Evaluating Precipitation Features and Rainfall Characteristics in a Multi‐Scale Modeling Framework. (21st August 2020)

• Record Type: Journal Article
• Authors: Chern, Jiun‐Dar; Tao, Wei‐Kuo; Lang, Stephen E.; Li, Xiaowen; Matsui, Toshihisa
• Is Part Of: Journal of advances in modeling earth systems. Volume 12:Number 8(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-08-21
• Subjects: Superparameterization -- multi‐scale modeling framework -- TRMM precipitation features -- two‐dimensionality -- cyclic boundary -- tropical precipitation; Geological modeling -- Periodicals; Climatology -- Periodicals; Geochemical modeling -- Periodicals; 551.5011
• DOI: 10.1029/2019MS002007
• Languages: English
• ISSNs: 1942-2466

Abstract

Abstract: Cloud and precipitation systems are simulated with a multi‐scale modeling framework (MMF) and compared over the Tropics and Subtropics against the Tropical Rainfall Measuring Mission (TRMM) Radar‐defined Precipitation Features (RPFs) product. A methodology, in close analogy to the TRMM RPFs, is developed to produce simulated precipitation features (PFs) from the output of the embedded two‐dimensional (2D) cloud‐resolving models (CRMs) within an MMF. Despite the limitations of 2D CRMs, the simulated population distribution, horizontal and vertical structure of PFs, and the geographical location and local rainfall contribution of mesoscale convective systems (MCSs) are in good agreement with the TRMM observations. However, some model discrepancies are found and can be identified and quantified within the PF distributions. Using model biases in relative population and rainfall contributions, PFs can be characterized into four size categories: small, medium to large, very large, and extremely large. Four different major mechanisms might account for the model biases in each different category: (1) the two‐dimensionality of the CRMs, (2) a positive convection‐wind‐evaporation feedback loop, (3) an artificial dynamic constraint in a bounded CRM domain with cyclic boundaries, and (4) the limited CRM domain size. The second and fourth mechanisms tend to contribute to the excessive tropical precipitation biases commonly found in most MMFs, whereas the other mechanisms reduce rainfall contributions from small and very large PFs. MMF sensitivity experiments with various CRM domain sizes and grid spacings showed that larger domains (higher resolutions) tend to shift PF populations toward larger (smaller) sizes. Plain Language Summary: Satellites are an excellent way to evaluate models over a large area, and of these, the Tropical Rainfall Measuring Mission satellite known as TRMM, the first satellite to have a precipitation radar in space, was especially valuable as it could detect precipitation within clouds. Radar data from TRMM have been used to build a database categorizing all precipitation that TRMM observed, including their size, rain intensity, and how high in the atmosphere their radar echoes extended. In this study, similar data from the embedded fine‐scale models of a hybrid global model are mined and compared with that observed by TRMM. In general, the simulated distributions of these precipitation areas in terms of their size, geographical location, and horizontal and vertical structure agree fairly well with TRMM observations. However, some discrepancies were found, which were identified and quantified in terms of precipitation size. Four different mechanisms might account for the model biases: (1) the use of two‐dimensional grids for the fine‐scale models, (2) a positive storm‐wind‐surface evaporation feedback loop in the overall hybrid model, (3) an artificial constraint imposed by using cyclic boundaries for the fine‐scale models, and (4) the limited spatial size of the fine‐scale model domains. Key Points: Many characteristics of simulated rain features are in reasonable agreement with satellite observations Four different major mechanisms might account for the rainfall and precipitation feature biases in the model Medium to large rain features contribute the most to the model tropical precipitation biases