A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction

Yang, Shun; Huang, Zih Mao; Huang, Ching Yuang; Tsai, Chih-Chien; Yeh, Teng Kuang

doi:10.1175/mwr-d-18-0418.1

Cited by 27 publications

(13 citation statements)

References 52 publications

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“…Ground‐based Global Navigation Satellite System (GNSS) meteorological data, which are by‐products of geodetic positioning, have been widely used for weather applications such as data assimilation in numerical weather prediction (NWP) systems (e.g., Mahfouf et al ., 2015; Mile et al ., 2019) or improvement of quantitative precipitation forecasts associated with heavy precipitating events (e.g., Yang et al ., 2020). The primary weather observation that can be derived from GNSS sensors is the tropospheric humidity content, also known as Integrated Water Vapour (IWV; Bevis et al ., 1992).…”

Section: Introductionmentioning

confidence: 99%

Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground‐based GNSS and fifth‐generation ECMWF reanalysis (ERA5) data

Lees

Bousquet

Roy

et al. 2020

Quart J Royal Meteoro Soc

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The spatial and temporal distribution of tropospheric water vapour in the South Indian Ocean (SIO) basin is investigated using observations collected from twelve GNSS stations spanning the basin. The comparison of GNSS‐derived integrated water vapour (IWV) content against radiosoundings and satellite‐borne microwave radiometer data shows good agreement, with global uncertainties ranging from 0.76 to 1.17 kg·m−2, depending on GNSS station locations. GNSS‐derived IWV contents show a strong seasonal cycle, characterized by higher water vapour content during the austral summer, when the InterTropical Convergence Zone (ITCZ) is located in the Southern Hemisphere. At the seasonal time‐scale, the observed annual IWV amplitude varies from 10 to 15 kg·m−2 near the Equator to 20 to 30 kg·m−2 in the Subtropics. The GNSS IWV signature of the Madden–Julian Oscillation (MJO) is hardly noticeable during the Austral winter, but varies from 1–2 to 4 kg·m−2 between the active and suppressed phases of the MJO during austral summer. At diurnal time‐scales, GNSS IWV shows larger diurnal amplitude over land (2–3 kg·m−2) than over open ocean (1–2 kg·m−2), with highest amplitudes (up to 7 kg·m−2) observed over large and mountainous islands. The phase analysis of the IWV diurnal cycle indicates that the diurnal maximum (minimum) is reached in the late afternoon/evening (morning) over land, at night (mid‐day) over ocean and in the early morning (late afternoon) at coastal locations. A comparison of GNSS‐derived IWV contents against fifth‐generation European Centre for Medium‐range Weather Forecasts (ECMWF) Reanalysis (ERA5) data shows that ERA5 generally correctly reproduces the IWV content at both seasonal, intra‐seasonal and diurnal time‐scales, although some discrepancies can be noticed over small islands characterised by steep orography. The signature of the MJO in ERA5 also shows good agreement with GNSS observations at most studied locations.

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Section: Introductionmentioning

confidence: 99%

Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground‐based GNSS and fifth‐generation ECMWF reanalysis (ERA5) data

Lees

Bousquet

Roy

et al. 2020

Quart J Royal Meteoro Soc

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“…The assimilation of GNSS products into NWP models can currently be done in terms of total delay in the zenith direction above a receiver. Their positive impact on the prediction of convective storms localization and timing has been proved in many research studies (Oigawa et al, 2018;Lagasio et al, 2019;Mascitelli et al, 2019;Hdidou et al, 2020;Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 93%

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

et al. 2020

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Atmospheric Phase Screens (APSs) derived from Interferometric Synthetic Aperture Radar (InSAR) observations contain the difference between the tropospheric watervapor-induced delay of two acquisition epochs, i.e., the slave and the master (or reference) epochs. Using estimates of the atmospheric state coming from independent sources, for example numerical models and/or Global Navigation Satellite System (GNSS) observations, the APSs can be transformed into absolute maps of Tropospheric Delay (Zenith Total Delay or ZTD), related to the columnar atmospheric water vapor content. In this work, a systematic comparison between various APS and ZTD products aims to determine a convenient strategy to go from APSs to InSAR-derived absolute ZTD maps, highlighting the uncertainties and approximations introduced in the entire processing. The main problem to solve is the evaluation of a sufficiently accurate highresolution master delay map. Different sources of data and two different approaches to derive the master are validated and compared to define the most suitable strategy for meteorological applications. Maps of ZTD obtained by an iterative interpolation of a global atmospheric circulation model values results in being more suited than those derived from the assimilation of GNSS observations into an NWP model. A time average approach to estimate the master map is more robust than the single epoch approach with respect to the choice of the master epoch. Still, the choice of a proper master epoch in the InSAR processing chain as well as that of the maps to be averaged crucially result in the estimate of the master.

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“…The terrain is higher in the west and lower in the east, and the ground elevation is mostly between 4.5 and 7 m. The river network is dense. The whole area lies in a temperate monsoon climate zone with abundant sunshine and average annual precipitation of 937.60 mm [30][31][32]. The soil texture of the topsoil (0-15 cm) in Shuyang County is mainly silt, and the contents of clay and sand are relatively small.…”

Section: Study Areamentioning

confidence: 99%

Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

Yang

Shang

Xiao

et al. 2022

IJGI

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Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient () and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI(654, 679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI(551, 1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI(654, 679) and V-RR-DSI(551, 1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping.

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A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction

Cited by 27 publications

References 52 publications

Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground‐based GNSS and fifth‐generation ECMWF reanalysis (ERA5) data

Analysis of diurnal to seasonal variability of Integrated Water Vapour in the South Indian Ocean basin using ground‐based GNSS and fifth‐generation ECMWF reanalysis (ERA5) data

On the Definition of the Strategy to Obtain Absolute InSAR Zenith Total Delay Maps for Meteorological Applications

Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

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