ALBERT

Description: New satellite and in situ observations show large intraseasonal (10–60 day) variability of surface winds and upper-ocean current in the equatorial Indian Ocean, particularly in the east. An ocean model forced by the Quick Scatterometer (QuikSCAT) wind stress is used to study the dynamics of the intraseasonal zonal current. The model has realistic upper-ocean currents and thermocline depth variabilities on intraseasonal to interannual scales. The quality of the simulation is directly attributed to the accuracy of the wind forcing. At the equator, moderate westerly winds are punctuated by strong 10–40-day westerly wind bursts. The wind bursts force swift, intraseasonal (20–50 day) eastward equatorial jets in spring, summer, and fall. The zonal momentum balance is between local acceleration, stress, and pressure, while nonlinearity deepens and strengthens the eastward current. The westward pressure force associated with the thermocline deepening toward the east rapidly arrests eastward jets and, subsequently, generates (weak) westward flow. Thus, in accord with direct observations in the east, the spring jet is a single intraseasonal event, there are intraseasonal jets in summer, and the fall jet is long lived but strongly modulated on an intraseasonal scale. The zonal pressure force is almost always westward in the upper 120 m, and changes sign twice a year in the 120–200-m layer. Transient eastward equatorial undercurrents in early spring and late summer are associated with semiannual Rossby waves generated at the eastern boundary following thermocline deepening by the spring and fall jets. An easterly wind stress is not necessary to generate the undercurrents. Experiments with a single westerly wind burst forcing show that apart from the intraseasonal response, the zonal pressure force and current in the east have an intrinsic 90-day time scale that arises purely from equatorial adjustment.

Description: This study addresses the problem of four-dimensional (4D) estimation of a cloudy atmosphere on cloud-resolving scales using satellite remote sensing measurements. The motivation is to develop a methodology for accurate estimation of cloud properties and the associated atmospheric environment on small spatial scales but over large regions to aid in better understanding of the clouds and their role in the atmospheric system. The problem is initially approached by the study of the assimilation of the Geostationary Operational Environmental Satellite (GOES) imager observations into a cloud-resolving model with explicit bulk cloud microphysical parameterization. A new 4D variational data assimilation (4DVAR) research system with the cloud-resolving capability is applied to a case of a multilayered cloud evolution without convection. In the experiments the information content of the IR window channels is addressed as well as the sensitivity of estimation to lateral boundary condition errors, model first guess, decorrelation length in the background statistical error model, and the use of a generic linear model error. The assimilation results are compared with independent observations from the Atmospheric Radiation Measurement (ARM) central facility archive. The modeled 3D spatial distribution and short-term evolution of the ice cloud mass is significantly improved by the assimilation of IR window channels when the model already contains conditions for the ice cloud formation. The assimilated ice cloud in this case is in good agreement with the independent cloud radar measurements. The simulation of liquid clouds below thick ice clouds is not influenced by the IR window observations. The assimilation results clearly demonstrate that increasing the observational constraint from individual to combined channel measurements and from less to more frequent observation times systematically improves the assimilation results. The experiments with the model error indicate that the current specification of this error in the form of a generic linear forcing, which was adopted from other data assimilation studies, is not suitable for the cloud-resolving data assimilation. Instead, a parameter estimation approach may need to be explored in the future. The experiments with varying decorrelation lengths suggest the need to use the model horizontal grid spacing that is several times smaller than the GOES imager native resolution to achieve equivalent spatial variability in the assimilation.

Description: Variability of the wind field over the equatorial Indian Ocean is spread throughout the intraseasonal (10–60 day) band. In contrast, variability of the near-surface υ field in the eastern, equatorial ocean is concentrated at biweekly frequencies and is largely composed of Yanai waves. The excitation of this biweekly variability is investigated using an oceanic GCM and both analytic and numerical versions of a linear, continuously stratified (LCS) model in which solutions are represented as expansions in baroclinic modes. Solutions are forced by Quick Scatterometer (QuikSCAT) winds (the model control runs) and by idealized winds having the form of a propagating wave with frequency σ and wavenumber kw. The GCM and LCS control runs are remarkably similar in the biweekly band, indicating that the dynamics of biweekly variability are fundamentally linear and wind driven. The biweekly response is composed of local (nonradiating) and remote (Yanai wave) parts, with the former spread roughly uniformly along the equator and the latter strengthening to the east. Test runs to the numerical models separately forced by the τx and τy components of the QuikSCAT winds demonstrate that both forcings contribute to the biweekly signal, the response forced by τy being somewhat stronger. Without mixing, the analytic spectrum for Yanai waves forced by idealized winds has a narrowband (resonant) response for each baroclinic mode: Spectral peaks occur whenever the wavenumber of the Yanai wave for mode n is sufficiently close to kw and they shift from biweekly to lower frequencies with increasing modenumber n. With mixing, the higher-order modes are damped so that the largest ocean response is restricted to Yanai waves in the biweekly band. Thus, in the LCS model, resonance and mixing act together to account for the ocean's favoring the biweekly band. Because of the GCM's complexity, it cannot be confirmed that vertical mixing also damps its higher-order modes; other possible processes are nonlinear interactions with near-surface currents, and the model's low vertical resolution below the thermocline. Test runs to the LCS model show that Yanai waves from several modes superpose to form a beam (wave packet) that carries energy downward as well as eastward. Reflections of such beams from the near-surface pycnocline and bottom act to maintain near-surface energy levels, accounting for the eastward intensification of the near-surface, equatorial υ field in the control runs.

Description: Information content analysis of the Geostationary Operational Environmental Satellite (GOES) sounder observations in the infrared was conducted for use in satellite data assimilation. Information content is defined as a first-order response of the top-of-atmosphere brightness temperature to perturbations of simulated temperature and humidity profiles, obtained from a cloud-resolving model, both in the presence and absence of clouds. Sensitivity to the perturbations was numerically evaluated using an observational operator for visible and infrared radiative transfer developed within a research satellite data assimilation system. The vertical distribution of the sensitivities was analyzed as a function of cloud optical thickness covering the range from a cloud-free scene to an optically thick cloud. The clear-sky sensitivities to temperature and humidity perturbations for each channel are representative of the corresponding channel weighting functions for a clear-sky case. For optically thin–moderate ice clouds, the vertical distributions of the sensitivities resemble clear-sky results, indicating that the use of infrared sounding observations in data assimilation can potentially improve temperature and humidity profiles below those clouds. This result is significant, as GOES infrared sounder data have until now only been used in cloud-cleared scenes. It is expected that the use of sounder data in data assimilation, even in the presence of optically thin to moderate high clouds, will help reduce errors in temperature and water vapor mixing ratio profiles below the clouds.