Browsing by Author "Stephens, Graeme L., advisor"

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Open Access
Characteristics of precipitation: CloudSat observations and model predictions of the current and future climate
(Colorado State University. Libraries, 2008) Ellis, Todd Douglas, author; Stephens, Graeme L., advisor
The overall purpose of this study is to examine the characteristics of precipitation as they are predicted to change in a typical climate change scenario and as they exist now and how well model reproduces those observations. The first part of this study examines the controls on global precipitation evident in a transient carbon dioxide doubling experiment conducted using coupled climate models collected for the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4). As noted in other studies, the ensemble mean changes in water vapor occur at a rate more than three times that of precipitation. A simple ratio of these changes is introduced as a type of measure of the efficiency of the atmospheric hydrologic cycle in responding to changes in moisture, and varies between about 0.09 and 0.25 for the models studied. It is shown that the change in precipitation sensitivity is primarily governed by how emission of radiation from the clear-sky atmosphere increases as water vapor increases. This relationship closely matches one derived from simple energy balance arguments involving changes to water vapor emission alone. The study also presents the precipitation incidence over the global oceans as calculated from the CloudSat satellite, showing precipitation into the high latitudes and calculating that precipitation occurs 11% of the time over the oceans. These data are verified using ship-based (ICOADS) and island-based (GSOD) data. This study then extends the use of these data to an analysis of the observed cloud structures that are associated with rainfall over the oceans and then comparing them to special runs of the ECMWF weather forecast and HadGAM1 climate prediction models. These comparisons show that the models don't predict shallow precipitation nor layered precipitation structures as often as they are observed, and predict incorrect global precipitation incidences.
Open Access
Estimation of snow microphysical properties with application to millimeter-wavelength radar retrievals for snowfall rate
(Colorado State University. Libraries, 2011) Wood, Norman Bryce, author; Stephens, Graeme L., advisor; Cotton, William R., committee member; Fassnacht, Steven R., committee member; Kummerow, Christian D., committee member; Matrosov, Sergey Y., committee member
The need for measuring snowfall is driven by the roles snow plays providing freshwater resources and affecting climate. Snow accumulations are an important resource for ecological and human needs and in many areas appear vulnerable to climate change. Snow cover modifies surface heat fluxes over areas extensive enough to influence climate at regional and perhaps global scales. Seasonal runoff from snowmelt, along with over-ocean snowfall, contributes to freshening in the Arctic and high-latitude North Atlantic oceans. Yet much of the Earth's area for which snowfall plays such significant roles is not well-monitored by observations. Radar reflectivity at 94 GHz is sensitive to scattering by snow particles and CloudSat, in a near-polar orbit, provides vertically resolved measurements of 94 GHz reflectivity at latitudes from 82 N to 82 S. While not global in areal coverage, CloudSat does provide observations sampled from regions where snowfall is the dominant form of precipitation and an important component of hydrologic processes. The work presented in this study seeks to exploit these observations by developing and assessing a physically-base snowfall retrieval which uses an explicit representation of snow microphysical properties. As the reflectivity-based snowfall retrieval problem is significantly underconstrained, a priori information about snow microphysical properties is required. The approaches typically used to develop relations between reflectivity and snowfall rate, so-called Ze-S relations, require assumptions about particle properties such as mass, area, fallspeed, and shape. Limited information about the distributions of these properties makes difficult the characterization of how uncertainties in the properties influence uncertainties in the Ze-S relations. To address this, the study proceeded in two parts. In the first, probability distributions for snow particle microphysical properties were assessed using optimal estimation applied to multi-sensor surface-based snow observations from a field campaign. Mass properties were moderately well determined by the observations, the area properties less so. The retrieval revealed nontrivial correlations between mass and area parameters not apparent in prior studies. Synthetic testing showed that the performance of the retrieval was hampered by uncertainties in the fallspeed forward model. The mass and area properties obtained from this retrieval were used to construct particle models including 94 GHz scattering properties for dry snow. These properties were insufficient to constrain scattering properties to match observed 94 GHz reflectivities. Vertical aspect ratio supplied a sufficient additional constraint. In the second part, the CloudSat retrieval, designed to estimate vertical profiles of snow size distribution parameters from reflectivity profiles, was applied to measurements from the field campaign and from an orbit of CloudSat observations. Uncertainties in the mass and area microphysical properties, obtained from the first part of this study, were substantial contributors to the uncertainties in the retrieved snowfall rates. Snowfall rate fractional uncertainties were typically 140% to 200%. Accumulations of snowfall calculated from the retrieval results matched observed accumulations to within 13%, however, when allowances were made for snowfall with properties likely inconsistent with the snow particle model. Information content metrics showed that the size distribution slope parameters were moderately to strongly constrained by the reflectivity observations, while the intercept parameters were determined primarily by the a priori constraints. Results from the CloudSat orbit demonstrated the ability of the CloudSat retrieval to represent a range of scene-dependent Ze-S relations.
Open Access
The near-global distribution of light precipitation from CloudSat
(Colorado State University. Libraries, 2008) Haynes, John M., author; Stephens, Graeme L., advisor
The W-band (94 GHz) Cloud Profiling Radar (CPR) on CloudSat is sensitive to both clouds and precipitation. A precipitation retrieval applicable to space-borne, millimeter wavelength radars is introduced. Measurements of the attenuated backscatter of the surface are used to derive the path integrated attenuation (PIA) through precipitating columns, which follows from the clear-sky scattering characteristics of the surface. Over ocean, this can be estimated as a function of near-surface wind speed and sea surface temperature. Assuming an exponential rain drop size distribution, the relationship between PIA and rain rate is derived from Mie theory for homogeneous columns of warm rain. Multiple scattering is found to be significant for rainfall rates exceeding 3 to 5 mm h-1. To correct for this effect, Monte Carlo modeling is used to simulate the relationship between rainfall and PIA for various vertical precipitation profiles. Multiple scattering is found to increase return power to the radar, acting opposite attenuation. A model of the melting layer is also incorporated to better represent attenuating characteristics near the bright band, where snow aggregates melt into rain. It is found that failure to account for extra attenuation caused by melting particles results in overestimation of precipitation rate. The retrieval algorithm is applied to near-global CloudSat observations. Precipitation in the tropics is found to prefer clouds with lowest-layer cloud tops near 2 and 15 km. A third mode, likely associated with congestus, is found to be common in the tropical western Pacific, Indian, and Atlantic basins. There are vast regions of the globe where nearly all precipitation falls from cloud with lowest-layer tops below 4.75 km. Over the tropical oceans as a whole, precipitation falls twice as often from these clouds as any other cloud type. Furthermore, multiple layered cloud systems are found to be ubiquitous globally. In the tropics, it is estimated that half the accumulated precipitation comes from multiple layered systems rather than the classic "deep convective" model. Outside the tropics, the CPR observes precipitation more often than the passive microwave AMSR-E, with greater resulting seasonal accumulations.