Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin

2018 ◽  
Vol 10 (04) ◽  
pp. 1850010
Author(s):  
Kimberly Leung ◽  
Aneesh C. Subramanian ◽  
Samuel S. P. Shen
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Southern Oscillation ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Statistical Characteristics ◽  
Convective Precipitation ◽  
Data Set ◽  
Best Estimates

This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.

An improvement in accuracy and spatiotemporal continuity of the MODIS precipitable water vapor product based on a data fusion approach

2020 ◽  
Author(s):  
Xueying Li ◽  
Di Long
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Data Fusion ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
The Tibetan Plateau ◽  
Monsoon Period ◽  
Yarlung Zangbo River ◽  
Key Variables ◽  
Fusion Approach

<p>Precipitable water vapor (PWV) is one of the key variables in the water and energy cycles, whereas current PWV products are subject to spatiotemporal discontinuity, low accuracy, and/or coarse resolution. Based on two widely used global PWV products, i.e., satellite-based MODIS and reanalysis-based ERA5 products, here we propose a data fusion approach to generate PWV maps of spatiotemporal continuity and high resolution (0.01°, daily) for the Upper Brahmaputra River (UBR, referred to as the Yarlung Zangbo River in China) basin in the Tibetan Plateau (TP) during the monsoon period (May‒September) from 2007‒2013. Results show that the fused PWV estimates have good agreement with ground-based PWV measurements from eight GPS stations (correlation coefficient = 0.87‒0.97, overall bias = -0.35‒1.78 mm, and root mean square error = 1.17‒2.04 mm), which greatly improve the accuracy of the MODIS PWV product. The high-resolution fused PWV maps provide detailed spatial variations which are generally consistent with those from the MODIS estimates under confident clear conditions and ERA5. During the monsoon period from 2007‒2013, monthly average PWV estimates across the UBR basin vary from ~6 to ~12 mm, and for each month high PWV values are found mainly along the UBR valley and at the basin outlet. The developed data fusion approach maximizes the potential of satellite and reanalysis-based PWV products for monitoring PWV and can be extended to other data available sources and study regions. The generated PWV estimates are highly valuable in understanding the water and energy cycles and retrieving atmospheric and surface variables for the south TP and its downstream areas.</p>

scholarly journals A tool for modelling telluric spectra

2009 ◽  
Vol 5 (H15) ◽  
pp. 533-534
Author(s):  
Alain Smette ◽  
Hugues Sana ◽  
Hannes Horst
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Nir Spectroscopy ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Synthetic Spectrum ◽  
Scientific Interest ◽  
Significant Change ◽  
The One ◽  
Large Telescopes

Accurate synthetic telluric spectra are required for efficient use of telescope time, in particular, with large telescopes and high-resolution NIR spectroscopy: (i) In the preparation of observations, are the telluric features at the same wavelength as spectroscopic features of scientific interest? Since water vapor is the molecule whose abundance varies most in the atmosphere, what values of precipitable water vapor are suitable to carry out successful observations? Are the observations of a telluric star required? Or better, can telluric features in the science spectrum be accurately represented by an appropriate synthetic spectrum? This point is also very important in the planning of telescope time, as observations of a telluric star may sometimes take longer than the one of the science target. (ii) In the analysis of the observations, how do telluric lines affect the scientifically interesting features in the observed spectrum? Is it possible to recover the useful information when telluric star observations could not be obtained, do not have sufficient SNR, or suffer from a significant change in instrumental or observing conditions?

Improved Daytime Column-Integrated Precipitable Water Vapor from Vaisala Radiosonde Humidity Sensors

2008 ◽  
Vol 25 (6) ◽  
pp. 873-883 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. D. Turner ◽  
E. J. Mlawer ◽  
S. A. Clough ◽  
...  
Keyword(s):  
Water Vapor ◽  
Great Plains ◽  
Weather Prediction ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Atmospheric Radiation Measurement ◽  
Atmospheric Transmittance ◽  
Dry Bias

Abstract Accurate water vapor profiles from radiosondes are essential for long-term climate prediction, weather prediction, validation of remote sensing retrievals, and other applications. The Vaisala RS80, RS90, and RS92 radiosondes are among the more commonly deployed radiosondes in the world. However, numerous investigators have shown that the daytime water vapor profiles measured by these instruments present a significant dry bias due to the solar heating of the humidity sensor. This bias in the column-integrated precipitable water vapor (PWV), along with variability due to calibration, can be removed by scaling the humidity profile to agree with the PWV retrieved from a microwave radiometer (MWR), as has been demonstrated by several previous studies. Infrared radiative closure analyses have shown that the MWR PWV does not present daytime versus nighttime differences; thus, scaling by the MWR is a possible approach for removing the daytime dry bias. However, MWR measurements are not routinely available at all radiosonde launch sites. Starting from a long-term series of sonde and MWR PWV measurements from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, the authors have developed a simple correction to the column-integrated sonde PWV, derived from an analysis of the ratio of the MWR and sonde measurements; this correction is a function of the atmospheric transmittance as determined by the solar zenith angle, and it effectively removes the daytime dry bias at all solar zenith angles. The correction was validated by successfully applying it to an independent dataset from the ARM tropical western Pacific (TWP) site.

scholarly journals A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor

2021 ◽  
Vol 24 (2) ◽  
pp. 744
Author(s):  
Wayan Suparta ◽  
Aris Warsita ◽  
Ircham Ircham
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Low Cost ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Weather System ◽  
Spatial And Temporal Scales

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I<sup>2</sup>C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

scholarly journals Interannual Variability of the GNSS Precipitable Water Vapor in the Global Tropics

Atmosphere ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1698
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Beata Latos ◽  
Dariusz B. Baranowski ◽  
Mariusz Figurski
Keyword(s):  
Water Vapor ◽  
Southern Oscillation ◽  
Linear Trend ◽  
Regional Climate ◽  
Singular Spectrum Analysis ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Small Scale ◽  
Climate Indices ◽  
Single Station

This paper addresses the subject of inter-annual variability of the tropical precipitable water vapor (PWV) derived from 18 years of global navigation satellite system (GNSS) observations. Non-linear trends of retrieved GNSS PWV were investigated using the singular spectrum analysis (SSA) along with various climate indices. For most of the analyzed stations (~49%) the GNSS PWV anomaly was related to the El Niño Southern Oscillation (ENSO), although its influence on the PWV variability was not homogeneous. The cross-correlations coefficient values estimated between the Multivariate ENSO Index (MEI) and PWV were up to 0.78. A strong cross-correlation was also found for regional climate pattern expressed through CAR, DMI, HAW, NPGO, TNA and TSA indices. A distinct agreement was also found when instead of climate indices, the local sea surface temperature was examined (average correlation 0.60). The SSA method made it also possible to distinguish small-scale phenomena that affect PWV, such as local droughts or wetter rainy seasons. The overall nature of the investigated changes was also verified through linear trend analysis. In general, not a single station was characterized by a negative trend and its weighted mean value, calculated for all stations was equal to 0.08 ± 0.01 mm/year.

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