Corsock Automatic Weather Station

Weather ◽  
2003 ◽  
Vol 58 (8) ◽  
pp. 291-294
Author(s):  
G. A. J. Bowles
Keyword(s):  
Weather Station ◽  
Automatic Weather Station

scholarly journals Uncertainty Evaluation of Wind Speed Sensor in Automatic Weather Station

2021 ◽  
Vol 768 (1) ◽  
pp. 012008
Author(s):  
Zhen Yang ◽  
Husheng Zhang ◽  
Qiang Wang ◽  
Cuicui Li ◽  
Wenlong Xu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Uncertainty Evaluation ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Speed Sensor

Ongoing Experience with Ohio’s Automatic Weather Station Network

1993 ◽  
Vol 9 (5) ◽  
pp. 437-441 ◽  
Author(s):  
D. L. Elwell ◽  
J. C. Klink ◽  
J. R. Holman ◽  
M. J. Sciarini
Keyword(s):  
Weather Station ◽  
Automatic Weather Station ◽  
Station Network

scholarly journals Snow surface height variations on the Antarctic ice sheet in Princess Elizabeth Land, Antarctica: 1 year of data from an automatic weather station

2004 ◽  
Vol 39 ◽  
pp. 181-187 ◽  
Author(s):  
Qin Dahe ◽  
Xiao Cunde ◽  
Ian Allison ◽  
Bian Lingen ◽  
Rod Stephenson ◽  
...  
Keyword(s):  
Ice Sheet ◽  
Cloud Amount ◽  
Snow Accumulation ◽  
Strong Wind ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Antarctic Ice Sheet ◽  
Total Cloud Amount ◽  
Surface Snow ◽  
The Antarctic

AbstractThe net surface snow accumulation on the Antarctic ice sheet is determined by a combination of precipitation, sublimation and wind redistribution. We present a 1 year record of hourly snow-height measurements that shows its seasonal variability. The measurements were made with an ultrasonic sensor mounted on an automatic weather station (AWS) installed at LGB69, Princess Elizabeth Land, Antarctica (70.835˚S, 77.075˚E; 1850 ma.s.l.). The average accumulation at this site is approximately 0.70 m snow a–1. Throughout the winter, between April and September, there was little change in surface snow height. The strongest accumulation occurred during the period October–March, with four episodic increases occurring during 2002. These episodic events coincided with obvious humidity ‘pulses’ and decreases of incoming solar radiation as recorded by the AWS. Observations of the total cloud amount at Davis station, 160 km north-northeast of LGB69, showed good correlation with major accumulation events recorded at LGB69. There was an obvious anticorrelation between the lowest cloud height at Davis and the daily accumulation rate at LGB69. Although there was no correlation over the total year between wind speed and accumulation at LGB69, large individual accumulation events are associated with episodes of strong wind. Strong accumulation events at LGB69 are associated with major storms in the region and inland transport of moist air masses from the coast.

scholarly journals APLIKASI SATELIT DALAM MENGESTIMASI EVAPORASI DI DAERAH WADUK (STUDI KASUS: WADUK SAGULING-JAWA BARAT)

2018 ◽  
Vol 19 (2) ◽  
pp. 61
Author(s):  
Rusmawan Suwarman ◽  
Dinda Mahardita ◽  
I Dewa Gede A. Junnaedhi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Input Data ◽  
Input Parameter ◽  
Weather Station ◽  
Automatic Weather Station

Estimasi evaporasi di daerah waduk menggunakan metode empiris dengan input data satelit dilakukan untuk mengatasi masalah ketersediaan data meteorologi dari observasi permukaan. Data satelit berupa Land Surface Temperature dari satelit Himawari dan profil atmosfer dari satelit MODIS digunakan untuk memperoleh informasi parameter temperatur, kelembapan relatif dan radiasi matahari untuk mengestimasi besaran evaporasi di daerah waduk. Metode empiris yang digunakan antara lain adalah Blaney-Criddle, Kharuffa, Hargreaves, Schendel dan Schendel yang dimodifikasi (Modified Schendel). Hasil estimasi evaporasi dibandingkan terhadap evaporasi acuan yang dihitung menggunakan metode kombinasi (Penman) dengan input parameter meteorologi hasil observasi. Observasi dilakukan menggunakan Automatic Weather Station di dua titik pengamatan di Waduk Saguling. Hasil penelitian menunjukkan estimasi evaporasi waduk dengan input data satelit dapat dilakukan dengan metode yang ada namun diperlukan modifikasi. Metode estimasi evaporasi waduk yang terbaik adalah Modified Schendel, namun belum bisa menunjukkan variasi spasial yang sesuai observasi. Penggunaan regresi Linier Berganda dan menambahkan parameter radiasi matahari pada Modified Schendel, didapatkan suatu persamaan yang baik secara statistik dan dapat menunjukkan variasi spasial evaporasi di Waduk Saguling yang sesuai observasi.

scholarly journals Evaluasi Pengukuran Curah Hujan Antara Hasil Pengukuran Permukaan (AWS, HELLMAN, OBS) dan Hasil Estimasi (Citra Satelit =GSMaP) Di Stasiun Klimatologi Mlati Tahun 2018

2020 ◽  
Vol 4 (1) ◽  
pp. 1-7
Author(s):  
Agusta Kurniawan
Keyword(s):  
Weather Station ◽  
Automatic Weather Station

Telah dilakukan perbandingan empat pengukuran curah hujan di Stasiun Klimatologi Mlati, Yogyakarta pada tahun 2018. Empat pengukuran curah hujan antara lain tiga hasil pengukuran permukaan (Automatic Weather Station, Penakar Hujan Hellman dan Penakar Hujan OBS=Ombrometer) dan hasil estimasi dari citra satelit (GsMap). Data mentah diolah menjadi aggregat waktu (rata-rata perjam, rata-rata harian dan bulanan).  Evaluasi pengukuran berdasarkan ketersediaaan data yang ada setiap bulan dan nilai korelasi antar masing-masing peralatan pengukuran. Hasil penelitian menunjukkan bahwa ada perbedaan pengukuran nilai curah hujan antara hasil pengukuran penakar hujan di permukaan bumi (OBS, Hellman dan AWS) dengan hasil estimasi (GSMaP) di Stasiun Klimatologi Mlati pada 2018. Nilai Korelasi Pearson data curah hujan per jam antara Hellman, AWS dan GSMaP menunjukkan nilai korelasi kecil disebabkan karena banyak data hilang dan ketidaksesuaian waktu, sedangkan korelasi Pearson data curah hujan per hari antara OBS, Hellman, AWS dan GSMap, nilai tertinggi adalah antara data OBS dengan Hellman, sebesar 0,62, sedangkan nilai pengukuran curah hujan akumulasi tahunan pada tahun 2018 paling besar adalah penakar hujan OBS, sebesar 2488,3 mm/tahun.

scholarly journals PROMICE automatic weather station data

2021 ◽  
Author(s):  
Robert S. Fausto ◽  
Dirk van As ◽  
Kenneth D. Mankoff ◽  
Baptiste Vandecrux ◽  
Michele Citterio ◽  
...  
Keyword(s):  
Surface Energy Balance ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Atmospheric Conditions ◽  
Production Chain ◽  
Near Surface ◽  
Surface Mass ◽  
Future Assessment

Abstract. The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheetproperties since 2007. Currently the PROMICE automatic weather station network includes 25 instrumented sites in Greenland.Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate is important for reliablepresent and future assessment of changes to the Greenland ice sheet. Here we present the PROMICE vision, methodology,and each link in the production chain for obtaining and sharing quality-checked data. In this paper we mainly focus on thecritical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic webbaseddatabase of known data quality issues is associated with the data products at (https://github.com/GEUS-PROMICE/PROMICE-AWS-data-issues/). As part of the living data option, the datasets presented and described here are available atDOI: 10.22008/promice/data/aws, https://doi.org/10.22008/promice/data/aws (Fausto and van As, 2019).

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