Radiative Flux ◽  
Transfer Model ◽  
Model Calculations ◽  
Aerosol Effects

scholarly journals Machine Learning the Warm Rain Process

2020 ◽  
Author(s):  
Andrew Gettelman ◽  
David John Gagne ◽  
Chih-Chieh Chen ◽  
Matthew Christensen ◽  
Zachary Lebo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warm Rain ◽  
Warm Rain Process
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