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G. David Alexander and William R. Cotton

Abstract

A method is described for parameterizing thermodynamic forcing by the mesoscale updrafts and downdrafts of mesoscale convective systems (MCSs) in models with resolution too coarse to resolve these drafts. The parameterization contains improvements over previous schemes, including a more sophisticated convective driver and inclusion of the vertical distribution of various physical processes obtained through conditional sampling of two cloud-resolving MCS simulations. The mesoscale parameterization is tied to a version of the Arakawa–Schubert convective parameterization scheme that is modified to employ a prognostic closure. The parameterized Arakawa–Schubert cumulus convection provides condensed water, ice, and water vapor, which drives the parameterization for the large-scale effects of mesoscale circulations associated with the convection. In the mesoscale parameterization, determining thermodynamic forcing of the large scale depends on knowing the vertically integrated values and the vertical distributions of phase transformation rates and mesoscale eddy fluxes of entropy and water vapor in mesoscale updrafts and downdrafts. The relative magnitudes of these quantities are constrained by assumptions made about the relationships between various quantities in an MCS’s water budget deduced from the cloud-resolving MCS simulations. The MCS simulations include one of a tropical MCS observed during the 1987 Australian monsoon season (EMEX9) and one of a midlatitude MCS observed during a 1985 field experiment in the Central Plains of the United States (PRE-STORM 23–24 June).

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G. David Alexander and George S. Young

Abstract

Characteristics of the prestorm environment of Equatorial Mesoscale Experiment (EMEX) mesoscale precipitation features (MPFs) are related to properties of them systems using regression analysis. Although environmental thermodynamic parameters are poorly correlated with EMEX MPF properties (mainly because environmental thermodynamic conditions varied little among MPFs), kinematic parameters are well correlated to these properties. Lines whose environments have low-level shear (the shear between about 950 and 750 mb) exceeding 5 m s−1 are oriented normal to the direction of this shear; lines where the low-level shear is under 5 m s−1 are oriented along the direction of the midlevel shear (the shear between about 800 and 400 mb). Convective line speeds correlate well with the maximum speed of the rear-to-front flow in the troposphere below 300 mb. Direction of MPF motion is nearly coincident with the tropospheric mean wind direction. convective line length is proportional to the magnitude of the men along-line wind in the cloud layer. The lower-tropospheric drying that a system causes is proportional to the shear within two different layers—800–400 and 1000–800 mb.

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G. David Alexander, George S. Young, and David V. Ledvina

Abstract

Rotated Principal component analysis (PCA) is applied to the combined vertical profiles of apparent heat source Q 1 and apparent moisture sink Q 2 from both disturbed and undisturbed periods of the Australian summer monsoon season. The data represent the heating and drying within two radiosonde arrays afforded by the Australian Monsoon Experiment (AMEX), The aim here is to identify dominant modes of variability in combined vertical profiles of Q 1 and Q 2. Rotation of the principal components (PCs)-done to assure stable, physically meaningful components-yields several PCs, deemed here to be statistically significant. The variation of individual Q 1 and Q 2 profiles from the mean profile can be expressed as linear combinations of the PCs; therefore, determination of the relative importance of each PC (through examination of its score) during differing convective conditions provides insight into their physical meaning. For instance, the contribution of PC 1 (that mode of variability that explains the maximum amount of variance between the profiles) is largest when mature cloud-cluster coverage is most expansive. Therefore, this PC is attributable to that combination of deep convection and associated stratiform anvil typical of mature cloud clusters. The remaining PCs WI into two categories: those whose contributions vary with the evolution of a convective system and those whose contributions vary diurnally. Principal components of the former group represent the effects of convection from shallow cumulus to stratiform anvil precipitation. Principal components of the latter group, those that show heating and drying patterns confined to the extremities of the troposphere, are attributable to diabatic boundary-layer fluxes and radiative processes at the top of the troposphere.

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G. David Alexander, James A. Weinman, and J. L. Schols

Abstract

A technique is described in which forecasts of the locations of features associated with marine cyclones may be improved through the use of microwave integrated water vapor (IWV) imagery and image warping of forecast mesoscale model fields. Here, image warping is used to optimally match mesoscale model output to observations of IWV measured by microwave sensors. In the mesoscale model simulations presented here (one of the March 1993 “superstorm,” one of a rapidly deepening cyclone observed in the North Atlantic in February 1992, and one of the ERICA IOP 4 cyclone), the Pennsylvania State University–National Center for Atmospheric Research MM5 model is initialized from the standard National Meteorological Center (recently renamed the National Centers for Environmental Prediction) operational analysis. The simulations are then run until a time at which a Special Sensor Microwave/Imager (SSM/I) overpass occurs. For each simulation, the forecast pattern of IWV is then compared to the field shown in the SSM/I image. In all three cases, the MM5 moves the cyclones too slowly, and therefore places distinguishing features in the forecast IWV fields significantly upstream of their locations as revealed in the microwave imagery. To rectify these errors, the grid on which the source image (forecast field) is defined is then warped to match the target image (remotely observed IWV field) by choosing pairs of tie points corresponding to similar features in the two images. The values of all model moisture variables at all vertical levels are then carried to the new warped grid points and interpolated back to the original model grid. Model integration then proceeds with the new model fields. The model results at a subsequent time after the warping is applied are then compared with simultaneous model results in simulations in which no warping was applied as well as with model simulations in which a standard nudging technique is applied. Warping results in improved forecasts of cyclone minimum sea level pressure, tracks, and IWV fields over both the control simulations and the nudged simulations.

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William H. G. Roberts, David S. Battisti, and Alexander W. Tudhope

Abstract

The offline linearized ocean–atmosphere model (LOAM), which was developed to quantify the impact of the climatological mean state on the variability of the El Niño–Southern Oscillation (ENSO), is used to illuminate why ENSO changed between the modern-day and early/mid-Holocene simulations in two climate modeling studies using the NCAR Climate System Model (CSM) and the Hadley Centre Coupled Model, version 3 (HadCM3). LOAM reproduces the spatiotemporal variability simulated by the climate models and shows both the reduction in the variance of ENSO and the changes in the spatial structure of the variance during the early/mid-Holocene. The mean state changes that are important in each model are different and, in both cases, are also different from those hypothesized to be important in the original papers describing these simulations. In the CSM simulations, the ENSO mode is stabilized by the mean cooling of the SST. This reduces atmospheric heating anomalies that in turn give smaller wind stress anomalies, thus weakening the Bjerknes feedback. Within the ocean, a change in the thermocline structure alters the spatial pattern of the variance, shifting the peak variance farther east, but does not reduce the overall amount of ENSO variance. In HadCM3, the ENSO mode is stabilized by a combination of a weaker thermocline and weakened horizontal surface currents. Both of these reduce the Bjerknes feedback by reducing the ocean’s SST response to wind stress forcing. This study demonstrates the importance of considering the combined effect of a mean state change on the coupled ocean–atmosphere system: conflicting and erroneous results are obtained for both models if only one model component is considered in isolation.

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G. David Alexander, James A. Weinman, V. Mohan Karyampudi, William S. Olson, and A. C. L. Lee

Abstract

Inadequate specification of divergence and moisture in the initial conditions of numerical models results in the well-documented “spinup” problem. Observational studies indicate that latent heat release can be a key ingredient in the intensification of extratropical cyclones. As a result, the assimilation of rain rates during the early stages of a numerical simulation results in improved forecasts of the intensity and precipitation patterns associated with extratropical cyclones. It is challenging, however, particularly over data-sparse regions, to obtain complete and reliable estimates of instantaneous rain rate. Here, a technique is described in which data from a variety of sources—passive microwave sensors, infrared sensors, and lightning flash observations—along with a classic image processing technique (digital image morphing) are combined to yield a continuous time series of rain rates, which may then be assimilated into a mesoscale model. The technique is tested on simulations of the notorious 1993 Superstorm. In this case, a fortuitous confluence of several factors—rapid cyclogenesis over an oceanic region, the occurrence of this cyclogenesis at a time inconveniently placed in between Special Sensor Microwave/Imager overpasses, intense lightning during this time, and a poor forecast in the control simulation—leads to a dramatic improvement in forecasts of precipitation patterns, sea level pressure fields, and geopotential height fields when information from all of the sources is combined to determine the rain rates. Lightning data, in particular, has a greater positive impact on the forecasts than the other data sources.

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Andrea J. Dittus, David J. Karoly, Sophie C. Lewis, Lisa V. Alexander, and Markus G. Donat

Abstract

The skill of eight climate models in simulating the variability and trends in the observed areal extent of daily temperature and precipitation extremes is evaluated across five large-scale regions, using the climate extremes index (CEI) framework. Focusing on Europe, North America, Asia, Australia, and the Northern Hemisphere, results show that overall the models are generally able to simulate the decadal variability and trends of the observed temperature and precipitation components over the period 1951–2005. Climate models are able to reproduce observed increasing trends in the area experiencing warm maximum and minimum temperature extremes, as well as, to a lesser extent, increasing trends in the areas experiencing an extreme contribution of heavy precipitation to total annual precipitation for the Northern Hemisphere regions. Using simulations performed under different radiative forcing scenarios, the causes of simulated and observed trends are investigated. A clear anthropogenic signal is found in the trends in the maximum and minimum temperature components for all regions. In North America, a strong anthropogenically forced trend in the maximum temperature component is simulated despite no significant trend in the gridded observations, although a trend is detected in a reanalysis product. A distinct anthropogenic influence is also found for trends in the area affected by a much-above-average contribution of heavy precipitation to annual precipitation totals for Europe in a majority of models and to varying degrees in other Northern Hemisphere regions. However, observed trends in the area experiencing extreme total annual precipitation and extreme number of wet and dry days are not reproduced by climate models under any forcing scenario.

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Alexander J. Stockham, David M. Schultz, Jonathan G. Fairman Jr., and Adam P. Draude

Abstract

Although rain shadows (i.e., leeside reductions of precipitation downwind of orography) are commonly described in textbooks, quantitative climatologies of the rain-shadow effect are rare. To test quantitatively a classic rain-shadow locality of the Peak District, United Kingdom, precipitation from 54 observing stations over 30 years (1981–2010) are examined. Under 850-hPa westerlies, annual and daily precipitation amounts are on average higher in Manchester in the west and the Peak District than in Sheffield in the east. More precipitation falls—and falls more frequently—frequently in Manchester than Sheffield on 197 westerly flow days annually. In contrast, more precipitation falls—and falls more frequently—in Sheffield than Manchester on 28 easterly flow days annually. These bulk precipitation statistics support a climatological rain shadow. However, when individual days are investigated, only 17% of westerly flow days occur where daily rainfall data might exhibit the rain-shadow effect (defined here as Manchester with precipitation and Sheffield with no precipitation). In contrast, only 10% of easterly flow days occur where daily rainfall data might exhibit the rain-shadow effect (Sheffield with precipitation and Manchester with no precipitation). Thus, westerly winds are more likely to exhibit a rain-shadow effect than easterly winds. Although the distribution of precipitation observed across the Peak District can sometimes be explained by the rain-shadow effect, the occurrence of the rain-shadow effect by our admittedly strict definition is not as frequent as one might expect to explain the local precipitation climate for which it has sometimes been previously credited. Thus, an attempt to understand the climatological relevance of the rain-shadow effect from one location reveals ambiguity in the definition of a rain shadow and in its interpretation from real rainfall data.

Open access
Ming Hu, Stanley G. Benjamin, Therese T. Ladwig, David C. Dowell, Stephen S. Weygandt, Curtis R. Alexander, and Jeffrey S. Whitaker

Abstract

The Rapid Refresh (RAP) is an hourly updated regional meteorological data assimilation/short-range model forecast system running operationally at NOAA/National Centers for Environmental Prediction (NCEP) using the community Gridpoint Statistical Interpolation analysis system (GSI). This paper documents the application of the GSI three-dimensional hybrid ensemble–variational assimilation option to the RAP high-resolution, hourly cycling system and shows the skill improvements of 1–12-h forecasts of upper-air wind, moisture, and temperature over the purely three-dimensional variational analysis system. Use of perturbation data from an independent global ensemble, the Global Data Assimilation System (GDAS), is demonstrated to be very effective for the regional RAP hybrid assimilation. In this paper, application of the GSI-hybrid assimilation for the RAP is explained. Results from sensitivity experiments are shown to define configurations for the operational RAP version 2, the ratio of static and ensemble background error covariance, and vertical and horizontal localization scales for the operational RAP version 3. Finally, a 1-week RAP experiment from a summer period was performed using a global ensemble from a winter period, suggesting that a significant component of its multivariate covariance structure from the ensemble is independent of time matching between analysis time and ensemble valid time.

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Eric P. James, Curtis R. Alexander, David C. Dowell, Stephen S. Weygandt, Stanley G. Benjamin, Geoffrey S. Manikin, John M. Brown, Joseph B. Olson, Ming Hu, Tatiana G. Smirnova, Terra Ladwig, Jaymes S. Kenyon, and David D. Turner

Abstract

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecast (WRF-ARW) Model that covers the conterminous United States and Alaska and runs hourly (for CONUS; every 3 h for Alaska) in real time at the National Centers for Environmental Prediction. The high-resolution forecasts support a variety of user applications including aviation, renewable energy, and prediction of many forms of severe weather. In this second of two articles, forecast performance is documented for a wide variety of forecast variables and across HRRR versions. HRRR performance varies across geographical domain, season, and time of day depending on both prevalence of particular meteorological phenomena and the availability of both conventional and nonconventional observations. Station-based verification of surface weather forecasts (2-m temperature and dewpoint temperature, 10-m winds, visibility, and cloud ceiling) highlights the ability of the HRRR to represent daily planetary boundary layer evolution and the development of convective and stratiform cloud systems, while gridded verification of simulated composite radar reflectivity and quantitative precipitation forecasts reveals HRRR predictive skill for summer and winter precipitation systems. Significant improvements in performance for specific forecast problems are documented for the upgrade versions of the HRRR (HRRRv2, v3, and v4) implemented in 2016, 2018, and 2020, respectively. Development of the HRRR model data assimilation and physics paves the way for future progress with operational convective-scale modeling.

Significance Statement

NOAA’s operational hourly updating convection-allowing model, the High-Resolution Rapid Refresh (HRRR), is a key tool for short-range weather forecasting and situational awareness. Improvements in assimilation of weather observations, as well as in physics parameterizations, has led to improvements in simulated radar reflectivity and quantitative precipitation forecasts since the initial implementation of HRRR in September 2014. Other targeted development has focused on improved representation of the diurnal cycle of the planetary boundary layer, resulting in improved near-surface temperature and humidity forecasts. Additional physics and data assimilation changes have led to improved treatment of the development and erosion of low-level clouds, including subgrid-scale clouds. The final version of HRRR features storm-scale ensemble data assimilation and explicit prediction of wildfire smoke plumes.

Open access