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.

An analysis of the relationship between cloud anomalies and sea surface temperature anomalies in a global circulation model

Abstract: The relationship between the sea surface temperature anomalies (SSTAs) and the anomalies of the monthly mean cloud cover (including the high-level, low-level, and total cloud cover), the outgoing longwave radiation, and the reflected solar radiation was analyzed using a least absolute deviations regression at each grid point over the open ocean for a 6-yr period. The results indicate that cloud change in association with a local 1-C increase in SSTAs cannot be used to predict clouds in a potential future world where all the oceans are 1-C warmer than at present, because much of the observed cloud changes are due to circulation changes, which in turn are related not only to changes in SSTAs but to changes in SSTA gradients. However, because SSTAs are associated with changes in the local ocean-atmosphere moisture and heat fluxes as well as significant changes in circulation (such as ENSO), SSTAs can serve as a surrogate for many aspects of global climate change.

The Colorado State University Convective CLoud Outflows and UpDrafts Experiment (C3LOUD-Ex)

Abstract: The intensity of deep convective storms is driven in part by the strength of their updrafts and cold pools. In spite of the importance of these storm features, they can be poorly represented within numerical models. This has been attributed to model parameterizations, grid resolution, and the lack of appropriate observations with which to evaluate such simulations. The overarching goal of the Colorado State University Convective CLoud Outflows and UpDrafts Experiment (C3LOUD-Ex) was to enhance our understanding of deep convective storm processes and their representation within numerical models. To address this goal, a field campaign was conducted during July 2016 and May–June 2017 over northeastern Colorado, southeastern Wyoming, and southwestern Nebraska. Pivotal to the experiment was a novel “Flying Curtain” strategy designed around simultaneously employing a fleet of uncrewed aerial systems (UAS; or drones), high-frequency radiosonde launches, and surface observations to obtain detailed measurements of the spatial and temporal heterogeneities of cold pools. Updraft velocities were observed using targeted radiosondes and radars. Extensive datasets were successfully collected for 16 cold pool–focused and seven updraft-focused case studies. The updraft characteristics for all seven supercell updraft cases are compared and provide a useful database for model evaluation. An overview of the 16 cold pools’ characteristics is presented, and an in-depth analysis of one of the cold pool cases suggests that spatial variations in cold pool properties occur on spatial scales from O(100) m through to O(1) km. Processes responsible for the cold pool observations are explored and support recent high-resolution modeling results.

Satellite-Based Estimation of Temporally Resolved Dust Radiative Forcing in Snow Cover

Abstract: Runoff from mountain snowpack is an important freshwater supply for many parts of the world The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud obser

A high-accuracy rainfall dataset by merging multiple satellites and dense gauges over the southern Tibetan Plateau for 2014–2019 warm seasons

Abstract: . Tibetan Plateau (TP) is well known as Asia's water tower from where many large rivers originate. However, due to complex spatial variability in climate and topography, there is still a lack of a high-quality rainfall dataset for hydrological modeling and flood prediction. This study therefore aims to establish a high-accuracy daily rainfall product through merging rainfall estimates from three satellites, i.e., GPM-IMERG, GSMaP and CMORPH, based on a high-density rainfall gauge network. The new merged daily rainfall dataset with a spatial resolution of 0.1 ∘ focuses on warm seasons (10 June–31 October) from 2014 to 2019. Statistical evaluation indicated that the new dataset outperforms the raw satellite estimates, especially in terms of rainfall accumulation and the detection of ground-based rainfall events. Hydrological evaluation in the Yarlung Zangbo River basin demonstrated high performance of the merged rainfall dataset in providing accurate and robust forcings for streamflow simulations. The new rainfall dataset additionally shows superiority to several other products of similar types, including MSWEP and CHIRPS. This new rainfall dataset is publicly accessible at https://doi.org/10.11888/Hydro.tpdc.271303 (Li and Tian, 2021).