A Pavement Design and Management System for Forest Service Road : A Conceptual Study

See this work in the Center for Transportation Research Library catalog: https://library.ctr.utexas.edu/Presto/catalogid=5738The design of pavements for low-cost, low-volume roads is a complex procedure involving numerous variables. Because of the development of new information in the pavement field during the past decade, the complexity of the interaction of these design variables has become better understood and the need for a systematic approach to the problem of pavement design and management has become evident. This report is an attempt to apply this systematic approach to the design and management of low-volume Forest Service roads. The report summarizes the problem analysis efforts of the project staff, beginning with the identification of the problem through its recognition and definition. Using the FPS type of working Pavement Design System developed in Texas as a conceptual base, an extensive examination of the major subsystems that make up the majority of existing pavement management systems for "higher type" roads was conducted. In attempting to define these basic components for the proposed low-volume road system, it was found that interaction between the project research staff and Forest Service personnel was of great importance. This interaction was achieved in the form of an interagency "brainstorming session" and later an "importance rating" of the ideas presented at this meeting. The results of this interaction along with the research efforts of the project staff allowed for an initial definition of the major components in the proposed system. Where complete definition of these subsystems was not possible, relevant questions and ideas were formulated for consideration in their further development. Finally, an example conceptual pavement management system for low-volume roads that incorporates all the ideas and concepts developed during the past year's research is presented. It is concluded that the development of pavement management systems for low-volume Forest Service roads is indeed feasible and should be pursued in Phase II of the project. Recommendations for major areas of further research are also given.Forest Service U.S. Department of Agriculture; Department of Transportation Office of University Research (Washington D.C.)Center for Transportation ResearchSee this work in the Center for Transportation Research Library catalog

Cloud_cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products

Parallel retrieval of aerosol and cloud

Due to similarities in their radiometric signature, it is not possible to retrieve aerosol and cloud properties simultaneously from satellite imagery. A plethora of filtering techniques have been developed to ensure aerosol and cloud are analysed separately, but this neglects the scientifically interesting regions of interaction between the two. It also limits the spatial coverage of such products, with up to 20% of the planet neglected because it is considered too cloudy to be suitable for an aerosol retrieval but insufficiently so for a cloud retrieval. The Optimal Retrieval of Aerosol and Cloud (ORAC) is a single algorithm that can retrieve the aerosol or cloud properties consistent with a single measurement. By performing radiative transfer calculations via look-up tables, various types of particle can be considered in parallel — such as liquid-phase cloud, different models of ice nuclei, and various clean and polluted aerosols — by simply running the program repeatedly using tables assuming different microphysical properties and vertical distributions. Bayesian statistics can determine the probability that the scene contains a specific species, classifying it as aerosol, cloud, or uncertain. The important but infrequently discussed `uncertain' region can then be used to investigate the impact of contamination and data coverage on existing products by, for example, observing how retrieved aerosol optical thickness varies as a function of the distance from the nearest cloud. It also provides a potential window for the study of aerosol-cloud interactions

Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: a framework applied to multiple satellite imaging sensors

We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02∘. By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (Cloud-Aerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea–West Africa. The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multi-instrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.</p

Unveiling aerosol-cloud interactions Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate

Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds

The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %

The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the 5 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the “fast” radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing 10 optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20%