Variational assimilation of GPS precipitable water vapor and hourly rainfall observations for a meso-β scale heavy precipitation event during the 2002 mei-yu season

Nowadays, precipitable water vapor (PWV) retrieved from ground-based Global Navigation Satellite Systems (GNSS) tracking stations has heralded a new era of GNSS meteorological applications, especially for severe weather prediction. Among the existing models that use PWV timeseries to predict heavy precipitation, the “threshold-based” models, which are based on a set of predefined thresholds for the predictors used in the model for predictions, are effective in heavy precipitation nowcasting. In previous studies, monthly thresholds have been widely accepted due to the monthly patterns of different predictors being fully considered. However, the primary weakness of this type of thresholds lies in their poor prediction results in the transitional periods between two consecutive months. Therefore, in this study, a new method for the determination of an optimal set of diurnal thresholds by adopting a 31-day sliding window was first proposed. Both the monthly and diurnal variation characteristics of the predictors were taken into consideration in the new method. Then, on the strength of the new method, an improved PWV-based model for heavy precipitation prediction was developed using the optimal set of diurnal thresholds determined based on the hourly PWV and precipitation records for the summer over the period 2010–2017 at the co-located HKSC–KP (King’s Park) stations in Hong Kong. The new model was evaluated by comparing its prediction results against the hourly precipitation records for the summer in 2018 and 2019. It is shown that 96.9% of heavy precipitation events were correctly predicted with a lead time of 4.86 h, and the false alarms resulting from the new model were reduced to 25.3%. These results suggest that the inclusion of the diurnal thresholds can significantly improve the prediction performance of the model.

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Predicted Changes in the Frequency of Extreme Precipitable Water Vapor Events

Journal of Climate ◽

10.1175/jcli-d-14-00679.1 ◽

2015 ◽

Vol 28 (18) ◽

pp. 7057-7070 ◽

Cited By ~ 4

Author(s):

Jacola Roman ◽

Robert Knuteson ◽

Steve Ackerman ◽

Hank Revercomb

Keyword(s):

United States ◽

Water Vapor ◽

Climate Models ◽

Global Climate ◽

Precipitable Water ◽

Heavy Precipitation ◽

Precipitable Water Vapor ◽

Global Climate Models ◽

Eastern United States ◽

Mean Trend

Abstract A high amount of precipitable water vapor (PWV) is a necessary requirement for heavy precipitation and extreme flooding events. This study determined the predicted shift in extreme PWV from a set of CMIP5 global climate models using the highest emission scenario over three different spatial resolutions (global, zonal, and regional) and four different case regions (India, China, Europe, and eastern United States). For the globe, the frequency of the extreme 1% of PWV events between 2006 and 2030 was predicted to increase by a median factor (herein called an X factor) of 9 by 2075–99. Areas of high PWV, like the tropics, tended toward higher factors. The annual median X factor for India, China, central Europe, and the eastern United States was 24, 17, 15, and 16, respectively. For India, the minimum median X factor was 10 during December–February (DJF) and the maximum was 48 during June–August (JJA). In China, the minimum median X factor (8) occurred during DJF, and the maximum was 42 in JJA. For Europe, DJF and September–November (SON) had the smallest median X factor of 15, whereas JJA had the largest median X factor of 30. The smallest median X factor for the eastern United States (11) occurred during March–May (MAM), whereas the largest median X factor (32) occurred in JJA. Regional X factors were significantly larger than global (1.5–2 times larger), illustrating the importance of regional assessments of extreme PWV. The mean trend in the extreme PWV was approximately linear for all regions with a slope of about 3% decade−1. Observations for 10 (20) years are needed for the extreme PWV to change by an amount that exceeds a 3% (5%) measurement error.

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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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Variability and climatology of precipitable water vapor from 12-year GPS observations in Taiwan

Advances in Space Research ◽

10.1016/j.asr.2021.01.021 ◽

2021 ◽

Author(s):

Shih-Chun Pang ◽

Ta-Kang Yeh ◽

Jing-Shan Hong ◽

Chieh-Hung Chen

Keyword(s):

Water Vapor ◽

Precipitable Water ◽

Precipitable Water Vapor ◽

Gps Observations

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Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

Water ◽

10.3390/w13131727 ◽

2021 ◽

Vol 13 (13) ◽

pp. 1727

Author(s):

Valerio Capecchi ◽

Andrea Antonini ◽

Riccardo Benedetti ◽

Luca Fibbi ◽

Samantha Melani ◽

...

Keyword(s):

Extreme Event ◽

Wrf Model ◽

Heavy Precipitation ◽

Radar Data ◽

Flash Floods ◽

Precipitation Event ◽

Weather Research And Forecasting ◽

Limited Area ◽

Heavy Precipitation Event ◽

The Town

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts.

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