Intraseasonal Oscillations during the TOGA-COARE IOP

Dual Doppler radar observations were carried out from November 1992 to January 1993 during the TOGA-COARE IOP (Tropical Ocean and Global Atmosphere-Coupled Ocean Atmosphere Response Experiment, Intensive Observation Phase) at Manus Island in Papua New Guinea. The heating profiles of six stratiform and eight convective cloud systems were calculated using three-dimensional wind field and hydrometeor distribution in a same way as Roux and Sun (1990). Relations between the heating profiles and the thermodynamic properties of environment were studied.Convective and stratiform cloud systems both showed structures consistent with past studies. The heating level varied significantly with environmental conditions that had been linked to the phase of Madden Julian Oscillation (MJO). Specifically, at the beginning of the active phase of MJO, convective cloud systems dominated and the heating level was high (7 km). These cloud systems heated and moistened the middle to upper troposphere and stabilized the atmosphere. In the middle of the active phase of MJO, stratiform cloud systems dominated and the heating level lowered (5 km). At the end of the active phase of the MJO, convective cloud systems again dominated.A composite profile for the entire observation period was calculated using the representative profiles and satellite data, and was compared with that derived by Lin and Johnson (1996) using radiosonde observation. The derived maximum heating rate coincided with their results, however, the heating rates of lower and upper troposphere were smaller and the level of maximum heating (5 km) was slightly lower.

Section: Large-scale Atmospheric Conditionsmentioning

confidence: 52%

“…During the IOP, two active phases of MJO passed (Nakazawa 1995) and many classifications of cloud systems were observed (Uyeda et al 1995). In this study, diabatic heating properties of various cloud systems are derived from Doppler radar observation.…”

Section: Introductionmentioning

confidence: 99%

Heating Distribution by Cloud Systems Derived from Doppler Radar Observation in TOGA-COARE

Ushiyama¹,

Kawashima

Fujiyoshi

2003

“…While a convective area slowly migrates to the east in association with the MJO (top 3 panels of the left column), sea surface warms up in prior to the convective peak and cools down afterward. An increase in SST leading MJO convection has been well recognized by past observational studies (Nakazawa 1995;Zhang 1996;Hendon and Glick 1997;Kemball-Cook and Weare 2001;Benedict and Randall 2007, among others). The amplitude of deep stratiform coverage and SST is greater in the southern hemisphere than in the northern hemisphere, since the current analysis is focused on austral summer events.…”

Section: Filtering and Compositing Methodsmentioning

confidence: 69%

A 9-season TRMM Observation of the Austral Summer MJO and Low-frequency Equatorial Waves

Masunaga

2009

A long-term observation from the Tropical Rainfall Measuring Mission (TRMM) is analyzed to investigate the Madden-Julian Oscillation (MJO), Kelvin wave, and equatorial Rossby (ER) wave in austral summer seasons. TRMM Precipitation Radar (PR) and Visible/Infrared Scanner (VIRS) measurements are jointly used to clarify convective progression associated with individual modes of the tropical oscillations. Variability in the dynamic and thermodynamic environment involving sea surface temperature (SST), column relative humidity (CRH), and moisture convergence is also examined. A sea surface warming is found to precede the peak MJO convection by ~10 days, while a prior SST increase is not as evident for the Kelvin and ER waves. Moisture convergence and CRH exhibit a horseshoe-like pattern in the composite MJO map, constituted of a pair of off-equatorial maxima and a weak equatorial peak. The Kelvin wave has a moist anomaly on the equator leading the convective peak as theoretically expected, while the moist anomaly also extends poleward without being accompanied by moisture convergence. Moisture convergence leads CRH by a day or two for the Kelvin and ER waves. Moisture convergence is, in contrast, virtually concurrent with CRH for the MJO. The correlation between CRH and deep stratiform coverage is diverse among the three modes of the tropical oscillations. Shallow cumulus and cumulus congestus lead the MJO convective peak by ~1 day, followed by lingering non-precipitating high clouds. ER wave convection is led by moisture convergence but lagged by CRH. The convective progression appears not to proceed in a monotonic way for the ER wave.A possible mechanism to explain MJO propagation is discussed as suggested by a synthesis of the present findings. The Kelvin wave guides the eastward migration of MJO convection onset over the Indian Ocean. The role of the Kelvin wave in MJO propagation diminishes as the MJO enters the west Pacific with the convective area shifting away from the equator. Instead, the Kelvin wave convective heating induces poleward moisture transport and moistens the off-equatorial mid troposphere. The resultant moist anomaly is hypothesized to help trigger the MJO convective burst upon the arrival of ER wave disturbances. Such cooperative processes involving the Kelvin and ER waves could act as a driving engine of some, if not all, MJO episodes.

“…To examine assimilated variables, assimilation was applied to meteorological cases during 19-20 Dec. 1992 over the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) domain, where large PWC and precipitating disturbances were accompanied by the supercluster for this period (Nakazawa, 1995). Results were compared with the TOGA COARE observation datasets.…”

Section: ) a Radiative Transfer Model (Rtm) Developedmentioning

confidence: 99%

Direct Assimilation of Multichannel Microwave Brightness Temperatures and Impact on Mesoscale Numerical Weather Prediction over the TOGA COARE Domain

Aonashi¹,

Liu

1999

Direct assimilation using 1-dimensional variational method in the vertical (1D-VAR) was developed to incorporate vertically polarized brightness temperatures (TB's), and rain flag data (index of existence of precipitation) from the special sensor microwave imager (SSM/I), into a mesoscale numerical weather prediction (NWP) model. We used a radiative transfer model (RTM) developed by Liu (1998) to calculate TB's from NWP model variables. Observational residuals of TB's are assumed to be nonlinear functions of precipitation rates in rainy areas, and of other thermodynamic variables in rain-free areas. Quasiequilibrium assumptions on humidity and convective instability in model precipitation areas are used to assimilate TB's in rainy areas and rain flag data, which are functions of precipitation. TB's in rain-free areas are assimilated into total water content (cloud water content + humidity). Smith's method (1990) is used to calculate cloud water content and humidity from total water content. To simplify 1D-VAR, the following approximations are introduced: 1) In the tropics, variations in temperature are much smaller than variations in humidity.2) Variations in divergence dominates the rainy areas, compared to vorticity and mass variables. Based on the above approximation, we assumed that the background error covariance of relative vorticity and the unbalanced component of mass variables is negligible, so we divided 1D-VAR into that for total water content and that for divergence. Newtonian iteration is used to solve these 1D-VAR problems.To study the assimilated variables, assimilation was applied to cases during 19-20 Dec. 1992 over the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) domain. Results indicate: 1) Precipitable water content (PWC) obtained by assimilation agreed well with PWC calculated from radiosonde data and PWC retrieved from TB's using Shibata's algorithm (1994).2) Consistency between humidity and cloud water content profiles was attained by Smith's method (1990), although no significant improvement was seen in humidity profile accuracy by assimilation. 3) Assimilation of rain flag and TB's in rainy areas successfully produced model precipitation areas agreeing with radar observation data by Short et al. (1997).To test the impact of assimilation on NWP forecasts, experiments were conducted assimilating TB data for 19 Dec. 1992. Results indicate: 1) Assimilation modified large-scale model humidity distribution, improving large-scale humidity and precipitation forecasts for 48 hours.