Evaluation of a New Approach for Entrainment and Detrainment Rate Estimation

Journal of Geophysical Research Atmospheres, Journal Year: 2024, Volume and Issue: 129(13)

Published: July 4, 2024

Abstract Entrainment and detrainment rates ( ε δ ) constitute the most critical free parameters in mass flux schemes commonly employed for cumulus parameterizations. Recently, Zhu et al. (2021) introduced a new approach that utilizes aircraft observations to simultaneously estimate clouds, overcoming limitation of other observation‐based approaches solely yield without offering insights into . This study aims comprehensively evaluate reliability this approach. First, evaluation using an Explicit Mixing Parcel Model demonstrates capability back‐calculate predetermined based on physical properties before after entrainment mixing. Second, large‐eddy simulations illustrates yields consistent profiles compared traditional Sensitivity tests indicate weak sensitivity estimated with entrained air source. A decrease proportion cloudy assumed detrained leads reduction , while remains unaffected. Finally, appropriate assumptions are discussed. Estimating parameterizations involves acquiring ambient more than 500 m away from cloud edge as air. Due implicit mean field approximations approach, determining optimal assumption proves challenging. confirms estimating providing confidence its application extensive observational data advancement parameterization.

Language: Английский

---

Improvement of cloud microphysical parameterization and its advantages in simulating precipitation along the Sichuan-Xizang Railway

Science China Earth Sciences, Journal Year: 2024, Volume and Issue: 67(3), P. 856 - 873

Published: Feb. 2, 2024

Language: Английский

---

Using Machine Learning to Predict Cloud Turbulent Entrainment‐Mixing Processes

Journal of Advances in Modeling Earth Systems, Journal Year: 2024, Volume and Issue: 16(8)

Published: Aug. 1, 2024

Abstract Different turbulent entrainment‐mixing mechanisms between clouds and environment are essential to cloud‐related processes; however, accurate representation of in weather/climate models still poses a challenge. This study exploits the use machine learning (ML) address this Four ML (Light Gradient Boosting Machine [LGB], eXtreme Boosting, Random Forest, Support Vector Regression) examined compared. It is found that LGB performs best, thus selected understand impact on microphysics using simulation data from Explicit Mixing Parcel Model. Compared with traditional parameterizations, trained provides more microphysical properties (number concentration cloud droplet spectral dispersion). The partial dependences predicted features exhibit strong alignment physical expectations, as determined by interpreting method, overcoming limitations “black box” scheme. underlying smaller number larger dispersion correspond inhomogeneous entrainment‐mixing. Specifically, after positively correlated adiabatic liquid water content affected entrainment‐mixing, inversely volume mean radius. Spectral negatively dissipation rate relative humidity entrained air. Sensitivity analysis further suggests mainly whereas influenced both environmental variables. results indicate scheme has potential enhance models.

Language: Английский

---

Aircraft Observations Reveal the Relationship Between Cumulus Entrainment Rate and Aerosol Loading

Geophysical Research Letters, Journal Year: 2024, Volume and Issue: 51(19)

Published: Oct. 1, 2024

Abstract The influence of entrainment, a key process characterized by the entrainment rate in cumulus parameterization, on aerosol‐cloud interactions has been widely recognized. However, despite qualitative links established between and aerosol loading, quantitative relationship based observational evidence remains elusive. This study utilizes aircraft observations clouds during two field campaigns to determine loading. In both campaigns, is negatively correlated with It speculated that increased loading enhances cloud edge droplet evaporation, which leads buoyancy vertical velocity within cloud, thereby reducing rate. Further analysis shows response perturbations more significant smaller weak less pronounced under opposite conditions. These findings shed new light improving description parameterizations.

Language: Английский

---

Exploring the interconnections between total cloud water content and water vapor mixing ratio with other cloud microphysical variables in northward-moving typhoon precipitation via information entropy: A hybrid causal analysis approach using wavelet coherence and Liang–Kleeman information flow

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 107914 - 107914

Published: Jan. 1, 2025

Language: Английский

---

A new approach for identifying dominant cloud types and relationships between cloud types and precipitation vertical structure in tropical regions

Atmospheric and Oceanic Science Letters, Journal Year: 2025, Volume and Issue: unknown, P. 100606 - 100606

Published: Feb. 1, 2025

Language: Английский

---

Implementation and Evaluation of Physics‐Driven Dynamic Entrainment‐Mixing Parameterization in a Climate Model and Its Impact on Low‐Cloud Simulation

Journal of Geophysical Research Atmospheres, Journal Year: 2025, Volume and Issue: 130(6)

Published: March 25, 2025

Abstract The turbulent entrainment‐mixing process in the Community Earth System Model version 1.2 (CESM1.2) is assumed to follow extremely inhomogeneous entrainment‐mixing. However, different scenarios can occur real clouds. To address this deficiency, a unifying parameterization that represents processes implemented and evaluated CESM1.2. results indicate homogeneous mixing degree values simulated by new CESM1.2 are predominantly greater than 50%, suggesting tendency toward mixing. Compared mechanism, increases cloud droplet number concentration ( N c ). More importantly, improves low‐cloud fraction (CLDLOW) simulation Northwest Pacific (NWP) Southeast (SEP) regions, with relative improvements of 2.95% 4.17%, respectively. Furthermore, reach up 44.6% 16.2% NWP SEP respectively, when considering relationship between CLDLOW. Further analysis reveals enhances optical depth, longwave radiative cooling effect, net condensation rate, water ratio, lower‐troposphere stability, CLDLOW increasing . These underscore importance improving climate models.

Language: Английский

---

Optimization of electric vehicle charging facility layout considering the enhancement of renewable energy consumption capacity and improvement of PSO algorithm

Energy Informatics, Journal Year: 2025, Volume and Issue: 8(1)

Published: April 28, 2025

Language: Английский

---

Extreme wind speed estimation in thunderstorm gales utilizing dual-polarization weather radar hydrometeor classification products

Atmospheric Research, Journal Year: 2025, Volume and Issue: unknown, P. 108202 - 108202

Published: May 1, 2025

Language: Английский

---

Shallow Convection Dataset Simulated by Three Different Large Eddy Models

Advances in Atmospheric Sciences, Journal Year: 2024, Volume and Issue: 41(4), P. 754 - 766

Published: Feb. 9, 2024

Abstract Shallow convection plays an important role in transporting heat and moisture from the near-surface to higher altitudes, yet its parameterization numerical models remains a great challenge, partly due lack of high-resolution observations. This study describes large eddy simulation (LES) dataset for four shallow cases that differ primarily inversion strength, which can be used as surrogate real data. To reduce uncertainty LES modeling, three different were used, including SAM (System Atmospheric Modeling), WRF (Weather Research Forecasting model), UCLA-LES. Results show generally exhibit similar behavior each case, despite some differences details convective structure. In addition grid-averaged fields, conditionally sampled variables, such in-cloud vertical velocity, are also provided, indispensable calculation entrainment/detrainment rate. Considering essentiality entraining/detraining process cumulus convection, presented this is potentially useful validation improvement convection.

Language: Английский

---