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.

A regional CO 2 observing system simulation experiment for the ASCENDS satellite mission

Abstract: . Top–down estimates of the spatiotemporal variations in emissions and uptake of CO2 will benefit from the increasing measurement density brought by recent and future additions to the suite of in situ and remote CO2 measurement platforms. In particular, the planned NASA Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) satellite mission will provide greater coverage in cloudy regions, at high latitudes, and at night than passive satellite systems, as well as high precision and accuracy. In a novel approach to quantifying the ability of satellite column measurements to constrain CO2 fluxes, we use a portable library of footprints (surface influence functions) generated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in combination with the Weather Research and Forecasting (WRF) model in a regional Bayesian synthesis inversion. The regional Lagrangian particle dispersion model framework is well suited to make use of ASCENDS observations to constrain weekly fluxes in North America at a high resolution, in this case at 1° latitude × 1° longitude. We consider random measurement errors only, modeled as a function of the mission and instrument design specifications along with realistic atmospheric and surface conditions. We find that the ASCENDS observations could potentially reduce flux uncertainties substantially at biome and finer scales. At the grid scale and weekly resolution, the largest uncertainty reductions, on the order of 50%, occur where and when there is good coverage by observations with low measurement errors and the a priori uncertainties are large. Uncertainty reductions are smaller for a 1.57 μm candidate wavelength than for a 2.05 μm wavelength, and are smaller for the higher of the two measurement error levels that we consider (1.0 ppm vs. 0.5 ppm clear-sky error at Railroad Valley, Nevada). Uncertainty reductions at the annual biome scale range from ~40% to ~75% across our four instrument design cases and from ~65% to ~85% for the continent as a whole. Tests suggest that the quantitative results are moderately sensitive to assumptions regarding a priori uncertainties and boundary conditions. The a posteriori flux uncertainties we obtain, ranging from 0.01 to 0.06 Pg C yr−1 across the biomes, would meet requirements for improved understanding of long-term carbon sinks suggested by a previous study.

An introduction to factor analysis for radio frequency interference detection on satellite observations

Abstract: A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C- and X-band radiometer channels as effectively as the conventional PCA method.

The Andes Cordillera. Part II: Rio Olivares Basin snow conditions (1979–2014), central Chile

Abstract: Snow cover extent, duration, and properties were simulated (1979/1980–2013/2014) for the Rio Olivares Basin (548 km2) in central Chilean Andes, in an effort to understand conditions and trends (linear) at a basin scale. The National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications products, together with the snow modelling software package SnowModel allowed simulations of first-order atmospheric forcings (mean annual air temperature (MAAT) and water-equivalent precipitation) and terrestrial snow features (snow cover extent, duration, snow water-equivalent depth, snow density, and runoff generated from snow melt). Simulated snow cover extent and depletion curves were verified against Moderate Resolution Imaging Spectroradiometer-derived snow cover data. For the Rio Olivares Basin, MAAT was −2.9 ± 0.6 °C with a mean 0° isotherm at 3325 m a.s.l. The greatest temporal and spatial changes in temperature over the 35-year period occurred in January and at the highest elevations, respectively. Mean annual precipitation was 1.86 ± 0.60 m w.e., indicating an increase in precipitation of ∼0.1 m w.e. 100 m−1 increase in elevation. On average, ∼90% of the basin precipitation fell as snow, varying from 70% at ∼2600 m a.s.l., to 95% at ∼4200 m a.s.l. In 20 out of 35 years the snow cover extent went to 0% (no basin snow cover) by end-of-summer (during March), and the snow duration increased on average by ∼10 days 100 m−1 increase in elevation. Approximately 85% of the basin outlet freshwater runoff originated from snowmelt, making snowmelt a dominant contributor to water resources. Snowmelt-derived basin runoff was dominated by variability in snow precipitation rather than by variability in MAAT.

A Tale of Two Dust Storms : analysis of a complex dust event in the Middle East

Abstract: . Lofted mineral dust over data-sparse regions presents considerable challenges to satellite-based remote sensing methods and numerical weather prediction alike. The southwest Asia domain is replete with such examples, with its diverse array of dust sources, dust mineralogy, and meteorologically driven lofting mechanisms on multiple spatial and temporal scales. A microcosm of these challenges occurred over 3–4 August 2016 when two dust plumes, one lofted within an inland dry air mass and another embedded within a moist air mass, met over the southern Arabian Peninsula. Whereas conventional infrared-based techniques readily detected the dry air mass dust plume, they experienced marked difficulties in detecting the moist air mass dust plume, becoming apparent when visible reflectance revealed the plume crossing over an adjacent dark water background. In combining information from numerical modeling, multi-satellite and multi-sensor observations of lofted dust and moisture profiles, and idealized radiative transfer simulations, we develop a better understanding of the environmental controls of this event, characterizing the sensitivity of infrared-based dust detection to column water vapor, dust vertical extent, and dust optical properties. Differences in assumptions of dust complex refractive index translate to variations in the sign and magnitude of the split-window brightness temperature difference commonly used for detecting mineral dust. A multi-sensor technique for mitigating the radiative masking effects of water vapor via modulation of the split-window dust-detection threshold, predicated on idealized simulations tied to these driving factors, is proposed and demonstrated. The new technique, indexed to an independent description of the surface-to-500 hPa atmospheric column moisture, reveals parts of the missing dust plume embedded in the moist air mass, with the best performance realized over land surfaces.

NOAA’S SENSING HAZARDS WITH OPERATIONAL UNMANNED TECHNOLOGY (SHOUT) EXPERIMENT: Observations and Forecast Impacts

Abstract: Capsule:Field operations and data impact studies examine how observations from high-altitude unmanned aircraft can improve forecasts of tropical cyclones and other high-impact weather events.