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John Hampson
and
Peter Haynes

Abstract

This paper investigates the occurrence of phase alignment of the tropical stratospheric quasi-biennial oscillation (QBO) with the annual cycle. First, updating previous studies, observational results are shown for NCEP reanalysis data and Singapore radiosondes: both datasets show strong phase alignment of the QBO at 24.5 km. Phase alignment is investigated in a 3D mechanistic stratospheric model including explicit large-scale planetary waves, forced by a lower boundary geopotential anomaly, and a simple equatorial wave parameterization. The model simulates a QBO-like oscillation, with the period depending on the lower boundary momentum flux of the parameterized waves. Phase alignment is manifested in two different ways. First, simulated oscillations of both integer and noninteger year periods are shown to lock on to a certain phase of the annual cycle. Second, when the magnitude of the lower boundary momentum flux is varied about a range implying oscillation period close to 2 yr, the period of the resulting oscillation is exactly 2 yr for a finite range of such magnitude. Analysis of the 3D model results suggest that the the phase alignment is due largely to the annual cycle in tropical upwelling. This hypothesis is supported by simulations with a 1D equatorial model including both parameterized waves and seasonally varying upwelling. The oscillations in this model show significant phase alignment when the upwelling parameters are tuned to correspond to the 3D model, although the phase alignment is weaker than that seen in the 3D model.

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John Hampson
and
Peter Haynes

Abstract

The work described here examines the influence of the equatorial quasi-biennial oscillation (QBO) on the extratropics in a zonally truncated 3D mechanistic stratospheric model. Model results show that the extratropical response to the QBO depends critically on the phase alignment of the QBO with the annual cycle: the signal of extratropical response varies by a factor of 8 between the phase alignment that gives minimum response and that which gives maximum response. Model simulations in which the time and height structure of the QBO are varied suggest that, in this zonally truncated model, the equatorial height of 21–23 km is most influential for the extratropical response and that late autumn/early winter is the time at which the QBO has the most influence over the extratropical circulation. The correlation coefficient between the QBO (measured by zonal wind) and the extratropics (measured by zonal wind or potential temperature) is as high as 0.95. The correlation coefficient is largest for simulations with lower boundary wave forcing weaker than that which gives largest extratropical interannual variability. For stronger extratropical wave forcing, the correlation coefficient is slightly smaller, but the regression coefficient of the linear term in a least squares fit is significantly larger.

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Zhengxin Zhu
,
John Thuburn
,
Brian J. Hoskins
, and
Peter H. Haynes

Abstract

A vertical discretization of the primitive equations in a general vertical coordinate is described that enables a primitive equation model to use terrain-following sigma levels near the ground and isentropic levels higher up, with a smooth transition region in between. Therefore, it combines many of the advantages of the computational efficiency of σ coordinates and the predictive and diagnostic potential of θ coordinates, and should be particularly useful for general circulation models to be used for studies of stratosphere-troposphere exchange and middle-atmosphere transport of trace gases. It is shown that the semi-implicit time scheme can be used in a straightforward manner with this discretization. A discussion is given of how to optimize the transition from sigma levels to isentropic levels so as to avoid model levels crossing each other. A numerical problem caused when very shallow, very strong inversions occur in the temperature field is countered by a form of vertical-scale selective dissipation. Baroclinic wave life cycles and full general circulation simulations have been successfully performed with a modified version of the European Centre for Medium-Range Weather Forecasts model.

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Josephine R. Brown
,
Christian Jakob
, and
John M. Haynes

Abstract

Observed regional rainfall characteristics can be analyzed by examining both the frequency and intensity of different categories of rainfall. A complementary approach is to consider rainfall characteristics associated with regional synoptic regimes. These two approaches are combined here to examine daily rainfall characteristics over the Australian region, providing a target for model simulations. Using gridded daily rainfall data for the period 1997–2007, rainfall at each grid point and averaged over several sites is decomposed into the frequency of rainfall events and the intensity of rainfall associated with each event. Daily sea level pressure is classified using a self-organizing map, and rainfall on corresponding days is assigned to the resulting synoptic regimes. This technique is then used to evaluate rainfall in the new Australian Community Climate and Earth-System Simulator (ACCESS) global climate model and separate the influence of large-scale circulation errors and errors due to the representation of subgrid-scale physical processes. The model exhibits similar biases to many other global climate models, simulating too frequent light rainfall and heavy rainfall of insufficient intensity. These errors are associated with particular synoptic regimes over different sectors of the Australian continent and surrounding oceans. The model simulates only weak convective rainfall over land during the summer monsoon, and heavy rainfall associated with frontal systems over southern Australia is also not simulated. As the model captures the structure and frequency of synoptic patterns, but not the associated rainfall intensity or frequency, it is likely that the source of the rainfall errors lies in model physical parameterizations rather than large-scale dynamics.

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Mark Smalley
,
Tristan L'Ecuyer
,
Matthew Lebsock
, and
John Haynes

Abstract

Because of its extensive quality control procedures and uniform space–time grid, the NCEP Stage IV merged Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and surface rain gauge dataset is often considered to be the best long-term gridded dataset of precipitation observations covering the contiguous United States. Stage IV accumulations are employed in a variety of applications, and while the WSR-88D systems are well suited for observing heavy rain events that are likely to affect flooding, limitations in surface radar and gauge measurements can result in missed precipitation, especially near topography and in the western United States. This paper compares hourly Stage IV observations of precipitation occurrence to collocated observations from the 94-GHz CloudSat Cloud Profiling Radar, which provides excellent sensitivity to light and frozen precipitation. Statistics from 4 yr of comparisons show that the CloudSat observes precipitation considerably more frequently than the Stage IV dataset, especially in northern states where frozen precipitation is prevalent in the cold season. The skill of Stage IV for precipitation detection is found to decline rapidly when the near-surface air temperature falls below 0°C. As a result, agreement between Stage IV and CloudSat tends to be best in the southeast, where radar coverage is good and moderate-to-heavy liquid precipitation dominates. Stage IV and CloudSat precipitation detection characteristics are documented for each of the individual river forecast centers that contribute to the Stage IV dataset to provide guidance regarding potential sampling biases that may impact hydrologic applications.

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Cristian Mitrescu
,
Tristan L’Ecuyer
,
John Haynes
,
Steven Miller
, and
Joseph Turk

Abstract

Identifying and quantifying the intensity of light precipitation at global scales is still a difficult problem for most of the remote sensing algorithms in use today. The variety of techniques and algorithms employed for such a task yields a rather wide spectrum of possible values for a given precipitation event, further hampering the understanding of cloud processes within the climate. The ability of CloudSat’s millimeter-wavelength Cloud Profiling Radar (CPR) to profile not only cloud particles but also light precipitation brings some hope to the above problems. Introduced as version zero, the present work uses basic concepts of detection and retrieval of light precipitation using spaceborne radars. Based on physical principles of remote sensing, the radar model relies on the description of clouds and rain particles in terms of a drop size distribution function. Use of a numerical model temperature and humidity profile ensures the coexistence of mixed phases otherwise undetected by the CPR. It also provides grounds for evaluating atmospheric attenuation, important at this frequency. Related to the total attenuation, the surface response is used as an additional constraint in the retrieval algorithm. Practical application of the profiling algorithm includes a 1-yr preliminary analysis of global rainfall incidence and intensity. These results underscore once more the role of CloudSat rainfall products for improving and enhancing current estimates of global light rainfall, mostly at higher latitudes, with the goal of understanding its role in the global energy and water cycle.

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John M. Haynes
,
Yoo-Jeong Noh
,
Steven D. Miller
,
Katherine D. Haynes
,
Imme Ebert-Uphoff
, and
Andrew Heidinger

Abstract

The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning–based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The random forest (RF) and neural network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively sensed cloud boundaries from the radar and lidar systems on board the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.

Significance Statement

Using satellites to detect the heights of clouds in the atmosphere is important for a variety of weather applications, including aviation weather forecasting. However, detecting low clouds can be challenging if there are other clouds above them. To address this, we have developed machine learning–based models that can be used with passive satellite instruments. These models use satellite observations at visible and infrared wavelengths, an estimate of relative humidity in the atmosphere, and geographic and surface-type information to predict whether low clouds are present. Our results show that these models have significant skill at predicting low clouds, even in the presence of higher cloud layers.

Open access
Carl E. Hane
,
John A. Haynes
,
David L. Andra Jr.
, and
Frederick H. Carr

Abstract

Mesoscale convective systems that affect a limited area within the southern plains of the United States during late morning hours during the warm season are investigated. A climatological study over a 5-yr period documents the initiation locations and times, tracks, associated severe weather, and relation to synoptic features over the lifetimes of 145 systems. An assessment is also made of system evolution in each case during the late morning. For a subset of 48 systems, vertical profiles of basic variables from Rapid Update Cycle (RUC) model analyses are used to characterize the environment of each system. Scatter diagrams and discriminant analyses are used to assess which environmental variables are most promising in helping to determine which of two classes of evolutionary character each system will follow.

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Yi Huang
,
Steven T. Siems
,
Michael J. Manton
,
Luke B. Hande
, and
John M. Haynes

Abstract

A climatology of the structure of the low-altitude cloud field (tops below 4 km) over the Southern Ocean (40°–65°S) in the vicinity of Australia (100°–160°E) has been constructed with CloudSat products for liquid water and ice water clouds. Averaging over longitude and time, CloudSat produces a roughly uniform cloud field between heights of approximately 750 and 2250 m across the extent of the domain for both winter and summer. This cloud field makes a transition from consisting primarily of liquid water at the lower latitudes to ice water at the higher latitudes. This transition is primarily driven by the gradient in the temperature, which is commonly between 0° and −20°C, rather than by direct physical observation.

The uniform lower boundary is a consequence of the CloudSat cloud detection algorithm being unable to reliably separate radar returns because of the bright surface versus returns due to clouds, in the lowest four range bins above the surface. This is potentially very problematic over the Southern Ocean where the depth of the boundary layer has been observed to be as shallow as 500 m. Cloud fields inferred from upper-air soundings at Macquarie Island (54.62°S, 158.85°E) similarly suggest that the peak frequency lies between 260 and 500 m for both summer and winter. No immediate explanation is available for the uniformity of the cloud-top boundary. This lack of a strong seasonal cycle is, perhaps, remarkable given the large seasonal cycles in both the shortwave (SW) radiative forcing experienced and the cloud condensation nuclei (CCN) concentration over the Southern Ocean.

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Shannon Mason
,
Jennifer K. Fletcher
,
John M. Haynes
,
Charmaine Franklin
,
Alain Protat
, and
Christian Jakob

Abstract

A deficit of shortwave cloud forcing over the Southern Ocean is persistent in many global climate models. Cloud regimes have been widely used in model evaluation studies to make a process-oriented diagnosis of cloud parameterization errors, but cloud regimes have some limitations in resolving both observed and simulated cloud behavior. A hybrid methodology is developed for identifying cloud regimes from observed and simulated cloud simultaneously.

Through this methodology, 11 hybrid cloud regimes are identified in the ACCESS1.3 model for the high-latitude Southern Ocean. The hybrid cloud regimes resolve the features of observed cloud and characterize cloud errors in the model. The simulated properties of the hybrid cloud regimes, and their occurrence over the Southern Ocean and in the context of extratropical cyclones, are evaluated, and their contributions to the shortwave radiation errors are quantified.

Three errors are identified: an overall deficit of cloud fraction, a tendency toward optically thin low and midtopped cloud, and an absence of a shallow frontal-type cloud at high latitudes and in the warm fronts of extratropical cyclones.

To demonstrate the utility of the hybrid cloud regimes for the evaluation of changes to the model, the effects of selected changes to the model microphysics are investigated.

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