University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and the Scanning Multichannel Microwave Radiometer (SMMR)

Abstract: [1] Two data sets of satellite surface soil moisture retrievals are first compared and then assimilated into the NASA Catchment land surface model. The first satellite data set is derived from 4 years of X-band (10.7 GHz) passive microwave brightness temperature observations by the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), and the second is from 9 years of C-band (6.6 GHz) brightness temperature observations by the Scanning Multichannel Microwave Radiometer (SMMR). Despite the similarity in the satellite instruments, the retrieved soil moisture data exhibit very large differences in their multiyear means and temporal variability, primarily because they are computed with different retrieval algorithms. The satellite retrievals are also compared to a soil moisture product generated by the NASA Catchment land surface model when driven with surface meteorological data derived from observations. The climatologies of both satellite data sets are different from those of the model products. Prior to assimilation of the satellite retrievals into the land model, satellite-model biases are removed by scaling the satellite retrievals into the land model's climatology through matching of the respective cumulative distribution functions. Validation against in situ data shows that for both data sets the soil moisture fields from the assimilation are superior to either satellite data or model data alone. A global analysis of the innovations (defined as the difference between the observations and the corresponding model values prior to the assimilation update) reveals how changes in model and observations error parameters may enhance filter performance in future experiments.

Stellar and Total Baryon Mass Fractions in Groups and Clusters Since Redshift 1

Abstract: We investigate if the discrepancy between estimates of the total baryon mass fraction obtained from observations of the cosmic microwave background (CMB) and of galaxy groups/clusters persists when a large sample of groups is considered. To this purpose, 91 candidate X-ray groups/poor clusters at redshift 0.1 ≤ z ≤ 1 are selected from the COSMOS 2 deg^2 survey, based only on their X-ray luminosity and extent. This sample is complemented by 27 nearby clusters with a robust, analogous determination of the total and stellar mass inside R_(500). The total sample of 118 groups and clusters with z ≤ 1 spans a range in M_(500) of ~10^(13)-10^(15) M_☉. We find that the stellar mass fraction associated with galaxies at R_(500) decreases with increasing total mass as M^(–0.37 ± 0.04)_(500), independent of redshift. Estimating the total gas mass fraction from a recently derived, high-quality scaling relation, the total baryon mass fraction (f^(stars+gas)_(500) = f^(stars)_(500) + f^(gas)_(500)) is found to increase by ~25%, when M_(500) increases from = 5 × 10^(13) M_☉ to = 7 × 10^(14) M_☉. After consideration of a plausible contribution due to intracluster light (11%-22% of the total stellar mass) and gas depletion through the hierarchical assembly process (10% of the gas mass), the estimated values of the total baryon mass fraction are still lower than the latest CMB measure of the same quantity (WMAP5), at a significance level of 3.3σ for groups of = 5 × 10^(13) M_☉. The discrepancy decreases toward higher total masses, such that it is 1σ at = 7 × 10^(14) M_☉. We discuss this result in terms of nongravitational processes such as feedback and filamentary heating.

Social networks applied

Abstract: Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise. The authors investigate the following areas concerning social networks: how to exploit our unprecedented wealth of data and how we can mine social networks for purposes such as marketing campaigns; social networks as a particular form of influence, i.e.., the way that people agree on terminology and this phenomenon's implications for the way we build ontologies and the Semantic Web; social networks as something we can discover from data; the use of social network information to offer a wealth of new applications such as better recommendations for restaurants, trustworthy email senders, or (maybe) blind dates; investigation of the richness and difficulty of harvesting FOAF (friend-of-a-friend) information; and by looking at how information processing is bound to social context, the resulting ways that network topology's definition determines its outcomes.