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.

Evaluating the Ability of Remote Sensing Observations to Identify Significantly Severe and Potentially Tornadic Storms

Abstract: Remote sensing observations, especially those from ground-based radars, have been used extensively to discriminate between severe and nonsevere storms. Recent upgrades to operational remote...

Air Quality Modeling in the South Coast Air Basin of California: What Do the Numbers Really Mean?

Abstract: This study evaluates air quality model sensitivity to input and to model components. Simulations are performed using the California Institute of Technology (CIT) airshed model. Results show the impacts on ozone (O3) concentration in the South Coast Air Basin (SCAB) of California because of changes in: (1) input data, including meteorological conditions (temperature, UV radiation, mixing height, and wind speed), boundary conditions, and initial conditions (ICs); and (2) model components, including advection solver and chemical mechanism. O3 concentrations are strongly affected by meteorological conditions and, in particular, by temperature. ICs also affect O3 concentrations, especially in the first 2 days of simulation. On the other hand, boundary conditions do not significantly affect the absolute peak O3 concentration, although they do affect concentrations near the inflow boundaries. Moreover, predicted O3 concentrations are impacted considerably by the chemical mechanism. In addition, dispersion of pollutants is affected by the advection routine used to calculate its transport. Comparison among CIT, California Photochemical Grid Model (CALGRID), and Urban Airshed Model air quality models suggests that differences in O3 predictions are mainly caused by the different chemical mechanisms used. Additionally, advection solvers contribute to the differences observed among model predictions. Uncertainty in predicted peak O3 concentration suggests that air quality evaluation should not be based solely on this single value but also on trends predicted by air quality models using a number of chemical mechanisms and with an advection solver that is mass conservative.

The death of an altocumulus cloud

Abstract: What causes altocumulus clouds to decay? To address this question, the authors examine an observational case study of a mid-level cloud that was measured during the Complex Layered Cloud Experiments (CLEX). The budget of liquid water reveals that the cloud was not dissipated by fallout of precipitation. Rather, the largest contributor to decay of liquid water was subsidence drying. The strong link between subsidence and cloud lifetime is an important difference between altocumuli and boundary layer clouds. The net effect of radiative transfer on our cloud is unclear: liquid water was directly increased by radiative cooling, but this was offset by radiatively induced entrainment drying.

Estimation of snow depth over open prairie environments using GOES imager observations

Abstract: We assess the potential for estimating snow depth using observations in the visible and infrared spectral bands from the imager instrument onboard the Geostationary Operational Environmental Satellites (GOES). The approach makes use of a correlation between depth of the snowpack and satellite-derived subpixel fractional snow cover over non-forested and sparsely forested areas. To retrieve the snow depth we propose a simple analytical formula approximating the statistical relationship between the snow depth and the snow fraction. The primary focus of this study was the US Great Plains and Canadian prairies area. Daily maps of snow depth at a spatial resolution of 4 km have been produced for this region for four winter seasons from late 1999 to the beginning of 2003. Validation of the algorithm developed was performed through comparison of the satellite-based product with snow depth measurements made at first-order synoptic stations, US Cooperative Network stations and Canadian climate stations. The accuracy of snow depth retrievals was found to be about 30% of the observed snow depth for snow depths below 30 cm.