Attribution of the United States “warming hole”: Aerosol indirect effect and precipitable water vapor

Mean monthly values of precipitable water for about 50 stations in the United States are used to describe the amount and variation of moisture over the area. The average precipitable water over the United States is found to be 1.75 cm and to range from 0.94 cm in February to 2.99 cm in July. Monthly and yearly averages for the period 1946 to 1956 indicate that there is considerable variation on a monthly and sectional basis but that the yearly and overall area amounts of precipitable water are rather stable.

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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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On the Climatology of Precipitable Water and Water Vapor Flux in the Mid-Atlantic Region of the United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0030.1 ◽

2015 ◽

Vol 16 (1) ◽

pp. 70-87 ◽

Cited By ~ 7

Author(s):

Young-Hee Ryu ◽

James A. Smith ◽

Elie Bou-Zeid

Keyword(s):

United States ◽

Water Vapor ◽

Diurnal Cycle ◽

Precipitable Water ◽

The United States ◽

Radiosonde Data ◽

Atlantic Region ◽

Water Vapor Flux ◽

Vapor Flux ◽

Diurnal Cycles

Abstract The seasonal and diurnal climatologies of precipitable water and water vapor flux in the mid-Atlantic region of the United States are examined. A new method of computing water vapor flux at high temporal resolution in an atmospheric column using global positioning system (GPS) precipitable water, radiosonde data, and velocity–azimuth display (VAD) wind profiles is presented. It is shown that water vapor flux exhibits striking seasonal and diurnal cycles and that the diurnal cycles exhibit rapid transitions over the course of the year. A particularly large change in the diurnal cycle of meridional water vapor flux between spring and summer seasons is found. These features of the water cycle cannot be resolved by twice-a-day radiosonde observations. It is also shown that precipitable water exhibits a pronounced seasonal cycle and a less pronounced diurnal cycle. There are large contrasts in the climatology of water vapor flux between precipitation and nonprecipitation conditions in the mid-Atlantic region. It is hypothesized that the seasonal transition of large-scale flow environments and the change in the degree of differential heating in the mountainous and coastal areas are responsible for the contrasting diurnal cycle between spring and summer seasons.

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Aerosol Indirect Effect and Cloud-base Height Observations in the North East of the United States

International Journal of Earth & Environmental Sciences ◽

10.15344/2456-351x/2017/128 ◽

2017 ◽

Vol 2 (1) ◽

Cited By ~ 1

Author(s):

Siwei Li

Keyword(s):

United States ◽

Indirect Effect ◽

The United States ◽

Cloud Base ◽

Aerosol Indirect Effect ◽

North East ◽

The North ◽

Cloud Base Height

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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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