Meteorological Data Processing

2009 ◽  
pp. 183-183-13
Author(s):  
RL Jenne ◽  
DH Joseph
Keyword(s):  
Data Processing ◽  
Meteorological Data

Model project for establishing an avalanche warning and evacuation system

1993 ◽  
Vol 18 ◽  
pp. 245-250
Author(s):  
Hideki Terada ◽  
Kazunori Fujisawa ◽  
Yoshimitsu Nakamura ◽  
Noriyuki Minami
Keyword(s):  
Data Processing ◽  
Surveillance System ◽  
Meteorological Data ◽  
The Other ◽  
Weather Data ◽  
Model Project ◽  
Standard Value ◽  
Avalanche Warning ◽  
Evacuation System ◽  
Danger Level

A project was started in 1990 to rationalize the avalanche warning and evacuation system. The major items in the model project were: (1) zoning methods for dangerous areas; (2) establishment of a surveillance system; and (3) methods of determining a warning and evacuation standard. Since the scale of a snow avalanche cannot be predicted, areas with more than 30 KN m2 of impact are identified. Equipment for conducting automatic surveillance and data processing was developed to telemeter snow and weather data. Issuing warning or evacuation advice is the focus of the discussion. Judgment is based on information on current conditions obtained from a telemeter and from residents, and on information provided by forecasts of snowfall and temperatures until the following morning. Two methods of judgment are under consideration: one is to set out a standard value based on meteorological data obtained by observation, the other is based on a discriminant of danger level.

scholarly journals From NEON Field Sites to Data Portal: A Community Resource for Surface–Atmosphere Research Comes Online

2019 ◽  
Vol 100 (11) ◽  
pp. 2305-2325 ◽  
Author(s):  
Stefan Metzger ◽  
Edward Ayres ◽  
David Durden ◽  
Christopher Florian ◽  
Robert Lee ◽  
...  
Keyword(s):  
Data Processing ◽  
Atmospheric Chemistry ◽  
Meteorological Data ◽  
The United States ◽  
Observation System ◽  
Ongoing Research ◽  
Continental Scale ◽  
Surface Atmosphere ◽  
Data Products ◽  
Data Portal

AbstractThe National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO2. EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities.

A Novel Approach in Improving I/O Performance of Small Meteorological Files on HDFS

2011 ◽  
Vol 117-119 ◽  
pp. 1759-1765 ◽  
Author(s):  
Sheng Jun Xue ◽  
Wu Bin Pan ◽  
Wei Fang
Keyword(s):  
Cloud Computing ◽  
Data Processing ◽  
Meteorological Data ◽  
Storage Space ◽  
Master Node ◽  
Reading And Writing ◽  
Computing Platform ◽  
Novel Approach ◽  
Computing Performance ◽  
Cloud Computing Platform

With the cloud computing is becoming mature, many of its characteristics for meteorological data processing is extremely important. Since HDFS is designed for reading and writing large files, it’s difficult to be taken advantage of small meteorological files. In this paper, an improved approach on HDFS is proposed for small meteorological files, small files are to be merged, indexed, and blocks are compressed, the pressure of memory on master node occupied by metadata is relieved, the speed of reading and writing small files is increased, read speed is increased by 50%, and write speed is up to 3-4 times of the original, saving about 2/3 of storage space and computing performance has also been improved. Thus, meteorological data processing can make use of cloud computing platform more closely.

scholarly journals Model project for establishing an avalanche warning and evacuation system

1993 ◽  
Vol 18 ◽  
pp. 245-250
Author(s):  
Hideki Terada ◽  
Kazunori Fujisawa ◽  
Yoshimitsu Nakamura ◽  
Noriyuki Minami
Keyword(s):  
Data Processing ◽  
Surveillance System ◽  
Meteorological Data ◽  
The Other ◽  
Weather Data ◽  
Model Project ◽  
Standard Value ◽  
Avalanche Warning ◽  
Evacuation System ◽  
Danger Level

A project was started in 1990 to rationalize the avalanche warning and evacuation system. The major items in the model project were: (1) zoning methods for dangerous areas; (2) establishment of a surveillance system; and (3) methods of determining a warning and evacuation standard. Since the scale of a snow avalanche cannot be predicted, areas with more than 30 KN m2 of impact are identified. Equipment for conducting automatic surveillance and data processing was developed to telemeter snow and weather data. Issuing warning or evacuation advice is the focus of the discussion. Judgment is based on information on current conditions obtained from a telemeter and from residents, and on information provided by forecasts of snowfall and temperatures until the following morning. Two methods of judgment are under consideration: one is to set out a standard value based on meteorological data obtained by observation, the other is based on a discriminant of danger level.

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