Effects of Moist Convection on Mesoscale Predictability

By Fuqing Zhang, C. Snyder, R. Rotunno. 2003

329 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/84baa65eb75f6f195ad8e89322f0cc8769d3e6ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Fuqing Zhang
2. Q. Xiao
3. Ming Xue
4. C. Snyder
5. Juanzhen Sun
6. D. Barker
7. Xuguang Wang
8. Xiangyu Huang

In Global and Planetary Change

1. Fuqing Zhang
2. Q. Xiao
3. Ming Xue
4. Juanzhen Sun
5. D. Barker
6. C. Snyder
7. Xuguang Wang
8. R. Plant

Citations in the corpus

• Next-Day Convection-Allowing WRF Model Guidance: A Second Look at 2-km versus 4-km Grid Spacing (2009, background): “However, as storm-scale predictability on both 2- and 4-km grids is lost as forecast time increases (Zhang et al. 2003; Hohenegger and Schär 2007), we hypothesize that higher resolution is less important at later forecast periods, when cumulative small-scale error growth renders the smallest…”
• Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results (2009, background): “The other possible reason is that the weather system is nonlinear in nature (Zhang et al. 2002, 2003, 2007).”
• Mesoscale Predictability of Moist Baroclinic Waves: Convection-Permitting Experiments and Multistage Error Growth Dynamics (2007, background): “The association of the initial error growth with conditional instability and moist convection was also found in Zhang et al. (2002, 2003), and TZRS04 and showed that moist convection is responsible for the rapid initial error growth in the simulations. b. Stage 2: Transition and adjustment (3–18…”
• A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges (2015, background): “At kilometer scales, deterministic predictability is limited to a few hours and small-scale structures are dominated by stochastic processes [Lorenz, 1969; Zhang et al., 2003; Hohenegger and Schär, 2007].”
• Atmospheric Predictability at Synoptic Versus Cloud-Resolving Scales (2007, background): “2001), for limited-area integrations (e.g., Ancell and Mass 2006; Errico and Raeder 1999; Zhang et al. 2003), for idealized simulations of moist convection with cloud-resolving models (e.”
• A Stochastic Parameterization for Deep Convection Based on Equilibrium Statistics (2008, background): “In such cases, moist convection reacts strongly to near-grid-scale noise, which can cause simulations to evolve in quite distinct ways (Zhang et al. 2003; Done et al. 2006).”
• Moist Orographic Convection: Physical Mechanisms and Links to Surface-Exchange Processes (2018, background): “While finer grids can reduce model error by reducing the reliance on uncertain subgrid parameterization schemes, they induce faster error growth on the smaller, newly resolved scales [149], which can grow upscale to contaminate the larger-scale flow [150].”
• SAL—A Novel Quality Measure for the Verification of Quantitative Precipitation Forecasts (2008, background): “Theoretical and model studies on error propagation (e.g., Zhang et al. 2002, 2003, 2006; Walser et al. 2004; Walser and Schär 2004; Hohenegger et al. 2006) indicate that the predictability limit falls off rapidly toward small scales (1–100 km) mainly due to upscale error propagation associated with…”