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Steven J. Ghan
,
L. Ruby Leung
, and
James McCaa

Abstract

Parallel simulations of clouds and radiation fields by a single-column model (SCM), a regional circulation model, and a global circulation model (GCM), each using the same treatment of all physical processes and approximately the same spatial resolution, are compared with observations at the Atmopheric Radiation Measurement Clouds and Radiation Testbed in the southern Great Plains. Significant differences between model simulations are evident for individual cloud systems, but these differences are not systematic, varying from cloud system to cloud system. Several systematic differences between model simulations and observations are identified. These biases are about the same for each model and are much larger than differences between model simulations, suggesting that for some purposes one model can serve as a testbed for parameterizations developed for another.

The role of nudging in the simulations is explored by driving the SCM with large-scale forcing from a GCM simulation. The authors find that nudging of SCM temperature and humidity toward the GCM simulation, using the inverse of the advective timescale for the nudging coefficient, reduces errors in the SCM simulation when artificial errors in the forcing are introduced. The authors also find that nudging of temperature and humidity hides physics errors introduced in the SCM, but only if the physics errors involve processes that directly influence temperature or humidity. Thus, errors in the treatment of nucleation, collision–coalescence, collection, and gravitational settling would not be hidden by nudging, but errors in the treatment of radiative heating, condensation/vapor deposition, evaporation/sublimation, melting, cumulus convection, and subgrid or resolved transport of heat and moisture would be hidden by nudging.

Full access
Man-Yau Chan
,
Fuqing Zhang
,
Xingchao Chen
, and
L. Ruby Leung

Abstract

Geostationary infrared satellite observations are spatially dense [>1/(20 km)2] and temporally frequent (>1 h−1). These suggest the possibility of using these observations to constrain subsynoptic features over data-sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against the independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than the EnSRF algorithm.

Free access
Paul J. Neiman
,
Natalie Gaggini
,
Christopher W. Fairall
,
Joshua Aikins
,
J. Ryan Spackman
,
L. Ruby Leung
,
Jiwen Fan
,
Joseph Hardin
,
Nicholas R. Nalli
, and
Allen B. White

Abstract

To gain a more complete observational understanding of atmospheric rivers (ARs) over the data-sparse open ocean, a diverse suite of mobile observing platforms deployed on NOAA’s R/V Ronald H. Brown (RHB) and G-IV research aircraft during the CalWater-2015 field campaign was used to describe the structure and evolution of a long-lived AR modulated by six frontal waves over the northeastern Pacific during 20–25 January 2015. Satellite observations and reanalysis diagnostics provided synoptic-scale context, illustrating the warm, moist southwesterly airstream within the quasi-stationary AR situated between an upper-level trough and ridge. The AR remained offshore of the U.S. West Coast but made landfall across British Columbia where heavy precipitation fell. A total of 47 rawinsondes launched from the RHB provided a comprehensive thermodynamic and kinematic depiction of the AR, including uniquely documenting an upward intrusion of strong water vapor transport in the low-level moist southwesterly flow during the passage of frontal waves 2–6. A collocated 1290-MHz wind profiler showed an abrupt frontal transition from southwesterly to northerly flow below 1 km MSL coinciding with the tail end of AR conditions. Shipborne radar and disdrometer observations in the AR uniquely captured key microphysical characteristics of shallow warm rain, convection, and deep mixed-phase precipitation. Novel observations of sea surface fluxes in a midlatitude AR documented persistent ocean surface evaporation and sensible heat transfer into the ocean. The G-IV aircraft flew directly over the ship, with dropsonde and radar spatial analyses complementing the temporal depictions of the AR from the RHB. The AR characteristics varied, depending on the location of the cross section relative to the frontal waves.

Full access
Markus Gross
,
Hui Wan
,
Philip J. Rasch
,
Peter M. Caldwell
,
David L. Williamson
,
Daniel Klocke
,
Christiane Jablonowski
,
Diana R. Thatcher
,
Nigel Wood
,
Mike Cullen
,
Bob Beare
,
Martin Willett
,
Florian Lemarié
,
Eric Blayo
,
Sylvie Malardel
,
Piet Termonia
,
Almut Gassmann
,
Peter H. Lauritzen
,
Hans Johansen
,
Colin M. Zarzycki
,
Koichi Sakaguchi
, and
Ruby Leung

Abstract

Numerical weather, climate, or Earth system models involve the coupling of components. At a broad level, these components can be classified as the resolved fluid dynamics, unresolved fluid dynamical aspects (i.e., those represented by physical parameterizations such as subgrid-scale mixing), and nonfluid dynamical aspects such as radiation and microphysical processes. Typically, each component is developed, at least initially, independently. Once development is mature, the components are coupled to deliver a model of the required complexity. The implementation of the coupling can have a significant impact on the model. As the error associated with each component decreases, the errors introduced by the coupling will eventually dominate. Hence, any improvement in one of the components is unlikely to improve the performance of the overall system. The challenges associated with combining the components to create a coherent model are here termed physics–dynamics coupling. The issue goes beyond the coupling between the parameterizations and the resolved fluid dynamics. This paper highlights recent progress and some of the current challenges. It focuses on three objectives: to illustrate the phenomenology of the coupling problem with references to examples in the literature, to show how the problem can be analyzed, and to create awareness of the issue across the disciplines and specializations. The topics addressed are different ways of advancing full models in time, approaches to understanding the role of the coupling and evaluation of approaches, coupling ocean and atmosphere models, thermodynamic compatibility between model components, and emerging issues such as those that arise as model resolutions increase and/or models use variable resolutions.

Open access