5:15 PM - 7:15 PM
[MTT37-P07] A study on local-scale variations in tropospheric delay using observational data sets with dense GNSS stations
Keywords:GNSS, Tropospheric delay, Water vapor
As meteorological perspectives, it is important to reveal mechanisms of generation, growth and disappearance for local-scale phenomena such as a cumulus cloud, a cloud over the island with an isolated mountain, and a sea breeze front, etc. Because atmospheric water vapor plays important roles in the progression of such phenomena including ones which are sequentially generated by them, it is expected to observe its spatial and temporal variations with a horizontal scale of a few kilometers. However, in general, it is a challenging subject to observe spatial and temporal changes of atmospheric water vapor with good resolutions for observing the phenomena. In this study, we focus on tropospheric delay variations due to water vapor distribution using data sets received by dense GNSS stations in order to extract and use the information for observing local-scale meteorological phenomena.
We consider two kinds of observations, which are horizontal and vertical variations of atmospheric water vapor distribution for local-scale phenomena. First, as the observation with a horizontal variation, we try to extract small variations in tropospheric delay within a few kilometers with measures to mitigate multipath and other error sources. The Electronic Navigation Research Institute (ENRI) has installed and operated a set of five GNSS stations within a horizontal distance of about 2 km each at New Ishigaki Airport and Sendai Airport in order to collect continuous GNSS data for research purposes. There, it is frequently observed that variations in tropospheric slant delay differences with a magnitude of up to about 0.1 m between two GNSS stations in sunny and warm days during spring to autumn seasons. This kind of observational events has also reported by the previous studies in US and Europe, and it has been pointed out that their variations are consistent with the surface wind velocities. Therefore, we aim to investigate the phenomena in detail with a wider GNSS stations including GEONET and SoftBank GNSS network stations in addition to our five GNSS stations.
As the second subject, which is the observation with a vertical variation, we consider possibility of observing the spatial variation of atmospheric water vapor in the vertical using a set of GNSS stations with height differences, including its temporal and seasonal variations. In general, atmospheric propaagtion delay derived from a ground GNSS station is obtained as the sum of the propagation delay from the upper atmosphere to the lower atmosphere. In order to extract tropospheric delay for a lower layer, we focus on two GNSS stations that are closely located with a height difference of over 500 meters and within a horizontal distance of about 10 kilometers. For example, a data set of GNSS stations near the top of an isolated mountain and at its foot is a possible candidate. Using such a data set of GNSS stations, it is expected to distinguish the tropospheric delay in lower layer below the top of the mountain from the upper atmosphere above the top based on the difference between their observational results for tropospheric delay. Although there are candidates in the GEONET network stations, inclusion of SoftBank stations has an advantage to expand candidate observation areas. Because these tropospheric delay differences are relatively small, one of the challenges is to extract these small variations due to water vapor with distinction from error sources. For example, radiosonde observations throughout a year in the Kanto region show that the contribution of water vapor to the tropospheric delay in the zenith direction from the ground to an altitude of 900 meters varies in the range of about 0.01 to 0.1 meters. Therefore, we try to examine whether seasonal changes and/or variations during meteorological disturbances such a passage of cold front can be reflected in the results. If these changes and variations can be extracted against error sources such as multipath, antenna phase variations and etc., it could be useful to investigate mechanisms and predictions of clouds over islands.
In the meeting, we plan to present initial results of the analysis using dense GNSS station data targeting these two kinds of GNSS measurements for observing local-scale weather phenomena.
Acknowledgements:
The SoftBank's GNSS observation data used in this study was provided by SoftBank Corp. and ALES Corp. through the framework of the "Consortium to utilize the SoftBank original reference sites for Earth and Space Science".
We consider two kinds of observations, which are horizontal and vertical variations of atmospheric water vapor distribution for local-scale phenomena. First, as the observation with a horizontal variation, we try to extract small variations in tropospheric delay within a few kilometers with measures to mitigate multipath and other error sources. The Electronic Navigation Research Institute (ENRI) has installed and operated a set of five GNSS stations within a horizontal distance of about 2 km each at New Ishigaki Airport and Sendai Airport in order to collect continuous GNSS data for research purposes. There, it is frequently observed that variations in tropospheric slant delay differences with a magnitude of up to about 0.1 m between two GNSS stations in sunny and warm days during spring to autumn seasons. This kind of observational events has also reported by the previous studies in US and Europe, and it has been pointed out that their variations are consistent with the surface wind velocities. Therefore, we aim to investigate the phenomena in detail with a wider GNSS stations including GEONET and SoftBank GNSS network stations in addition to our five GNSS stations.
As the second subject, which is the observation with a vertical variation, we consider possibility of observing the spatial variation of atmospheric water vapor in the vertical using a set of GNSS stations with height differences, including its temporal and seasonal variations. In general, atmospheric propaagtion delay derived from a ground GNSS station is obtained as the sum of the propagation delay from the upper atmosphere to the lower atmosphere. In order to extract tropospheric delay for a lower layer, we focus on two GNSS stations that are closely located with a height difference of over 500 meters and within a horizontal distance of about 10 kilometers. For example, a data set of GNSS stations near the top of an isolated mountain and at its foot is a possible candidate. Using such a data set of GNSS stations, it is expected to distinguish the tropospheric delay in lower layer below the top of the mountain from the upper atmosphere above the top based on the difference between their observational results for tropospheric delay. Although there are candidates in the GEONET network stations, inclusion of SoftBank stations has an advantage to expand candidate observation areas. Because these tropospheric delay differences are relatively small, one of the challenges is to extract these small variations due to water vapor with distinction from error sources. For example, radiosonde observations throughout a year in the Kanto region show that the contribution of water vapor to the tropospheric delay in the zenith direction from the ground to an altitude of 900 meters varies in the range of about 0.01 to 0.1 meters. Therefore, we try to examine whether seasonal changes and/or variations during meteorological disturbances such a passage of cold front can be reflected in the results. If these changes and variations can be extracted against error sources such as multipath, antenna phase variations and etc., it could be useful to investigate mechanisms and predictions of clouds over islands.
In the meeting, we plan to present initial results of the analysis using dense GNSS station data targeting these two kinds of GNSS measurements for observing local-scale weather phenomena.
Acknowledgements:
The SoftBank's GNSS observation data used in this study was provided by SoftBank Corp. and ALES Corp. through the framework of the "Consortium to utilize the SoftBank original reference sites for Earth and Space Science".