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.

Large snowmelt versus rainfall events in the mountains

Abstract: While snow is the dominant precipitation type in mountain regions, estimates of rainfall are used for design, even though snowmelt provides most of the runoff. Daily data were used to estimate the 10 and 100 year, 24 h snowmelt, precipitation, and rainfall events at 90 Snow Telemetry stations across the Southern Rocky Mountains. Three probability distributions were compared, and the Pearson type III distribution yielded the most conservative estimates. Precipitation was on average 33% and 28% more than rainfall for the 10 and 100 year events. Snowfall exceeded rainfall at most of the stations and was on average 53% and 38% more for the 10 and 100 year events. On average, snowmelt was 15% and 8.9% more than precipitation. Where snow accumulation is substantial, it is recommended that snowmelt be considered in conjunction with rainfall and precipitation frequencies to develop flood frequencies.

Estimating noise levels of remotely sensed measurements from satellites using spatial structure analysis

Abstract: A technique is presented whereby the noise level of satellite measurements of the atmosphere and earth can be estimated. The technique analyzes a spatial array of data measured by a satellite instrument. A minimum of about 200 satellite measurements is required, preferably in a regular pattern. Statistical structure analysis is used to describe a combination of the mean gradient and noise in the data. The noise level is then estimated by separating out the gradient information and leaving only the noise. Results are presented for four satellite sounding instruments, and effective blackbody or brightness temperature noise levels were compared to prelaunch specifications or inflight calibrations for each instrument. Comparisons showed that in the absence of cloud-contaminated measurements (in the case of infrared data) and away from the highly variable ground surface, the noise level of various satellite instruments can be obtained without the need for calibration data. The noise levels imply how much spatial averaging is possible, without smearing the detected geophysical gradient, and how much is necessary, to meet the absolute signal accuracy requirements for the intended use of the satellite measurements.

The ARM cloud radar simulator for global climate models bridging field data and climate models

Abstract: C louds play an important role in Earth’s radiation budget and hydrological cycle. However, current global climate models (GCMs) have difficulties in accurately simulating clouds and precipitation. To improve the representation of clouds in climate models, it is crucial to identify where simulated clouds differ from real-world observations of them. This can be difficult, since significant differences exist between how a climate model represents clouds and what instruments observe, both in terms of spatial scale and the properties of the hydrometeors that are either modeled or observed. To address these issues and minimize impacts of instrument limitations, the concept of instrument “simulators,” which convert model variables into pseudoinstrument observations, has evolved with the goal to facilitate and improve the comparison of modeled clouds with observations. Many simulators have been (and continue to be) developed for a variety of instruments and purposes. A community satellite simulator package, the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP; Bodas-Salcedo et al. 2011), contains several independent satellite simulators and is being widely used in the GCM community to exploit satellite observations for model cloud evaluation (e.g., Kay et al. 2012; Klein et al. 2013; Suzuki et al. 2013; Zhang et al. 2010). This article introduces a ground-based cloud radar simulator developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program for comparing climate model clouds with ARM observations from its vertically pointing 35-GHz radars. As compared to the radar measurements made by CloudSat [a satellite carrying the first spaceborne 94-GHz (3.2-mm wavelength) cloud radar], which provides near-global sampling of profiles of cloud condensate and precipitation with a vertical resolution of 500 m (Stephens et al. 2002), ARM radar measurements occur with higher temporal resolution (10 s) and finer vertical resolution (45 m). This enables users to investigate more fully the detailed vertical structures within clouds, resolve thin clouds, and quantify the diurnal variability of clouds. Particularly, ARM radars are sensitive to low-level clouds, which are difficult for the CloudSat radar to detect due to both surface contamination (Mace et al. 2007; Marchand et al. 2008) and a radar sensitivity of approximately −28 dBZ near the surface. Therefore, the ARM ground-based cloud observations complement measurements from space.