Engineering topics

McFarlane, Sally A

Publications and source records attributed to McFarlane, Sally A.

15 records

DOE OSTI2013

Uncertainty Quantification and Parameter Tuning in the CAM5 Zhang-McFarlane Convection Scheme and Impact of Improved Convection on the Global Circulation and Climate

In this study, we applied an uncertainty quantification (UQ) technique to improve convective precipitation in the global climate model, the Community Atmosphere Model version 5 (CAM5), in which the convective and stratiform precipitation partitioning is very different from observational estimates. We examined the sensitivity of precipitation and circulation to several key parameters in the Zhang-McFarlane deep convection scheme in CAM5, using a stochastic importance-sampling algorithm that can progressively converge to optimal parameter values. The impact of improved deep convection on the global circulation and climate was subsequently evaluated. Our results show that the simulated convective precipitation is most sensitive to the parameters of the convective available potential energy consumption time scale, parcel fractional mass entrainment rate, and maximum downdraft mass flux fraction. Using the optimal parameters constrained by the observed Tropical Rainfall Measuring Mission, convective precipitation improves the simulation of convective to stratiform precipitation ratio and rain-rate spectrum remarkably. When convection is suppressed, precipitation tends to be more confined to the regions with strong atmospheric convergence. As the optimal parameters are used, positive impacts on some aspects of the atmospheric circulation and climate, including reduction of the double Intertropical Convergence Zone, improved East Asian monsoon precipitation, and improved annual cycles of the cross-equatorial jets, are found as a result of the vertical and horizontal redistribution of latent heat release from the revised parameterization. Positive impacts of the optimal parameters derived from the 2 simulations are found to transfer to the 1 simulations to some extent.

CAM5↗

DOE OSTI2012

Life Cycle of Midlatitude Deep Convective Systems in a Lagrangian Framework

Deep Convective Systems (DCSs) consist of intense convective cores (CC), large stratiform rain (SR) regions, and extensive non-precipitating anvil clouds (AC). This study focuses on the evolution of these three components and the factors that affect convective AC production. An automated satellite tracking method is used in conjunction with a recently developed multi-sensor hybrid classification to analyze the evolution of DCS structure in a Lagrangian framework over the central United States. Composite analysis from 4221 tracked DCSs during two warm seasons (May-August, 2010-2011) shows that maximum system size correlates with lifetime, and longer-lived DCSs have more extensive SR and AC. Maximum SR and AC area lag behind peak convective intensity and the lag increases linearly from ~1-hour for short-lived systems to more than 3-hours for long-lived ones. The increased lag, which depends on the convective environment, suggests that changes in the overall diabatic heating structure associated with the transition from CC to SR and AC could prolong the system lifetime by sustaining stratiform cloud development. Longer-lasting systems are associated with up to 60% higher mid-tropospheric relative humidity and up to 40% stronger mid-to-upper tropospheric wind shear. Regression analysis shows that the areal coverage of thick AC is strongly correlated with the size of CC, updraft strength, and SR area. Ambient upper tropospheric wind speed and wind shear also play an important role for convective AC production where for systems with large AC (radius>120-km) they are 24% and 20% higher, respectively, than those with small AC (radius=20 km).

Anvil↗

DOE OSTI2012

Climatology and Formation of Tropical Midlevel Clouds at the Darwin ARM Site

A 4-yr climatology of midlevel clouds is presented from vertically pointing cloud lidar and radar measurements at the Atmospheric Radiation Measurement Program (ARM) site at Darwin, Australia. Few studies exist of tropical midlevel clouds using a dataset of this length. Seventy percent of clouds with top heights between 4 and 8 km are less than 2 km thick. These thin layer clouds have a peak in cloud-top temperature around the melting level (0°C) and also a second peak around -12.5°C. The diurnal frequency of thin clouds is highest during the night and reaches a minimum around noon, consistent with variation caused by solar heating. Using a 1.5-yr subset of the observations, the authors found that thin clouds have a high probability of containing supercooled liquid water at low temperatures: ~20% of clouds at -30°C, ~50% of clouds at -20°C, and ~65% of clouds at -10°C contain supercooled liquid water. The authors hypothesize that thin midlevel clouds formed at the melting level are formed differently during active and break monsoon periods and test this over three monsoon seasons. A greater frequency of thin midlevel clouds are likely formed by increased condensation following the latent cooling of melting during active monsoon periods when stratiform precipitation is most frequent. This is supported by the high percentage (65%) of midlevel clouds with preceding stratiform precipitation and the high frequency of stable layers slightly warmer than 0°C. In the break monsoon, a distinct peak in the frequency of stable layers at 0°C matches the peak in thin midlevel cloudiness, consistent with detrainment from convection.

Climatology↗

DOE OSTI2012

Comparison of Simulated and Observed Continental Tropical Anvil Clouds and Their Radiative Heating Profiles

Vertically pointing millimeter-wavelength radar observations of anvil clouds extending from mesoscale convective systems (MCSs) that pass over an Atmospheric Radiation Measurement Program (ARM) field site in Niamey, Niger, are compared to anvil structures generated by the Weather Research and Forecasting (WRF) mesoscale model using six different microphysical schemes. The radar data provide the statistical distribution of the radar reflectivity values as a function of height and anvil thickness. These statistics are compared to the statistics of the modeled anvil cloud reflectivity at all altitudes. Requiring the model to be statistically accurate at all altitudes is a stringent test of the model performance. The typical vertical profile of radiative heating in the anvil clouds is computed from the radar observations. Variability of anvil structures from the different microphysical schemes provides an estimate of the inherent uncertainty in anvil radiative heating profiles. All schemes underestimate the optical thickness of thin anvils and cirrus, resulting in a bias of excessive net anvil heating in all of the simulations.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

RACORO Extended-Term Aircraft Observations of Boundary-Layer Clouds

A first-of-a-kind, extended-term cloud aircraft campaign was conducted to obtain an in-situ statistical characterization of boundary-layer clouds needed to investigate cloud processes and refine retrieval algorithms. Coordinated by the Atmospheric Radiation Measurement (ARM) Aerial Facility (AAF), the Routine AAF Clouds with Low Optical Water Depths (CLOWD) Optical Radiative Observations (RACORO) field campaign operated over the ARM Southern Great Plains (SGP) site from 22 January to 30 June 2009, collecting 260 h of data during 59 research flights. A comprehensive payload aboard the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft measured cloud microphysics, solar and thermal radiation, aerosol properties, and atmospheric state parameters. Proximity to the SGP's extensive complement of surface measurements provides ancillary data that supports modeling studies and enables evaluating a variety of surface retrieval algorithms. The five-month duration enabled sampling a range of conditions associated with the seasonal transition from winter to summer. Although about two-thirds of the cloud flights occurred in May and June, boundary-layer cloud fields were sampled under a variety of environmental and aerosol conditions, with about 75% of the flights occurring in cumulus and stratocumulus. Preliminary analyses show how these data are being used to analyze cloud-aerosol relationships, determine the aerosol sizes that are responsible for nucleating cloud drops, characterize the horizontal variability of the cloud radiative impacts, and evaluate air-borne and surface-based cloud property retrievals. We discuss how conducting an extended-term campaign requires a simplified operating paradigm that is different from that used for typical, short-term, intensive aircraft field programs.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Toward understanding of differences in current cloud retrievals of ARM ground-based measurements

Accurate observations of cloud microphysical properties are needed for evaluating and improving the representation of cloud processes in climate models and better estimate of the Earth radiative budget. However, large differences are found in current cloud products retrieved from ground-based remote sensing measurements using various retrieval algorithms. Understanding the differences is an important step to address uncertainties in the cloud retrievals. In this study, an in-depth analysis of nine existing ground-based cloud retrievals using ARM remote sensing measurements is carried out. We place emphasis on boundary layer overcast clouds and high level ice clouds, which are the focus of many current retrieval development efforts due to their radiative importance and relatively simple structure. Large systematic discrepancies in cloud microphysical properties are found in these two types of clouds among the nine cloud retrieval products, particularly for the cloud liquid and ice particle effective radius. Note that the differences among some retrieval products are even larger than the prescribed uncertainties reported by the retrieval algorithm developers. It is shown that most of these large differences have their roots in the retrieval theoretical bases, assumptions, as well as input and constraint parameters. This study suggests the need to further validate current retrieval theories and assumptions and even the development of new retrieval algorithms with more observations under different cloud regimes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

TWP-ICE Global Atmospheric Model Intercomparison: Convection Responsiveness and Resolution Impact

Results are presented from an intercomparison of global atmospheric model (GAM) simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE). The distinct cloud properties, precipitation, radiation, and vertical diabatic heating profiles associated with three different monsoon regimes (wet, dry, and break) from available observations are used to evaluate 9 GAM forecasts initialized daily from realistic global analyses. All models well captured the evolution of large-scale circulation and the thermodynamic fields, but cloud properties differed substantially among models. For example, liquid water path and ice water path differed by up to two orders of magnitude. Compared with the relatively well simulated top-heavy heating structures during the wet and break period, most models had difficulty in depicting the bottom-heavy heating profiles associated with cumulus congestus. The best performing models during this period were the ones whose convection scheme was most responsive to the free tropospheric humidity. Compared with the large impact of cloud and convective parameterizations on model cloud and precipitation characteristics, resolution has relatively minor impact on simulated cloud properties. However, one feature that was influence by the resolution study in several models was the diurnal cycle of precipitation. Peaking at a different time from convective precipitation, large-scale precipitation generally increases in high resolution forecasts and modulates the total precipitation diurnal cycle. Overall, the study emphasizes the importance of more environmental responsive convective parameterizations to capture various types of convection and the substantial diversity among large-scale cloud and precipitation schemes in current GAMs. This experiment has also demonstrated itself to be a very useful testbed for those developing cloud and convection schemes in these models.

58 GEOSCIENCES↗

DOE OSTI2012

Comparison of Methods to Determine Tropical Tropopause Layer Cirrus Formation Mechanisms

A new method of estimating Tropical Tropopause Layer Cirrus (TTLC) formation mechanism (object method) is compared to interpretations of formation from previous studies using back trajectory calculations matched to convection identified from satellites and statistical relationships of TTLC with temperature and water vapor. The object method groups neighboring Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) TTLC cloud profiles into cloud objects and classifies them as convective (35% of TTLC) if they are directly attached to a convective cloud along the CALIPSO track. The percentage of back trajectory calculations that intersect convection (39-95% of TTLC within 5 days) depends strongly on the spatial and temporal resolution of the convection data set, and the manner in which deep convection is identified. Using minimum brightness temperature in 3 hourly, 1{sup o} resolution grid boxes to define convection, 46% of non-convective TTLC (37% of all TTLC) did not trace back to convection within 24 h. Back trajectory calculations of thin cirrus identified by the High Resolution Dynamics Limb Sounder (HIRDLS) gave similar results. Temperature is not a good proxy for formation mechanism as convective and non-convective TTLC frequencies both increase monotonically with decreasing temperature at approximately the same rate. Non-convective TTLC frequencies have a stronger relationship with relative humidity than convective TTLC frequencies, though are not sufficiently different to distinguish object method categories. A decrease in TTL cirrus frequency found at low temperatures in previous studies is caused by insufficient variability in reanalysis temperature data and is not indicative of TTLC formation mechanism.

58 GEOSCIENCES↗

DOE OSTI2012

Quantification of the Impact of Nauru Island on ARM Measurements

Nauru Island at times generates low clouds that impact low-level cloud statistics and downwelling shortwave radiation measurements made at the Atmospheric Radiation Measurement Program (ARM) site. This study uses five years of Nauru data to quantify the island impact on the site measurements. The results indicate that the solar-heating-produced Nauru island effect occurs about 11% of the time during daylight hours. The island effect increases the 500-1000-m cloud base occurrence by 15%-20% when clouds occur, but because the island effect only occurs 11% of the time the overall increase in daylight low-cloud statistics is 2%, or 1% for 24-h statistics. In a similar way, the island effect produces a reduction of about 17% in the downwelling shortwave (SW) radiation across the daylight hours during the 11% of the time it occurs, an overall 2% daylight (or 1% for 24 h) average reduction. The island effect produces frequent positive downwelling SW cloud effects, in particular during the morning, which tend to somewhat mitigate the overall decrease in downwelling SW radiation that is due to clouds. This produces 17 W m22 less daylight average SW cloud effect relative to non-island-effect times, in particular for the convectively suppressed regime that typifies island-effect-producing conditions. For long-term overall statistical studies such as model and satellite comparisons, the2%daylight (or1%per 24 h) average increase in low-level cloud occurrence and decrease in downwelling SW are not of large concern as long as researchers are aware of them. For shorter-term studies, however, or those that separate data by conditions such as convectively active/suppressed regimes, the Nauru island effect can have significant impacts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2012

Evaluation of cloud fraction and its radiative effect simulated by IPCC AR4 global models against ARM surface observations

Cloud Fraction (CF) is the dominant modulator of radiative fluxes. In this study, we evaluate CF simulations in the IPCC AR4 GCMs against ARM ground measurements, with a focus on the vertical structure, total amount of cloud and its effect on cloud shortwave transmissivity, for both inter-model deviation and model-measurement discrepancy. Our intercomparisons of three CF or sky-cover related dataset reveal that the relative differences are usually less than 10% (5%) for multi-year monthly (annual) mean values, while daily differences are quite significant. The results also show that the model-observation and the inter-model deviations have a similar magnitude for the total CF (TCF) and the normalized cloud effect, and they are twice as large as the surface downward solar radiation and cloud transmissivity. This implies that the other cloud properties, such as cloud optical depth and height, have a similar magnitude of disparity to TCF among the GCMs, and suggests that a better agreement among the GCMs in solar radiative fluxes could be the result of compensating errors in either cloud vertical structure, cloud optical depth or cloud fraction. Similar deviation pattern between inter-model and model-measurement suggests that the climate models tend to generate larger bias against observations for those variables with larger inter-model deviation. The simulated TCF from IPCC AR4 GCMs are very scattered through all seasons over three ARM sites: Southern Great Plains (SGP), Manus, Papua New Guinea and North Slope of Alaska (NSA). The GCMs perform better at SGP than at Manus and NSA in simulating the seasonal variation and probability distribution of TCF; however, the TCF in these models is remarkably underpredicted and cloud transmissivity is less susceptible to the change of TCF than the observed at SGP. Much larger inter-model deviation and model bias are found over NSA than the other sites in estimating the TCF, cloud transmissivity and cloud-radiation interaction, suggesting that the Arctic region continues to challenge cloud simulations in climate models. Most of the GCMs tend to underpredict CF and fail to capture the seasonal variation of CF at middle and low levels in the tropics. The high altitude CF is much larger in the GCMs than the observation and the inter-model variability of CF also reaches maximum at high levels in the tropics. Most of the GCMs tend to underpredict CF by 50-150% relative to the measurement average at low and middle levels over SGP. While the GCMs generally capture the maximum CF in the boundary layer and vertical variability, the inter-model deviation is largest near surface over the Arctic. The internal variability of CF simulated in ensemble runs with the same model is very minimal.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Evaluation of Cloud-Resolving Model Intercomparison Simulations Using TWP-ICE Observations: Precipitation and Cloud Structure

The Tropical Warm Pool – International Cloud Experiment (TWP-ICE) provided high quality model forcing and observational datasets through which detailed model and observational intercomparisons could be performed. In this first of a two part study, precipitation and cloud structures within nine cloud-resolving model simulations are compared with scanning radar reflectivity and satellite infrared brightness temperature observations during an active monsoon period from 19 to 25 January 2006. Most simulations slightly overestimate volumetric convective rainfall. Overestimation of simulated convective area by 50% or more in several simulations is somewhat offset by underestimation of mean convective rain rates. Stratiform volumetric rainfall is underestimated by 13% to 53% despite overestimation of stratiform area by up to 65% because stratiform rain rates in every simulation are much lower than observed. Although simulations match the peaked convective radar reflectivity distribution at low levels, they do not reproduce the peaked distributions observed above the melting level. Simulated radar reflectivity aloft in convective regions is too high in most simulations. In stratiform regions, there is a large spread in model results with none resembling observed distributions. Above the melting level, observed radar reflectivity decreases more gradually with height than simulated radar reflectivity. A few simulations produce unrealistically uniform and cold 10.8-μm infrared brightness temperatures, but several simulations produce distributions close to observed. Assumed ice particle size distributions appear to play a larger role than ice water contents in producing incorrect simulated radar reflectivity distributions aloft despite substantial differences in mean graupel and snow water contents across models.

Varble, Adam↗

DOE OSTI2011

Shortwave absorptance in a tropical cloudy atmosphere: Reconciling calculations and observations

The absorption of shortwave (SW) by clouds is a topic surrounded by contradictory reports and controversy. Some studies have shown large discrepancies between observed SW absorption and absorption predicted by models, while others have found no significant dfference. In this study, values of column SW absorptance obtained by combining collocated top-of atmosphere (TOA) and surface observations at an island site in the Tropical Western Pacific are compared to radiative transfer model (RTM) output. To compensate for the field of view difference between satellite and surface instruments, the surface data are averaged over time. Scatterplots and statistical measures show that there is a signficant discrepancy between models and observations with the RTMs apparently underestimating SW absorptance. In previous observational studies showing highly enhanced absorption compared to models, the slope of a linear fit to d*TOA/dT (the derivative of TOA albedo with respect to transmittance) was used to quantify cloud SW absorption while non-linearity of d*TOA/dT was interpreted as a sign of sampling issues. Here, the models produce a steeper slope (about -0.9) than observations (-0.6 to -0.8), indicating that models predict too little cloud SW absorption. However, when the surface observations are averaged over a longer period, their slope grows steeper and the root mean square di*erence between linear and quadratic fits to d*TOA/dT is reduced. This implies that insufficient averaging of surface data contributes to the observed SW absorption discrepancy. Reexamination of the observational data using the difference between cloud fraction estimated from satellite and surface measurements as an estimate of field of view mismatch supports this hypothesis. High mea sured absorptance values are shown to correspond to occasions of large field of view mismatch. When such data are excluded, the difference between the linear and quadratic fits is reduced and the slope of the best fit line becomes steeper. We conclude that averaging surface data over three hours or less is not always sufficient to eliminate sampling issues. However, the possibility that shortcomings of the RTMs contribute to the discrepancy in SW absorption values cannot be excluded.

58 GEOSCIENCES↗

DOE OSTI2011

Evaluating In-Clique and Topological Parallelism Strategies for Junction Tree-Based Bayesian Inference Algorithm on the Cray XMT

Long viewed as a strong statistical inference technique, Bayesian networks have emerged to be an important class of applications for high-performance computing. We have applied an architecture-conscious approach to parallelizing the Lauritzen-Spiegelhalter Junction Tree algorithm for exact inferencing in Bayesian networks. In optimizing the Junction Tree algorithm, we have implemented both in-clique and topological parallelism strategies to best leverage the fine-grained synchronization and massive-scale multithreading of the Cray XMT architecture. Two topological techniques were developed to parallelize the evidence propagation process through the Bayesian network. One technique involves performing intelligent scheduling of junction tree nodes based on its topology and relative size. The second technique involves decomposing the junction tree into a much finer tree-like representation to offer much more opportunities for parallelism. We evaluate these optimizations on five different Bayesian networks and report our findings and observations. Another important contribution of this paper is to demonstrate the application of massive-scale multithreading for load balancing and use of implicit parallelism-based compiler optimizations in designing scalable inferencing algorithms.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Development of a high spectral resolution surface albedo product for the ARM Southern Great Plains Central Facility

We present a method for identifying dominant surface type and estimating high spectral resolution surface albedo at the Atmospheric Radiation Measurement (ARM) facility at the Southern Great Plains (SGP) site in Oklahoma for use in radiative transfer calculations. Given a set of 6-channel narrowband visible and near-infrared irradiance measurements from upward and downward looking multi-filter radiometers (MFRs), four different surface types (snow-covered, green vegetation, partial vegetation, non-vegetated) can be identified. A normalized difference vegetation index (NDVI) is used to distinguish between vegetated and non-vegetated surfaces, and a scaled NDVI index is used to estimate the percentage of green vegetation in partially vegetated surfaces. Based on libraries of spectral albedo measurements, a piecewise continuous function is developed to estimate the high spectral resolution surface albedo for each surface type given the MFR albedo values as input. For partially vegetated surfaces, the albedo is estimated as a linear combination of the green vegetation and non-vegetated surface albedo values. The estimated albedo values are evaluated through comparison to high spectral resolution albedo measurements taken during several Intensive Observational Periods (IOPs) and through comparison of the integrated spectral albedo values to observed broadband albedo measurements. The estimated spectral albedo values agree well with observations for the visible wavelengths constrained by the MFR measurements, but have larger biases and variability at longer wavelengths. Additional MFR channels at 1100 nm and/or 1600 nm would help constrain the high resolution spectral albedo in the near infrared region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2010

Fault Detection in Distributed Climate Sensor Networks using Dynamic Bayesian Networks

The Atmospheric Radiation Measurement program operated by U.S. Department of Energy is one of the largest climate research programs dedicated to the collection of long-term continuous measurements of cloud properties and other key components of the earth’s climate system. Given the critical role that collected ARM data plays in the analysis of atmospheric processes and conditions and in the enhancement and evaluation of global climate models, the production and distribution of high-quality data is one of ARM’s primary mission objectives. Fault detection in ARM’s distributed sensor network is one critical ingredient towards maintaining high quality and useful data. We are modeling ARM’s distributed sensor network as a dynamic Bayesian network where key measurements are mapped to Bayesian network variables. We then define the conditional dependencies between variables by discovering highly correlated variable pairs from historical data. The resultant dynamic Bayesian network provides an automated approach to identifying whether certain sensors are malfunctioning or failing in the distributed sensor network. A potential fault or failure is detected when an observed measurement is not consistent with its expected measurement and the observed measurements of other related sensors in the Bayesian network. We present some of our experiences and promising results with the fault detection dynamic Bayesian network.

54 ENVIRONMENTAL SCIENCES↗