Numerical Experiments on Formation Processes of Thin Moist Layers in the Tropical Mid-Troposphere over Ocean
Water vapor is important in terms of atmospheric radiation and convection, especially in the tropics. When the water vapor distribution has fine structures, they may have large effects on atmospheric radiation and cumulus convections. Though vertically fine layered structures in water vapor are often observed in radiosonde observations and airborne observations, mechanisms to create such layers are not well understood due to sparseness of observations in the tropics and coarseness of global objective analyses. In this study, we perform numerical experiments to obtain three-dimensional data with high vertical resolution to investigate formation processes of thin moist layers in the tropical mid-troposphere over ocean. We diagnose formation processes of thin moist layers using the advection equation of water vapor. Because a thin moist layer is a local maximum of water vapor in vertical, we evaluate D(∂2q/∂z2)/Dt, where q denotes water vapor mixing ratio. The formation processes of thin moist layers by advection can be classified into two types: “intrusion” (∂2u/∂z2 ∂q/∂x) and “linear-shear” (2∂u/∂z ∂2q/∂x∂z), where u denotes a horizontal wind component. In this study, we used relative humidity instead of water vapor mixing ratio. We performed numerical experiments using a non-hydrostatic regional model PSU/NCAR MM5. We apply the above method to the case study of an observed thin moist layer in September 1999 over equatorial eastern Pacific. The diagnosis method correctly capture the shear flow which produces the thin moist layer. We also performed an idealized numerical experiment over equatorial eastern Pacific. We found that the formation process by intrusion dominates over the equator, while the formation process by linear-shear dominates around 10 degrees north and south of the equator.
Baroclinic vorticity and its application in Typhoon
Typhoon is an important rotating system. Therefore vorticity plays significant roles in the study of typhoon. Vorticity can be decomposed as barotropic and baroclinic components. Because barotropic property is the basic one of the atmosphere, each particle possess barotropic vorticity, while baroclinic vorticity is not possessed by all particles. Therefore baroclinic vorticity anomaly is associated with the development of strong convection and heavy precipitation in typhoon. Thus, a new vorticity variable, baroclinic vorticity, is introduced. It is denoted by the cross product between the gradient of integral temperature with time and the gradient of potential temperature, which can reflect the distribution of baroclinic vorticity in typhoon. Then a case study of typhoon is performed. The numerical simulation of typhoon “Morakot” is carried out. Then the model results are used to calculate the baroclinic vorticity. It is found that the baroclinic vorticity has close coeherence with the spiral rainband in tyhpoon, and to some extent it can reflect the development of mesoscale convective system in typhoon. It is an effective variable to diagnose typhoon structure.
Island Effects on Mei-Yu Jet/Front Systems and Rainfall Distribution during TiMREX IOP #3
From 29 May - 1 June 2008, a Mei-Yu jet/ front system passed over the Taiwan area. In the pre-frontal southwesterly flow regime, heavy afternoon showers occurred on the western windward side with significant flow splitting off the southwestern windward coast. During the passage of the Mei-Yu front during the evening of 30 May, heavy rainfall > 130 mm occurred over northwestern Taiwan. The shallow Mei-Yu front passed the island of Taiwan during the night of 30-31 May and interacted with the island terrain and island-induced airflow with strong post-frontal low-level (< 1 km) northeasterly flow over the Taiwan Strait. Above the shallow Me-Yu front, southwesterly flow still prevailed. The following day, a mesoscale Mei-Yu frontal cycle brought in significant orographic rainshowers (> 90 mm) over the southwestern windward slopes of the Central Mountain Range. In this study, the island/orographic effects on the complex Mei-Yu jet/front system and rainfall distribution will be presented based on the analysis of sounding, surface, dropwindsonde, rainguage, radar and satellite data and high resolution mesoscale model simulations.
A Study on the Effects of Convective Momentum Transport Associated with Rain Bands within the Madden-Julian Oscillation
Using the output data of the Madden Julian Oscillation (MJO) 2006 experiment conducted by Miura et al. (2007) in a global cloud resolving Non-hydrostatic Icosahedral Atmospheric Model (NICAM, Satoh et al. 2008), we analyzed the Convective Momentum Transport (CMT) in relation with the phase of a successfully reproduced MJO. The objectives of this study were (I) to clarify the distribution structure of CMT acceleration involved in rainbands within the MJO convection, (II) to quantify their ensemble impacts on the environmental zonal wind, and (III) to analyze what components account for the ensemble. In the MJO 2006 experiment of NICAM, 60,997 rainband cases were detected in the analysis region (100 - 170E, 12S-12N) , 15,221 cases of which came along with complete 3-d dataset necessary to calculate CMT. Rainbands that involve strong upscale CMT acceleration to the environment were most frequently found between -20 to 0 degrees (longitude) relative to the MJO center. Their upscale zonal acceleration ensemble formed a three-storied structure: positive at lower levels (below 1.6km); negative at mid levels (2km-6.5km); positive at upper levels (above 11km). The upscale acceleration due to CMT accounted for -160% on the 2km-6.5km averaged wind difference between +20 and -30 degrees relative to the MJO center. CMT operated to slow down the eastward propagation of the westerly wind at the low-mid troposphere, thereby slowing down the propagation speed of the entire MJO. Lastly, CMT components that account for the three-storied structure were explored. While more than half of the negative acceleration at 2.5km-6km were due to the contribution from updrafts involving up gradient CMT, the contribution from updrafts that mix the mid-level (2.5km-6km) with the upper levels (above 7km) and contribution from rear-inflow type down drafts were also relatively large. It is concluded that; (1) CMT involved with rainbands within the MJO has a clear preference to construct a three-storied structure which positively/negatively/positively accelerate environmental zonal wind at the lower/middle/upper levels, (2) the acceleration due to CMTs has a significant impact on the change of environmental zonal wind (up to -160% at 2.5km-6km), possibly slowing down the phase speed of the MJO, (3) both up/down gradient CMTs largely account for such a strong impact.
Climatology of the East Pacific ITCZ detected using a statistical model
The location of the ITCZ has been identified in 3 hourly satellite data from 1980 to 2008 using a statistical model. The model has been developed to identify the ITCZ as a discrete region using information from satellites. The algorithm uses prior knowledge of the ITCZ character learned from manual labels to create a base image using inputs from satellite infra-red, visual and total precipitable water. A Markov-Random Field `nearest neighbor’ is applied to every pixel in space and time so that the local neighborhood of pixels is taken into account in the decision on whether a pixel is classified as ITCZ or non-ITCZ. By this method the algorithm is able to emulate what a human would `see’ as organized cloud belonging to the convergence zone as opposed to isolated thunderstorms. This method of identification is particularly useful for examining the dynamical aspects of the observed ITCZ as well as the climatology. The information has been used to produce a climatology of ITCZ occurrence and variability. The seasonal cycle of the location and extent of the ITCZ will be discussed with particular focus on how the East Pacific varies from the Central Pacific ITCZ in character. Inter-annual variability and climatic trends in area will also be presented and the relationship between the ITCZ and ENSO highlighted.
An example of an ITCZ label automatically identified by the statistical model for 19 August 2000 at 2100 UTC overlaid on IR satellite image.