2,668 research outputs found
Jennifer Jacobs Professor of Civil and Environmental Engineering (CEPS) travels to England
The UNHâs Center for International Education grant provided financial support to accept a seminar invitation and to meet with colleagues at Nottingham University and Newcastle University, England about climate change and infrastructure. Upon my arrival in Birmingham on November 16, 2015 I was met by Dr. Jo Daniel at the Birmingham Airport. Jo is a colleague in UNHâs Civil and Environmental Engineering, and my research partner in infrastructure and climate research. She was on a Fulbright at Nottingham University since August. From that point forward, we began a whirlwind week of travel, meetings and many opportunities for cultural events
Jets and high hadrons in dense matter: recent results from STAR
We review recent measurements of high transverse momentum (high ) hadron
production in nuclear collisions by the STAR Collaboration at RHIC. The
previously observed suppression in central Au+Au collisions has been extended
to much higher . New measurements from d+Au collisions are presented which
help disentangle the mechanisms responsible for the suppression. Inclusive
single hadron spectra are enhanced in d+Au relative to p+p, while two-particle
azimuthal distributions are observed to be similar in p+p, d+Au and peripheral
Au+Au collisions. The large suppression of inclusive hadron production and
absence of the away-side jet-like correlations in central Au+Au collisions are
shown to be due to interactions of the jets with the very dense medium produced
in these collisions.Comment: 6 pages, 5 figures, joint proceedings for the CIPANP '03 Conferenc
Temporal variability corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) surface soil moisture: case study in Little River Region, Georgia, U. S.
Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-E\u27s limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R-2 values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques
Temporal Variability Corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) Surface Soil Moisture: Case Study in Little River Region, Georgia, U.S.
Statistical correction methods, the Cumulative Distribution Function (CDF) matching technique and Regional Statistics Method (RSM) are applied to adjust the limited temporal variability of Advanced Microwave Scanning Radiometer E (AMSR-E) data using the Common Land Model (CLM). The temporal variability adjustment between CLM and AMSR-E data was conducted for annual and seasonal periods for 2003 in the Little River region, GA. The results showed that the statistical correction techniques improved AMSR-Eâs limited temporal variability as compared to ground-based measurements. The regression slope and intercept improved from 0.210 and 0.112 up to 0.971 and -0.005 for the non-growing season. The R2 values also modestly improved. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) products were able to identify periods having an attenuated microwave brightness signal that are not likely to benefit from these statistical correction techniques
Remote sensing observatory validation of surface soil moisture using Advanced Microwave Scanning Radiometer E, Common Land Model, and ground based data: Case study in SMEX03 Little River Region, Georgia, U.S.
Optimal soil moisture estimation may be characterized by intercomparisons among remotely sensed measurements, groundâbased measurements, and land surface models. In this study, we compared soil moisture from Advanced Microwave Scanning Radiometer E (AMSRâE), groundâbased measurements, and a SoilâVegetationâAtmosphere Transfer (SVAT) model for the Soil Moisture Experiments in 2003 (SMEX03) Little River region, Georgia. The Common Land Model (CLM) reasonably replicated soil moisture patterns in dry down and wetting after rainfall though it had modest wet biases (0.001â0.054 m3/m3) as compared to AMSRâE and ground data. While the AMSRâE average soil moisture agreed well with the other data sources, it had extremely low temporal variability, especially during the growing season from May to October. The comparison results showed that highest mean absolute error (MAE) and root mean squared error (RMSE) were 0.054 and 0.059 m3/m3 for short and long periods, respectively. Even if CLM and AMSRâE had complementary strengths, low MAE (0.018â0.054 m3/m3) and RMSE (0.023â0.059 m3/m3) soil moisture errors for CLM and soil moisture low biases (0.003â0.031 m3/m3) for AMSRâE, care should be taken prior to employing AMSRâE retrieved soil moisture products directly for hydrological application due to its failure to replicate temporal variability. AMSRâE error characteristics identified in this study should be used to guide enhancement of retrieval algorithms and improve satellite observations for hydrological sciences
Assessment of clear and cloudy sky parameterizations for daily downwelling longwave radiation over different land surfaces in Florida, USA
Clear sky downwelling longwave radiation (Rldc) and cloudy sky downwelling longwave radiation (Rld) formulas were tested across eleven sites in Florida. The Brunt equation, using air vapor pressure and temperature measurements, provides the best Rldc estimates with a root mean square error of less than around 12 Wmâ2 across all sites. The Crawford and Duchon\u27s cloudiness factor with Brunt equation is recommended for Rld calculations. This combined approach requires no local calibration and estimates Rld with a root mean square error of less than around 13 Wmâ2 and squared correlation coefficients that typically exceed 0.9
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