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.

Landscape transform and spatial metrics for mapping spatiotemporal land cover dynamics using Earth Observation data-sets

Abstract: Analysis of Earth observation (EO) data, often combined with geographical information systems (GIS), allows monitoring of land cover dynamics over different ecosystems, including protected or conservation sites. The aim of this study is to use contemporary technologies such as EO and GIS in synergy with fragmentation analysis, to quantify the changes in the landscape of the Rajaji National Park (RNP) during the period of 19 years (1990–2009). Several statistics such as principal component analysis (PCA) and spatial metrics are used to understand the results. PCA analysis has produced two principal components (PC) and explained 84.1% of the total variance, first component (PC1) accounted for the 57.8% of the total variance while the second component (PC2) has accounted for the 26.3% of the total variance calculated from the core area metrics, distance metrics and shape metrics. Our results suggested that notable changes happened in the RNP landscape, evidencing the requirement of taking appropriate...

The Maximum Likelihood Ensemble Filter as a non‐differentiable minimization algorithm

Abstract: The Maximum Likelihood Ensemble Filter (MLEF) equations are derived without the differentiability requirement for the prediction model and for the observation operators. The derivation reveals that a new non-differentiable minimization method can be defined as a generalization of the gradient-based unconstrained methods, such as the preconditioned conjugate-gradient and quasi-Newton methods. In the new minimization algorithm the vector of first-order increments of the cost function is defined as a generalized gradient, while the symmetric matrix of second-order increments of the cost function is defined as a generalized Hessian matrix. In the case of differentiable observation operators, the minimization algorithm reduces to the standard gradient-based form. The non-differentiable aspect of the MLEF algorithm is illustrated in an example with one-dimensional Burgers model and simulated observations. The MLEF algorithm has a robust performance, producing satisfactory results for tested non-differentiable observation operators. Copyright © 2008 Royal Meteorological Society

Aerosol optical depth retrieval from GOES-8: Uncertainty study and retrieval validation over South America

Abstract: [1] An algorithm for aerosol optical depth τ retrieval from the Geostationary Observational Environmental Satellite (GOES) series is described, where the darkest pixels are used to create a spatial composite of surface reflectance. The data are calibrated and corrected for atmospheric extinction to retrieve the surface reflectance which is then used to retrieved τ. Analysis suggests that τ retrieval uncertainty is ±18–34% depending on the certainty of the assumed radiative transfer model parameters. Retrieval uncertainty is less over low surface reflectances and at large scattering angles. The retrieval algorithm is validated against Sun-sky radiometer τ measurements for aerosols emitted by biomass burning in South America during 1995 and 1998. The relative differences between observed and retrieved τ are within the estimated uncertainty, having correlations ranging from 0.78 to 0.97. Further, the GOES retrievals are compared to τ retrieved using the Moderate-Resolution Imaging Spectroradiometer (MODIS) airborne simulator (MAS). The average relative difference in this comparison is 11%, thus retrieval validations are again within the estimated algorithm uncertainty. These results suggest that the GOES satellite can be used to monitor aerosols over land, while the agreement between MAS and GOES retrievals suggests the ability to combine the spectral abilities of MODIS with the temporal observations of GOES.

A Comparison of Precipitation Occurrence from the NCEP Stage IV QPE Product and theCloudSatCloud Profiling Radar

Abstract: Because of its extensive quality control procedures and uniform space–time grid, the NCEP Stage IV merged Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and surface rain gauge dataset is often considered to be the best long-term gridded dataset of precipitation observations covering the contiguous United States. Stage IV accumulations are employed in a variety of applications, and while the WSR-88D systems are well suited for observing heavy rain events that are likely to affect flooding, limitations in surface radar and gauge measurements can result in missed precipitation, especially near topography and in the western United States. This paper compares hourly Stage IV observations of precipitation occurrence to collocated observations from the 94-GHz CloudSat Cloud Profiling Radar, which provides excellent sensitivity to light and frozen precipitation. Statistics from 4 yr of comparisons show that the CloudSat observes precipitation considerably more frequently than the Stage IV dataset...

An aerosol climatology for a rapidly growing arid region (southern Arizona): Major aerosol species and remotely sensed aerosol properties

Abstract: [1] This study reports a comprehensive characterization of atmospheric aerosol particle properties in relation to meteorological and back trajectory data in the southern Arizona region, which includes two of the fastest growing metropolitan areas in the United States (Phoenix and Tucson). Multiple data sets (MODIS, AERONET, OMI/TOMS, MISR, GOCART, ground-based aerosol measurements) are used to examine monthly trends in aerosol composition, aerosol optical depth (AOD), and aerosol size. Fine soil, sulfate, and organics dominate PM2.5 mass in the region. Dust strongly influences the region between March and July owing to the dry and hot meteorological conditions and back trajectory patterns. Because monsoon precipitation begins typically in July, dust levels decrease, while AOD, sulfate, and organic aerosol reach their maximum levels because of summertime photochemistry and monsoon moisture. Evidence points to biogenic volatile organic compounds being a significant source of secondary organic aerosol in this region. Biomass burning also is shown to be a major contributor to the carbonaceous aerosol budget in the region, leading to enhanced organic and elemental carbon levels aloft at a sky-island site north of Tucson (Mt. Lemmon). Phoenix exhibits different monthly trends for aerosol components in comparison with the other sites owing to the strong influence of fossil carbon and anthropogenic dust. Trend analyses between 1988 and 2009 indicate that the strongest statistically significant trends are reductions in sulfate, elemental carbon, and organic carbon, and increases in fine soil during the spring (March–May) at select sites. These results can be explained by population growth, land-use changes, and improved source controls.