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Joseph G. Alfieri, William P. Kustas, John H. Prueger, Lawrence E. Hipps, José L. Chávez, Andrew N. French, and Steven R. Evett

substantially both spatially and temporally due to localized differences in both surface and atmospheric conditions. For example, Alfieri et al. (2007) found that while evapotranspiration (ET) during drought is strongly linked to soil moisture content, water availability is only one of several key environmental factors that influence the moisture flux over time. Therefore, measurements of surface fluxes collected across the continuum of surface and atmospheric conditions are requisite both for

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Renée Elio, Johannes De Haan, and G. S. Strong

experience with convective complexes. The strategyfor interpreting the model using surface moisture fieldsand advection principles is given by Strong and Wilson(1983) and more recently detailed by Strong (1986).This strategy is summarized as follows. By consideringthe advection of complexes by the mean wind, it hasbeen found that these systems usually form on the upwind side (at 700 mb) of the strongest Sc4 gradients,i.e., within regions of strong negative Sc4 advection,indicated by the gray areas in Fig

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Shelley L. Knuth and John J. Cassano

section. 3. Flux calculation methodology and corrections The methodology described below uses atmospheric data (temperature, pressure, wind speed, and moisture) to estimate changes in the energy present in the atmosphere over the UAV flight path via the integral method. The general approach, described in section 3a , uses methods similar to those of Kottmeier and Engelbart (1992) and Serreze et al. (1992) , as well as those in the ocean community to estimate carbon dioxide flux through inverse

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Thomas Foken, Arden L. Buck, Russell A. Nye, and Randy D. Horn

section per water vapor mass unit. Further, k can be affected by operating conditions such as the presence of liquid moisture. Also, error in the assumed value of pathlength d can result in an equivalent error in the determination of fluctuations in ρ υ . Therefore, to allow for any unknown departures of d ρ υ from assumed values, a calibration scheme is required. Since a change in output signal can be produced by either a change in humidity or a change in pathlength, it is possible by a change

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Wen Chen, Rachel T. Pinker, Yingtao Ma, Glynn Hulley, Eva Borbas, Tanvir Islam, Kerry-A. Cawse-Nicholson, Simon Hook, Chris Hain, and Jeff Basara

seasonal and diurnal variability and climatological characteristics of LST over the United States. The importance of the LST diurnal changes has been amply recognized and a wide range of attempts have been made to estimate it (e.g., Dai and Trenberth 2004 ; Aires et al. 2004 ; Sun et al. 2006 ; Duan et al. 2014 ). Early studies have demonstrated that there is a strong connection between soil moisture and soil surface temperature. This concept served as a basis for the Heat Capacity Mapping Mission

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Jerald A. Brotzge, J. Wang, C. D. Thorncroft, E. Joseph, N. Bain, N. Bassill, N. Farruggio, J. M. Freedman, K. Hemker Jr., D. Johnston, E. Kane, S. McKim, S. D. Miller, J. R. Minder, P. Naple, S. Perez, James J. Schwab, M. J. Schwab, and J. Sicker

all of New York State, which is an insufficient number of ground stations to adequately cover the complex terrain and diverse climates of the state. In addition, ASOS sites are not equipped with sensors to measure solar radiation, soil moisture, and snow depth—critical information for monitoring severe storms, flooding, and winter weather. Tropical Storm Lee and Hurricane Irene in 2011 and Hurricane Sandy in 2012 revealed the deficiencies of the sparse ASOS network as the greatest impacts from

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J. A. Schroeder, E. R. Westwater, P. T. May, and L. M. McMillin

requires no. foreknowledge oflocal climatology. The blending problem was especiallysimple for this dataset, because temperature and Tvwere virtually indistinguishable due to the extremelycold, dry conditions present during the measurementperiod. Ordinarily, the RASS Tv would be convertedto temperature prior to blending the TOVS and RASSsoundings, using some assumed moisture distributionat RASS measurement altitudes to facilitate the conversion T= Tv(1 + 0.61q)-~,where T = temperature in kelvin

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Lasse Makkonen

represented in Fig.1, where also the curve showing the saturation watervapor pressure with respect to ice (frost point) is drawnas a solid line. The frost-point curve in Fig. 1 is calculated according to the formulas given by Huffmanand Norman (1988). Figure 1 shows, as Anderson (1'994) noted, that ateach temperature there is a maximum value of RHwaterthat is measured, because the sensor surface providesnucleation sites for moisture to sublimate and, consequently, at RHwater values above the frost

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Richard D. Rosen, John M. Henderson, and David A. Salstein

radiosonde stations, Oort was able to simulate the effects of temporal or spatial gaps in the radiosonde network on hemispheric-scale climate statistics. More recently, Yarosh et al. (1999) use a similar approach on a regional scale to determine that closing the moisture budget over the central United States is strongly dependent on adequate temporal and spatial sampling. Comparing output from the National Centers for Environmental Prediction (NCEP) Eta model analyses at full resolution with that at

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L. C. Bender III, S. D. Howden, D. Dodd, and N. L. Guinasso Jr.

accelerometer data into significant wave heights and periods. All of the motion sensors operated through the storm and the raw data were saved on board the buoy’s computer to be retrieved when the buoy was recovered on 20 September 2005. In this paper we use the dual-frequency PPP GPS positions to extend the GPS wave record through the entire storm, something that was not possible in BEN , and we demonstrate that the atmospheric moisture retrieval algorithms are accurate through a hurricane. We also show

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