Data assimilation experiment of precipitable water vapor observed by a hyper-dense GNSS receiver network using a nested NHM-LETKF system
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- Material Type
- 記事
- Author/Editor
- Masanori OigawaToshitaka TsudaHiromu Seko
- Publication, Distribution, etc.
- Publication Date
- 2018-05-04
- Publication Date (W3CDTF)
- 2018-05-04
- Periodical title
- EPS : Earth, Planets and Space
- No. or year of volume/issue
- 70(74)
- Volume
- 70(74)
- ISSN (Periodical Title)
- 1880-5981
- ISSN-L (Periodical Title)
- 1343-8832
- Text Language Code
- eng
- DOI
- 10.1186/s40623-018-0851-3
- Persistent ID (NDL)
- info:ndljp/pid/11125842
- Collection
- Collection (Materials For Handicapped People:1)
- Collection (particular)
- 国立国会図書館デジタルコレクション > 電子書籍・電子雑誌 > その他
- Acquisition Basis
- オンライン資料収集制度
- Date Accepted (W3CDTF)
- 2018-08-10T20:26:03+09:00
- Date Captured (W3CDTF)
- 2018-08-10
- Format (IMT)
- application/pdf
- Access Restrictions
- 国立国会図書館内限定公開
- Service for the Digitized Contents Transmission Service
- 図書館・個人送信対象外
- Availability of remote photoduplication service
- 可
- Periodical Title (URI)
- Periodical Title (Persistent ID (NDL))
- info:ndljp/pid/11067456
- Data Provider (Database)
- 国立国会図書館 : 国立国会図書館デジタルコレクション
- Summary, etc.
- We studied the assimilation of high-resolution precipitable water vapor (PWV) data derived from a hyper-dense global navigation satellite system network around Uji city, Kyoto, Japan, which had a mean inter-station distance of about 1.7 km. We focused on a heavy rainfall event that occurred on August 13–14, 2012, around Uji city. We employed a local ensemble transform Kalman filter as the data assimilation method. The inhomogeneity of the observed PWV increased on a scale of less than 10 km in advance of the actual rainfall detected by the rain gauge. Zenith wet delay data observed by the Uji network showed that the characteristic length scale of water vapor distribution during the rainfall ranged from 1.9 to 3.5 km. It is suggested that the assimilation of PWV data with high horizontal resolution (a few km) improves the forecast accuracy. We conducted the assimilation experiment of high-resolution PWV data, using both small horizontal localization radii and a conventional horizontal localization radius. We repeated the sensitivity experiment, changing the mean horizontal spacing of the PWV data from 1.7 to 8.0 km. When the horizontal spacing of assimilated PWV data was decreased from 8.0 to 3.5 km, the accuracy of the simulated hourly rainfall amount worsened in the experiment that used the conventional localization radius for the assimilation of PWV. In contrast, the accuracy of hourly rainfall amounts improved when we applied small horizontal localization radii. In the experiment that used the small horizontal localization radii, the accuracy of the hourly rainfall amount was most improved when the horizontal resolution of the assimilated PWV data was 3.5 km. The optimum spatial resolution of PWV data was related to the characteristic length scale of water vapor variability.
- DOI
- 10.1186/s40623-018-0851-3
- Access Restrictions
- インターネット公開
- Rights (production)
- © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Related Material (URI)
- References
- A High-Resolution, Precipitable Water Vapor Monitoring System Using a Dense Network of GNSS ReceiversEstimation of tropospheric delay for microwaves from surface weather dataData assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008Data Assimilation Using an Ensemble Kalman Filter TechniqueAssimilation of Precipitable Water Measurements into a Mesoscale Numerical ModelInter-technique validation of tropospheric slant total delaysThe 10,240‐member ensemble Kalman filtering with an intermediate AGCMSensing atmospheric water vapor with the global positioning systemThe benefit of GPS zenith delay assimilation to high‐resolution quantitative precipitation forecasts: a case‐study from COPS IOP 9Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman FilterSequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statisticsDistance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman FilterGPS meteorology: Remote sensing of atmospheric water vapor using the global positioning systemEfficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filterFour-Dimensional Variational Data Assimilation of Heterogeneous Mesoscale Observations for a Strong Convective CaseEstimation of Local-Scale Precipitable Water Vapor Distribution Around Each GNSS Station Using Slant Path Delay: Evaluation of a Severe Tornado Case Using High-Resolution NHMStudy of Water Vapor Variations Associated with Meso-γ Scale Convection: Comparison between GNSS and Non-Hydrostatic Model DataAssimilation of Nationwide and Global GPS PWV Data for a Heavy Rain Event on 28 July 2008 in Hokuriku and Kinki, JapanTsukuba GPS Dense Net Campaign Observation : Improvement in GPS Analysis of Slant Path Delay by Stacking One-way Postfit Phase Residuals (1.Ground-Based GPS Meteorology)A Numerical Study on a Mesoscale Convective System over a Subtropical Island with 4D-Var Assimilation of GPS Slant Total DelaysNumerical Simulation on Retrieval of Meso-γ Scale Precipitable Water Vapor Distribution with the Quasi-Zenith Satellite System (QZSS)Localizing the Error Covariance by Physical Distances within a Local Ensemble Transform Kalman Filter (LETKF)Estimation of Local-scale Precipitable Water Vapor Distribution Around Each GNSS Station Using Slant Path DelayA Multi-Scale Localization Approach to an Ensemble Kalman filterImpacts of GPS-derived Water Vapor and Radical Wind Measured by Doppler Radar on Numerical Prediction of Precipitation (3.Application of GPS Data to Atmospheric Science)The 1000-Member Ensemble Kalman Filtering with the JMA Nonhydrostatic Mesoscale Model on the K ComputerAn assimilation and forecasting experiment of the Nerima heavy rainfall with a cloud-resolving nonhydrostatic 4-dimensional variational data assimilation systemNonhydrostatic atmospheric models operational development at JMA
- Data Provider (Database)
- 国立情報学研究所 : CiNii Research
- Original Data Provider (Database)
- 学術機関リポジトリデータベース雑誌記事索引データベースCrossrefCiNii Articles科学研究費助成事業データベース科学研究費助成事業データベース
- Bibliographic ID (NDL)
- 11125842
- NAID
- 120006528345