Tropospheric profiles of wet refractivity and humidity from the combination of remote sensing data sets and measurements on the ground

Abstract

Abstract. We reconstruct atmospheric wet refractivity profiles for the western part of Switzerland with a least-squares collocation approach from data sets of (a) zenith path delays that are a byproduct of the GPS (global positioning system) processing, (b) ground meteorological measurements, (c) wet refractivity profiles from radio occultations whose tangent points lie within the study area, and (d) radiosonde measurements. Wet refractivity is a parameter partly describing the propagation of electromagnetic waves and depends on the atmospheric parameters temperature and water vapour pressure. In addition, we have measurements of a lower Vband microwave radiometer at Payerne. It delivers temperature profiles at high temporal resolution, especially in the range from ground to 3000 m a.g.l., though vertical information content decreases with height. The temperature profiles together with the collocated wet refractivity profiles provide near-continuous dew point temperature or relative humidity profiles at Payerne for the study period from 2009 to 2011.In the validation of the humidity profiles, we adopt a twostep procedure. We first investigate the reconstruction quality of the wet refractivity profiles at the location of Payerne by comparing them to wet refractivity profiles computed from radiosonde profiles available for that location. We also assess the individual contributions of the data sets to the reconstruction quality and demonstrate a clear benefit from the data combination. Secondly, the accuracy of the conversion from wet refractivity to dew point temperature and relative humidity profiles with the radiometer temperature profiles is examined, comparing them also to radiosonde profiles.For the least-squares collocation solution combining GPS and ground meteorological measurements, we achieve the following error figures with respect to the radiosonde reference: maximum median offset of relative refractivity error is −16 % and quartiles are 5 % to 40 % for the lower troposphere. We further added 189 radio occultations that met our requirements. They mostly improved the accuracy in the upper troposphere. Maximum median offsets have decreased from 120 % relative error to 44 % at 8 km height. Dew point temperature profiles after the conversion with radiometer temperatures compare to radiosonde profiles as to: absolute dew point temperature errors in the lower troposphere have a maximum median offset of −2 K and maximum quartiles of 4.5 K. For relative humidity, we get a maximum mean offset of 7.3 %, with standard deviations of 12-20 %.The methodology presented allows us to reconstruct humidity profiles at any location where temperature profiles, but no atmospheric humidity measurements other than from GPS are available. Additional data sets of wet refractivity are shown to be easily integrated into the framework and strongly aid the reconstruction. Since the used data sets are all operational and available in near-realtime, we envisage the methodology of this paper to be a tool for nowcasting of clouds and r...

Tropospheric refractivity and zenith path delays from least-squares collocation of meteorological and GNSS data

“…Firstly, we perform the collocation procedure on the data set 'AWS/GNSS'. In the previous study from Hurter and Maier (2013), this data set exhibits the best accuracy w.r.t. the RS data in Payerne.…”

“…The software allows collocation and interpolation of meteorological parameters (temperature, air pressure, humidity) as well as ZTD and tropospheric refractivity. The most recent studies on this topic from Hurter and Maier (2013) are related to the 4-D interpolation of wet refractivity, dew point temperature and relative humidity for a 3-year period (2009)(2010)(2011) in Switzerland. Authors reconstruct the wet refractivity profiles from GNSS data, ground-based meteorological parameters and radio occultations at the location of the radiosonde (RS) station in Payerne and validate the results against RS observations.…”

“…Location of the RS in Payerne is marked with a star. Original version: Hurter and Maier (2013) have been applied: GNSS tomography (Notarpietro et al 2011;Troller et al 2002), NWP tomography (Rohm and Bosy 2011), algebraic reconstruction techniques (ART) (Bender et al 2011), radio occultations (Heise et al 2006), raytracing (Gegout et al 2011) and many others. The advantage of using the collocation technique is that it can provide a methodology to investigate individual instrument accuracies, because of a relatively easy implementation of additional data sources.…”

Overview: Tropospheric profiling: state of the art and future challenges – introduction to the AMT special issue

“…Results obtained considering pilot instrumentation should be extended to instrument networks, such as ground-based weather radars (Maiello et al, 2014) or microwave radiometers (Güldner, 2013). Several authors pointed out that instrument synergy should be better exploited to overcome intrinsic limitations and reduce the uncertainties of products, such as the PBL height Summa et al, 2013), atmospheric refractivity (Hurter and Maier, 2013), aerosol profiles (Marcos et al, 2013), and rainfall rates (Réchou et al, 2014). The availability of near-real-time data and smart information and communications technology tools is recognized as crucial for applications such as flight trajectory prediction , weather nowcasting (Hurter and Maier, 2013), and hazard early warning (Adachi et al, 2013).…”

“…Several authors pointed out that instrument synergy should be better exploited to overcome intrinsic limitations and reduce the uncertainties of products, such as the PBL height Summa et al, 2013), atmospheric refractivity (Hurter and Maier, 2013), aerosol profiles (Marcos et al, 2013), and rainfall rates (Réchou et al, 2014). The availability of near-real-time data and smart information and communications technology tools is recognized as crucial for applications such as flight trajectory prediction , weather nowcasting (Hurter and Maier, 2013), and hazard early warning (Adachi et al, 2013). On the other hand, Bock et al (2013) noted that long-term historical data sets may be reprocessed with new insight to infer by-products, such as integrated water vapor from astronomical spectrometers available starting at the beginning of the 20th century.…”