Estimation of tropospheric delay for microwaves from surface weather data

Askne, J.; Nordius, H.

doi:10.1029/rs022i003p00379

Cited by 472 publications

(277 citation statements)

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“…In a second step, the ZWD is converted into IWV (in kg m −2 or mm) using the following relationship (Hogg et al, 1981;Askne and Nordius, 1987;Bevis et al, 1992Bevis et al, , 1994:…”

Section: Ztd To Iwv Conversion Schemementioning

confidence: 99%

A multi-site intercomparison of integrated water vapour observations for climate change analysis

et al. 2014

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Abstract. Water vapour plays a dominant role in the climate change debate. However, observing water vapour over a climatological time period in a consistent and homogeneous manner is challenging. On one hand, networks of groundbased instruments able to retrieve homogeneous integrated water vapour (IWV) data sets are being set up. Typical examples are Global Navigation Satellite System (GNSS) observation networks such as the International GNSS Service (IGS), with continuous GPS (Global Positioning System) observations spanning over the last 15+ years, and the AErosol RObotic NETwork (AERONET), providing long-term observations performed with standardized and well-calibrated sun photometers. On the other hand, satellite-based measurements of IWV already have a time span of over 10 years (e.g. AIRS) or are being merged to create long-term time series (e.g. GOME, SCIAMACHY, and GOME-2).This study performs an intercomparison of IWV measurements from satellite devices (in the visible, GOME/SCIAMACHY/GOME-2, and in the thermal infrared, AIRS), in situ measurements (radiosondes) and ground-based instruments (GPS, sun photometer), to assess their use in water vapour trends analysis. To this end, we selected 28 sites world-wide for which GPS observations can directly be compared with coincident satellite IWV observations, together with sun photometer and/or radiosonde measurements. The mean biases of the different techniques compared to the GPS estimates vary only between −0.3 to 0.5 mm of IWV. Nevertheless these small biases are accompanied by large standard deviations (SD), especially for the satellite instruments. In particular, we analysed the impact of clouds on the IWV agreement. The influence of specific issues for each instrument on the intercomparison is also investigated (e.g. the distance between the satellite ground pixel centre and the co-located ground-based station, the satellite scan angle, daytime/nighttime differences). Furthermore, we checked if the properties of the IWV scatter plots between these different instruments are dependent on the geography and/or altitude of the station. For all considered instruments, the only dependency clearly detected is with latitude: the SD of the IWV observations with respect to the GPS IWV retrievals decreases with increasing latitude and decreasing mean IWV.

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“…In a second step, the ZWD is converted into IWV (in kg m −2 or mm) using the following relationship (Hogg et al, 1981;Askne and Nordius, 1987;Bevis et al, 1992Bevis et al, , 1994:…”

Section: Ztd To Iwv Conversion Schemementioning

confidence: 99%

A multi-site intercomparison of integrated water vapour observations for climate change analysis

et al. 2014

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“…The wet delay is the propagation delay experienced by GPS signals due mainly to water vapour. The physical relation for the quotient Q, between the wet delay (l w ) and the IPWV (I) is given by Askne & Nordius (1987): where ρ is the density of liquid water, R v is the specific gas constant of water vapour, equal to 461.524 J kg -1 K -1 , and the atmospheric refractivity constants k 3 and k 2 ′ are approximately 3.7 × 10 5 K 2 mbar -1 and 22 K mbar -1 respectively. T m is defined by where ρ v is the water vapour density, T is the temperature, and z is the vertical coordinate.…”

Section: Introductionmentioning

confidence: 99%

On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere

Emardson

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2000

Meteorological Applications

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“…Table 10 summarizes the current user requirements for NWP nowcasting; however, during the new COST Action ES1206 (GNSS4SWEC), these requirements will be revised. The typical value of the dimensionless conversion factor Q (Askne and Nordius 1987) used for the conversion of ZWD to IWV is approximately 6, and therefore, 1 kg/m 2 of IWV is equivalent to about 6 mm of ZTD (Glowacki et al GPS Solut (2016) 20:187-199 195 2006). Using this equivalence, the accuracy requirements for IWV can be translated into their equivalent for ZTD, which are 6 mm (0.6 cm) target and 30 mm (3 cm) threshold values.…”

Section: Discussionmentioning

confidence: 99%

Comparative analysis of real-time precise point positioning zenith total delay estimates

et al. 2014

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The continuous evolution of global navigation satellite systems (GNSS) meteorology has led to an increased use of associated observations for operational modern low-latency numerical weather prediction (NWP) models, which assimilate GNSS-derived zenith total delay (ZTD) estimates. The development of NWP models with faster assimilation cycles, e.g., 1-h assimilation cycle in the rapid update cycle NWP model, has increased the interest of the meteorological community toward sub-hour ZTD estimates. The suitability of real-time ZTD estimates obtained from three different precise point positioning software packages has been assessed by comparing them with the state-of-the-art IGS final troposphere product as well as collocated radiosonde (RS) observations. The ZTD estimates obtained by BNC2.7 show a mean bias of 0.21 cm, and those obtained by the G-Nut/Tefnut software library show a mean bias of 1.09 cm to the IGS final troposphere product. In comparison with the RS-based ZTD, the BNC2.7 solutions show mean biases between 1 and 2 cm, whereas the G-Nut/Tefnut solutions show mean biases between 2 and 3 cm with the RS-based ZTD, and the ambiguity float and ambiguity fixed solutions obtained by PPP-Wizard have mean biases between 6 and 7 cm with the references. The large biases in the time series from PPP-Wizard are due to the fact that this software has been developed for kinematic applications and hence does not apply receiver antenna eccentricity and phase center offset (PCO) corrections on the observations. Application of the eccentricity and PCO corrections to the a priori coordinates has resulted in a 66 % reduction of bias in the PPP-Wizard solutions. The biases are found to be stable over the whole period of the comparison, which are criteria (rather than the magnitude of the bias) for the suitability of ZTD estimates for use in NWP nowcasting. A millimeter-level impact on the ZTD estimates has also been observed in relation to ambiguity resolution. As a result of a comparison with the established user requirements for NWP nowcasting, it was found that both the G-Nut/Tefnut solutions and one of the BNC2.7 solutions meet the threshold requirements, whereas one of the BNC2.7 solution and both the PPPWizard solutions currently exceed this threshold.

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Estimation of tropospheric delay for microwaves from surface weather data

Cited by 472 publications

References 6 publications

A multi-site intercomparison of integrated water vapour observations for climate change analysis

A multi-site intercomparison of integrated water vapour observations for climate change analysis

On the relation between the wet delay and the integrated precipitable water vapour in the European atmosphere

Comparative analysis of real-time precise point positioning zenith total delay estimates

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