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Rapid linear algorithm retrieves meteorological parameters from microwave radiometric data. Versatile 2-step, multiple-linear-regression algorithm examines all possible subsets of available channels and selects optimum subset for retrieving given geophysical or meteorological parameter.
The data from the MAPS radiometer launched on board the first shuttle flight was analyzed. The meteorological data relevant to the MAPS measurements was also analyzed so that the in situ calibration measurements and any anomalous features within the data set can be evaluated in the context of the atmospheric circulation during the period of observation. The three dimensioned isentropic analysis scheme was used as the main analytical tool. A graphical output of the N and delta channel signals, black body reference temperatures, reduced CO concentrations and ground tracks for orbits 15 and 16 were provided. The insentropic trajectory methods was emphasized because it is a basic requirement that the analysis be extended to incorporate daily data within the tropics.
This data package contains a QA/QC-ed meteorological time series dataset from 1985 to 2018, including relative humidity, temperature, solar radiation, rainfall, wind speed, and barometric pressure at Barro Colorado Island (PA-BCI) in Panama. The data were collected at the elevations of 42 m and 48 m at the BCI Lutz station, except for rainfall that was collected at the BCI Clearing station at the elevation of 1 m. Individual time series datasets were collected using electro-mechanical (electronic sensors, data loggers, chart recorders, etc.). The rainfall dataset was collected using the tipping bucket method, and the data could have been underestimated by 13-17% (likely the large values). The data is included as a single CSV file.
Meteorological and geomorphological data acquired by Nimbus satellite during July, 1966
The analysis scheme and meteorological applications of the GEMPAK data analysis and display software system developed by NASA are described. The program was devised to permit objective, versatile, and practical analysis of satellite meteorological data using a minicomputer and a display system with graphics capability. A data area can be selected within the data file for the globe, and data-sparse regions can be avoided. Distances between observations and the nearest observation points are calculated in order to avoid errors when determining synoptic weather conditions. The Barnes (1973) successive correction method is employed to restore the amplitude of small yet resolvable wavelengths suppressed in an initial filtering pass. The rms deviation is then calculated in relation to available measured data. Examples are provided of treatment of VISSR data from the GOES satellite and a study of the impact of incorrect cloud height data on synoptic weather field analysis.
Documentation of meteorological and geomorphological data acquired from the Nimbus 2 meteorological satellite is provided. The observation period extends from 1 October through 15 November 1966. In addition, supplemental and amended data for previous volumes are included.
Solar and other renewable energy systems are optimized using geophysical parameters describing information about the solar resource and meteorological environments and how those environments may be changing in time. The Prediction of Worldwide Renewable Energy Resource (POWER) team at the NASA LaRC Research Center deployed its first version of the revitalized POWER geophysical parameter website that employs Esri Geographic Information System (GIS) tools. This web application provides access to both time series and climatological data sets spanning from a few days behind real-time back to the early 1980’s with a spatial resolution of 0.5 x 0.5 degree, thus extending over 30 years. The GIS tools enable, generate and store climatological averages using spatial queries and calculations in a spatial database resulting in greater accessibility to government agencies, industry and individuals. There is an API that provides data through a URL coding and can also invoked inside user software packages. Output data formats now include ASCII, CSV, geoTiff, JSON and .netCDF. Since May 2018, over 10.4 TB of data have been delivered to fulfill over 110 million data requests from 240,000 unique user IPs (https://power.larc.nasa.gov). This presentation provides an overview of this project and the current version of the new POWER web capabilities through to the end usage. Surface solar parameters are adapted from both the NASA/GEWEX (Global and Energy Water Cycle Exchange) SRB (Surface Radiation Budget) project and the CERES (Clouds and Earth’s Radiant Energy System) data products. The meteorological parameters are adapted from NASA’s MERRA-2 (Modern Era Retrospective-analysis for Research and Applications). We review uncertainty of various basic parameters using surface measurements. After the introduction, we specifically discuss various clusters of parameters for equator pointing tilted surfaces that provide for the estimation of optimal tilt angle by month and year. The discussion will emphasize a few examples of how solar and building engineers are using the data products. We will then preview the capabilities of Version 2 featuring examples of hourly solar and meteorological data products, customized user reports and expanded web mapping services from CERES data products. The web services provide a unique, expandable resource for renewable energy systems engineers to design, evaluate and optimize to environments worldwide.
Reliable, long term offshore atmospheric data is critical to development of the US offshore wind industry. There exists significant lack of meteorological, oceanographic, and geological data at potential US offshore sites1. Assessment of wind resources at heights in the range of 25-200m is needed to understand and characterize offshore wind turbine performance. Data from the US Department of Energy owned WindSentinel buoy from two US offshore sites and one European site is analyzed. Low Level Jet (LLJ) phenomena and its potential impact on the performance of an offshore wind turbine is investigated.
This paper presents results from AMSR-E validation activities during a collaborative international cruise onboard the RV Aurora Australis to the East Antarctic sea ice zone (64-65 deg.S, 110-120 deg.E) in the early Austral spring of 2003. The validation strategy entailed an IS-day survey of the statistical characteristics of sea ice and snowcover over a Lagrangian grid 100 x 50 km in size (demarcated by 9 drifting ice beacons) i.e. at a scale representative of Ah4SR pixels. Ice conditions ranged h m consolidated first-year ice to a large polynya offshore from Casey Base. Data sets collected include: snow depth and snow-ice interface temperatures on 24 (?) randomly-selected floes in grid cells within a 10 x 50 km area (using helicopters); detailed snow and ice measurements at 13 dedicated ice stations, one of which lasted for 4 days; time-series measurements of snow temperature and thickness at selected sites; 8 aerial photography and thermal-IR radiometer flights; other satellite products (SAR, AVHRR, MODIS, MISR, ASTER and Envisat MERIS); ice drift data; and ancillary meteorological (ship-based, meteorological buoys, twice-daily radiosondes). These data are applied to a validation of standard AMSR-E ice concentration, snowcover thickness and ice-temperature products. In addition, a validation is carried out of ice-surface skin temperature products h m the NOAA AVHRR and EOS MODIS datasets.
5 minute averages of surface meteorology and flux data collected by the NCAR/EOL Integrated Surface Flux System (ISFS) at 15 sites during the RELAMPAGO field campaign. These data have been quality-controlled and are available in NetCDF format. Winds reported by the sonic anemometers have been tilt corrected and rotated into geographic coordinates. Data providence, citation, and acknowledgement This ARM data set is a copy of v2.0 of the NCAR data set obtained on 6-Jun-2024 from https://doi.org/10.26023/ZPHJ-JW9W-2B0Y. The citation for the original data source is: NCAR/EOL In-situ Sensing Facility, Oncley, S. 2021. NCAR/EOL ISFS Surface Meteorology and Flux Products, 5-minute. Version 2.0. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/ZPHJ-JW9W-2B0Y Accessed 06 Jun 2024. In addition to the citation reference and any other acknowledgements, please acknowledge NCAR/EOL in your publications with text such as: "Data provided by NCAR/EOL under the sponsorship of the National Science Foundation. https://data.eol.ucar.edu/"
Tropical peatlands are among the most carbon-dense ecosystems on Earth, and their water storage dynamics strongly control these carbon stocks. The hydrological functioning of tropical peatlands differs from that of northern peatlands, which has not yet been accounted for in global land surface models (LSMs). Here, we integrated tropical peat-specific hydrology modules into a global LSM for the first time, by utilizing the peatland-specific model structure adaptation (PEATCLSM) of the NASA Catchment Land Surface Model (CLSM). We developed literature-based parameter sets for natural (PEATCLSM(Trop,Nat)) and drained (PEATCLSM(Trop,Drain)) tropical peatlands. Simulations with PEATCLSM(Trop,Nat) were compared against those with the default CLSM version and the northern version of PEATCLSM (PEATCLSM(North,Nat)) with tropical vegetation input. All simulations were forced with global meteorological reanalysis input data for the major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia. The evaluation against a unique and extensive data set of in situ water level and eddy covariance-derived evapotranspiration showed an overall improvement in bias and correlation compared to the default CLSM version. Over Southeast Asia, an additional simulation with PEATCLSM(Trop,Drain) was run to address the large fraction of drained tropical peatlands in this region. PEATCLSM(Trop,Drain) outperformed CLSM, PEATCLSM(North,Nat) and PEATCLSM(Trop,Nat) over drained sites. Despite the overall improvements of PEATCLSM(Trop,Nat) over CLSM, there are strong differences in performance between the three study regions. We attribute these performance differences to regional differences in accuracy of meteorological forcing data, and differences in peatland hydrologic response that are not yet captured by our model.
A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A variety of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology, precipitation, and radiation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.
A diversity of instruments are used to measure various quantities related to meteorology and precipitation near the Earth’s surface. Typically, a standard suite of instruments is deployed to monitor meteorological state variables.