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Roger M. Wakimoto
and
Chinghwang Liu

Abstract

On 16 May 1995, a supercell storm produced an F1 tornado near Garden City, Kansas, during VORTEX (Verification of the Origins of Rotation in Tornadoes Experiment). This event provided the first opportunity to synthesize data collected by a new airborne radar platform called ELDORA (Electra Doppler radar) developed by the National Center for Atmospheric Research. The evolution of the low- and midlevel mesocyclone was presented in a companion paper. In this paper, the sequence of events that triggered tornadogenesis within the mesocyclone circulation is shown. The occlusion downdraft, documented in Part I, appears to promote “vortex breakdown” resulting in the generation of multiple vorticity centers. It is one of these centers that intensifies into the Garden City tornado.

The structural relationship between the observed wall cloud and the radar reflectivity associated with the developing hook echo and the kinematic wind field is also presented. Although the wall cloud appears to be uniform there are pronounced asymmetries in the echo and ground-relative flow fields.

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J. G. McLay
and
M. Liu

Abstract

This study looks for evidence of correlation among model physical parameters in the sensitive parameter space defined by those randomly sampled physical parameter vectors that induce the most notable response in some forecast metric. These “sensitive parameter vectors” are identified through an ensemble methodology. The correlation analysis is facilitated by two established techniques from statistical inference theory. The random parameter vectors are found to induce a considerable range of forecast responses in terms of five metrics, such as bias and variance. The metrics enable measurement not only of the biggest forecast response but also of the most beneficial forecast response (e.g., in terms of reduction of forecast error). For most metrics, multiple parameter pairs exhibit significantly more correlation than would be expected from random sampling processes. The correlations frequently involve parameters from two different physical routines. These inference results are independently supported by a Monte Carlo simulation. The results suggest that correlations among parameters must be taken into account in order to gain the most response from a model when carrying out parameter variation experiments. Also, they reinforce the idea that parameter estimation efforts need to be expanded so that they simultaneously estimate the joint distribution of parameters across multiple physical routines.

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Elizabeth M. Sims
and
Guosheng Liu

Abstract

When estimating precipitation using remotely sensed observations, it is important to correctly classify the phase of precipitation. A misclassification can result in order-of-magnitude errors in the estimated precipitation rate. Using global ground-based observations over multiple years, the influence of different geophysical parameters on precipitation phase is investigated, with the goal of obtaining an improved method for determining precipitation phase. The parameters studied are near-surface air temperature, atmospheric moisture, low-level vertical temperature lapse rate, surface skin temperature, surface pressure, and land cover type. To combine the effects of temperature and moisture, wet-bulb temperature, instead of air temperature, is used as a key parameter for separating solid and liquid precipitation. Results show that in addition to wet-bulb temperature, vertical temperature lapse rate affects the precipitation phase. For example, at a near-surface wet-bulb temperature of 0°C, a lapse rate of 6°C km−1 results in an 86% conditional probability of solid precipitation, while a lapse rate of −2°C km−1 results in a 45% probability. For near-surface wet-bulb temperatures less than 0°C, skin temperature affects precipitation phase, although the effect appears to be minor. Results also show that surface pressure appears to influence precipitation phase in some cases; however, this dependence is not clear on a global scale. Land cover type does not appear to affect precipitation phase. Based on these findings, a parameterization scheme has been developed that accepts available meteorological data as input and returns the conditional probability of solid precipitation.

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Zhengyu Liu
,
M. Notaro
,
J. Kutzbach
, and
Naizhuang Liu

Abstract

The feedback between global vegetation greenness and surface air temperature and precipitation is assessed using remote sensing observations of monthly fraction of photosynthetically active radiation (FPAR) for 1982 to 2000 with a 2.5° grid resolution. Lead/lag correlations are used to infer vegetation–climate interactions. Furthermore, a statistical method is used to quantify the efficiency of vegetation feedback on climate in the observations. This feedback analysis provides a first quantitative assessment of global vegetation feedback on climate. In northern mid- and high latitudes, vegetation variability is found to be driven predominantly by temperature; in the meantime, vegetation also exerts a strong positive feedback on temperature with the feedback accounting for over 10%–25% of the total monthly temperature variance. The strongest positive feedback occurs in the boreal regions of southern Canada/northern United States, northern Europe, and southern Siberia, where the feedback efficiency exceeds 1°C (0.1 FPAR)−1. Over most of the Tropics and subtropics (outside the equatorial rain belt), vegetation is driven primarily by precipitation. However, little vegetation feedback is found on local precipitation when averaged year-round, with the feedback explained variance usually accounting for less than 5% of the total precipitation variance. Nevertheless, in a few isolated small regions such as Northeast Brazil, East Africa, East Asia, and northern Australia, there appears to be some positive vegetation feedback on local precipitation, with the feedback efficiency over 1 cm month−1 (0.1 FPAR)−1. Further studies suggest a significant seasonal variation of the vegetation feedback in some regions. A preliminary analysis also seems to suggest an enhanced intensity of the vegetation feedback, especially on precipitation, at longer time scales and over a larger grid box area. Limitations and implications of the assessment of vegetation feedback are also discussed. The assessed vegetation feedback is shown to be valuable for the evaluation of vegetation–climate feedback in coupled climate–vegetation models.

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Roger M. Wakimoto
,
Warren Blier
, and
Chinghwang Liu

Abstract

Observation taken during the Experiment on Rapidly Intensifying Cyclones over the Atlantic (ERICA) have permitted analyses of explosive oceanic cyclogenesis of unprecedented detail. The most intense of the cyclones that occurred during this experiment was that of intensive observing period 4 (IOP 4). This storm not only contained the lowest sea level pressure ever observed south of 40°N in an extratropical cyclone over the Atlantic but was well-sampled by the specially deployed observing systems (aircraft, airbone Doppler radar, dropwindsondes, and buoys. This paper presents detailed analysis of this case. The primary issues addressed here are 1) the finescale structure of the fronts, and 2) the structure and organization of the associated precipitating features.

Both of these issues have previously been investigated primarily through numerical simulation of various cases of intense cyclogenesis. Analysis of the resulting model output has indicated a structural evolution of such cyclones that departs significantly from that described by the Norwegian cyclone model. Diagnosis of the output has indicated that latent heat release plays a significant role, both in the cyclone intensification and in the evolution of the associated fronts. The detailed in situ observations in the present case allow for observational evaluation of the attendant conclusions of these prior modeling studies.

Principal findings include:

1) Confirmation of the existence of a “bent-back%rdquo; warm front wrapping to the west around the cyclone center; the frontal structure is very different from that of the occluded front that would here by analyzed according to the Norwegian model.

2) The presence of an extremely sharp warm front, with Kelvin-Helmholtz waves and an intense line of convection found along the front.

3) A continuous extension of convective activity along the cold front to the point of intersection with the warm front, with no evident "fracture” zone.

4) The presence of only scattered convective cells along and to the north of the bent-back warm front.

5) A significant displacement between the cold front and the main cloud band. The cold front lay along a narrow line of intense convection well to the rear of the main comma-shaped cloud mass evident in the satellite imagery.

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M. Notaro
,
Z. Liu
, and
J. W. Williams

Abstract

Observed vegetation feedbacks on temperature and precipitation are assessed across the United States using satellite-based fraction of photosynthetically active radiation (FPAR) and monthly climate data for the period of 1982–2000. This study represents the first attempt to spatially quantify the observed local impact of vegetation on temperature and precipitation over the United States for all months and by season. Lead–lag correlations and feedback parameters are computed to determine the regions where vegetation substantially impacts the atmosphere and to quantify this forcing. Temperature imposes a significant instantaneous forcing on FPAR, while precipitation's impact on FPAR is greatest at one-month lead, particularly across the prairie. An increase in vegetation raises the surface air temperature by absorbing additional radiation and, in some cases, masking the high albedo of snow cover. Vegetation generally exhibits a positive forcing on temperature, strongest in spring and particularly across the northern states. The local impact of FPAR on precipitation appears to be spatially inhomogeneous and relatively weak, potentially due to the atmospheric transport of transpired water. The computed feedback parameters can be used to evaluate vegetation–climate interactions simulated by models with dynamic vegetation.

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M. Stephens
,
Zhengyu Liu
, and
Haijun Yang

Abstract

The evolution of decadal subduction temperature anomalies in the subtropical North Pacific is studied using a simple and a complex ocean model. It is found that the amplitude of the temperature anomaly decays faster than a passive tracer by about 30%–50%. The faster decay is caused by the divergence of group velocity of the subduction planetary wave, which is contributed to, significantly, by the divergent Sverdrup flow in the subtropical gyre. The temperature anomaly also seems to propagate southward slower than the passive tracer, or mean ventilation flow. This occurs because the mean potential vorticity gradient in the ventilated zone is directed eastward; the associated general beta effect produces a northward propagation for the temperature anomaly, partially canceling the southward advection by the ventilation flow.

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Roger M. Wakimoto
,
Chinghwang Liu
, and
Huaqing Cai

Abstract

Analysis of a supercell storm that produced an F1 tornado near Garden City, Kansas, is presented. This event provided one of the first opportunities to synthesize data collected by a new airborne radar platform called ELDORA (Electra Doppler radar) developed by the National Center for Atmospheric Research. The early stages of development of the midlevel mesocyclone and the entire evolution of the low-level mesocyclone are captured over a 70-min period. The low-level mesocyclone began as an incipient shallow circulation along a synoptic-scale trough. The circulation intensified and grew in depth via vortex stretching under the influence of a strong updraft. As this rotation built up from the boundary layer, it initially remained separate and distinct from the midlevel mesocyclone. Subsequently, the two mesocyclones merge to produce a single column of rotation 4–5 km in diameter. An occlusion downdraft develops within the mesocyclone circulation during the last passes by the storm signaling the beginning of the tornadic phase. Perturbation pressure retrievals provide conclusive evidence that this downdraft is driven by a downward-directed pressure gradient force.

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S. C. Liu
and
T. M. Donahue

Abstract

A model for H2O, CH4, H2 and odd hydrogen is developed that properly relates the measured mixing ratios in the stratosphere to escape of H in the form of Jeans flux, charge exchange and polar wind. The resulting model predicts a temperature-dependent jeans flux in agreement with recent measurements.

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Yongqiang Liu
,
Filippo Giorgi
, and
Warren M. Washington

Abstract

A summertime season climate over east Asia is simulated with a regional climate model (RegCM) developed at the National Center for Atmospheric Research (NCAR) to validate the model's capability to produce the basic characteristics of monsoon climate over the region. The RegCM used here is a modified version of the NCAR-Pennsylvania State University Mesoscale Model (MM4), in which the Biosphere-Atmosphere Transfer Scheme and a detailed radiative transfer package have been implemented for climate application. The model horizontal resolution is 50 km, and the domain covers a 5200 km × 4700 km area encompassing eastern Asia and adjacent ocean regions. The simulation period is June–August 1990, and the model-driving initial and lateral boundary conditions are from European Centre for Medium-Range Weather Forecasts analyses of observations. The simulated patterns of the monsoon circulation, precipitation, and land-surface temperature are in general agreement with observations, although the model is somewhat too dry and cool. Furthermore, the RegCM captures terrain-induced local rain maxima and temperature centers. Three special aspects of the model results are examined for assessment of model performance: 1) the RegCM reproduces the entire progress of a summer monsoon and the accompanying rain belt, including different steady phases and sudden transitions between two adjacent phases; 2) the paths of tropical storms occurring during the simulated period are closely traced by the model; and 3) realistic patterns of soil moisture are simulated.

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