Establishment and analysis of global gridded Tm − Ts relationship model

Lan, Zeying; Bao, Zhejing; Geng, Yichao

doi:10.1016/j.geog.2016.02.001

Cited by 19 publications

(11 citation statements)

References 15 publications

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“…The HGPT model follows the time-segmentation concept and was based on 20 years of 0.25° × 0.25° spatial resolution and 1-h temporal resolution data from the ECMWF ERA5 reanalysis model. Lan et al [55] calculated linear regression coefficients between T m and T s at every 2 • × 2.5 • grid point using T s data from the ERA-Interim model and T m data from Global Geodetic Observing System (GGOS), and compared the results with the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and radiosonde data to obtain accuracies of 3.1 and 3.8 K, respectively. When compared with our results, we obtained a global accuracy improvement of about 33%.…”

Section: Discussionmentioning

confidence: 99%

“…In a similar analysis for temperature, we found a global standard deviation of 1.5 K, and assuming there was no error in α and β (see Equation (6)), we obtained a global T m uncertainty of 1.1 K with a maximum value of 3.5 K over the Atlantic Ocean, close to the Amazon Rainforest (Figure 9). Lan et al [55] calculated linear regression coefficients between and at every 2° × 2.5° grid point using data from the ERA-Interim model and data from Global Geodetic Observing System (GGOS), and compared the results with the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) and radiosonde data to obtain accuracies of 3.1 and 3.8 K, respectively. When compared with our results, we obtained a global accuracy improvement of about 33%.…”

Section: Accuracy Assessmentmentioning

confidence: 99%

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An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

et al. 2020

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The Global Navigation Satellite System (GNSS) meteorology contribution to the comprehension of the Earth’s atmosphere’s global and regional variations is essential. In GNSS processing, the zenith wet delay is obtained using the difference between the zenith total delay and the zenith hydrostatic delay. The zenith wet delay can also be converted into precipitable water vapor by knowing the atmospheric weighted mean temperature profiles. Improving the accuracy of the zenith hydrostatic delay and the weighted mean temperature, normally obtained using modeled surface meteorological parameters at coarse scales, leads to a more accurate and precise zenith wet delay estimation, and consequently, to a better precipitable water vapor estimation. In this study, we developed an hourly global pressure and temperature (HGPT) model based on the full spatial and temporal resolution of the new ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The HGPT model provides information regarding the surface pressure, surface air temperature, zenith hydrostatic delay, and weighted mean temperature. It is based on the time-segmentation concept and uses the annual and semi-annual periodicities for surface pressure, and annual, semi-annual, and quarterly periodicities for surface air temperature. The amplitudes and initial phase variations are estimated as a periodic function. The weighted mean temperature is determined using a 20-year time series of monthly data to understand its seasonality and geographic variability. We also introduced a linear trend to account for a global climate change scenario. Data from the year 2018 acquired from 510 radiosonde stations downloaded from the National Oceanic and Atmospheric Administration (NOAA) Integrated Global Radiosonde Archive were used to assess the model coefficients. Results show that the GNSS meteorology, hydrological models, Interferometric Synthetic Aperture Radar (InSAR) meteorology, climate studies, and other topics can significantly benefit from an ERA5 full-resolution model.

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Section: Discussionmentioning

confidence: 99%

Section: Accuracy Assessmentmentioning

confidence: 99%

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

et al. 2020

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“…For decades, several methods have been studied, such as radiosondes and water vapor radiometers, sun photometers and GPS (Campmany et al, 2010;Ciesielski et al, 2010;Liu et al, 2013;Perez-Ramirez et al, 2014;Li et al, 2016). Compared with the traditional water vapor observations, ground-based GPS water vapor measurement has the advantages of high accuracy, high spatial-temporal resolution, all-weather availability and low-cost (Haase et al, 2003;Pacione and Vespe, 2008;Lee et al, 2010;Means, 2013;Lu et al, 2015). Ground-based GPS water vapor products, mainly including precipitable water vapor (PWV), are widely used in many fields such as real-time vapor monitoring, weather and climate research, and numerical weather prediction (NWP) (Van Baelen and Penide, 2009;Karabatic et al, 2011;Rohm et al, 2014;Adams et al, 2017).…”

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confidence: 99%

Development of time-varying global gridded Ts–Tm model for precise GPS–PWV retrieval

Jiang

et al. 2019

Atmos. Meas. Tech.

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Abstract. Water-vapor-weighted mean temperature, Tm, is the key variable for estimating the mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). For the near-real-time GPS–PWV retrieval, estimating Tm from surface air temperature Ts is a widely used method because of its high temporal resolution and fair degree of accuracy. Based on the estimations of Tm and Ts at each reanalysis grid node of the ERA-Interim data, we analyzed the relationship between Tm and Ts without data smoothing. The analyses demonstrate that the Ts–Tm relationship has significant spatial and temporal variations. Static and time-varying global gridded Ts–Tm models were established and evaluated by comparisons with the radiosonde data at 723 radiosonde stations in the Integrated Global Radiosonde Archive (IGRA). Results show that our global gridded Ts–Tm equations have prominent advantages over the other globally applied models. At over 17 % of the stations, errors larger than 5 K exist in the Bevis equation (Bevis et al., 1992) and in the latitude-related linear model (Y. B. Yao et al., 2014), while these large errors are removed in our time-varying Ts–Tm models. Multiple statistical tests at the 5 % significance level show that the time-varying global gridded model is superior to the other models at 60.03 % of the radiosonde sites. The second-best model is the 1∘ × 1∘ GPT2w model, which is superior at only 12.86 % of the sites. More accurate Tm can reduce the contribution of the uncertainty associated with Tm to the total uncertainty in GPS–PWV, and the reduction augments with the growth of GPS–PWV. Our theoretical analyses with high PWV and small uncertainty in surface pressure indicate that the uncertainty associated with Tm can contribute more than 50 % of the total GPS–PWV uncertainty when using the Bevis equation, and it can decline to less than 25 % when using our time-varying Ts–Tm model. However, the uncertainty associated with surface pressure dominates the error budget of PWV (more than 75 %) when the surface pressure has an error larger than 5 hPa. GPS–PWV retrievals using different Tm estimates were compared at 74 International GNSS Service (IGS) stations. At 74.32 % of the IGS sites, the relative differences of GPS–PWV are within 1 % by applying the static or the time-varying global gridded Ts–Tm equations, while the Bevis model, the latitude-related model and the GPT2w model perform the same at 37.84 %, 41.89 % and 29.73 % of the sites. Compared with the radiosonde PWV, the error reduction in the GPS–PWV retrieval can be around 1–2 mm when using a more accurate Tm parameterization, which accounts for around 30 % of the total GPS–PWV error.

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“…The Lan's global gridded model (Lan et al, 2016) is replaced by our static global gridded model because of its much lower spatial resolution(4°×5°) than our model (0.75°×0.75°). Actually Bevis model is established using regional radiosonde data so it should be a regional model, but it has been adopted in many other regional research so we regraded it as a global applicable equation.…”

Section: Assessments Of Ts-tm Modelsmentioning

confidence: 99%

Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

Jiang¹,

Ye²,

Lu³

et al. 2018

Preprint

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Water-vapor-weighted mean temperature, Tm, is the key variable to estimate mapping factor between GPS zenith wet delay (ZWD) and precipitable water vapor (PWV). In near real-time GPS-PWV retrieving, estimating Tm from surface air 10 temperature Ts is a widely used method because of its high temporal resolution and a fair degree of accuracy. Based on the Tm estimates and the extracted Ts parameters at each reanalysis grid node, analyses of the relationship between Tm and Ts were performed without smoothing of data which will produce superior results than other similar studies. Analyses demonstrate that

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Establishment and analysis of global gridded Tm − Ts relationship model

Cited by 19 publications

References 15 publications

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

Development of time-varying global gridded <i>T</i><sub>s</sub>–<i>T</i><sub>m</sub> model for precise GPS–PWV retrieval

Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

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Establishment and analysis of global gridded Tm − Ts relationship model

Cited by 19 publications

References 15 publications

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

An ERA5-Based Hourly Global Pressure and Temperature (HGPT) Model

Development of time-varying global gridded &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;s&lt;/sub&gt;–&lt;i&gt;T&lt;/i&gt;&lt;sub&gt;m&lt;/sub&gt; model for precise GPS–PWV retrieval

Development of time-varying global gridded Ts-Tm model for precise GPS-PWV retrieval

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Development of time-varying global gridded <i>T</i><sub>s</sub>–<i>T</i><sub>m</sub> model for precise GPS–PWV retrieval