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Surface temperature is an important parameter of the physical processes that govern the fluxes of energy and water at the biosphere-atmosphere interface. The energy and water fluxes have major influences on the Earth's weather and climate.
Atmospheric measurements from the Arctic Summer Cloud Ocean Study (ASCOS) are used to evaluate the performance of three atmospheric reanalyses (European Centre for Medium Range Weather Forecasting (ECMWF)- Interim reanalysis, National Center for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) reanalysis, and NCEP-DOE (Department of Energy) reanalysis) and two global climate models (CAM5 (Community Atmosphere Model 5) and NASA GISS (Goddard Institute for Space Studies) ModelE2) in simulation of the high Arctic environment. Quantities analyzed include near surface meteorological variables such as temperature, pressure, humidity and winds, surface-based estimates of cloud and precipitation properties, the surface energy budget, and lower atmospheric temperature structure. In general, the models perform well in simulating large-scale dynamical quantities such as pressure and winds. Near-surface temperature and lower atmospheric stability, along with surface energy budget terms, are not as well represented due largely to errors in simulation of cloud occurrence, phase and altitude. Additionally, a development version of CAM5, which features improved handling of cloud macro physics, has demonstrated to improve simulation of cloud properties and liquid water amount. The ASCOS period additionally provides an excellent example of the benefits gained by evaluating individual budget terms, rather than simply evaluating the net end product, with large compensating errors between individual surface energy budget terms that result in the best net energy budget.
An atmospheric flight vehicle in hover is typically controlled by varying its thrust vector. Achieving both levitation and attitude control with the propulsion system places considerable demands on it for agility and precision, particularly if the vehicle is statically unstable, or nearly so. These demands can be relaxed by introducing an appropriately sized angular momentum bias aligned with the vehicle's yaw axis, thus providing an additional margin of attitude stability about the roll and pitch axes. This paper describes a methodical approach for trading off angular momentum bias level needed with desired levels of vehicle response due to the design disturbance environment given a vehicle's physical parameters. It also describes several simplifications that provide a more physical and intuitive understanding of dual-spin dynamics for hovering atmospheric vehicles. This approach also mitigates the need for control torques and inadvertent actuator saturation difficulties in trying to stabilize a vehicle via control torques produced by unsteady aerodynamics, thrust vectoring, and unsteady throttling. Simulation results, based on a subscale laboratory test flying platform, demonstrate significant improvements in the attitude control robustness of the vehicle with respect to both wind disturbances and off-center of gravity payload changes during flight.
The clean-air temperature profile accuracy yielded by a localized linear statistical retrieval operator applied to passive aircraft-based 118-GHz spectra is demonstrated. A comparison of the statistically and physically derived correlation coefficients of antenna temperature and kinetic temperature furnishes a physical justification of the statistical retrieval technique. The atmospheric temperature mean and covariance significantly depend on such geophysical parameters as latitude, longitude, local season, and time, as well as the prevailing meteorological state and orographic effects.
The GOES Product Generation System (GPGS) is a set of computer codes and scripts that enable the assimilation of real-time Geostationary Operational Environmental Satellite (GOES) data into regional-weather-forecasting mathematical models. The GPGS can be used to derive such geophysical parameters as land surface temperature, the amount of precipitable water, the degree of cloud cover, the surface albedo, and the amount of insolation from satellite measurements of radiant energy emitted by the Earth and its atmosphere. GPGS incorporates a priori information (initial guesses of thermodynamic parameters of the atmosphere) and radiometric measurements from the geostationary operational environmental satellites along with mathematical models of physical principles that govern the transfer of energy in the atmosphere. GPGS solves the radiative-transfer equation and provides the resulting data products in formats suitable for use by weather-forecasting computer programs. The data-assimilation capability afforded by GPGS offers the potential to improve local weather forecasts ranging from 3 hours to 2 days - especially with respect to temperature, humidity, cloud cover, and the probability of precipitation. The improvements afforded by GPGS could be of interest to news media, utility companies, and other organizations that utilize regional weather forecasts.
We report high-precision interferometric and radial velocity (RV) observations of the M-dwarf binary Gl 268. Combining measurements conducted using the IOTA interferometer and the ELODIE and Harvard Center for Astrophysics RV instruments leads to a mass of 0.22596 plus-minus 0.00084 Mass compared to the sun for component A and 0.19230 plus-minus 0.00071 Mass compared to the sun for component B. The system parallax as determined by these observations is 0.1560 plus-minus 0.0030 arcsec - a measurement with 1.9% uncertainty in excellent agreement with Hipparcos (0.1572 plus-minus 0.0033). The absolute H-band magnitudes of the component stars are not well constrained by these measurements; however, we can place an approximate upper limit of 7.95 and 8.1 for Gl 268A and B, respectively.We test these physical parameters against the predictions of theoretical models that combine stellar evolution with high fidelity, non-gray atmospheric models. Measured and predicted values are compatible within 2sigma. These results are among the most precise masses measured for visual binaries and compete with the best adaptive optics and eclipsing binary results.
In recent years, much progress has been made in observing CO2 distributions from space. However, the use of these observations to infer source/sink distributions in inversion studies continues to be complicated by difficulty in quantifying atmospheric transport model errors. We will present results from several different experiments designed to quantify different aspects of transport error using the Goddard Earth Observing System, Version 5 (GEOS-5) Atmospheric General Circulation Model (AGCM). In the first set of experiments, an ensemble of simulations is constructed using perturbations to parameters in the model s moist physics and turbulence parameterizations that control sub-grid scale transport of trace gases. Analysis of the ensemble spread and scales of temporal and spatial variability among the simulations allows insight into how parameterized, small-scale transport processes influence simulated CO2 distributions. In the second set of experiments, atmospheric tracers representing model error are constructed using observation minus analysis statistics from NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA). The goal of these simulations is to understand how errors in large scale dynamics are distributed, and how they propagate in space and time, affecting trace gas distributions. These simulations will also be compared to results from NASA's Carbon Monitoring System Flux Pilot Project that quantified the impact of uncertainty in satellite constrained CO2 flux estimates on atmospheric mixing ratios to assess the major factors governing uncertainty in global and regional trace gas distributions.
The transport of water, heat, and momentum from the surface to the atmosphere is dependent in part on the 10 characteristics of the land surface. Together with the model physics, parameterization schemes, and parameters employed, land datasets determine the spatial variability in land surface states (i.e., soil moisture and temperature) and fluxes. Despite the importance of these datasets, they are often chosen out of convenience or regional limitations without due assessment of their impacts on model results. Irrigation is an anthropogenic form of land heterogeneity that has been shown to alter the land surface energy balance, ambient weather, and local circulations. As such, irrigation schemes are becoming more 15 prevalent in weather and climate models with rapid developments in dataset availability and parameterization scheme complexity. Thus, to address pragmatic issues related to modeling irrigation, this study uses a high-resolution, regional coupled modeling system to investigate the impacts of irrigation dataset selection on land-atmosphere (L-A) coupling using a case study from the Great Plains Irrigation Experiment (GRAINEX) field campaign. The simulations are assessed in the context of irrigated versus non-irrigated regions, subregions across the irrigation gradient, and sub-grid scale process 20 representation in coarser scale models. The results show that L-A coupling is sensitive to the choice of irrigation dataset and resolution and that the irrigation impact on surface fluxes and near surface meteorology can be dominant, conditioned on the details of the irrigation map (i.e., boundaries, heterogeneity, etc), or minimal. A consistent finding across several analyses was that even a low percentage of irrigation fraction (i.e., 4-16%) can have significant local and downstream atmospheric impacts (e.g., lower PBL height), suggesting that representation of boundaries and heterogeneous areas within irrigated 25 regions is particularly important for the modeling of irrigation impacts on the atmosphere in this model. When viewing the simulations presented here as a proxy for ‘ideal’ tiling in a Earth System Model scale gridbox, the results show that some ‘tiles’ will reach critical nonlinear moisture and planetary boundary layer (PBL) thresholds that could be important for clouds and convection, implying that heterogeneity resulting from irrigation should be taken into consideration in new sub-grid land-atmosphere exchange parameterizations.
Correlative evidence accumulating since 1926 suggests that there must be some physical coupling mechanism between solar activity and thunderstorm occurrence in middle to high latitudes. Such a link may be provided by alteration of atmospheric electric parameters through the combined influence of high-energy solar protons and decreased cosmic ray intensities, both of which are associated with active solar events. The protons produce excess ionization near and above 20km, while the Forbush decreases a lowered conductivity and enhanced fair-weather atmospheric electric field below that altitude. Consequent effects ultimately lead to a charge distribution similar to that found in thunderclouds, and then other cloud physics processes take over to generate the intense electric fields required for lightning discharge.
The goal of this study is to validate the global cloud parameters derived from the satellite-borne HIRS2 and MSU atmospheric sounding instrument measurements, and to use the analysis of these data as one prototype for studying large geophysical data sets in general. The HIRS2/MSU data set contains a total of 40 physical parameters, filling 25 MB/day; raw HIRS2/MSU data are available for a period exceeding 10 years. Validation involves developing a quantitative sense for the physical meaning of the derived parameters over the range of environmental conditions sampled. This is accomplished by comparing the spatial and temporal distributions of the derived quantities with similar measurements made using other techniques, and with model results. The need to work with Level 2 (point) data, rather than Level 3 (gridded) data for validation purposes is discussed, and some techniques developed for charting the assumptions made in deriving an algorithm and generating a code to produce geophysical quantities from measured radiances are presented.
We are validating the global cloud parameters derived from the satellite-borne HIRS2 and MSU atmospheric sounding instrument measurements, and are using the analysis of these data as one prototype for studying large geophysical data sets in general. The HIRS2/MSU data set contains a total of 40 physical parameters, filling 25 MB/day; raw HIRS2/MSU data are available for a period exceeding 10 years. Validation involves developing a quantitative sense for the physical meaning of the derived parameters over the range of environmental conditions sampled. This is accomplished by comparing the spatial and temporal distributions of the derived quantities with similar measurements made using other techniques, and with model results. The data handling needed for this work is possible only with the help of a suite of interactive graphical and numerical analysis tools. Level 3 (gridded) data is the common form in which large data sets of this type are distributed for scientific analysis. We find that Level 3 data is inadequate for the data comparisons required for validation. Level 2 data (individual measurements in geophysical units) is needed. A sampling problem arises when individual measurements, which are not uniformly distributed in space or time, are used for the comparisons. Standard 'interpolation' methods involve fitting the measurements for each data set to surfaces, which are then compared. We are experimenting with formal criteria for selecting geographical regions, based upon the spatial frequency and variability of measurements, that allow us to quantify the uncertainty due to sampling. As part of this project, we are also dealing with ways to keep track of constraints placed on the output by assumptions made in the computer code. The need to work with Level 2 data introduces a number of other data handling issues, such as accessing data files across machine types, meeting large data storage requirements, accessing other validated data sets, processing speed and throughput for interactive graphical work, and problems relating to graphical interfaces.
Environmental parameters affecting growth of bacteria are compared with current atmospheric models for Jupiter and Saturn, and with the available physical data for their satellites. Different zones of relative probability of growth are identified for Jupiter and Saturn. Of the more than two dozen satellites, only the largest (Io, Europa, Ganymede, Callisto, and Titan) are found to be interesting biologically. Titan's atmosphere may produce a substantial greenhouse effect providing increased surface temperatures. Models predicting a dense atmosphere are compatible with microbial growth for a range of pressures at Titan's surface. For Titan's surface the probability of growth would be enhanced if: (1) the surface is entirely or partially liquid; (2) volcanism is present; or (3) access to internal heat sources is significant.
The principal objective of the two AEROS satellites is the study of the physical and chemical processes in the earth's upper atmosphere and ionosphere by means of simultaneous measurements of various parameters. The parameters include the neutral atmosphere composition and density, the ion composition and density, electron density and flux of suprathermal electrons, and the solar EUV ionizing radiation. Temperature measurements of neutral particles, ions, and electrons are also conducted. The scientific payload is discussed along with details regarding the satellite orbits.
Techniques have been developed and used to invert limb scan measurements for vertical profiles of atmospheric state parameters. The parameters which can be found are concentrations of Rayleigh scatters, ozone, NO2, and aerosols, and aerosol physical properties including a Junge-size distribution parameter and real and imaginary parts of the index of refraction.
Numerical modelling results are reported from a pilot study investigating the feasibility of developing a technique for daily soil moisture measurement throughout the world, based on GOES infrared data. A detailed one-dimensional boundary layer-surface-soil model was used in order to determine which physical parameters observable from GOES are most sensitive to soil moisture, and which are most effected by seasonal changes, atmospheric effects and vegetation cover. The results of the sensitivity test show that the mid-morning differential of surface temperature with respect to absorbed solar radiation is optimally sensitive to soil moisture. A case study comparing model results with GOES infrared data confirms the sensitivity of this parameter to soil moisture and also confirms the applicability of the model to predicting area-averaged surface temperature changes. Model measurements of soil moisture are expected to be most accurate for dry or marginal agricultural areas where drought is common. Sources of error, including the advection of clouds, are examined and methods of minimizing error are discussed.
Global average temperature in a 100-year control run of a model used for greenhouse gas response simulations showed low-frequency natural variability comparable in magnitude to that observed over the last 100 years. The model variability was found to be barotropic in the atmosphere, and located in the tropical strip with largest values near the equator in the Pacific. The model variations were traced to complex, low-frequency interactions between the meridional sea surface temperature gradients in the eastern equatorial Pacific, clouds at both high and low levels, and features of the tropical atmospheric circulation. The variations in these and other model parameters appear to oscillate between two limiting climate states. The physical scenario accounting for the oscillations on decadal time scales is almost certainly not found in the real world on shorter time scales due to limited resolution and the omission of key physics (e.g., equatorial ocean dynamics) in the model. The real message is that models with dynamical limitations can still produce significant long-term variability. Only a thorough physical diagnosis of such simulations and comparisons with decadal-length data sets will allow one to decide if faith in the model results is, or is not, warranted.
The report present the atmosphere, ocean color, land and solar irradiation data sets. The data presented: total ozone, aerosol, cloud optical and physical parameters, temperature and humidity profiles, radiances, rain fall, drop size distribution.
In an earlier paper, we proposed that Jupiter's alternating wind bands are a manifestation of the global interaction between rotation and convection in a shallow layer. The model, however, was obtained from linearization of the 2D equations of motions. At HKUST/Hong Kong, we are now trying to study this problem by rigorous numerical simulation. Using a three-dimensional spectral numerical code, we compute models for the outermost layer of Jupiter's convective envelope. Two cases have been studied. In one the atmospheric pressure varies from 1 to 23 bar, and in the other from 1 to 115 bar. The physical parameters (internal energy flux, rotation rate) are chosen to be close to those expected, but solar heating, chemistry, as well as dynamical influences from deeper layers are ignored. The models generate wind field patterns that contain alternating jet streams with resemblance to the Jovian bands. Instantaneous values of the mean zonal flow at the equator reach 80 m/sec. Yet the mean meridional flows are less than 1% of such value. The meridional temperature profile at the cloud top level also shows a double hump structure of a few degrees (as observed) in the subtropics. Though there is not complete quantitative agreement (caused perhaps by neglected effects like solar radiation), these models demonstrate, in principle, the feasibility of generating a Jovian type wind pattern through the interaction of fast rotation and convection in a thin shell.