Comparing GPS Radio Occultation Observations with Radiosonde Measurements in the Australian Region

Norman, Robert; Marshall, J. Le; Zhang, K.; Wang, C. S.; Carter, Brett; Rohm, Witold; Manning, Toby; Gordon, S.; Li, Y.

doi:10.1007/978-3-642-37222-3_7

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…Furthermore, the RO profiles are not completely vertical, and the location of tangent points in each profile can vary up to 100 kilometers horizontally. [10]. To clarify that table (1) illustrates the mean, SD, MED, and MAE for long track location and short track location between GPS RO and RS at the days 20070515, and 20071023 respectively for El Arish station, the statistical values have lower values in short track location day than those of the long track location.…”

Section: Temperature Histogramsmentioning

confidence: 99%

“…Figure (10) shows relative frequency histograms for vapor pressure bias between RS stations and GPS RO data during the period (2007-2019) at available six stations over Egypt. As the relative frequency histograms for temperature bias, most of data are clustered around Zero, and the shape of the columns forms a symmetrical bell shape (normally distributed), and the same pattern occurs for relative frequency histograms of all stations as shown in figure (11).…”

Section: Vapor Pressure Histogramsmentioning

confidence: 99%

“…The time window is 1 hr, the comparison made between 925 and 10 hPa, according to their findings the global mean temperature bias was −0.09 K, with a standard deviation (SD) of 1.72 K, the results indicated that the RO temperature data agreed well with the RS data. The comparison over Antarctica showed in Norman et al (2014), the results showed good agreement between two techniques with biases less than 0.5 o C, the biases between 500 and 100 hPa were less than 0.25 o C. Xu et al (2017) compared two techniques over China, and the results showed that the vertical mean biases of temperature, pressure, and vapor pressure are − 0.10 K, 0.69 hPa, and − 0.01 hPa, respectively.…”

Section: Introductionmentioning

confidence: 95%

“…The collaboration Taiwan and USA satellite program (COSMIC) Constellation Observing System for Meteorology, Ionosphere and Climate launched in April 2006; this constellation orbits the earth at 800 km altitude and is considered a low earth orbiter (LEO). The system is composed of six micro-satellites; COSMIC data offers a unique source of meteorological data that gives detailed information about the earth's atmosphere, and improves atmospheric analyses and predictions [10].…”

Section: Introductionmentioning

confidence: 99%

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Statistical Validation of GNSS Radio Occultation data over Egypt

gomaa,

mousa,

Yousef

et al. 2024

Advances in Basic and Applied Sciences

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Radiosonde (RS) measurements of the upper atmosphere have long been the principal data source, with measurements spanning more than 60 years . RS has a limited number of stations in Egypt. Thus; it has the poor spatial and temporal resolution, and This negatively affects meteorological research, atmospheric observation, and weather forecasting studies over Egypt. We need to find an additional technique characterized by the length of the available time series and with the high vertical resolution. Global Positioning System Radio Occultation (GPS RO) is the satellite technique received by a low-Earth orbiting satellite to profile earth's atmosphere with high vertical resolution and global coverage. This technique been found to enhance weather forecasting and climate monitoring; In this study, the GPS RO temperature and vapor pressure profiles of the COSMIC satellite mission over Egypt from 2007 to 2019 been validated with those of the available six Radiosonde stations (RS); GPS RO and RS profiles collocated by matching the position and the time, The horizontal distance between the chosen RS stations and the GPS RO event is within 150 km, and the time window was 3 hours; The comparison was performed only on the standard pressure level. One of the objectives of this paper is to check the accuracy of the GPS RO at different elevations; For this aim, the differences between RS and GPS RO were sorted according to pressure level in 4 groups; It is found from the results that GPS RO profiles agree well with RS data; The mean temperature bias for all stations is equal (0.22) with a standard deviation (1.83); On the other hand, mean vapor pressure bias is equal (-0.02) with standard deviation (0.12); According to pressure level sorting, the change of temperature means bias and standard deviation increases with the pressure level decreasing.

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Section: Temperature Histogramsmentioning

confidence: 99%

Section: Vapor Pressure Histogramsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Statistical Validation of GNSS Radio Occultation data over Egypt

gomaa,

mousa,

Yousef

et al. 2024

Advances in Basic and Applied Sciences

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“…The tool package consists of sensors measuring of temperature, pressure, relative humidity, and wind speed profiles of the atmosphere from the Earth's surface to a height of 30 km. Although it is one of the major networks and excellent instrument to measure PWV vertically, it has some drawbacks for short-term studies in covering temporal and spatial variability as the device restricts the number of launches to twice per day (00:00 UTC and 12:00 UTC) [4]. The PWV estimated from GPS has been applied such as for study the El Niño-Southern Oscillation (ENSO) activity [5,6], flash floods [7], lightning activity [8], climate change [9,10] To overcome the limitation of radiosonde, Bevis et al [3] has proposed the principle of estimating tropospheric PWV by using ground-based GPS.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Atmospheric Water Vapor from ANFIS Technique and Its Validation with GPS Data

Suparta¹,

Alhasa

2019

INFOTEL

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Adaptive neuro-fuzzy inference system (ANFIS) is a prospective approach in modeling weather parameters based on learning from historical data used. This study presented the comparison of tropospheric precipitable water vapor (PWV) between ANFIS and Global Positioning System (GPS) for areas in Pekan, Pahang, Malaysia. The PWV value was estimated with the ANFIS model with the surface meteorological data as inputs. The accuracy of PWV from ANFIS has been validated with PWV from GPS measurements for the period of 2010. The result showed that the ANFIS PWV has a similar trend with the GPS PWV (r = 0.999 at the 99% confidence level) and found a difference of 0.024%. The PWV from ANFIS was calculated 0.035% higher compared to GPS PWV and found a similar character in two seasonal monsoons. This indicates that the PWV obtained with ANFIS model agreed very well with GPS measurements and it can be implemented to monitor atmospheric variability as well as climate change studies in the absence of GPS data

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