Dan Braithwaite
University of California, Irvine
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Featured researches published by Dan Braithwaite.
Bulletin of the American Meteorological Society | 2015
Hamed Ashouri; Kuolin Hsu; Soroosh Sorooshian; Dan Braithwaite; Kenneth R. Knapp; L. Dewayne Cecil; Brian R. Nelson; Olivier P. Prat
AbstractA new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset ...
FEBS Letters | 1997
Yi-Ping Huang; Dan Braithwaite; Junetsu Ito
The nucleotide sequences of the dnaQ genes from Salmonella typhimurium and Buchnera aphidicola, encoding the ϵ‐subunit of the DNA polymerase III holoenzyme, have been determined. The Salmonella typhimurium dnaQ protein consists of 243 amino acid residues with a calculated molecular weight of 27 224. The Buchnera aphidicola dnaQ protein contains 233 amino acid residues with a calculated molecular weight of 27 170. A multiple sequence alignment of the amino acid sequences of the dnaQ proteins and those of DNA polymerase IIIs from Gram‐positive bacteria produced six homologous segments. These homologous segments contain highly conserved amino acid sequence motifs involved in catalytically important metal ion bindings (ligands 1, 2 and 3). However, metal ligand 4 is found to be altered in the 3′‐5′ exonuclease domain of the family C DNA polymerases and dnaQ proteins in Gram‐negative bacteria. From these results, we propose that the last common ancestor of the dnaQ gene of Gram‐negative bacteria and the DNA polymerase III gene (pol C gene) of Gram‐positive bacteria was a single gene containing both 3′‐5′ exonuclease and DNA polymerase domains and then the dnaQ gene separated from the polymerase gene in Gram‐negative bacteria.
Eos, Transactions American Geophysical Union | 2014
Phu Nguyen; Scott Sellars; Andrea Thorstensen; Yumeng Tao; Hamed Ashouri; Dan Braithwaite; Kuolin Hsu; Soroosh Sorooshian
Typhoon Haiyan, which struck Southeast Asia in November 2013, might be the strongest storm on record, with a 10-minute sustained wind speed of 230 kilometers per hour. In the Philippines alone, the damage was immense—the storm killed more than 6000 and completely leveled cities and towns, particularly on the island of Leyte.
Journal of Hydrometeorology | 2016
Hamed Ashouri; Phu Nguyen; Andrea Thorstensen; Kuolin Hsu; Soroosh Sorooshian; Dan Braithwaite
AbstractThis study aims to investigate the performance of Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in a rainfall–runoff modeling application over the past three decades. PERSIANN-CDR provides precipitation data at daily and 0.25° temporal and spatial resolutions from 1983 to present for the 60°S–60°N latitude band and 0°–360° longitude. The study is conducted in two phases over three test basins from the Distributed Hydrologic Model Intercomparison Project, phase 2 (DMIP2). In phase 1, a more recent period of time (2003–10) when other high-resolution satellite-based precipitation products are available is chosen. Precipitation evaluation analysis, conducted against stage IV gauge-adjusted radar data, shows that PERSIANN-CDR and TRMM Multisatellite Precipitation Analysis (TMPA) have close performances with a higher correlation coefficient for TMPA (~0.8 vs 0.75 for PERSIANN-CDR) and almost the same root-mean-square deviati...
Journal of Geophysical Research | 2016
Zhongwen Yang; Kuolin Hsu; Soroosh Sorooshian; Xinyi Xu; Dan Braithwaite; Koen Verbist
Author(s): Yang, Z; Hsu, K; Sorooshian, S; Xu, X; Braithwaite, D; Verbist, KMJ | Abstract:
Molecular Microbiology | 1998
Junetsu Ito; Dan Braithwaite
In our continuing interest in the evolution of DNA polymerase superfamilies, we have conducted homology searches for the editing 38 to 58 exonuclease domains of DNA polymerases (Huang, Braithwaite and Ito, 1997, FEBS Lett 400: 94–98). Very recently, we learned that Mian has carried out comparative sequence analyses of ribonuclease families (Mian, 1997, Nucleic Acids Res 25: 3187–3195). Her study showed that the ribonuclease D family has significant sequence homology with the 38 to 58 exonuclease domain of family A DNA polymerases of which E. coli DNA polymerase I is the prototype. Although our results are similar to those of Mian, we found an additional homology between ribonuclease and DNA polymerases that could be significant. As shown in Fig. 1, the RNase T family is closely related to dnaQ proteins from Gram-negative bacteria and DNA polymerase III proteins from Gram-positive bacteria. RNase T, reported first in 1984 (Deutscher, Marlor and Zaniewski,1984, Proc Natl Acad Sci USA, 81: 4290–4293), is one of seven distinct exoribonucleases that have been identified in eubacteria (Deutscher, 1990, Prog Nucleic Acids Res Mol Biol 39: 209–240). All of these exoribonucleases catalyse RNA hydrolysis in the 38 to 58 direction. It is known that RNase T is responsible for the removal of the 38 terminal AMP residue of tRNA during the end-turnover process (Huang and Deutscher, 1992, J Biol Chem 267: 25609–25613). The dnaQ gene specifies the e-subunit of DNA polymerase III holoenzyme that has proofreading 38 to 58 exonuclease activity (Scheuermann and Echols, 1984, Proc Natl Acad Sci USA 81: 7747–7751). Based on the X-ray crystallographic data and site-directed mutagenesis studies of the Klenow fragment of E. coli DNA polymerase I, the two metal ion catalysis mechanism has been proposed for the 38 to 58 exonuclease activity (Joyce and Steitz, 1994, Annu Rev Biochem 63: 777–822). These two metal ions are co-ordinated by four carboxylic amino acids (Beese and Steitz, 1991, EMBO J 10: 25–33) As shown in Fig. 1, all four amino acid ligands indicated by the arrows (L1–L4) are conserved in these proteins. Although structural data on the dna Q or family C DNA polymerases are currently not available, site-directed mutagenesis studies of the B. subtilis DNA polymerase III indicated that amino acid residues H and D in the 38-Exo IIIC Molecular Microbiology (1998) 27(1), 235–237
Journal of Geophysical Research | 2017
Negar Karbalaee; Kuolin Hsu; Soroosh Sorooshian; Dan Braithwaite
Author(s): Karbalaee, N; Hsu, K; Sorooshian, S; Braithwaite, D | Abstract:
Spie Newsroom | 2012
Kuolin Hsu; Soroosh Sorooshian; Xiaogang Gao; Dan Braithwaite; Amir AghaKouchak
Figure 1. The cloud image segmentation, feature extraction, classification, and rainfall estimation of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) algorithm. Geostationary satellites used include Geostationary Operational Environment Satellites (GOES) 8 and 10, Geostationary Meteorological Satellite (GMS), and Meteosat 6 and 7. Polar and near-polar instruments contributing data include Precipitation Radar (PR), Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), Special Sensor Microwave Imager (SSMI), the Advanced Microwave Scanning Radiometer (AMSR), the NASA Earth Observation System (NASA-EOS), the AMSR for EOS (AMSR/E), the EOS Aqua satellite, weather satellites 15, 16 and 17 run by the National Oceanic and Atmospheric Administration (NOAA) and weather satellites F13, F14 and F15 run by the Defense Meteorological Satellite program (DMSP). Tb-P: Brightness-temperature and rainfall rate. ANN: Artificial neural network. and dangerous than ever, as population in urban areas increases and the global climate becomes more extreme and variable. Data shows that each year there are more than a hundred million people affected by flood events with a cost of more than
Journal of Geophysical Research | 2017
Zhongwen Yang; Kuolin Hsu; Soroosh Sorooshian; Xinyi Xu; Dan Braithwaite; Yuan Zhang; Koen Verbist
100 billion.1 While accurate precipitation monitoring is a key element for improving flood forecasting, traditional means of precipitation observation, such as ground-based gauges and radars, are limited in their spatial coverage. Recent advances in satellite
Bulletin of the American Meteorological Society | 2017
Phu Nguyen; Soroosh Sorooshian; Andrea Thorstensen; Hoang Tran; Phat Huynh; Thanh T. Pham; Hamed Ashouri; Kuolin Hsu; Amir AghaKouchak; Dan Braithwaite
Author(s): Yang, Z; Hsu, K; Sorooshian, S; Xu, X; Braithwaite, D; Zhang, Y; Verbist, KMJ | Abstract: