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Institution

University of Maryland, Baltimore County

EducationBaltimore, Maryland, United States
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.


Papers
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Journal ArticleDOI
TL;DR: This article found that comments about the content of the storybook were the most common type of utterance during reading interactions and the affective quality of the reading interaction was the most powerful predictor of children's motivations for reading.

310 citations

Journal ArticleDOI
TL;DR: The thought processes, advances in synthetic chemistry and lessons learned from antiviral testing that led to a few molecules being moved forward to eventual approval for human therapies, while others were discarded.

310 citations

Journal ArticleDOI
TL;DR: In this paper, a cloud masking algorithm based on the spatial variability of reflectances at the top of the atmosphere in visible wavelengths was developed for the retrieval of aerosol properties by MODIS.
Abstract: ] A cloud masking algorithm based on the spatial variability ofreflectances at the top of the atmosphere in visible wavelengths wasdeveloped for the retrieval of aerosol properties by MODIS. It isshown that the spatial pattern of cloud reflectance as observed fromspace, is very different from that of aerosols. Clouds show a veryhigh spatial variability in the scale of hundred meters to fewkilometers, whereas aerosols in general are very homogeneous. Theconcept of spatial variability of reflectances at the top of theatmosphere is mainly applicable over the ocean where the surfacebackground is sufficiently homogeneous for the separation betweenaerosols and clouds. Aerosol retrievals require a particular cloudmasking approach since a conservative mask will screen out strongaerosol episodes and a less conservative mask could allow forcloud contamination that tremendously affect the retrieved aerosoloptical properties (e.g. aerosol optical depth and effective radii). Adetailed study on the effect of cloud contamination on aerosolretrievals is performed and parameters are established determiningthe threshold value for the MODIS aerosol cloud mask (3X3-STD)over the ocean. The 3X3-STD algorithm discussed in this paper isthe operational cloud mask used for MODIS aerosol retrievals overthe ocean. I

309 citations

Journal ArticleDOI
TL;DR: The second phase of the Global Land-Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture as mentioned in this paper.
Abstract: The second phase of the Global Land–Atmosphere Coupling Experiment (GLACE-2) is a multi-institutional numerical modeling experiment focused on quantifying, for boreal summer, the subseasonal (out to two months) forecast skill for precipitation and air temperature that can be derived from the realistic initialization of land surface states, notably soil moisture. An overview of the experiment and model behavior at the global scale is described here, along with a determination and characterization of multimodel “consensus” skill. The models show modest but significant skill in predicting air temperatures, especially where the rain gauge network is dense. Given that precipitation is the chief driver of soil moisture, and thereby assuming that rain gauge density is a reasonable proxy for the adequacy of the observational network contributing to soil moisture initialization, this result indeed highlights the potential contribution of enhanced observations to prediction. Land-derived precipitation forec...

309 citations


Authors

Showing all 8862 results

NameH-indexPapersCitations
Robert C. Gallo14582568212
Paul T. Costa13340688454
Igor V. Moskalenko13254258182
James Chiang12930860268
Alex K.-Y. Jen12892161811
Alan R. Shuldiner12055771737
Richard N. Zare120120167880
Vince D. Calhoun117123462205
Rita R. Colwell11578155229
Kendall N. Houk11299754877
Elliot K. Fishman112133549298
Yoram J. Kaufman11126359238
Paulo Artaxo10745444346
Braxton D. Mitchell10255849599
Sushil Jajodia10166435556
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202371
2022165
20211,065
20201,091
2019989
2018929