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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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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 M. Forsythe
and
John M. Haynes

Abstract

Polar lows generate hazardous weather conditions in the Arctic, and satellites have played a key role in understanding their genesis and dynamics. For the first time, an overpass of the CloudSat 94-GHz cloud radar over a polar low has been recorded. The case occurred in November 2013 in the Labrador Sea between Canada and Greenland, and had a striking convective appearance with an eyelike feature. A deep cloud band was observed by the radar, with radar reflectivity up to 5-km in altitude in a 50-km-wide band. It is likely that more such matchups exist in the CloudSat mission data.

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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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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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Wesley Berg
,
Tristan L’Ecuyer
, and
John M. Haynes
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Wesley Berg
,
Tristan L’Ecuyer
, and
John M. Haynes

Abstract

A combination of rainfall estimates from the 13.8-GHz Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) and the 94-GHz CloudSat Cloud Profiling Radar (CPR) is used to assess the distribution of rainfall intensity over tropical and subtropical oceans. These two spaceborne radars provide highly complementary information: the PR provides the best information on the total rain volume because of its ability to estimate the intensity of all but the lightest rain rates while the CPR’s higher sensitivity provides superior rainfall detection as well as estimates of drizzle and light rain. Over the TRMM region between 35°S and 35°N, rainfall frequency from the CPR is around 9%, approximately 2.5 times that detected by the PR, and the CPR estimates indicate a contribution by light rain that is undetected by the PR of around 10% of the total. Stratifying the results by total precipitable water (TPW) as a proxy for rainfall regime indicates dramatic differences over stratus-dominated subsidence regions, with nearly 20% of the total rain occurring as light rain. Over moist tropical regions, the CPR substantially underestimates rain from intense convective storms because of large attenuation and multiple-scattering effects while the PR misses very little of the total rain volume because of a lower relative contribution from light rain. Over low-TPW regions, however, inconsistencies between estimates from the PR and the CPR point to uncertainties in the algorithm assumptions that remain to be understood and addressed.

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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
John M. Haynes
,
Christian Jakob
,
William B. Rossow
,
George Tselioudis
, and
Josephine Brown

Abstract

Clouds over the Southern Ocean are often poorly represented by climate models, but they make a significant contribution to the top-of-atmosphere (TOA) radiation balance, particularly in the shortwave portion of the energy spectrum. This study seeks to better quantify the organization and structure of Southern Hemisphere midlatitude clouds by combining measurements from active and passive satellite-based datasets. Geostationary and polar-orbiter satellite data from the International Satellite Cloud Climatology Project (ISCCP) are used to quantify large-scale, recurring modes of cloudiness, and active observations from CloudSat and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are used to examine vertical structure, radiative heating rates, and precipitation associated with these clouds. It is found that cloud systems are organized into eight distinct regimes and that ISCCP overestimates the midlevel cloudiness of these regimes. All regimes contain a relatively high occurrence of low cloud, with 79% of all cloud layers observed having tops below 3 km, but multiple-layered clouds systems are present in approximately 34% of observed cloud profiles. The spatial distribution of regimes varies according to season, with cloud systems being geometrically thicker, on average, during the austral winter. Those regimes found to be most closely associated with midlatitude cyclones produce precipitation the most frequently, although drizzle is extremely common in low-cloud regimes. The regimes associated with cyclones have the highest in-regime shortwave cloud radiative effect at the TOA, but the low-cloud regimes, by virtue of their high frequency of occurrence over the oceans, dominate both TOA and surface shortwave effects in this region as a whole.

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