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

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.

scholarly journals Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Qin Zhang ◽  
Junhua Ye ◽  
Shuangcheng Zhang ◽  
Fei Han
Keyword(s):  
Water Vapor ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Reanalysis Data ◽  
Precipitable Water Vapor ◽  
Reanalysis Dataset ◽  
High Positive Correlation ◽  
Advantages And Disadvantages ◽  
Ecmwf Reanalysis

Precipitable water vapor (PWV) content detection is vital to heavy rain prediction; up to now, lots of different measuring methods and devices are developed to observe PWV. In general, these methods can be divided into two categories, ground-based or space-based. In this study, we analyze the advantages and disadvantages of these technologies, compare retrieved atmosphere parameters by different RO (radio occultation) observations, like FORMOSAT-3/COSMIC (Formosa Satellite-3 and Constellation Observing System for Meteorology, Ionosphere, and Climate) and FY3C (China Feng Yun 3C), and assess retrieved PWV precision with a radiosonde. Besides, we interpolate PWV from NWP (numerical weather prediction) reanalysis data for more comparison and analysis with RO. Specifically, ground-based GNSS is of high precision and continuous availability to monitor PWV distribution; in our paper, we show cases to validate and compare GNSS retrieving PWV with a radiosonde. Except GNSS PWV, we give two different radio occultation sounding results, COSMIC and FY3C, to validate the precision to monitor PWV from space in a global area. FY3C results containing Beidou (China Beidou Global Satellite Navigation System) radio occultation events need to be emphasized. So, in our study, we get the retrieved atmospheric profiles from GPS and Beidou radio occultation observations and derive atmosphere PWV by a variational retrieval method based on these data over a global area. Besides, other space-based methods, such as microwave satellite, are also useful in detecting PWV distribution situations in a global area from space; in this study, we present a case of retrieved PWV using microwave satellite observation. NWP reanalysis data ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim and the new-generation reanalysis data ERA5 provide global grid atmosphere parameters, like surface temperature, different-level pressures, and precipitable water. We show cases of retrieved PWV and validate the precision with radiosonde results and compare new reanalysis dataset ERA5 with ERA-Interim, finding that ERA5 can get higher precision-retrieved atmosphere parameters and PWV. In the end, from our comparison, we find that the retrieved PWV from RO (FY3C and COSMIC) and ECMWF reanalysis data (ERA-Interim and ERA5) have a high positive correlation and that almost all R2 values exceed 0.9, compare retrieved PWV with a radiosonde, and find that whether it is RO and ECMWF reanalysis data, ground-based GNSS, or microwave satellite, they all show small biases.

scholarly journals Assessments of GMI-Derived Precipitable Water Vapor Products over the South and East China Seas Using Radiosonde and GNSS

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Biyan Chen ◽  
Wujiao Dai ◽  
Zhizhao Liu ◽  
Lixin Wu ◽  
Pengfei Xia
Keyword(s):  
Water Vapor ◽  
Weather Forecasting ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
East China ◽  
China Seas ◽  
The South ◽  
Mean Square Errors

Satellite remote sensing of the atmospheric water vapor distribution over the oceans is essential for both weather and climate studies. Satellite onboard microwave radiometer is capable of measuring the water vapor over the oceans under all weather conditions. This study assessed the accuracies of precipitable water vapor (PWV) products over the south and east China seas derived from the Global Precipitation Measurement Microwave Imager (GMI), using radiosonde and GNSS (Global Navigation Satellite System) located at islands and coasts as truth. PWV measurements from 14 radiosonde and 5 GNSS stations over the period of 2014–2017 were included in the assessments. Results show that the GMI 3-day composites have an accuracy of better than 5 mm. A further evaluation shows that RMS (root mean square) errors of the GMI 3-day composites vary greatly in the range of 3∼14 mm at different radiosonde/GNSS sites. GMI 3-day composites show very good agreements with radiosonde and GNSS measured PWVs with correlation coefficients of 0.896 and 0.970, respectively. The application of GMI products demonstrates that it is possible to reveal the weather front, moisture advection, transportation, and convergence during the Meiyu rainfall. This work indicates that the GMI PWV products can contribute to various studies such as climate change, hydrologic cycle, and weather forecasting.

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.

scholarly journals Validation of Precipitable Water Vapor within the NCEP/DOE Reanalysis Using Global GPS Observations from One Decade

2010 ◽  
Vol 23 (7) ◽  
pp. 1675-1695 ◽  
Author(s):  
Sibylle Vey ◽  
Reinhard Dietrich ◽  
Axel Rülke ◽  
Mathias Fritsche ◽  
Peter Steigenberger ◽  
...  
Keyword(s):  
Time Series ◽  
Water Vapor ◽  
Seasonal Cycle ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Atmospheric Temperature ◽  
Interannual Variations ◽  
Seasonal Signals ◽  
The Tropics

Abstract In contrast to previous studies validating numerical weather prediction (NWP) models using observations from the global positioning system (GPS), this paper focuses on the validation of seasonal and interannual variations in the water vapor. The main advantage of the performed validation is the independence of the GPS water vapor estimates compared to studies using water vapor datasets from radiosondes or satellite microwave radiometers that are already assimilated into the NWP models. Tropospheric parameters from a GPS reanalysis carried out in a common project of the Technical Universities in Munich and Dresden were converted into precipitable water (PW) using surface pressure observations from the WMO and mean atmospheric temperature data from ECMWF. PW time series were generated for 141 globally distributed GPS sites covering the time period from the beginning of 1994 to the end of 2004. The GPS-derived PW time series were carefully examined for their homogeneity. The validation of the NWP model from NCEP shows that the differences between the modeled and observed PW values are time dependent. In addition to establishing a long-term mean, this study also validates the seasonal cycle and interannual variations in the PW. Over Europe and large parts of North America the seasonal cycle and the interannual variations in the PW from GPS and NCEP agree very well. The results reveal a submillimeter accuracy of the GPS-derived PW anomalies. In the regions mentioned above, NCEP provides a highly accurate database for studies of long-term changes in the atmospheric water vapor. However, in the Southern Hemisphere large differences in the seasonal signals and in the PW anomalies were found between GPS and NCEP. The seasonal signal of the PW is underestimated by NCEP in the tropics and in Antarctica by up to 40% and 25%, respectively. Climate change studies based on water vapor data from NCEP should consider the large uncertainties in the analysis when interpreting these data, especially in the tropics.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2015 ◽  
Vol 8 (10) ◽  
pp. 10755-10792
Author(s):  
A. M. Dzambo ◽  
D. D. Turner ◽  
E. J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km MSL), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, mid-latitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RH are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

scholarly journals Evaluation of two Vaisala RS92 radiosonde solar radiative dry bias correction algorithms

2016 ◽  
Vol 9 (4) ◽  
pp. 1613-1626 ◽  
Author(s):  
Andrew M. Dzambo ◽  
David D. Turner ◽  
Eli J. Mlawer
Keyword(s):  
Water Vapor ◽  
Statistical Significance ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Upper Troposphere ◽  
Clear Sky ◽  
Microwave Radiometers ◽  
The Impact ◽  
Dry Bias ◽  
Better Than

Abstract. Solar heating of the relative humidity (RH) probe on Vaisala RS92 radiosondes results in a large dry bias in the upper troposphere. Two different algorithms (Miloshevich et al., 2009, MILO hereafter; and Wang et al., 2013, WANG hereafter) have been designed to account for this solar radiative dry bias (SRDB). These corrections are markedly different with MILO adding up to 40 % more moisture to the original radiosonde profile than WANG; however, the impact of the two algorithms varies with height. The accuracy of these two algorithms is evaluated using three different approaches: a comparison of precipitable water vapor (PWV), downwelling radiative closure with a surface-based microwave radiometer at a high-altitude site (5.3 km m.s.l.), and upwelling radiative closure with the space-based Atmospheric Infrared Sounder (AIRS). The PWV computed from the uncorrected and corrected RH data is compared against PWV retrieved from ground-based microwave radiometers at tropical, midlatitude, and arctic sites. Although MILO generally adds more moisture to the original radiosonde profile in the upper troposphere compared to WANG, both corrections yield similar changes to the PWV, and the corrected data agree well with the ground-based retrievals. The two closure activities – done for clear-sky scenes – use the radiative transfer models MonoRTM and LBLRTM to compute radiance from the radiosonde profiles to compare against spectral observations. Both WANG- and MILO-corrected RHs are statistically better than original RH in all cases except for the driest 30 % of cases in the downwelling experiment, where both algorithms add too much water vapor to the original profile. In the upwelling experiment, the RH correction applied by the WANG vs. MILO algorithm is statistically different above 10 km for the driest 30 % of cases and above 8 km for the moistest 30 % of cases, suggesting that the MILO correction performs better than the WANG in clear-sky scenes. The cause of this statistical significance is likely explained by the fact the WANG correction also accounts for cloud cover – a condition not accounted for in the radiance closure experiments.

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