Data assimilation experiment of precipitable water vapor observed by a hyper-dense GNSS receiver network using a nested NHM-LETKF system

AbstractWe assess the impact of GPS precipitable water vapor (GPS-PWV) data assimilation (DA) on short-range North American monsoon (NAM) precipitation forecasts, across 38 days with weak synoptic forcing, during the NAM GPS Hydrometeorological Network field campaign in 2017 over northwest Mexico. Utilizing an ensemble-based data assimilation technique, the GPS-PWV data retrieved from 18 observation sites are assimilated every hour for 12 hours into a 30-member ensemble convective-permitting (2.5 km) Advanced Research version of the Weather Research and Forecasting (WRF-ARW) model. As the assimilation of the GPS-PWV improves the initial condition of WRF by reducing the root mean square error and bias of PWV across 1200-1800 UTC, this also leads to an improvement in capturing nocturnal convection of mesoscale convective systems (MCSs; after 0300 UTC) and to an increase by 0.1 mm h-1 in subsequent precipitation during the 0300-0600 UTC period relative to no assimilation of the GPS-PWV (NODA) over the area with relatively more observation sites. This response is consistent with observed precipitation from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Final Precipitation product. Moreover, compared to the NODA, we find that the GPS-PWV DA decreases cloud top temperature, increases most unstable convective available energy and surface dewpoint temperature, and thus creates a more favorable condition for convective organization in the region.

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Mesoscale Data Assimilation of Myanmar Cyclone Nargis Part II: Assimilation of GPS-Derived Precipitable Water Vapor

Journal of the Meteorological Society of Japan Ser II ◽

10.2151/jmsj.2011-105 ◽

2011 ◽

Vol 89 (1) ◽

pp. 67-88 ◽

Cited By ~ 21

Author(s):

Yoshinori SHOJI ◽

Masaru KUNII ◽

Kazuo SAITO

Keyword(s):

Water Vapor ◽

Data Assimilation ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Cyclone Nargis

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Comparisons of Performance Using Data Assimilation and Data Fusion Approaches in Acquiring Precipitable Water Vapor: A Case Study of a Western United States of America Area

Water ◽

10.3390/w12102943 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2943

Author(s):

Zhaohui Xiong ◽

Jizhang Sang ◽

Xiaogong Sun ◽

Bao Zhang ◽

Junyu Li

Keyword(s):

United States ◽

Water Vapor ◽

Data Assimilation ◽

United States Of America ◽

Weather Prediction ◽

Wrf Model ◽

Precipitable Water ◽

The United States ◽

Satellite System ◽

Precipitable Water Vapor

There are two main types of methods available to obtain precipitable water vapor (PWV) with high accuracy. One is to assimilate observations into a numerical weather prediction (NWP) model, for example, the Weather Research and Forecasting (WRF) model, to improve the accuracy of meteorological parameters, and then obtain the PWV with improved accuracy. The other is the direct fusion of multi-source PWV products. Regarding the two approaches, we conduct a comparison experiment on the West Coast of the United States of America with the data from May 2018, in which the WRF data assimilation (DA) system is used to assimilate the Global Navigation Satellite System (GNSS) PWV, while the method by Zhang et al. to fuse the GNSS PWV, ERA5 PWV and MODIS (moderate-resolution imaging spectroradiometer) PWV. As a result, four groups of PWV products are generated: the assimilated GNSS PWV, the unassimilated GNSS PWV, PWV from the fusion of the GNSS PWV and ECWMF (European Centre for Medium-Range Weather Forecasts) ERA5 (ECWMF Reanalysis 5) PWV, and PWV from the fusion of the GNSS PWV, ERA5 PWV and MODIS PWV. Experiments show that the data assimilation based on the WRF model (WRFDA) and adopted fusion method can generate PWV products with similar accuracy (1.47 mm vs. 1.52 mm). Assimilating the GNSS PWV into the WRF model slightly improves the accuracy of the inverted PWV by 0.18 mm. The fusion of the MODIS PWV, GNSS PWV and ERA5 PWV results in a higher accuracy than the fusion of GNSS PWV and ERA5 PWV by a margin of 0.35 mm. In addition, the inland canyon topography appears to have an influence on the inversion accuracy of both the methods.

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Precipitable Water Vapor Retrieval from Shipborne GNSS Observations on the Korean Research Vessel ISABU

Sensors ◽

10.3390/s20154261 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4261

Author(s):

Dong-Hyo Sohn ◽

Byung-Kyu Choi ◽

Yosup Park ◽

Yoon Chil Kim ◽

Bonhwa Ku

Keyword(s):

Water Vapor ◽

Low Cost ◽

Precipitable Water ◽

Satellite System ◽

Precipitable Water Vapor ◽

Dual Frequency ◽

Gnss Receiver ◽

Ocean Science ◽

Rms Error ◽

Gnss Observations

We estimate precipitable water vapor (PWV) from data collected by the low-cost Global Navigation Satellite System (GNSS) receiver at a vessel. The dual-frequency GNSS receiver that the vessel ISABU is equipped with that is operated by the Korea Institute of Ocean Science and Technology. The ISABU served in the Pacific Ocean for scientific research during a period from August 30 to September 21, 2018. It also performs radiosonde observations to obtain a vertical profile of troposphere on the vessel’s path. The GNSS-derived PWV is compared to radiosonde observations and the Atmospheric Infrared Sounder (AIRS) on NASA’s Aqua satellite output. A bias and root-mean-square (RMS) error between shipborne GNSS-PWV and radiosonde-PWV were −1.48 and 5.22 mm, respectively. When compared to the ground GNSS-PWV, shipborne GNSS-PWV has a relatively large RMS error in comparison with radiosonde-PWV. However, the GNSS observations on the vessel are still in good agreement with radiosonde observations. On the other hand, the GNSS-PWV is not well linearly correlated with AIRS-PWV. The RMS error between the two observations was approximately 8.97 mm. In addition, we showed that the vessel on the sea surface has significantly larger carrier phase multipath error compared to the ground-based GNSS observations. This also can result in reducing the accuracy of shipborne GNSS-PWV. However, we suggest that the shipborne GNSS has sufficient potential to derive PWV with the kinematic precise point positioning (PPP) solution on the vessel.

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Data Assimilation of GPS Precipitable Water Vapor into the JMA Mesoscale Numerical Weather Prediction Model and its Impact on Rainfall Forecasts

Journal of the Meteorological Society of Japan Ser II ◽

10.2151/jmsj.2004.441 ◽

2004 ◽

Vol 82 (1B) ◽

pp. 441-452 ◽

Cited By ~ 41

Author(s):

Hajime NAKAMURA ◽

Ko KOIZUMI ◽

Nobutaka MANNOJI

Keyword(s):

Water Vapor ◽

Data Assimilation ◽

Prediction Model ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Precipitable Water ◽

Numerical Weather Prediction Model ◽

Precipitable Water Vapor ◽

Numerical Weather ◽

Mesoscale Numerical Weather Prediction

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GNSS Precipitable Water Vapor from an Amazonian Rain Forest Flux Tower

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-11-00082.1 ◽

2011 ◽

Vol 28 (10) ◽

pp. 1192-1198 ◽

Cited By ~ 16

Author(s):

David K. Adams ◽

Rui M. S. Fernandes ◽

Jair M. F. Maia

Keyword(s):

Water Vapor ◽

Rain Forest ◽

Deep Convection ◽

Precipitable Water ◽

Satellite System ◽

Precipitable Water Vapor ◽

Gnss Receiver ◽

Flux Tower ◽

Complex Interactions ◽

Amazon Rain Forest

Abstract Understanding the complex interactions between water vapor fields and deep convection on the mesoscale requires observational networks with high spatial (kilometers) and temporal (minutes) resolution. In the equatorial tropics, where deep convection dominates the vertical distribution of the most important greenhouse substance—water—these mesoscale networks are nonexistent. Global Navigational Satellite System (GNSS) meteorological networks offer high temporal/spatial resolution precipitable water vapor, but infrastructure exigencies are great. The authors report here on very accurate precipitable water vapor (PWV) values calculated from a GNSS receiver installed on a highly nonideal Amazon rain forest flux tower. Further experiments with a mechanically oscillating platform demonstrate that errors and biases of approximately 1 mm (2%–3% of PWV) can be expected when compared with a stable reference GNSS receiver for two different geodetic grade receivers/antennas and processing methods [GPS-Inferred Positioning System (GIPSY) and GAMIT]. The implication is that stable fixed antennas are unnecessary for accurate calculation of precipitable water vapor regardless of processing techniques or geodetic grade receiver.

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KORELASI MUSIMAN ANTARA ZENITH WET DELAY (ZWD) DAN DEBIT SUNGAI DI DAS CIKAPUNDUNG HULU, KAWASAN BANDUNG UTARA, JAWA BARAT

Seminar Nasional Geomatika ◽

10.24895/sng.2018.3-0.1033 ◽

2019 ◽

Vol 3 ◽

pp. 741

Author(s):

Wedyanto Kuntjoro ◽

Z.A.J. Tanuwijaya ◽

A. Pramansyah ◽

Dudy D. Wijaya

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Zenith Wet Delay

Kandungan total uap air troposfer (precipitable water vapor) di suatu tempat dapat diestimasi berdasarkan karakteristik bias gelombang elektromagnetik dari satelit navigasi GPS, berupa zenith wet delay (ZWD). Pola musiman deret waktu ZWD sangat penting dalam studi siklus hidrologi khususnya yang terkait dengan kejadian-kejadian banjir. Artikel ini menganalisis korelasi musiman antara ZWD dan debit sungai Cikapundung di wilayah Bandung Utara berdasarkan estimasi rataan pola musimannya. Berdasarkan rekonstruksi sejumlah komponen harmonik ditemukan bahwa pola musiman ZWD memiliki kemiripan dan korelasi yang kuat dengan pola musiman debit sungai. Pola musiman ZWD dan debit sungai dipengaruhi secara kuat oleh fenomena pertukaran Monsun Asia dan Monsun Australia. Korelasi linier di antara keduanya menunjukkan hasil yang sangat kuat, dimana hampir 90% fluktuasi debit sungai dipengaruhi oleh kandungan uap air di troposfer dengan level signifikansi 95%. Berdasarkan spektrum amplitudo silang dan koherensi, kedua kuantitas ini nampak didominasi oleh siklus monsun satu tahunan disertai indikasi adanya pengaruh siklus tengah tahunan dan 4 bulanan.

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HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

Remote Sensing ◽

10.3390/rs13112179 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2179

Author(s):

Pedro Mateus ◽

Virgílio B. Mendes ◽

Sandra M. Plecha

Keyword(s):

Water Vapor ◽

Relative Humidity ◽

Real Time ◽

Prediction Models ◽

Weather Prediction ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Global Navigation Satellite Systems ◽

International Gnss Service ◽

Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

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