Cooperative Institute for Research in the Atmosphere

About: Cooperative Institute for Research in the Atmosphere is a based out in . It is known for research contribution in the topics: Snow & Data assimilation. The organization has 332 authors who have published 997 publications receiving 38835 citations. The organization is also known as: CIRA.

The Potential of 8-mm Radars for Remotely Sensing Cloud Drop Size Distributions

Abstract: This paper describes the use of a vertically pointing 8.6-mm-wavelength Doppler radar for measuring drop size spectra in clouds. The data used were collected in the Atlantic Stratocumulus Transition Experiment in 1992. This paper uses the full Doppler velocity spectrum from the time series of Doppler radial velocities to extract information farther into the small-drop regime than previously attempted. The amount of liquid residing in the cloud regime is compared with that found in the precipitation regime where drop fall velocities are resolvable. Total liquid is compared with that measured with a collocated three-channel microwave radiometer. Examples of number density spectra, liquid water spectra, and flux spectra are shown and compared with what is known of these quantities from various in situ measurements by aircraft in similar clouds. Error estimates and uncertainties are discussed. It is concluded that 8-mm Doppler radars have the potential for broader use in cloud and precipitation studi...

Modeling Snow–Canopy Processes on an Idealized Mountain

Abstract: Snow interception in a coniferous forest canopy is an important hydrological feature, producing complex mass and energy exchanges with the surrounding atmosphere and the snowpack below. Subcanopy snowpack accumulation and ablation depends on the effects of canopy architecture on meteorological conditions and on interception storage by stems, branches, and needles. Mountain forests are primarily composed of evergreen conifer species that retain their needles throughout the year and hence intercept snow efficiently during winter. Canopy-intercepted snow can melt, fall to the ground, and/or sublimate into the air masses above and within the canopy. To improve the understanding of snow–canopy interception processes and the associated influences on the snowpack below, a series of model experiments using a detailed, physically based snow–canopy and snowpack evolution model [Alpine Multiscale Numerical Distributed Simulation Engine (AMUNDSEN)] driven with observed meteorological forcing was conducted. A ...

Land use/land cover change effects on temperature trends at U.S. Climate Normals stations

Abstract: [1] Alterations in land use/land cover (LULC) in areas near meteorological observation stations can influence the measurement of climatological variables such as temperature. Urbanization near climate stations has been the focus of considerable research attention, however conversions between non-urban LULC classes may also have an impact. In this study, trends of minimum, maximum, and average temperature at 366 U.S. Climate Normals stations are analyzed based on changes in LULC defined by the U.S. Land Cover Trends Project. Results indicate relatively few significant temperature trends before periods of greatest LULC change, and these are generally evenly divided between warming and cooling trends. In contrast, after the period of greatest LULC change was observed, 95% of the stations that exhibited significant trends (minimum, maximum, or mean temperature) displayed warming trends.

CIRA/CSU Four-Dimensional Variational Data Assimilation System

Abstract: A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.