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

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.

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

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2007jtecha1027.1 ◽

2008 ◽

Vol 25 (6) ◽

pp. 873-883 ◽

Cited By ~ 72

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.

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Spectral Recalibration of NOAA HIRS Longwave CO2 Channels toward a 40+ Year Time Series for Climate Studies

Atmosphere ◽

10.3390/atmos12101317 ◽

2021 ◽

Vol 12 (10) ◽

pp. 1317

Author(s):

Bin Zhang ◽

Changyong Cao ◽

Tung-Chang Liu ◽

Xi Shao

Keyword(s):

Time Series ◽

Spectral Response ◽

Weather Prediction ◽

Weather Forecast ◽

Atmospheric Temperature ◽

Spectral Shift ◽

University Of Wisconsin ◽

Science Community ◽

The University

The High-Resolution Infrared Radiation Sounder (HIRS) on NOAA and MetOp A/B satellites has been observing the Earth continuously for over four decades, providing essential data for operational numerical weather prediction, retrieval of atmospheric vertical profile, and total column information on atmospheric temperature, moisture, water vapor, ozone, cloud climatology, and other geophysical parameters globally. Although the HIRS data meets the needs of the short-term weather forecast, there are inconsistencies when the long-term decadal time series is used for time series analysis. The discrepancies are caused by several factors, including spectral response differences between the HIRS models on the satellites and spectral response uncertainties and other calibration issues. Previous studies have demonstrated that significant improvements can be achieved by recalibrating some of the HIRS longwave CO2 channels (Channels 4, 5, 6, and 7), which has helped make the time series more consistent. The current study aims to extend the previous study to the remaining longwave infrared sounding channels, including Channels 1, 2, 3, and 8, using a similar approach. Similar to previous findings, the spectral shift of the HIRS bands has helped improve the consistency in the time series from NOAA-06 to MetOp-A and B for these channels. We also found that HIRS channels on MetOp-B also have bias relative to Infrared Atmospheric Sounding Interferometer (IASI) on the same satellite, especially Channel 4, and a spectral shift significantly reduced the bias. To bridge the observation gap in time series in the mid-1980s between NOAA-07 and NOAA-09, the global mean method has been used since no transfer radiometers between them was available for this period, and the spectral response function corrections, therefore, can be applied to the earliest satellites (NOAA-06) for these channels. The recalibration parameters have been provided to other scientists at the University of Wisconsin for improving the time series in their long-term studies using historical HIRS data and are now made available to the science community.

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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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Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

10.5194/amt-2019-38 ◽

2019 ◽

Author(s):

David D. Parrish ◽

Richard G. Derwent ◽

Simon O'Doherty ◽

Peter G. Simmonds

Keyword(s):

Time Series ◽

Seasonal Cycle ◽

Linear Trend ◽

Power Series Expansion ◽

Mathematical Representation ◽

Seasonal Cycles ◽

Data Sets ◽

Significant Information ◽

Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

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

Journal of Sensors ◽

10.1155/2018/3428303 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 6

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.

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Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

Atmospheric Measurement Techniques ◽

10.5194/amt-9-4861-2016 ◽

2016 ◽

Vol 9 (9) ◽

pp. 4861-4877 ◽

Cited By ~ 10

Author(s):

Zofia Baldysz ◽

Grzegorz Nykiel ◽

Andrzej Araszkiewicz ◽

Mariusz Figurski ◽

Karolina Szafranek

Keyword(s):

Time Series ◽

Point Of View ◽

Trend Test ◽

Data Sets ◽

Total Delay ◽

University Of Technology ◽

Time Period ◽

Seasonal Signals ◽

Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of ZTD (zenith total delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EUREF (Reference Frame Sub-Commission for Europe) EPN (Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT AC (Military University of Technology Analysis Centre) scenarios. Firstly, Lomb–Scargle periodograms were generated for 57 EPN stations to obtain a characterisation of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (least squares estimation) approach. The Mann–Kendall trend test was also carried out to verify the presence of linear long-term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. All these analyses were conducted for the ZTD time series of two lengths: a shortened 16-year series and a full 18-year one. In the case of spectral analysis, amplitudes of the annual and semi-annual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, for the reprocessing performed in 2008, the trends values were usually higher. In general, shortening of the analysed time period by 2 years resulted in a decrease of the linear trends values of about 0.07 mm yr−1. This was confirmed by analyses based on two data sets.

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Connecting the Time Series of Microwave Sounding Observations from AMSU to ATMS for Long-Term Monitoring of Climate

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-13-00232.1 ◽

2014 ◽

Vol 31 (10) ◽

pp. 2206-2222 ◽

Cited By ~ 5

Author(s):

Xiaolei Zou ◽

Fuzhong Weng ◽

H. Yang

Keyword(s):

Time Series ◽

Atmospheric Temperature ◽

Advanced Technology ◽

Central Frequency ◽

Brightness Temperatures ◽

Microwave Sounding Unit ◽

Microwave Sounding ◽

Cross Calibration ◽

Term Monitoring

Abstract The measurements from the Microwave Sounding Unit (MSU) and the Advanced Microwave Sounding Unit-A (AMSU-A) on board NOAA polar-orbiting satellites have been extensively utilized for detecting atmospheric temperature trend during the last several decades. After the launch of the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite on 28 October 2011, MSU and AMSU-A time series will be overlapping with the Advanced Technology Microwave Sounder (ATMS) measurements. While ATMS inherited the central frequency and bandpass from most of AMSU-A sounding channels, its spatial resolution and noise features are, however, distinctly different from those of AMSU. In this study, the Backus–Gilbert method is used to optimally resample the ATMS data to AMSU-A fields of view (FOVs). The differences between the original and resampled ATMS data are demonstrated. By using the simultaneous nadir overpass (SNO) method, ATMS-resampled observations are collocated in space and time with AMSU-A data. The intersensor biases are then derived for each pair of ATMS–AMSU-A channels. It is shown that the brightness temperatures from ATMS now fall well within the AMSU data family after resampling and SNO cross calibration. Thus, the MSU–AMSU time series can be extended into future decades for more climate applications.

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

Advances in Data Science and Adaptive Analysis ◽

10.1142/s2424922x18500109 ◽

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.

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