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 Effective Radius Retrieval for Thick Ice Clouds Using GOES

Abstract: Satellite retrieval of cirrus cloud microphysical properties is an important but difficult problem because of uncertainties in ice-scattering characteristics. Most methods have been developed for instruments aboard polar-orbiting satellites, which have better spatial and spectral resolution than geostationary sensors. The Geostationary Operational Environmental Satellite (GOES) series has the advantage of excellent temporal resolution, so that the evolution of thunderstorm-cloud-top properties can be monitored. In this paper, the authors discuss the development of a simple ice cloud effective radius retrieval for thick ice clouds using three bands from the GOES imager: one each in the visible, shortwave infrared, and window infrared portion of the spectrum. It is shown that this retrieval compares favorably to the MODIS effective radius algorithm. In addition, a comparison of the retrieval for clouds viewed simultaneously from GOES-East and GOES-West reveals that the assumed ice-scattering proper...

Using Deep Learning to Emulate and Accelerate a Radiative Transfer Model

Abstract: This paper describes the development of U-net++ models, a type of neural network that performs deep learning, to emulate the shortwave Rapid Radiative-transfer Model (RRTM). The goal is to emulate the RRTM accurately in a small fraction of the computing time, creating a U-net++ that could be used as a parameterization in numerical weather prediction (NWP). Target variables are surface downwelling flux, top-of-atmosphere upwelling flux (), net flux, and a profile of radiative-heating rates. We have devised several ways to make the U-net++ models knowledge-guided, recently identified as a key priority in machine learning (ML) applications to the geosciences. We conduct two experiments to find the best U-net++ configurations. In Experiment 1, we train on non-tropical sites and test on tropical sites, to assess extreme spatial generalization. In Experiment 2, we train on sites from all regions and test on different sites from all regions, with the goal of creating the best possible model for use in NWP. The selected model from Experiment 1 shows impressive skill on the tropical testing sites, except four notable deficiencies: large bias and error for heating rate in the upper stratosphere, unreliable for profiles with single-layer liquid cloud, large heating-rate bias in the mid-troposphere for profiles with multi-layer liquid cloud, and negative bias at lowzenith angles for all flux components and tropospheric heating rates. The selected model from Experiment 2 corrects all but the first deficiency, and both models run ~104 times faster than the RRTM. Our code is available publicly.

Modeling regional haze during the BRAVO study using CMAQ-MADRID: 2. Source region attribution of particulate sulfate compounds

Abstract: [1] Regional source attribution is conducted for fine particulate sulfate at Big Bend National Park, Texas, using a comprehensive regional air quality model, Community Multiscale Air Quality model augmented with the Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (CMAQ-MADRID), as part of the Big Bend Regional Aerosol Visibility and Observational (BRAVO) Study. The overall PM2.5 sulfate load at Big Bend National Park over the 9 July to 28 October 1999 period is attributed as follows: 31% to Mexico, 19% to Texas, 39% to the eastern United States, 6% to the western United States, and 5% to areas outside the modeling domain (boundary conditions). The arithmetic mean of the daily PM2.5 sulfate loads at BBNP over the 9 July to 28 October 1999 period is attributed as follows: 42% to Mexico, 14% to Texas, 27% to the eastern United States, 9% to the western United States, and 7% to areas outside the modeling domain. These results illustrate the potential for significant contributions from distant sources to regional haze in remote areas. An examination of source contributions and model performance by month and for specific episodes shows that model performance can affect the results of a source attribution. Therefore caution is advised when interpreting the results of source attribution obtained using Eulerian air quality models. In lieu of estimating the uncertainty of the apportionment procedures, source region attribution results obtained for sulfate using CMAQ-MADRID are refined using an inverse modeling technique. Comparison of original attribution results with refined attribution estimates obtained using inverse modeling techniques shows that these methods can reduce in part the biases introduced in the model by uncertainties and errors in the emissions, meteorology, and chemical transport modeling. The refined attribution estimates of overall PM2.5 sulfate load at Big Bend National Park over the 9 July to 28 October 1999 period using inverse modeling are 37% to Mexico, 17% to Texas, 31% to the eastern United States, 9% to the western United States, and 6% to areas outside the modeling domain.

Regional impacts of COVID-19 on carbon dioxide detected worldwide from space.

Abstract: Activity reductions in early 2020 due to the coronavirus disease 2019 pandemic led to unprecedented decreases in carbon dioxide (CO2) emissions. Despite their record size, the resulting atmospheric...