Abstract A new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily 0.25° rainfall estimates the latitude band 60°S–60°N period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR aimed at addressing need consistent, long-term, high-resolution, global studying changes...

PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed the estimation of rainfall geosynchronous satellite longwave infared imagery (GOES-IR) at a resolution 0.25° × every half-hour. The accuracy product is improved by adaptively adjusting network parameters instantaneous rain-rate estimates Tropical Rainfall Measurement Mission (TRMM) microwave imager (TMI 2A12), and random errors are further reduced...

This is an update of earlier compilation and alignment DNA polymerase sequences (Ito Braithwaite, 1991). As in the previous compilation, we attempted to compile complete sequences, facilitate identification conserved viable regions polymerases (1). includes, for first time, three from Archaea (2); two new members Family A polymerases; 19 B polymerases. In addition, included nucleases that have related amino acid E.coli I, sequence HI (e-subunit) was aligned C due its homology Bacillus...

Abstract The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), PERSIANN-Climate Record (CDR). These have potential widespread use various researchers, professionals including engineers,...

Abstract Satellite-derived high-resolution precipitation products (HRPP) have been developed to address the needs of user community and are now available with 0.25° × (or less) subdaily resolutions. This paper evaluates a number commonly satellite-derived HRPPs covering northwest Europe over 6-yr period. Precipitation include Tropical Rainfall Measuring Mission (TRMM) Multisatellite Analysis (TMPA), Climate Prediction Center (CPC) morphing (CMORPH) technique, CPC merged microwave Naval...

Abstract Accurate long-term global precipitation estimates, especially for heavy rates, at fine spatial and temporal resolutions is vital a wide variety of climatological studies. Most the available operational estimation datasets provide either high resolution with short-term duration estimates or lower estimates. Furthermore, previous research has stressed that most satellite-based products show poor performance capturing extreme events resolution. Therefore, there need product reliably...

Abstract Accurate and timely precipitation estimates are critical for monitoring forecasting natural disasters such as floods. Despite having high-resolution satellite information, estimation from remotely sensed data still suffers methodological limitations. State-of-the-art deep learning algorithms, renowned their skill in accurate patterns within large complex datasets, appear well suited to the task of estimation, given ample amount data. In this study, effectiveness applying...

Abstract With high spatial‐temporal resolution, Satellite‐based Precipitation Estimates (SPE) are becoming valuable alternative rainfall data for hydrologic and climatic studies but subject to considerable uncertainty. Effective merging of SPE ground‐based gauge measurements may help improve precipitation estimation in both better resolution accuracy. In this study, a framework satellite is developed based on three steps, including bias adjustment, observation gridding, merging, with the...

Abstract This 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 past three decades. PERSIANN-CDR provides precipitation data at daily and 0.25° temporal spatial resolutions 1983 present for 60°S–60°N latitude band 0°–360° longitude. The is conducted two phases test basins Distributed Hydrologic Model Intercomparison Project,...

Abstract Satellite‐based precipitation estimates (SPEs) are promising alternative data for climatic and hydrological applications, especially regions where ground‐based observations limited. However, existing satellite‐based rainfall estimations subject to systematic biases. This study aims adjust the biases in Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Cloud Classification System (PERSIANN‐CCS) over Chile, gauge as reference. A novel bias...

Abstract Little dispute surrounds the observed global temperature changes over past decades. As a result, there is widespread agreement that corresponding response in hydrologic cycle must exist. However, exactly how such manifests remains unsettled. Here we use unique recently developed long-term satellite-based record to assess precipitation across spatial scales. We show warm climate regions exhibit decreasing trends, while arid and polar increasing trends. At country scale, seems have...

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 Philippines alone, damage was immense—the killed more than 6000 and completely leveled cities towns, particularly island Leyte.

Abstract This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information Artificial Neural Networks‐Cloud Classification System (PERSIANN‐CCS). The PERSIANN‐CCS algorithm collects information infrared images to estimate rainfall. is one the algorithms used in Integrated Multisatellite Retrievals GPM (Global Mission) time period PMW estimations are limited or not available. Continued...

DNA polymerases have been classified, based on their primary sequence similarities, into two major groups: family A and B (1).Family polymerases, represented by E. coli polymerase I (2), includes Streptococcus pneumoniae (S.p. pol I)(3), Thermus aquaticus (Taq I)(4), the of bacteriophages T5 (5), T7 (6), Spo2 (7,8).All these are prokaryotic sensitive to dideoxynucleotide inhibitor, but resistant aphidicolin.Family eukaryotic replicative alpha delta, aphidicolin relatively...

Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based estimations are a promising alternative to rain gauges providing homogeneous information. Most satellite-based products suffer from short-term data records, which make them unsuitable various hydrological applications. However, Precipitation Estimation Remotely Sensed Information using Artificial Neural Networks-Climate Data Record...

Abstract. In the framework of Soil Moisture and Ocean Salinity (SMOS) Calibration/Validation (Cal/Val) activities, this study addresses use PERSIANN-CCS1database in hydrological applications to accurately simulate a whole SMOS pixel by representing spatial temporal heterogeneity soil moisture fields over wide area (50×50 km2). The focuses on Valencia Anchor Station (VAS) experimental site, Spain, which is one main Cal/Val sites Europe. A faithful representation distribution at scale km2)...

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The nucleotide sequences of the dnaQ genes from Salmonella typhimurium and Buchnera aphidicola, encoding epsilon-subunit DNA polymerase III holoenzyme, have been determined. protein consists 243 amino acid residues with a calculated molecular weight 27224. aphidicola contains 233 27170. A multiple sequence alignment proteins those IIIs Gram-positive bacteria produced six homologous segments. These segments contain highly conserved motifs involved in catalytically important metal ion bindings...