Atmospheric & Oceanic Science
Permanent URI for this communityhttp://hdl.handle.net/1903/2264
Formerly known as the Department of Meteorology.
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Item A Study of the Oklahoma City Urban Heat Island Effect Using a WRF/Single-Layer Urban Canopy Model, a Joint Urban 2003 Field Campaign, and MODIS Satellite Observations(MDPI, 2017-09-07) Zhang, Hengyue; Jin, Menglin S.; Leach, MartinThe urban heat island effect (UHI) for inner land regions was investigated using satellite data, ground observations, and simulations with an Single-Layer Urban Canopy Parameterization (SLUCP) coupled into the regional Weather Research Forecasting model (WRF, http://wrf-model.org/index.php). Specifically, using the satellite-observed surface skin temperatures (Tskin), the intensity of the UHI was first compared for two inland cities (Xi’an City, China, and Oklahoma City (OKC)), which have different city populations and building densities. The larger population density and larger building density in Xi’an lead to a stronger skin-level UHI by 2 °C. However, the ground observed 2 m surface air temperature (Tair) observations showed an urban cooling island effect (UCI) over the downtown region in OKC during the daytime of 19 July 2003, from a DOE field campaign (Joint Urban 2003). To understand this contrast between satellite-based Tskin and ground-based Tair, a sensitivity study using WRF/SLUCP was analyzed. The model reproduced a UCI in OKC. Furthermore, WRF/Noah/SLUCM simulations were also compared with the Joint Urban 2003 ground observations, including wind speeds, wind directions, and energy fluxes. Although the WRF/SLUCM model failed to simulate these variables accurately, it reproduced the diurnal variations of surface temperatures, wind speeds, wind directions, and energy fluxes reasonably well.Item Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics(MDPI, 2018-08-06) Grodsky, Semyon A.; Vandemark, Douglas; Feng, HuiMonitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.Item Tidal Mixing Signatures in the Hong Kong Coastal Waters from Satellite-Derived Sea Surface Temperature(MDPI, 2018-12-20) Susanto, R. Dwi; Pan, Jiayi; Devlin, Adam T.Tidal mixing in the coastal waters of Hong Kong was investigated using a combination of in situ observations and high-resolution satellite-derived sea surface temperature (SST) data. An indicator of tide-induced mixing is a fortnightly (spring-neap cycle) signature in SST due to nonlinear interactions between the two principal diurnal and the two principal semi-diurnal tides. Both semi-diurnal and diurnal tides have strong tidal amplitudes and currents near Hong Kong. As a result, both the near-fortnightly (Mf) and fortnightly (MSf) tides are enhanced due to nonlinear tidal signal interactions. In addition, these fortnightly tidal signals are modulated by seasonal variability, with the maximum seasonal modulation of fortnightly tides occurring during the monsoon transition periods in May and October. The largest fortnightly signals are found in the southwestern part of the Pearl River estuary. Tidal constituent properties vary by space and depth, and high-resolution SST plays a pivotal role in resolving the spatial characteristics of tidal mixing.Item Validation and Improvement of the WRF Building Environment Parametrization (BEP) Urban Scheme(MDPI, 2019-09-10) Gohil, Kanishk; Jin, Menglin S.The building environment parameterization scheme (BEP) is a built-in “urban physics” scheme in the weather research and forecasting (WRF) model. The urbanized College Park (CP) in Maryland state (MD) in the United States (US) covers an approximate land area of 14.8 km2 and has a population of 32,000 (reported by The United States Census Bureau, as of 2017). This study was an effort to validate and improve the BEP urban physics scheme for a small urban setting, College Park, MD. Comparing the WRF/BEP-simulated two-meter air temperatures with the local rooftop WeatherBug® observations and with the airport observations, systemic deficiencies in BEP for urban heat island effect simulation are evident. Specifically, WRF/BEP overestimates the two-meter air temperature by about 10 °F during clear summer nights and slightly underestimates it during noon of the same days by about 1–3 °F. Similar deficiencies in skin temperature simulations are also evident in WRF/BEP. Modification by adding an anthropogenic heat flux term resulted in better estimates for both skin and two-meter air temperatures on diurnal and seasonal scales.Item Towards a Unified and Coherent Land Surface Temperature Earth System Data Record from Geostationary Satellites(MDPI, 2019-06-12) Pinker, Rachel T.; Ma, Yingtao; Chen, Wen; Hulley, Glynn; Borbas, Eva; Islam, Tanvir; Hain, Chris; Cawse-Nicholson, Kerry; Hook, Simon; Basara, JeffOur objective is to develop a framework for deriving long term, consistent Land Surface Temperatures (LSTs) from Geostationary (GEO) satellites that is able to account for satellite sensor updates. Specifically, we use the Radiative Transfer for TOVS (RTTOV) model driven with Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) information and Combined ASTER and MODIS Emissivity over Land (CAMEL) products. We discuss the results from our comparison of the Geostationary Operational Environmental Satellite East (GOES-E) with the MODIS Land Surface Temperature and Emissivity (MOD11) products, as well as several independent sources of ground observations, for daytime and nighttime independently. Based on a six-year record at instantaneous time scale (2004–2009), most LST estimates are within one std from the mean observed value and the bias is under 1% of the mean. It was also shown that at several ground sites, the diurnal cycle of LST, as averaged over six years, is consistent with a similar record generated from satellite observations. Since the evaluation of the GOES-E LST estimates occurred at every hour, day and night, the data are well suited to address outstanding issues related to the temporal variability of LST, specifically, the diurnal cycle and the amplitude of the diurnal cycle, which are not well represented in LST retrievals form Low Earth Orbit (LEO) satellites.Item The College Park, Maryland, Tornado of 24 September 2001(MDPI, 2019-10-22) Pryor, Kenneth L.; Wawrzyniak, Tyler; Zhang, Da-LinThe 24 September 2001 College Park, Maryland, tornado was a long-track and strong tornado that passed within a close range of two Doppler radars. It was the third in a series of three tornadoes associated with a supercell storm that developed in Stafford County, Virginia, and initiated 3–4 km southwest of College Park and dissipated near Columbia, Howard County. The supercell tracked approximately 120 km and lasted for about 126 min. This study presents a synoptic and mesoscale overview of favorable conditions and forcing mechanisms that resulted in the severe convective outbreak associated with the College Park tornado. The results show many critical elements of the tornadic event, including a negative-tilted upper-level trough over the Ohio Valley, a jet stream with moderate vertical shear, a low-level warm, moist tongue of the air associated with strong southerly flow over south-central Maryland and Virginia, and significantly increased convective available potential energy (CAPE) during the late afternoon hours. A possible role of the urban heat island effects from Washington, DC, in increasing CAPE for the development of the supercell is discussed. Satellite imagery reveals the banded convective morphology with high cloud tops associated with the supercell that produced the College Park tornado. Operational WSR-88D data exhibit a high reflectivity “debris ball” or tornadic debris signature (TDS) within the hook echo, the evolution of the parent storm from a supercell structure to a bow echo, and a tornado cyclone signature (TCS). Many of the mesoscale features could be captured by contemporary numerical model analyses. This study concludes with a discussion of the effectiveness of the coordinated use of satellite and radar observations in the operational environment of nowcasting severe convection.Item THE DIURNAL AND SEASONAL RADIATIVE EFFECTS OF CIRRUS CLOUDS UTILIZING LARGE AIRBORNE AND SPACE-BORNE LIDAR DATASETS(2019) Ozog, Scott; Dickerson, Russell R; Yorks, John E; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Cirrus clouds are globally the most common cloud type, however, their radiative impact on the Earth remains a large source of uncertainty in global climate models. Cirrus are unique in that they are absorptive to terrestrial outgoing longwave radiation, while also relatively transmissive to incoming solar radiation. The interactions of this greenhouse and albedo effect determine the sign and magnitude of cirrus radiative effects. Cirrus are microphysically complex, and can exhibit a variety of different ice crystal shapes and sizes depending on the thermodynamic environment in which they form, and their dynamic formation mechanism. Our ability to reliably model cirrus radiative effects is dependent upon accurate observations and parameterizations incorporated into radiative transfer simulations. Laser lidar instruments provide valuable measurements of cirrus clouds unavailable by other radar systems, passive remote sensors, or in-situ instruments alone. In this dissertation I developed and tested an improved calibration technique for the ACATS lidar instrument, and its impact on the direct retrieval of cirrus HSRL optical properties. HSRL retrievals theoretically have reduced uncertainty over those from a standard backscatter lidar. ACATS flew on two field campaigns in 2012 and 2015 where it was unable to consistently calibrate its etalon. It has been operating from the lab in NASA GSFC collecting zenith pointing data of cirrus layers where the improved calibration has resulted in consistent and reliable separation of the particulate and Rayleigh signal components. The diurnal trend of cirrus influence on the global scale has primarily been limited to data provided by satellites in sun-synchronous orbit, which provide only a snapshot of conditions at two times a day. Utilizing data from the CATS lidar aboard the ISS I investigated cirrus at four periods throughout the day in morning, afternoon, evening, and night across all seasons. Cirrus radiative effects were found to have a large latitudinal dependence, and have a greater potential to cool than many studies suggest with their primary warming contributions skewed towards the nighttime hours. Constrained lidar retrievals reduce the assumptions made in retrieving cirrus optical properties. Utilizing the expansive airborne CPL dataset from six flight campaigns I model the radiative effects of over twenty thousand constrained cirrus observations. Mid-latitude cirrus were found to have a mean positive daytime forcing equivalent to that of the CO2 greenhouse effect. However, synoptic cirrus were found to have a greater warming effect than convective cirrus, which were more likely to have a cooling effect.Item SEA-SURFACE TEMPERATURE BASED STATISTICAL PREDICTION OF THE SOUTH ASIAN SUMMER MONSOON RAINFALL DISTRIBUTION(2019) Sengupta, Agniv; Nigam, Sumant; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The South Asian summer monsoon brings copious amounts of rainfall accounting for over 70% of the annual rainfall over India. Summer monsoon predictions have drawn considerable public/policy attention lately as South Asia has become a resource-stressed and densely populated region. This environmental backdrop and the livelihood concerns of a billion-plus people generate the demand for more accurate monsoon predictions. The prediction skill, however, has remained marginal and stagnant for several decades despite advances in the representation of physical processes, numerical model resolution, and data assimilation techniques, leading to the following key question: what is the potential predictability of summer monsoon rainfall at lead times of one month to a season? This dissertation examines the role of influential climate system components with large thermal inertia and reliable long-term observational records, like sea-surface temperature (SST) in forecasting the seasonal distribution of South Asian monsoon rainfall. First, an evolution-centric SST analysis is conducted in the global oceans using the extended-Empirical Orthogonal Function technique to uncover the recurrent modes of spatiotemporal variability and their potential inter-basin linkages. A statistical forecast model is next developed using these extracted modes of SST variability as predictors. Assessment of the forecasting system’s long-term performance from reconstruction and hindcasting over an independent verification period demonstrates high forecast skill over core monsoon regions – the Indo-Gangetic Plain and southern peninsular India, indicating prospects for improved seasonal predictions. The influence of SSTs on the northeast winter monsoon is subsequently investigated, especially, its evolution, interannual variability and the El Niño–Southern Oscillation (ENSO) influence. Key findings from this study include evidence of increased rainfall over southeastern peninsular India and Sri Lanka (generated by an off-equatorial anticyclonic circulation centered over the Bay of Bengal) during El Niño winters. This dissertation provides the first quantitative assessment of the potential predictability of summer monsoon rainfall anomalies – the maximum predictable summer rainfall signal (amount, distribution) over South Asia from prior SST information – at various seasonal leads, and notably, at SST-mode resolution. The improved skill of the SST-based statistical forecast establishes the bar – an evaluative benchmark – for the dynamical prediction of summer monsoon rainfall.Item Improving our Understanding of Tropical Cyclone Unusual Motion and Rapid Intensification(2019) Miller, William James Schouler; Zhang, Da-Lin; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite steady improvement in their tropical cyclone (TC) track and intensity forecasts over recent decades, operational numerical weather prediction (NWP) models still struggle at times in predicting two TC phenomena: climatologically unusual motion and rapid intensification (RI). Atlantic TCs typically move clockwise along curved tracks skirting the southern, western, and northwestern periphery of the Western Atlantic Ridge. Hurricane Joaquin (2015) followed a particularly unusual hairpin loop-shaped track that was poorly predicted by most operational NWP models, including the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Over recent years, considerable interest has also developed in understanding the cause-and-effect relationship between RI, defined here as a maximum surface wind (VMAX) intensification rate exceeding 15 m s-1 (24 h-1), and outbreaks of inner core deep convection, known as convective bursts (CBs), that have been observed to precede or coincide with RI in some TCs. A deeper physical understanding of the atmospheric processes governing TC unusual motion and RI, together with retrospective case study analyses of model forecast errors, will help us to identify NWP model components – data assimilation and physical parameterizations, for example – that may need further improvement. This research project seeks to (i) identify the atmospheric features that steered Hurricane Joaquin (2015) along the southwestward leg of its looping track and (ii) investigate the thermodynamic and three-dimensional characteristics of CBs as a first step toward developing a more comprehensive understanding of how CBs may facilitate RI. To accomplish (i), we generate a high-resolution Weather Research and Forecasting (WRF) model Control (CTL) simulation of Hurricane Joaquin (2015) that reproduces its looping track and intensification trends. Comparing CTL forecast fields against sensitivity WRF simulations initialized from perturbed analyses and against two representative GFS forecasts, we find that a sufficiently strong mid-to-upper level ridge northwest of Joaquin and a vortex sufficiently deep to interact with northeasterly geostrophic flows surrounding the ridge are both necessary for steering Joaquin southwestward. These results suggest that more accurate track forecasts for TCs developing in vertically sheared environments may be at least partly contingent on improved vortex initialization; for these cases, assimilation of more inner-core observations such as cloudy radiances and airborne radar-derived winds could be particularly beneficial. We address (ii) by comparing parcel traces, thermodynamic variables, and vertical accelerations along trajectories run through CB updraft cores with trajectories representative of the background eyewall ascent in a Hurricane Wilma (2005) WRF simulation. We compute three-dimensional trajectories from WRF-output winds using a model developed for this study that implements an experimental advection correction algorithm designed to reduce time interpolation errors, with the latter confirmed by tests on analytical and numerically-simulated flows. Results show that Wilma’s CBs are characterized by significant thermal buoyancy, particularly in the upper troposphere; this is consistent with their lower environmental air entrainment rates and reduced midlevel hydrometeor loading relative to the background ascent, and with their updrafts being rooted in portions of the boundary layer where ocean surface heat and moisture fluxes are locally higher.Item IMPROVING U.S. EXTREME PRECIPITATION PREDICTION AND PROCESS UNDERSTANDING USING A MESOSCALE CLIMATE MODEL MULTI-PHYSICS ENSEMBLE APPROACH(2019) sun, chao; Liang, Xin-zhong; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Despite many recent improvements, climate models continue to poorly simulate extreme precipitation. I attempted to improve prediction of extreme precipitation, focusing on daily 95th percentile (P95) events, and to better understand the source of model biases in three ways: 1) determine which physics processes P95 is most sensitive to and which parameterization schemes best represent these processes; 2) understand the underlying mechanisms through which these processes impact P95; and 3) maximize advantages from the ensemble of the best performing models. First, to determine the sensitive processes affecting P95, I tested a 25-member ensemble of different physics configurations in the regional Climate-Weather Research and Forecasting model (CWRF) for 36-yr historical U.S. simulations. Of these, P95 simulation was most sensitive to cumulus parameterization. Overall, the ensemble cumulus parameterization best represented P95 seasonal mean spatial patterns and interannual variations, while one traditional cumulus scheme generally overestimated P95 and the other three severely underestimated P95, especially over the Gulf States (GS) and the Central-Midwest States (CM) in convection-dominated seasons. Second, I built structural equation models (SEMs) to identify the underlying processes through which cumulus parameterization affects precipitation. I discovered five distinct physical mechanisms, each involving unique interplays among water and energy supplies and surface and cloud forcings. The relative importance of these factors varied significantly by season and region. For example, water supply is the dominant factor for P95 in CM, but its effect reversed from positive in summer to negative in winter due to changes in the prevailing precipitation system. In contrast, the predominant factors affecting P95 in GS were cloud forcing in summer, but surface forcing in winter. Since the choice of cumulus parameterization affected how water and energy supplies acted through surface and cloud forcings, it determined CWRF’s ability to simulate extreme precipitation. Third, I improved P95 prediction by developing an optimized multi-model ensemble based on the Bayesian Model Averaging (BMA) approach. BMA is a model-selection method that weights ensemble members to create an optimal composite. However, many BMA methods rely on maximum likelihood estimation and thus may be flawed when the true solution is not among the ensemble, as is the case in extreme precipitation. To resolve this issue, I adapted three BMA variations to fit the needs of extreme precipitation problems. These methods significantly improved performance compared to both the ensemble mean and the single best model and provided a more reliable confidence interval. My work shows that to improve extreme precipitation simulation, a better understanding of physics processes, especially cumulus processes, is critical. For this, I applied the SEM framework, for the first time in the climate community, to uncover the underlying physical mechanisms essential to regional extreme precipitation predictions. Furthermore, I adapted new BMA methods into extreme precipitation ensembles to maximize the benefits from the most physically advanced models. These advances may help improve the prediction of extreme precipitation occurrences and future changes, one of the most difficult modeling challenges and one with huge socioeconomic significance.