| 英文摘要: | This project uses the National Center for Atmospheric Research S-PolKa radar to address a primary goal of the Dynamics of the Madden-Julian Oscillation (MJO) field experiment (DYNAMO): to document the evolution of the cloud population of the MJO throughout its cycle from suppressed to active and back to suppressed conditions. S-PolKa was uniquely capable of achieving this goal because of: (1) its ability to see not only the precipitating convection but also the nonprecipitating clouds; and (2) its dual-polarization technology that allowed determination of the microphysical as well as kinematic and precipitation structure of the precipitating convection. Working in concert with other radars, S-PolKa provided a wealth of information on the convective cloud evolution in the MJO, and this project determines the organization and structure of the cloud population at all of its stages of the MJO through analysis of the S-PolKa data and comparison with model output. The high sensitivity of the S-band S-PolKa radar provided observations of nonprecipitating clouds. In highly suppressed conditions, the MJO cloud population occurs initially in lines of small cumulus. When some of these clouds precipitate, the showers form cold pools, which take over the boundary layer structure. The cold pools trigger more and larger convection until the deep precipitating convection begins to dominate. The nonprecipitating clouds are seen by S-PolKa because they produce Bragg scattering, with identifiable dual-polarization signatures. Analysis of the S-PolKa scans will document the organization and evolution of the clouds into lines and cold pool patterns. A co-located vertically pointing Ka-band radar (the DOE KAZR) will add vertical structure information. The empirical characteristics of the shallow clouds will be compared to fine resolution numerical model output. S-PolKa?s dual-polarization data indicates the dominant microphysical characteristics of deeper convection and mesoscale convective systems (MCSs). Two types of MCSs occurred: squall-lines with trailing stratiform precipitation and non-squall systems in which stratiform precipitation formed from the dying of convective cells. The microphysical and kinematic structures of both types of MCS will be determined from S-PolKa. A nearly co-located C-band Doppler radar (SMART-R)will provide additional kinematic information. Comparison with regional model output will give insight into how the MCSs developed and how they were affected by wind shear and other environmental factors. Comparison of the DYNAMO observations with other NSF deployments (TiMREX and NAME) will indicate how the equatorial convection differs from MCSs in the subtropics near and over land and complex terrain.
The characteristics of shallow, deep, and mesoscale convective elements derived from S-PolKa and the other radars will facilitate understanding of the contributions of the individual components to the aggregated cloud ensemble of the MJO. It is also important to understand how the composition of the cloud population evolves. The DYNAMO S-PolKa data shows that the MJO cloud population development is multiscale. The convectively active period of an MJO lasts from 1-4 weeks. Within this period the convection is controlled by intermediate-scale equatorial waves, which concentrate the convective population into shorter 2-4 day periods in which the convective population goes through a similar cycle of growth and decline. This intermediate scale is important, lying between cloud scale and MJO scale. To study this intermediate-scale behavior, the S-PolKa data will be examined in light of Indian Ocean basin-wide simulations of the multiscale behavior of the MJO.
A primary goal of DYNAMO was to determine the details of the evolution of the cloud population of the MJO as it develops over the Indian Ocean. This project will be a major contribution toward the achievement of that aim.
Broader Impacts:
The MJO affects weather globally, from variations in the Asian monsoon to variations in winter weather over the U.S. The physical understanding provided by this project's analysis of the S-PolKa dataset underpins the development of better models to forecast the MJO. In addition, the project contributes to the training of women scientists. |