Atmospheric & Oceanic Science

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Formerly known as the Department of Meteorology.

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End date: 2009

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Now showing 1 - 10 of 47
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    Potential Predictability of 500 mb Geopotential Heights on both Monthly and Seasonal Scales over the Northern Hemisphere
    (1989) Singh, Ramdas Ram; Shukla, Jagadish; Department of Meteorology; Digital Repository at the University of Maryland; University of Maryland (College Park, MD)
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    ENHANCEMENT OF ATMOSPHERIC LIQUID WATER ESTIMATION USING SPACE-BORNE REMOTE SENSING DATA
    (2009) Chen, Ruiyue; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Clouds strongly affect the energy balance and water cycle, two dominant processes in the climate system. Low-level liquid clouds have the most significant influence on cloud radiative forcing due to their areal extent and frequency. Estimation of atmospheric liquid water contained in low-level clouds and the precipitation underneath them is very important in meteorology, hydrology, and climatology. Space-borne remote sensing data are widely used for global estimation of atmospheric liquid water, given that they have a wider spatial coverage than other data sources and are spanning many years. However, previous space-borne remote sensing techniques have some limitations for estimation of atmospheric liquid water in low-level liquid clouds, namely, the vertical variation of droplet effective radius (DER) is neglected in the calculation of cloud liquid water path (LWP) and the rain underneath low-level liquid clouds can be overlooked. Comprising many state-of-art passive and active instruments, the recently launched NASA A-Train series of satellites provides comprehensive simultaneous information about cloud and precipitation processes. Utilizing A-Train satellite data and ship-borne data from the East Pacific Investigation of Climate (EPIC) campaign, in this study investigated is the estimation of liquid water in low-level liquid clouds, and assessed is the potential of cloud microphysical parameters in the estimation of rain from low-level liquid clouds. This study demonstrates that assuming a constant cloud DER can cause biases in the calculation of LWP. It is also shown that accounting for the vertical variation of DER can reduce the mean biases. This study shows that DER generally increases with height in non-drizzling clouds, consistent with aircraft observations. It is found that in drizzling clouds, the vertical gradient of DER is significantly smaller than that in non-drizzling clouds, and it can become negative when the drizzle is heavier than approximately 0.1 mm hr-1. It is shown that the warm rain underneath low-level liquid clouds accounts for 45.0% of occurrences of rain and 27.5% of the rainfall amount over the global ocean areas. Passive microwave techniques underestimate the warm rain over oceans by nearly 48%. Among the cloud microphysical parameters, LWP calculated with DER profile shows the best potential for estimating warm rain, which is neglected by traditional techniques of precipitation estimation.
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    Assimilation of trace gas retrievals with the Local Ensemble Transform Kalman Filter
    (2009) Kuhl, David Derieg; Kalnay, Eugenia; Szunyogh, Istvan; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Over the 50 year history of Numerical Weather Prediction (NWP), the focus has been on the modeling and prediction of meteorological parameters such as surface pressure, temperature, wind, and precipitation. However, due to concerns over pollution and to recent advancements in satellite technologies, an increasing number of NWP systems have been upgraded to include capabilities to analyze and predict the concentration of trace gases. This dissertation explores some of the specific issues that have to be addressed for an efficient modeling of the concentration of the trace gases. These issues include modeling the effects of convective mixing on the concentration of the trace gases and the multivariate assimilation of space-based observations of the concentration of the trace gases. In this dissertation, we assimilate observations of the concentration of trace gases with an implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation system on the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) NWP model. We use a modified version of the NCEP GFS model that was operational in 2004 at resolution T62/L28. We modify the model by adding parameterization for the process of convective mixing of the trace gases. We consider two specific trace gases: ozone (O3) and carbon monoxide (CO). We incorporate these gases into the model by using 3-dimensional time-dependent O3 and CO production-loss values from the Real-time Air Quality Modeling System (RAQMS) global chemical model. The O3 observations we assimilate are from the Solar Backscatter UltraViolet generation 2 (SBUV/2) satellite instrument (version 8) flown on the NOAA 16 and 17 satellites. The CO observations we assimilate are from the Measurements Of Pollution In The Troposphere (MOPITT) instrument (version 3) flown on the NASA TERRA satellite. We also develop a new observation operator for the assimilation of retrievals with the LETKF. We carry out numerical experiment for the period between 000UTC 1 July 2004 to 000UTC 15 August in the summer of 2004. The analysis and forecast impact of the assimilation of trace gas observations on the meteorological fields is assessed by comparing the analyses and forecasts to the high resolution operational NCEP GFS analyses and to radiosonde observations. The analysis and forecast impact on the trace gas fields is assessed by comparing the analyzed and predicted fields to observations collected during the Intercontinental Chemical Transport Experiment (INTEX-A) field mission. The INTEX-A field mission was conducted to characterize composition of pollution over North America, thus providing us with ozonesonde and aircraft based verification data. We find that adding the process of convective mixing to the parameterization package of the model and the assimilation of observations of the trace gases improves the analysis and forecast of the concentration of the trace gases. In particular, our system is more accurate in quantifying the concentration of O3 in the troposphere than the original NCEP GFS. Also, our system is competitive with the state-of-the-art RAQMS atmospheric chemical model in analyzing the concentration of O3 and CO throughout the full atmospheric model column. The assimilation of O3 and CO observations has a mixed impact on the analysis and forecast of the meteorological fields. We find that most of the negative impact on the meteorological fields can be eliminated, without much reduction to the positive impact on the trace gas fields, by inflating the prescribed variance of the trace gas observations. The appendices of this dissertation reproduces two papers on related research. The first paper covers the northward front movement and rising surface temperatures in the great planes. The second paper covers the assessment of predictability with a Local Ensemble Kalman Filter.
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    MADDEN-JULIAN OSCILLATION AND SEA SURFACE TEMPERATURE INTERACTIONS IN A MULTI-SCALE FRAMEWORK
    (2009) Zhou, Lei; Murtugudde, Raghu; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    The ocean-atmosphere coupling can play a role in initiating and sustaining the Madden-Julian Oscillations (MJOs), which are the major intraseasonal oscillations in the atmosphere. In this thesis, the oceanic influence on MJOs is studied with reanalysis products, numerical models, and idealized theoretical models. The energy sources for MJOs are calculated with NCEP reanalysis. The perturbed potential energy is found to be the most important energy source for most MJO events. In some MJO events, the sea surface is warmed due to the reduced latent heat flux during the suppressed phase of MJOs. As a result, warm sea surface temperature anomalies (SSTAs) occur, which appear to prolong the life time of these MJO events. In a minority of the MJO events, warm SSTAs can drive the atmosphere actively and trigger MJO events. In these events, the warm SSTAs are attributable to the internal oceanic processes influenced by the warm Indonesian Throughflow (ITF), which spreads from the southeastern Indian Ocean to the western Indian Ocean and modifies the subtle balance between stratification and mixing in the western Indian Ocean. In addition, during the transit period between monsoon seasons, a few MJO events are sustained by the energy obtained from the mean kinetic energy. Since the MJO events have different energy sources, their mechanisms should be considered in the context of these energy sources. While the spatial scale of the SSTAs in the Indian Ocean is only of order 100 km, the scale of MJOs is of order 1000 km, raising the potential for interactions between the oceanic and the atmospheric oscillations with different scales and this is demonstrated to be possible with analytical solutions to idealized linear governing equations. With a reasonable choice of parameters, the meso-scale oceanic and the large-scale atmospheric oscillations can interact with each other and lead to unstable waves in the intraseasonal band in this linear coupled model. The coupling and frequency shifts between oscillations with different scales and the atmospheric/oceanic responses to small variations in the external forcing are also tested with numerical models. Incorporating the oceanic influence on MJOs and the multi-scale interaction appropriately in a numerical model is supposed to help improve the simulation and forecast of MJOs. The hypothesis of multi-scale interaction is also expected to have wide applications in other studies, in addition to the MJO-SST interaction. The theoretical and numerical approach adopted here should also serve as a prototype for enhancing the process understanding of intraseasonal variability and lead to improved predictive understanding.
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    Carbon cycle data assimilation using a coupled atmosphere-vegetation and the Local Ensemble Transform Kalman Filter
    (2009) Kang, Ji-Sun; Kalnay, Eugenia; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    We develop and test new methodologies to best estimate CO2 fluxes on the Earth's surface by assimilating observations of atmospheric CO2 concentration, using the Local Ensemble Transform Kalman Filter. We perform Observing System Simulation Experiments and assimilate simultaneously atmospheric observations and atmospheric carbon observations, but no surface fluxes of carbon. For the experiments, we modified an atmospheric general circulation model to transport atmospheric CO2 and coupled this model with a dynamical terrestrial carbon model and a simple physical land model. The state vector of the model prognostic variables was augmented by the diagnosed carbon fluxes CF, so that the carbon fluxes were updated by the background error covariance with other variables. We designed three types of analysis systems: a C-univariate system where CF errors are coupled only with CO2, a multivariate system where all the error covariances are coupled, and a one-way multivariate analysis where the wind is included in the carbon error covariance, but there is no feedback on the winds. With perfect model experiments, the one-way multivariate analysis has the best results in CO2 analysis. For the imperfect model experiments, we applied techniques of model bias correction and adaptive inflation. With those, we obtained a high-quality analysis of surface CO2 fluxes. Furthermore, the adaptive inflation technique also provides a good estimate of observation errors. A new approach in the multivariate data assimilation with "variable localization", where the error correlations between unrelated variables are zeroed-out further improved the multivariate analyses surface CO2 fluxes. We note that with the simultaneous assimilation of winds and carbon variables, we are able to transport atmospheric CO2 with winds as well as, for the first time, couple their error covariances. As a result, the multivariate systems perform well, and do not require any kind of a-priori information that should be pre-calculated by independent observations or model simulations. The many new techniques that we developed and tested put us on a solid basis to tackle the assimilation of real atmospheric and CO2 observations, a project being carried out collaboratively by Dr. Junjie Liu under the direction of Prof. Inez Fung at UC Berkeley.
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    A STUDY OF OPTICAL, PHYSICAL AND CHEMICAL PROPERTIES OF AEROSOLS USING IN SITU MEASUREMENTS
    (2009) Chaudhry, Zahra; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Using a simple sampling apparatus, aerosol particles were collected on a polycarbonate substrate in various locations around the world. The focus of this study was Xianghe, China, an industrial town 70 km southeast of Beijing. The Nuclepore filters were collected in two size ranges (coarse, 2.5μm < d < 10μm, and fine, d < 2.5μm) from January-December 2005, with a focus on the Intensive Observation Campaign (IOC) in March 2005. The collected filters were analyzed for aerosol mass concentration and aerosol absorption efficiency; selected filters were analyzed for chemical composition. For fine mode aerosols measured during the Xianghe 2005 IOC, the average spectral absorption efficiency equates well to a &lamda;-1 model, while the coarse mode shows a much flatter spectral dependence, consistent with large particle models. The coarse mode absorption efficiency was compatible with that of the fine mode in the near-IR region, indicating the much stronger absorption of the coarse mode due to its composition and sizeable mass. Ground-based measurements were compared to remote sensing instruments that measure similar parameters for the total column. A co-located lidar assisted in determination of vertical homogeneity. For cases of vertical homogeneity, the ground-based measurements were able to represent total column measurements well. For cases of vertical inhomogeneity, ground-based measurements did not equate well to total column measurements. The layers of aerosols that form in the atmosphere have significant effects on the temperature profile. An instrument was developed to measure aerosol absorption and scattering, the Scattering and Absorption Sonde (SAS). This instrument was launched seven times at two locations in China in 2008. Vertical profiles of scattering coefficient were measured and several aerosol layers were identified. The aerosol characterized at Xianghe, China was compared to aerosol characteristics from Kanpur, India and Mexico City, Mexico. The aerosol at Mexico City differs greatly from that at Xianghe, based on the measured mass concentration, aerosol size distribution from AERONET, and measured aerosol absorption efficiency. The aerosol at Kanpur resembles well the aerosol characterized at Xianghe in the fine mode, with a correlation of 0.998 for the aerosol absorption efficiency.
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    High-Resolution Clouds and Radiative Fluxes from Satellites: Transferability of Methods and Application to Monsoon Regions
    (2009) Wonsick, Margaret; Pinker, Rachel T.; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    High-resolution information on clouds and radiative fluxes is produced for the Indian and African monsoon regions of interest to the GEWEX Project as articulated under the Coordinated Energy and Water Cycle Observations Project (CEOP). Such data are needed to provide forcing parameters for regional climate models, to evaluate them, and to facilitate their transferability to various climatic regions. Emphasis is placed on capturing the small-scale spatial variability and the diurnal cycle of cloud systems and on improving flux retrievals under the challenging conditions of high elevation and abundant aerosol loads that are characteristic of the various monsoon regions. Once developed, the data are applied to several issues investigated under CEOP and related to hydro-climate and aerosols. Documentation of the diurnal cycle of clouds and convection throughout the progression of the Indian monsoon has been limited due to lack of hourly satellite data over the region prior to 1998. This study adds to the base of knowledge by contrasting the diurnal cycle of clouds and convection in six diverse sectors of the Indian monsoon region and compositing the data for the pre-, peak-, and post-monsoon seasons to better understand the evolution of the monsoon. Comparison of satellite-observed clouds to model-predicted values points out model deficiencies in simulating clouds during the peak-monsoon season and at locations with elevated terrain. The high-resolution cloud information and cloud optical depth data derived with the radiative flux inference scheme are used to re-evaluate the "Elevated Heat Pump" (EHP) hypothesis. The hypothesis predicts early initiation and enhancement of monsoon precipitation in northern India and the Bay of Bengal due to anomalous warming caused by high aerosol loads in the Indo-Gangetic Basin. Newly derived information on convection is used to study the contrast in precipitation patterns during years with high and low aerosol loads. Evidence of the EHP effect is not found. This may be attributed to aerosol indirect effects or air-sea interactions which are not accounted for in the model simulations that were used to develop the hypothesis. Experiments are conducted with different aerosol treatments in the radiative flux inference scheme over Africa with the goal of determining whether using observed aerosol inputs can improve on fluxes retrieved with climatological aerosol values. This question is pertinent to the African Monsoon Multidisciplinary Analysis (AMMA) program, a subprogram of CEOP, which seeks to improve prediction of the West African Monsoon. The radiation component of the surface forcing database used for all AMMA land surface models overestimates clear-sky radiation under high aerosol loads due to poor representation of aerosols. The experiments show that flux retrievals improve when observed aerosol values are used, but biases are reduced even more significantly when aerosol absorbing properties are incorporated into the inference scheme as well. The improved scheme is then used to study the spatial and seasonal variations in downwelling surface shortwave flux and surface albedo over the African continent.
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    THE GENERATION AND APPLICATIONS OF A SPECTRALLY RESOLVED INFRARED RADIANCE CLIMATOLOGY DERIVED FROM THE ATMOSPHERIC INFRARED SOUNDER
    (2009) Goldberg, Mitchell David; Kalnay, Eugenia; Li, Zhanqing; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    There is growing consensus that persistent and increasing anthropogenic emissions, since the beginning of the industrial revolution in the 19th century, are increasing atmospheric temperatures, increasing sea levels, melting ice caps and glaciers, increasing the occurrence of severe weather, and causing regional shifts in precipitation patterns. Changes in these parameters or occurrences are responses to changes in climate forcing terms, notably greenhouse gases. The NASA Atmospheric InfraRed Sounder (AIRS), launched in May of 2002, is the first high spectral resolution infrared sounder with nearly complete global coverage on a daily basis. High spectral resolution in the infrared provides sensitivity to nearly all climate forcings, responses and feedbacks. The AIRS radiances are sensitive to changes in carbon dioxide, methane, carbon monoxide, ozone, water vapor, temperature, clouds, aerosols, and surface characteristics. This study uses the raw AIRS data to generate the first ever spectrally resolved infrared radiance (SRIR) dataset (2002- 2006) for monitoring changes in atmospheric temperature and constituents and for assessing the accuracy of climate and weather model analyses and forecasts. The SRIR dataset is a very powerful tool. Spectral signatures derived from the dataset confirmed the largest depletion of ozone over the Arctic in 2005, and also verified that the European Center for Medium Range Weather (ECMWF) model analysis water vapor fields are significantly more accurate than the analyses of the National Centers for Environmental Prediction (NCEP). The NCEP moisture fields are generally 20% more moist than those from ECMWF. This research included computations of radiances from NCEP and ECMWF atmospheric states and compared the calculated radiances with those obtained from the SRIR dataset. Comparisons showed very good agreement between the SRIR data and ECMWF simulated radiances, while the agreement with NCEP values was rather poor. Interannual differences of radiances computed from ECMWF analyses were nearly identical to those derived from the SRIR dataset, while the corresponding NCEP interannual differences were in poorer agreement. However, further comparisons with the SRIR dataset in 2006 found degradation in the ECMWF upper tropospheric water vapor fields due to an operational change in ECMWF assimilation procedures. This unexpected result demonstrates the importance of continuous routine monitoring. The SRIR climatology will be extended into the future using AIRS and other high spectral resolution sounders.
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    The Frequency Distribution of Daily Precipitation over the United States
    (2008) Becker, Emily Jones; Berbery, Ernesto Hugo; Atmospheric and Oceanic Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    This study examines the seasonal frequency distribution of daily precipitation and related variables over the United States using the North American Regional Reanalysis. Regions where the seasonal mean precipitation is dominated by heavy and extreme daily events or by more consistent lighter events are identified. The distributions are related to the variability of the vertically integrated moisture flux convergence (MFC) and precipitable water. The modulation of daily precipitation by ENSO and the Madden-Julian Oscillation (MJO) during winter is investigated. Assuming that the frequency of daily precipitation can be approximated by a gamma distribution, the scale and shape parameters are useful proxies to estimate the observed frequency distribution of precipitation. During winter, most areas of the country with high mean precipitation have a strong contribution from extreme events, particularly along the West and Gulf Coasts. During summer, the wettest areas of the country are Florida, where the mean precipitation is dominated by more-frequent light and moderate rainfall days, and the central Plains, dominated by variable rains and extreme events. Throughout the year, the MFC mean and scale parameter patterns strongly resemble those of precipitation, and areas with more heavy and extreme precipitation have stronger MFC daily values. These results suggest that the frequency distribution of MFC can be used as a proxy for the frequency distribution of modeled forecast precipitation. Changes in the winter total precipitation between the phases of ENSO are largely attributable to changes in the heavy and extreme events. Areas showing increased mean precipitation during the warm phase show an even greater increase in extremes. Similar to precipitation, strong values of MFC are more sensitive to ENSO phase than is the mean MFC. While the ENSO variability of the frequency distribution of MFC shows a strong relationship to that of precipitation, the variability of precipitable water does not. MJO modulation of winter daily precipitation over the central U.S. occurs primarily during MJO Phases 5 and 6, when MJO-related enhanced convection is located in the western Pacific. During these phases, the winter storm track is enhanced, and positive MFC anomalies are present in the central U.S.