Performance of the NCMRWF convection-permitting model during contrasting monsoon phases of the 2016 INCOMPASS field campaignJayakumar, A., Abel, S. J., Turner, A. G. ORCID: https://orcid.org/0000-0002-0642-6876, Mohandas, S., Sethunadh, J., O'Sullivan, D., Mitra, A. K. and Rajagopal, E. N. (2020) Performance of the NCMRWF convection-permitting model during contrasting monsoon phases of the 2016 INCOMPASS field campaign. Quarterly Journal of the Royal Meteorological Society, 146 (731). pp. 2928-2948. ISSN 1477-870X
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1002/qj.3689 Abstract/SummaryThis study uses INCOMPASS aircraft, radiosonde and satellite observations for verifying hydrometeors and associated state variables predicted by the regional model of the NCMRWF (NCUM-R) for contrasting phases of the 2016 monsoon. INCOMPASS flights B957 and B975 took place between Lucknow in northern India and Bhubaneswar near the east coast, and represent a contrast between dry pre-monsoon and active monsoon conditions, respectively. A moist profile above 4 km in Bhubaneswar measured on B957 showed a dry-air intrusion being eroded by mid-level clouds, whereas the Lucknow profile showed a drier, pre-monsoon profile. Aerosol extinction coefficient and cloud-top height measured using lidar showed an influx of continental aerosol, and intermittent multiple clouds below the aircraft in the mid-troposphere and boundary layer. Measurements from B975 match well with cyclonic wind patterns estimated from satellite observations and the convective tendency represented in radiosonde profiles. Extensive clouds were detected below 5 km during the active monsoon. Two-model formulations for cloud representation (prognostic cloud and prognostic condensate, PC2, and diagnostic schemes, Diag) are compared with observations during the campaign. Vertical structures of state variables from both schemes are generally in agreement along the flight tracks. Surface energy budget and cloud diagnoses indicate higher cloud cover in Diag consistent with lower surface temperatures through reduced surface downwelling shortwave flux than in PC2, while the latent heat flux is found to be insensitive to cloud scheme chosen. In-cloud water content is larger in PC2 for lower cloud fraction, and the autoconversion process is faster with respect to Diag. Higher total condensed-water content in the model with respect to aircraft measurements and an enhanced light precipitation bias with respect to satellite data is common to both cloud schemes. Further work to improve the representation of clouds and precipitation for the tropical implementation of the model is clearly warranted.
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