Abstract

Utilizing data from the Quick Scatterometer (QuikSCAT), a new observational parameter related to mesoscale cold pool activity [termed cold pool kinetic energy (CPKE)] is developed and investigated. CPKE and the Climate Prediction Center (CPC) morphing technique (CMORPH) rainfall product (both scaled to 2.25°) are geolocated to 25 tropical island radiosonde sites. CPKE and radiosonde-derived nondilute CAPE, entraining CAPE (ECAPE), saturation fraction, and a new measure of convective inhibition (that takes into account stable layers above the LFC) are investigated with respect to rainfall time tendencies. Over the life cycle of rainfall, the composite temporal evolutions of CPKE and convective inhibition are quantitatively similar, but slightly out of phase. The maximum in CPKE precedes the maximum in convective inhibition by 3–6 h, thus allowing for an oscillation in the ratio of convective inhibition to CPKE relative to maximum rainfall. This ratio falls below unity at the time rainfall begins increasing and averages to near unity over the entire life cycle. These results imply a lagged, coupled relationship between CPKE and convective inhibition during rainfall. The rapid increase in rainfall occurs when saturation fraction and ECAPE exceed approximately 70% and 280 J kg⁻¹, respectively, consistent with previously noted thresholds for deep convection transition. However, since similar thermodynamic conditions are found before the increase in rainfall, observations support a hypothesis that the onset time for transition from light to heavy rainfall occurs when triggering energy (as captured in CPKE) approaches and exceeds convective inhibition. The observed onset and time scale for CAPE depletion by convection is nearly equivalent to the initial temporal appearance and time duration (6–12 h) that CPKE exceeds convective inhibition.

Abstract

Over coastal mountain ranges of the Asian monsoon region, heavy orographic rainfall is frequently associated with low precipitation-top heights (PTHs). This leads to conspicuous underestimation of rainfall using microwave radiometer algorithms, which conventionally assume that heavy rainfall is associated with high PTHs. Although topographically forced upward motion is important for rainfall occurrence, it does not fully constrain precipitation profiles in this region. This paper focuses on the thermodynamic characteristics of the atmosphere that determine PTHs in tropical coastal mountains of Asia (Western Ghats, Arakan Yoma, Bilauktaung, Cardamom, Annam Range, and the Philippines).

PTHs of heavy orographic rainfall generally decrease with enhanced low- and midlevel relative humidity, especially during the summer monsoon. In contrast, PTHs over the Annam Range of the Indochina Peninsula increase with enhanced low-level and midlevel relative humidity during the transition from boreal summer to winter monsoon, demonstrating that convection depth is not simply a function of humidity. Instead, PTHs of heavy orographic rainfall decreased with increasing low-level stability for all monsoon regions considered in this study, as well as the Annam Range during the transition from boreal summer to winter monsoon. Therefore, low-level static stability, which inhibits cloud growth and promotes cloud detrainment, appears to be the most important parameter in determining PTHs of heavy rainfall in the Asian monsoon region.

Abstract

This paper presents a critical review of a number of popular methods that have been developed to retrieve cloud and precipitation properties from satellite radiance measurements. The emphasis of the paper is on the retrieval uncertainties associated with these methods, as these shape future data assimilation applications, either in the form of direct radiance assimilation or assimilation of retrieved geophysical data, or even in the use of retrieved information as a source of model error characterization. It is demonstrated throughout the paper how cloud and precipitation observing systems developed around seemingly simple concepts are in fact very complex and largely underconstrained, which explains, in part, why assigning realistic errors to these properties has been so elusive in the past. Two primary sources of error that define the observing system are highlighted throughout: (i) the first source is errors associated with the identification of cloudy scenes from clear scenes and the identification of precipitation in cloudy scenes from nonprecipitating cloudy scenes. The problems of discriminating of cloud clear and cloud precipitation are illustrated using examples drawn from microwave cloud liquid water path and precipitation retrievals. (ii) The second source is errors introduced by the forward model and its related parameters. The forward model generally contains two main components: a model of the atmosphere and the cloud and precipitation structures imbedded in that atmosphere and a forward model of the radiative transfer that produces the synthetic measurement that is ultimately compared to the measurement. The vast majority of methods developed for deriving cloud and precipitation information from satellite measurements is highly sensitive to these model parameters, which merely reflects the underconstrained nature of the problem and the need for other information in deriving solutions. The cloud and precipitation retrieval examples presented in this paper are most often constructed around very unrealistic atmosphere models typically composed of just a few layers. The consequence is that the retrievals become too sensitive to the unobserved parameters of those layers and the atmosphere above and below. Clearly a better definition of the atmospheric state, and the vertical structure of clouds and precipitation, are needed to improve the information extracted from satellite observations, and it is for this reason that the combination of active and passive measurements offers much hope for improving cloud and precipitation retrievals.

Abstract

A satellite data analysis is performed to explore the Madden–Julian oscillation (MJO) focusing on the potential roles of the equatorial Rossby (ER) and Kelvin waves. Measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and Visible/Infrared Scanner (VIRS) are analyzed in the frequency–wavenumber domain to identify and ultimately filter primary low-frequency modes in the Tropics. The space–time spectrum of deep-storm fraction estimated by PR and VIRS exhibits notable Kelvin wave signals at wavenumbers 5–8, a distinct MJO peak at wavenumbers 1–7 and periods of about 40 days, and a signal corresponding to the ER wave. These modes are separately filtered to study the individual modes and possible relationship among them in the time–longitude space. In 10 cases analyzed here, an MJO event is often collocated with a group of consecutive Kelvin waves as well as an intruding ER wave accompanied with the occasional onset of a stationary convective phase. The spatial and temporal relationship between the MJO and Kelvin wave is clearly visible in a lag composite diagram, while the ubiquity of the ER wave leads to a less pronounced relation between the MJO and ER wave. A case study based on the Geostationary Meteorological Satellite (GMS) imagery together with associated dynamic field captures the substructure of the planetary-scale waves. A cross-correlation analysis confirms the MJO-related cycle that involves surface and atmospheric parameters such as sea surface temperature, water vapor, low clouds, shallow convection, and near-surface wind as proposed in past studies. The findings suggest the possibility that a sequence of convective events coupled with the linear waves may play a critical role in MJO propagation. An intraseasonal radiative–hydrological cycle inherent in the local thermodynamic conditions could be also a potential factor responsible for the MJO by loosely modulating the envelope of the entire propagation system.

Abstract

Observed and modeled rainfall occurrence from shallow (warm) maritime clouds and their composite statistical relationships with cloud macrophysical properties are analyzed and directly compared. Rain falls from ~25% of warm, single-layered, maritime clouds observed by CloudSat and from ~27% of the analogous warm clouds simulated within a large-domain, fine-resolution radiative–convective equilibrium experiment performed using the Regional Atmospheric Modeling System (RAMS), with its sophisticated bin-emulating bulk microphysical scheme. While the fractional occurrence of observed and simulated warm rainfall is found to increase with both increasing column-integrated liquid water and cloud depth, calculations of rainfall occurrence as a joint function of these two macrophysical quantities suggest that the modeled bulk cloud-to-rainwater conversion process is more efficient than observations indicate—in agreement with previous research. Unexpectedly and in opposition to the model-derived relationship, deeper CloudSat-observed warm clouds with little column water mass are more likely to rain than their corresponding shallow counterparts, despite having lower cloud-mean water contents. Given that these composite relationships were derived from statically identified warm clouds, an attempt is made to quantitatively explore rainfall occurrence within the context of the warm cloud life cycle. Extending a previously established cloud-top buoyancy analysis technique, it is shown that rainfall likelihoods from positively buoyant RAMS-simulated clouds more closely resemble the surprising observed relationships than do those derived from negatively buoyant simulated clouds. This suggests that relative to the depiction of warm clouds within the RAMS output, CloudSat observes higher proportions of positively buoyant, developing warm clouds.