Paper published in JGR about convective clustering

Our paper on objective classification of convective clustering during the AMIE/DYNAMO field campaign has been published in JGR Atmospheres. The lead author is a graduate student who I mentored in the analysis of the radar data. I participated in regular discussions about the overall research and assisted in the writing of the manuscript. We are working on a follow-on paper to this study investigating the mechanisms responsible for the clustering described in this study.

Citation: Cheng, W.‐Y., Kim, D., & Rowe, A. (2018). Objective quantification of convective clustering observed during the AMIE/DYNAMO two‐day rain episodes. Journal of Geophysical Research: Atmospheres, 123. https://doi.org/10.1029/2018JD028497

Abstract

One critical bottleneck in developing and evaluating ways to represent the mesoscale organization of convection in cumulus parameterization schemes is that there is no single accepted method of objectively quantifying the degree of convective organization or clustering from observations. This study addresses this need using high‐quality S‐PolKa radar data from the Atmospheric Radiation Measurement Madden‐Julian Oscillation Investigation Experiment/Dynamics of the Madden‐Julian Oscillation (AMIE/DYNAMO) field campaign. We first identify convective elements (contiguous convective echoes [CCEs]) from radar reflectivity observations using the rain type classification algorithm of Powell et al. (2016, https://doi.org/10.1175/JTECH‐D‐15‐0135.1). Then we apply scalar clustering metrics, including the organization index (I_org) of Tompkins and Semie, to the radar CCEs to test their ability of quantifying convective clustering during the observed two‐day rain episodes. Our results show two distinct phases of convective clustering during the two‐day rain episodes, with each phase covering about 10 hr before (Phase 1) and after (Phase 2) the time of peak rain rate. In Phase 1 clustering, the number of CCEs increases and convective cells cluster as new cells form preferentially near existing convective entities. The number of CCEs decreases as the environment stabilizes in Phase 2 clustering, during which already clustered cells with associated stratiform clouds are preferred over the isolated ones. I_org is capable of capturing convective clustering in both phases. The possible mechanisms for convective clustering are discussed, including cold pool‐updraft feedback, moisture‐convection interaction, and mesoscale circulations. Our results suggest that parameterizations of convective organization should represent the feedback processes that are responsible for the convective clustering during both phases.

Paper on DYNAMO CRM Intercomparison and validation published in JGR Atmos

Our paper “Evolution of Precipitation Structure During the November DYNAMO MJO Event: Cloud-Resolving Model Intercomparison and Cross Validation Using Radar Observations” has been published in the Journal of Geophysical Research Atmospheres. I contributed to the radar analysis and provided feedback on the comparisons with the model simulations.

Citation: Li, X., Janiga, M. A., Wang, S., Tao, W.‐K., Rowe, A., Xu, W., et al. (2018). Evolution of precipitation structure during the November DYNAMO MJO event: Cloud‐resolving model intercomparison and cross validation using radar observations. Journal of Geophysical Research: Atmospheres, 123, 3530-3555. https://doi.org/10.1002/2017JD027775

Abstract

Evolution of precipitation structures are simulated and compared with radar observations for the November Madden‐Julian Oscillation (MJO) event during the DYNAmics of the MJO (DYNAMO) field campaign. Three ground‐based, ship‐borne, and spaceborne precipitation radars and three cloud‐resolving models (CRMs) driven by observed large‐scale forcing are used to study precipitation structures at different locations over the central equatorial Indian Ocean. Convective strength is represented by 0‐dBZ echo‐top heights, and convective organization by contiguous 17‐dBZ areas. The multi‐radar and multi‐model framework allows for more stringent model validations. The emphasis is on testing models’ ability to simulate subtle differences observed at different radar sites when the MJO event passed through. The results show that CRMs forced by site‐specific large‐scale forcing can reproduce not only common features in cloud populations but also subtle variations observed by different radars. The comparisons also revealed common deficiencies in CRM simulations where they underestimate radar echo‐top heights for the strongest convection within large, organized precipitation features. Cross validations with multiple radars and models also enable quantitative comparisons in CRM sensitivity studies using different large‐scale forcing, microphysical schemes and parameters, resolutions, and domain sizes. In terms of radar echo‐top height temporal variations, many model sensitivity tests have better correlations than radar/model comparisons, indicating robustness in model performance on this aspect. It is further shown that well‐validated model simulations could be used to constrain uncertainties in observed echo‐top heights when the low‐resolution surveillance scanning strategy is used.

Annual DOE ASR Meeting

This week, I’ll be attending the DOE’s 2018 Joint ARM User Facility and ASR PI meeting in Vienna, Virginia. I’ll be presenting two posters as Co-Investigator for two ASR-funded projects and co-leading a breakout session about GOAmazon updates.  You can view the two posters here:

• Serra, Y. and Rowe, A.: Local and Large-scale Controls of Moisture Variability in the Shallow-to-deep Transition During GOAmazon
• Cheng, W., Kim, D., Rowe, A., Moon, Y., and Park, S.: Objective Quantification of Convective Clustering Using Ground-based Radar Reflectivity During AMIE/DYNAMO