Abstract

Two types of El Niño–Southern Oscillation (ENSO) simulated by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) model are examined. The model is found to produce both the eastern Pacific (EP) and central Pacific (CP) types of ENSO with spatial patterns and temporal evolutions similar to the observed. The simulated ENSO intensity is comparable to the observed for the EP type, but weaker than the observed for the CP type. Further analyses reveal that the generation of the simulated CP ENSO is linked to extratropical forcing associated with the North Pacific Oscillation (NPO) and that the model is capable of simulating the coupled air–sea processes in the subtropical Pacific that slowly spreads the NPO-induced SST variability into the tropics, as shown in the observations. The simulated NPO, however, does not extend as far into the deep tropics as it does in the observations and the coupling in the model is not sustained as long as it is in the observations. As a result, the extratropical forcing of tropical central Pacific SST variability in the CFS model is weaker than in the observations. An additional analysis with the Bjerknes stability index indicates that the weaker CP ENSO in the CFS model is also partially due to unrealistically weak zonal advective feedback in the equatorial Pacific. These model deficiencies appear to be related to an underestimation in the amount of the marine stratus clouds off the North American coasts inducing an ocean surface warm bias in the eastern Pacific. This study suggests that a realistic simulation of these marine stratus clouds can be important for the CP ENSO simulation.

Abstract

A quasi balance with respect to parcel buoyancy at cloud base between destabilizing processes and convection is imposed as a constraint on convective cloud-base mass flux in a modified version of the Kain–Fritsch cumulus parameterization. Supporting evidence is presented for this treatment, showing a cloud-base quasi balance (CBQ) on a time scale of approximately 1–3 h in explicit simulations of deep convection over the U.S. Great Plains and over the tropical Pacific Ocean with the Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). With the exception of the smaller of two convective events in the Great Plains simulation, a CBQ is still observed upon restriction of the data analysis to instances where the available buoyant energy (ABE) exceeds a threshold value of 1000 J kg⁻¹. This observation is consistent with the view that feedbacks between convection and cloud-base parcel buoyancy can control the rate of convection on shorter time scales than those associated with the elimination of buoyant energy and supports the addition of a CBQ constraint to the Kain–Fritsch mass-flux closure.

Tests of the modified Kain–Fritsch scheme in single-column-model simulations based on the explicit three-dimensional simulations show a significant improvement in the representation of the main convective episodes, with a greater amount of convective rainfall. The performance of the scheme in COAMPS precipitation forecast experiments over the continental United States is also investigated. Improvements are obtained with the modified scheme in skill scores for middle to high rainfall rates.

Abstract

This study reveals a possible cause of model bias in simulating the western Pacific subtropical high (WPSH) variability via an examination of an Atmospheric Model Intercomparison Project (AMIP) simulation produced by the atmospheric general circulation model (AGCM) developed at Taiwan’s Central Weather Bureau (CWB). During boreal summer, the model overestimates the quasi-biennial (2–3 yr) band of WPSH variability but underestimates the low-frequency (3–5 yr) band of variability. The overestimation of the quasi-biennial WPSH sensitivity is found to be due to the model’s stronger sensitivity to the central Pacific El Niño–Southern Oscillation (CP ENSO) that has a leading periodicity in the quasi-biennial band. The model underestimates the low-frequency WPSH variability because of its weaker sensitivity to the eastern Pacific (EP) ENSO that has a leading periodicity in the 3–5-yr band. These different model sensitivities are shown to be related to the relative strengths of the mean Hadley and Walker circulations simulated in the model. An overly strong Hadley circulation causes the CWB AGCM to be overly sensitive to the CP ENSO, while an overly weak Walker circulation results in a weak sensitivity to the EP ENSO. The relative strengths of the simulated mean Hadley and Walker circulations are critical to a realistic simulation of the summer WPSH variability in AGCMs. This conclusion is further supported using AMIP simulations produced by three other AGCMs, including the CanAM4, GISS-E2-R, and IPSL-CM5A-MR models.

Abstract

A methodology is proposed for examining the effect of model parameters (assumed to be continuous) on the spatial structure of forecasts. The methodology involves several statistical methods of sampling and inference to assure the sensitivity results are statistically sound. Specifically, Latin hypercube sampling is employed to vary the model parameters, and multivariate multiple regression is used to account for spatial correlations in assessing the sensitivities. The end product is a geographic “map” of p values for each model parameter, allowing one to display and examine the spatial structure of the sensitivity. As an illustration, the effect of 11 model parameters in a mesoscale model on forecasts of convective and grid-scale precipitation, surface air temperature, and water vapor is studied. A number of spatial patterns in sensitivity are found. For example, a parameter that controls the fraction of available convective clouds and precipitation fed back to the grid scale influences precipitation forecasts mostly over the southeastern region of the domain; another parameter that modifies the surface fluxes distinguishes between precipitation forecasts over land and over water. The sensitivity of surface air temperature and water vapor forecasts also has distinct spatial patterns, with the specific pattern depending on the model parameter. Among the 11 parameters examined, there is one (an autoconversion factor in the microphysics) that appears to have no influence in any region and on any of the forecast quantities.

Abstract

We investigate a class of tropical cyclones (TCs) that undergo rapid intensification (RI) in moderate vertical wind shear through analysis of a series of idealized model simulations. Two key findings derived from observational analysis are that the average 200–850-hPa shear value is 7.5 m s⁻¹ and that the TCs displayed coherent cloud structures, deemed tilt-modulated convective asymmetries (TCA), which feature pulses of deep convection with periods of between 4 and 8 h. Additionally, all of the TCs are embedded in an environment that is characterized by shear associated with anticyclones, a factor that limits depth of the strongest environmental winds in the vertical. The idealized TC develops in the presence of relatively shallow environmental wind shear of an anticyclone. An analysis of the TC tilt in the vertical demonstrates that the source of the observed 4–8-h periodicity of the TCAs can be explained by smaller-scale nutations of the tilt on the longer, slower upshear precession. When the environmental wind shear occurs over a deeper layer similar to that of a trough, the TC does not develop. The TCAs are characterized as collections of updrafts that are buoyant throughout the depth of the TC since they rise into a cold anomaly caused by the tilting vortex. At 90 h into the simulation, RI occurs, and the tilt nutations (and hence the TCAs) cease to occur.

Abstract

The impact of ice phase cloud microphysical processes on prediction of tropical cyclone environment is examined for two microphysical parameterizations using the Coupled Ocean–Atmosphere Mesoscale Prediction System–Tropical Cyclone (COAMPS-TC) model. An older version of microphysical parameterization is a relatively typical single-moment scheme with five hydrometeor species: cloud water and ice, rain, snow, and graupel. An alternative newer method uses a hybrid approach of double moment in cloud ice and rain and single moment in the other three species. Basin-scale synoptic flow simulations point to important differences between these two schemes. The upper-level cloud ice concentrations produced by the older scheme are up to two orders of magnitude greater than the newer scheme, primarily due to differing assumptions concerning the ice nucleation parameterization. Significant (1°–2°C) warm biases near the 300-hPa level in the control experiments are not present using the newer scheme. The warm bias in the control simulations is associated with the longwave radiative heating near the base of the cloud ice layer. The two schemes produced different track and intensity forecasts for 15 Atlantic storms. Rightward cross-track bias and positive intensity bias in the control forecasts are significantly reduced using the newer scheme. Synthetic satellite imagery of Hurricane Igor (2010) shows more realistic brightness temperatures from the simulations using the newer scheme, in which the inner core structure is clearly discernible. Applying the synthetic satellite imagery in both quantitative and qualitative analyses helped to pinpoint the issue of excessive upper-level cloud ice in the older scheme.

Abstract

Interactions between the upper-level outflow of a sheared, rapidly intensifying tropical cyclone (TC) and the background environmental flow in an idealized model are presented. The most important finding is that the divergent outflow from convection localized by the tilt of the vortex serves to divert the background environmental flow around the TC, thus reducing the local vertical wind shear. We show that this effect can be understood from basic theoretical arguments related to Bernoulli flow around an obstacle. In the simulation discussed, the environmental flow diversion by the outflow is limited to 2 km below the tropopause in the 12–14-km (250–150 hPa) layer. Synthetic water vapor satellite imagery confirms the presence of upshear arcs in the cloud field, matching satellite observations. These arcs, which exist in the same layer as the outflow, are caused by slow-moving wave features and serve as visual markers of the outflow–environment interface. The blocking effect where the outflow and the environmental winds meet creates a dynamic high pressure whose pressure gradient extends nearly 1000 km upwind, thus causing the environmental winds to slow down, to converge, and to sink. We discuss these results with respect to the first part of this three-part study, and apply them to another atypical rapid intensification hurricane: Matthew (2016).

Abstract

The Operational Multiscale Environment model with Grid Adaptivity (OMEGA) is an atmospheric simulation system that links the latest methods in computational fluid dynamics and high-resolution gridding technologies with numerical weather prediction. In the fall of 1999, OMEGA was used for the first time to examine the structure and evolution of a hurricane (Floyd, 1999). The first simulation of Floyd was conducted in an operational forecast mode; additional simulations exploiting both the static as well as the dynamic grid adaptation options in OMEGA were performed later as part of a sensitivity–capability study. While a horizontal grid resolution ranging from about 120 km down to about 40 km was employed in the operational run, resolutions down to about 15 km were used in the sensitivity study to explicitly model the structure of the inner core. All the simulations produced very similar storm tracks and reproduced the salient features of the observed storm such as the recurvature off the Florida coast with an average 48-h position error of 65 km. In addition, OMEGA predicted the landfall near Cape Fear, North Carolina, with an accuracy of less than 100 km up to 96 h in advance. It was found that a higher resolution in the eyewall region of the hurricane, provided by dynamic adaptation, was capable of generating better-organized cloud and flow fields and a well-defined eye with a central pressure lower than the environment by roughly 50 mb. Since that time, forecasts were performed for a number of other storms including Georges (1998) and six 2000 storms (Tropical Storms Beryl and Chris, Hurricanes Debby and Florence, Tropical Storm Helene, and Typhoon Xangsane). The OMEGA mean track error for all of these forecasts of 101, 140, and 298 km at 24, 48, and 72 h, respectively, represents a significant improvement over the National Hurricane Center (NHC) 1998 average of 156, 268, and 374 km, respectively. In a direct comparison with the GFDL model, OMEGA started with a considerably larger position error yet came within 5% of the GFDL 72-h track error. This paper details the simulations produced and documents the results, including a comparison of the OMEGA forecasts against satellite data, observed tracks, reported pressure lows and maximum wind speed, and the rainfall distribution over land.