- Email: [email protected]
The usefulness of the Global Navigation Satellite Systems (GNSS) in the analysis of precipitation events
The usefulness of the Global Navigation Satellite Systems (GNSS) in the analysis of precipitation events Stefania Bonafoni, Riccardo Biondi PII: DOI: Reference:
S0169-8095(15)00223-9 doi: 10.1016/j.atmosres.2015.07.011 ATMOS 3458
To appear in:
Atmospheric Research
Received date: Revised date: Accepted date:
13 January 2015 13 July 2015 15 July 2015
Please cite this article as: Bonafoni, Stefania, Biondi, Riccardo, The usefulness of the Global Navigation Satellite Systems (GNSS) in the analysis of precipitation events, Atmospheric Research (2015), doi: 10.1016/j.atmosres.2015.07.011
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Dept. of Engineering, University of Perugia, via G. Duranti 93, 06125, Perugia, Italy; ph. +390755853663, [email protected] 2
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Stefania Bonafoni1*, Riccardo Biondi2,
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The usefulness of the Global Navigation Satellite Systems (GNSS) in the analysis of precipitation events
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
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*Corresponding author: [email protected]; Tel.: +390755853663,
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Abstract It is well known that the use of the Global Navigation Satellite Systems (GNSS), both with ground-based and Low Earth Orbit (LEO) receivers, allows to retrieve atmospheric parameters in all the weather conditions. Ground-based GNSS technique provides the integrated precipitable water vapor (IPWV) with temporal continuity at a specific receiver station, while the GNSS LEO technique allows for Radio Occultation (RO) observations of the atmosphere, providing a detailed atmospheric profiling but without temporal continuity at a specific site. In this work, several precipitation events occurred in Italy were analyzed exploiting the potential of the two GNSS techniques (i.e. ground-based and space-based GNSS receivers). From ground-based receivers, time series of IPWV were produced at specific locations with the purpose of analysing the water vapor behaviour during precipitation events. From LEO receivers, the profiling potential was exploited to retrieve the cloud top altitude of convective events, taking into account that although GNSS RO could capture the dynamics of the atmosphere with high vertical resolution, the temporal resolution is not enough to continuously monitor such an event in a local area. Therefore, the GNSS technique can be considered as a supplemental meteorological system useful in studying precipitation events, but with very different spatial and temporal features depending on the receiver positioning. Keywords: GNSS technique; precipitation events; integrated precipitable water vapor; cloud top altitude. 1. Introduction
It is well known that signals from the Global Navigation Satellite Systems (GNSS), which include for example the U.S. Global Positioning System (GPS), the Russian GLONASS, the future European Galileo, the Chinese COMPASS, commonly processed for navigation purposes, can also be used to characterize media where they propagate in. In the last decade, GNSS atmospheric and Earth’s surface remote sensing has become more and more important, thanks to technical improvements applied to the processing of such “free-of-charge”, everywhere available and weather insensitive signal. For example, the remote sensing of the wet part of the troposphere is possible “extracting” the atmospheric delays from the carrier phases of GNSS observations. Part of these delays, accumulated by the signal along its propagation path, can be associated to the water vapor. Considering the growing employment of ground-based GNSS receivers for the estimation of integrated precipitable water vapor (IPWV), time series of IPWV become available with high temporal and spatial resolution, taking into account that GNSS can be considered an all-weather system (Bevis et al., 1992, 1994; Koulali et al., 2012; Rohm et al., 2014). Concerning the impact of the rain on the GNSS signal, Solheim et al. (1999) proposed theoretical results
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on the microwave propagation delays induced by rain and other atmospheric constituents, while Champollion et al. (2004) and Brenot et al. (2006) analyzed the sensitivity of the total delay in the presence of severe precipitation events. Experimental measurements for monitoring the atmospheric water vapor are important for enabling reliable climate studies and to characterise the influence of the atmosphere on microwave propagation. For instance, the knowledge of water vapor field comes usually from radiosonde observations (RAOB’s), but the availability of data two or four times per day does not meet the requirement of frequent sampling of such parameter, taking into account the high degree of variability. Also, the ground-based microwave radiometers, that are able to work continuously for the retrieval the IPWV with a high temporal resolution, provide measurements not reliable during rainfall. Currently, different techniques employing in situ and remote sensing sensors are used to monitor the atmospheric water vapor, as reported and analyzed in Kämpfer (2013). More recently it was found that the GNSS satellite constellation also allows for Radio Occultation (RO) observations of the Earth’s atmosphere using one or more GNSS receivers onboard Low Earth Orbit (LEO) satellites. These space-based RO observations permit routine profiling of the atmospheric parameters in any meteorological condition with global coverage, high vertical resolution and precision based on limb sounding geometry (Kursinski et al., 1997, Steiner et al., 2013). Pilot study investigated the viability of using space-based GNSS RO technique to study and monitor precipitation events, especially severe thunderstorms and cyclones. Huang et al. (2005) demonstrated the improvement in forecasting the tropical cyclone (TC) best tracks using the RO refractivity; Biondi et al. (2012 and 2013) showed the thermal structure of convective systems and tropical cyclones developing a new technique for detecting their cloud top altitude; Vergados et al. (2014) estimated the wind intensity of hurricanes using temperature profiles from GPS RO measurements. Biondi et al. (2014) showed the different thermal structure of TCs reaching different altitudes according to the development ocean basin.With more than 10 years of GNSS RO observation availability, these acquisitions are important and well suited for climatological studies (Foelsche et al., 2008) thanks to the RO long-term stability and self-calibrated nature, improving operational analysis and global and regional weather forecast (Cardinali, 2009). However, the lower troposphere is a region of great uncertainty when using the GNSS RO technique to retrieve atmospheric profiles, due to the large amount of water vapor which introduces super-refraction and multipath effects (Kursinski et al., 1997). Therefore, GNSS RO allows a detailed atmospheric profiling excepting near the Earth’s surface, but without the temporal resolution required to continuously monitor precipitation events which could take place in a local area and over different period of time. Instead, the ground-based GNSS technique provides reliable integrated water vapor values with high temporal resolution at the receiver station. The spatial resolution of the distribution of the IPWV is dependent on the density of the GNSS stations. In this work, several precipitation events occurred in different zones of Italy and during different years are analyzed exploiting the potential of the two GNSS techniques, i.e. ground-based and LEO GNSS receivers. First, on the basis of previous experimental assessments of IPWV retrieval by GNSS measurements at specific receiver locations with high accuracy (Basili et al., 2001, Bonafoni et al., 2013), the detection of the behaviour of the IPWV during precipitative systems is proposed. For this purpose, a statistical characterization of the trend of the atmospheric water vapor content just before and during the precipitation events is provided. The precipitation systems have been selected during about two years of measurements at four Italian GNSS stations with available co-located automatic rainfall recorders. From literature, different works have been dealt with the analysis of the water vapor from ground-based GNSS during precipitation events, frequently single severe events, (Choy et al, 2013, Champollion et al., 2004; Koulali et al., 2012; Brenot, 2013; Rohm et al., 2014): in this work both a statistical study of events classified in terms of duration and intensity and a detailed study of single significant events is performed, considering four Italian stations with different climatological and geographical conditions.
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Concerning the GNSS LEO, the cloud top altitude from the RO profiles is retrieved during convective systems selected again in different zones of Italy and during different years: then, a first analysis aimed to find a link between the cloud top and the rain intensity is carried out.
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2. IPWV measurements using ground-based GNSS receiver
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In this study 4 Italian GNSS receiver stations, Perugia, Genova, Cagliari and L’Aquila, were selected (coordinates in Tab. 1) belonging to the Italian GNSS fiducial network managed by ASI/CGS (Agezia Spaziale Italiana/Centro di Geodesia Spaziale), with available co-located weather stations with rainfall measurement systems. ASI/CGS delivers GNSS data to the users through the EUREF (European Geodetic Reference Systems) local data center GeoDAF (Geodetic Data Archiving Facility, http://geodaf.mt.asi.it/). The use of ground-based GNSS receivers for IPWV estimation is a well established technique (Bevis et al., 1992, 1994; Rocken et al., 1995; Wolfe and Gutman, 2000; Basili et al., 2001; Bonafoni et al., 2013). It is based on measurements of the tropospheric delay affecting the signals during their propagation from the GNSS satellites to the receivers on ground. Basically, the Zenith Total Delay (ZTD), that is the excess path length due to the signal travel through the troposphere at zenith, is used to estimate the IPWV. The dispersive ionospheric effect is removed by a linear combination of dual frequency data (Klobuchar and Kunches, 2001). ASI/CGS provides to EUREF daily final Zenith Total Delay (ZTD) estimates in SINEX TRO files of the considered network. The delivered GNSS data are processed by using the JPL GIPSY-OASIS II software, with a time resolution of 15 minutes. The retrieval algorithm to estimate IPWV from the zenith total delay is based to the decomposition of ZTD into two components: the Zenith Hydrostatic Delay (ZHD), that is mainly dependent on the dry air gasses in the atmosphere and accounts for approximately 90% of the delay, and the Zenith Wet Delay (ZWD) that depends entirely on the moisture content of the atmosphere. Therefore, the ZWD is given by: ZWD = ZTD - ZHD
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Applying the well-known Saastamoinen model (Saastamoinen, 1972) to accurate surface pressure measurements, the ZHD can be predicted with high accuracy and compute the ZWD using (1). Then, it is possible to convert the ZWD values into IPWV ones by using the following relationship: IPWV = П·ZWD
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where the factor П is a function of various physical constants and of the weighted mean temperature Tm of the whole atmosphere above the GNSS antenna (Davis et al., 1985; Askne and Nordius, 1987): ( e / T )dz Tm 2 (e / T )dz 10
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Rv [( k3 / Tm ) (k 2 mk 1 )]
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where dz has units of length in the zenith, T is the air temperature (K), e is the partial pressure of water vapor (hPa), is the density of liquid water (g/m3), Rv is the specific gas constant for water vapor, m is the ratio of molar masses of water vapor and dry air, and k1, k2, k3 are the constants defined by Thayer (1974) as: k1 77.6036 ; k2 64.79; k3 3.776 10 5 .
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3. IPWV monitoring of precipitation events
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The transformation of ZWD into IPWV assumes that the wet path delay is entirely due to water vapor, and that liquid water and ice do not contribute significantly to it (Duan et al., 1996). The time-varying parameter can be estimated with such an accuracy that very little uncertainty is introduced during the computation of (2). Bevis et al. (1994) showed that in most practical conditions the uncertainty in the parameter estimationis essentially due to the uncertainty for Tm (predicted from the surface temperature Ts on the basis of a linear regression), leading to a relative error in on the order of 2%. In this work we have computed estimating Tm by linear regression on Ts (available from the weather stations) with coefficients determined using RAOB observations at seven Italian stations (12128 RAOB atmospheric profiles from 1999 to 2000): this lead to an estimate error on Tm having a standard deviation of 3.4 K, corresponding to a relative error on the value of slightly grater to 1%. Examples of different approaches and accuracies in the computation of and in the estimation of IPWV are shown in Basili et al. (2001). Concerning the prediction of ZHD with the Saastamoinen model, we have employed the surface pressure values corresponding in time to the ZTD data set of the GNSS stations available from the co-located weather stations.
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The automatic rain recorder systems of Perugia, Genova, Cagliari, L’Aquila, co-located near the GNSS receivers, provide values of cumulated rain on a time period of fifteen, thirty, fifteen and sixty minutes, respectively. For each rain event, an atmospheric water vapor monitoring has been performed exploiting approximately two years (2000 and 2001) of IPWV estimations from GNSS observations. Based on these rainfall measurements, taking into account that the four sites present different climatological and geographical conditions, a two-step classification of precipitation events has been performed on the basis of time duration and cumulated rain. First, for each station, the precipitation events have been classified into few classes of different duration, statistically significant for the site considered: for such classes a first study of the IPWV behaviour just before and during the rainfall has been performed. Table 1 synthesises the classification of the precipitation events for the four stations in terms of duration, reporting also the cumulated rain. Genova site exhibits more intense and frequent rainy phenomena.
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Table 1. Classification of precipitation events for the four Italian stations
Duration (hours)
MIN cumulated MAX cumulated Averaged Number of rain (mm) rain (mm) cumulated rain (mm) events Perugia 43.119 °N, 12.355 °E (from January 2000 to October 2001) >3 4.8 25.8 11.6 15 >1 and <3 0.8 38.2 4.70 94 <1 0.4 20.2 2.23 172 Genova 44.419 °N, 8.921 °E (from January 1999 to October 2001) >9 16.8 89.6 47.11 16 >3 and <9 1.6 116.4 15.80 94 >1 and <3 0.4 59.6 3.40 292 Cagliari 39.135 °N, 8.973 °E (from December 2000 to October 2001) >3 8.2 21 13.23 6 >1 and <3 1.2 19.6 5.02 27 <1 0.4 5.6 1.42 59
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L’Aquila 42.368°N, 13.350°E (from December 2000 to October 2001) >8 10.2 25 17.6 >3 and <8 0.6 19.4 6.01 >1 and <2 0.03 7.4 0.71
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Fig.1 shows the averaged IPWV time series for each station from one hour and half before until two hours after the beginning of the rainfall for the classified events (except for L’Aquila where the time intervals are greater since the cumulated rain is provided hourly). In these figures precipitation events of the same class (i.e. of the same time duration) with different cumulated rain are assembled together. Even if the mean level of IPWV is different at each sites, a general increase of IPWV field just before the rainfall can be noted. During the rainfall, the averaged IPWV evolution has not the same behaviour, but with the water vapor values reaching a plateau or decreasing: the only exception is for heavy rain of long duration, as the case of precipitation events longer than 9 hours, observed at Genova, or longer than 8 hours, at L’Aquila.
Fig. 1. Averaged IPWV (mm) values for rainy events classified in terms of duration (hours), for the four GNSS stations. The vertical line marks the beginning of the rainfall.
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Taking into account the statistically significant rain rate values for each site, the Fig. 2 shows the averaged IPWV time series three hours before the rainfall and for the first hour of precipitation for events classified in terms of rain rate. Again, a general increase of water vapor content can be recognized, but now is more evident the increased IPWV mean level for rain rate growing values. This fact suggests that most probably the convective systems are characterized by a high IPWV pattern.
Fig. 2. Averaged IPWV (mm) values for rainy events classified in terms of rain rate (mm/h), for the four GNSS stations. The vertical line marks the beginning of the rainfall. Due to the occurrence of less intense rainy events for Cagliari and L’Aquila sites the rain rate values of the selected classes are lower than the ones selected for Genova and Perugia, but the same average trend is noted. The results of Fig. 1 and Fig. 2 can be summarized in Table 2, where the percentage increase of the IPWV before the beginning of the precipitation and the percentage of events having a positive IPWV gradient are reported.
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Perugia 12% (100%) 5.5% (78%) 5.4% (72%)
Cagliari -1.5% (0%) 5.4% (84%) 7% (86 %)
L’Aquila 1.5% (100%) 5.9% (90%) 2.2% (68%)
3.4% (53%) 2.2% (44%) 0.4% (43%)
16% (70%) -1.7% (60%) 10% (75%)
13.8% (78%) 3.6% (44%)
8.3% (79%) 13.7% (55%)
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Genova 2.3% (92%) 4.1% (84%) -2.5% (57%)
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Duration Long Medium Short Intensity High Medium Low
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Table 2. Percentage increase of the IPWV before the beginning of the precipitation (1.5 hours before for the duration classification, 3 hours before for the intensity classification). In the brackets the percentage of events having a positive IPWV gradient is reported. Duration and intensity refer to the classifications reported in Fig. 1 and Fig. 2.
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Table 2 suggests that the IPWV average increase before the precipitation is variable, considering that the four stations have different climatological and geographical conditions, and that some events inside each class have a decreasing trend despite the mean behaviour. For instance, all the events with duration greater than 3 hours occurred at Cagliari have a decreasing IPWV trend before precipitation. Now, a monitoring of the IPWV for specific events is proposed, taking into account the intensity of the rainfall. In Fig. 3, the IPWV behaviour (blue line) and the correspondent rain rate (green line) are plotted together (before, during and after the precipitation), for a long rainy event occurred at Genova the 5 th November 2000. We can recognize the increase of IPWV before the rainfall, but also the continuous climbing trends until the maximum rain rate (16 mm/h), and after a decreasing. Also, a rapid upward trend just before the end of rainfall is present, where a relative peak of rainfall has been recorded. In general, a light lead time of the IPWV peaks with respect to to the rain rate peaks can be noted. This behaviour seems to suggest that the water vapor fuels the rain event and reveals a correlation between the IPWV trend and the amount of precipitation.
Fig. 3. Genova: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 5th November 2000, h.22.30, duration 20 hours.
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Fig. 4 reports the same analysis for the longest event occurred at Perugia the 15 th October 2000. The IPWV time series shows an upward trend before the rainfall and an evident decreasing until the end of the precipitation (IPWV difference before and after of about 10 mm). Here the peak during the rainfall are not so evident as in the previous case of Genova since the rain rate level are less discontinuous.
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Fig. 4. Perugia: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 15th November 2000, h.08.00, duration about 6 hours.
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Fig. 5 and 6 show the same analysis for the Cagliari and L’Aquila sites, where again the greater IPWV peak is in advance with respect to the rain rate peak.
Fig. 5. Cagliari: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 21st December 2000, h.15.30, duration 5.30 hours.
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Fig. 6. L’Aquila: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 17th August 2000, h.17.00, duration 4 hours.
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This study proves that precipitable water vapor data from ground-based GNSS receivers can be useful in weather analysis and modelling applications (Pacione et al., 2001; De Pondeca et al., 2001; Nakamura et al., 2004; Seko et al., 2004; Zhang et al., 2007) taking into account that GNSS ground stations are in abundance and the system is not very sensitive to rain for intensities below 40 mm/h (Solheim et al., 1999). This preliminary results show that GNSS data could help in analysing some features of a rain event but to tune a “quantitative” criteria for the definition of a precipitation event other information are necessary, such as cloud profile, cloud liquid content, radar/lidar measurements and so on (Brenot et al., 2013; Champollion et al., 2004; Michaelides et al., 2009; Yan et al., 2014). In fact the IPWV average increase before the precipitation is variable and also depends on climatological and geographical conditions, with some events showing even a decreasing trend. Therefore, even if this methodology cannot directly predict precipitations, it can describe the IPWV behaviour in the troposphere including the short-term variability. Also, the IPWV was analyzed at a single station, providing information of the horizontal water vapor distribution only on the surrounding area of the station: to provide an enlarged horizontal inspection a dense local network of ground-based GNSS receivers is required. For instance, a suggestion of possible strategies to properly distribute the GNSS stations to achieve a desired spatial resolution in the IPWV estimation, crowding them in some prescribed regions, are provided in Bonafoni et al. (2013). 4. The GNSS radio occultation technique The radio occultation technique uses phase and amplitude of the two L-band signals transmitted from GNSS satellites and measured by the receivers on board of LEO satellites. When the signal passes through the atmosphere it is refracted due to refractive index gradients and the correspondent measurement can be characterized by the bending angle, , as a function of the impact parameter a. Assuming local spherical symmetry, the refractive index profile, n, related to a given impact parameter (rp ), can be derived from and a using the Abel inversion (Kursinski et al., 1997):
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ar p
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Pd P P 72 w 3.75 105 w2 T T T
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where arp = n(rp)rp is the impact parameter for the ray whose tangent radius is rp. The refractivity profile is then N=(n-1)106, that at microwave wavelength is given by (Thayer, 1974): (6)
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where Pd is the pressure of dry air in hPa, Pw the partial pressure of water vapor in hPa, T is the atmospheric temperature in Kelvin. This equation is accurate to within 0.5% for frequencies up to 100 GHz. The water vapor term (third of eq. 6) is small in the upper troposphere and stratosphere, so the refractivity can be used to directly calculate the so-called dry temperature and dry pressure. Commonly eq. 6 can be solved to obtain estimates of Pw, Pd, and T by assimilating the refractivity together with first-guess profiles from, e.g., European Centre for Medium-Range Weather Forecasts (ECMWF), using a one-dimensional variational (1DVar) technique (Healy and Eyre, 2000). The vertical resolution provided by RO ranges from 60 m in the troposphere to 1 km in the stratosphere (Gorbunov et al., 2004) and the soundings have the highest accuracy between 5 and 25 km of altitude with observational errors, including measurement and representativeness errors, in the range 0.3% to 0.5% in refractivity (ScherllinPirscher et al., 2011). The RO horizontal resolution can range from 70 km up to about 300 km and this creates some uncertainty issues when studying small scale events. For this work we have used the GPS RO products Level 2 (L2) processed by the Wegener Center for Climate and Global Change (WEGC) through the new Occultation Processing System (OPS) version 5.6 (Kirchengast et al., 2007) based on University Corporation for Atmospheric Research (UCAR) orbit and excess phase data, version 2010.2640 (Schwaerz et al., 2013). The WEGC OPSv5.6 is based on a geometrics optics retrieval combined with a wave optics retrieval in the lower and middle troposphere. A bending angle optimization is performed at high altitudes with co-located short-range forecast profiles of the ECMWF. The physical temperature is retrieved on the basis of an optimal estimation with co-located ECMWF short-term forecast profiles as background data. Although the GNSS RO technique is exploited to obtain profiles of refractivity, temperature, pressure and humidity in the atmosphere at global scale, the integrated water vapor values (retrieved by GNSS ground-based receivers as previously shown) are not measurable with reliability by the LEO receivers due to spatial and temporal reasons: - GNSS RO allows a detailed atmospheric profiling, excepting near the Earth’s surface: since the lower troposphere is the region with the largest and variable amount of water vapor, the computation of the IPWV from the RO profiles would not be reliable; - ground-based GNSS technique provides the integrated water vapor retrieval with high temporal resolution, allowing the analysis of the IPWV behavior before, during and after the precipitation event at a specific site. GNSS RO can capture the vertical dynamics of the atmosphere at a random instant with respect to the temporal evolution of the precipitation event, preventing the continuous monitor of such an event in a local area. Also, only some events have RO profiles associated. Therefore, in the next section a useful atmospheric parameter achievable from the RO profiles during precipitation events, i.e. the cloud top, is retrieved, and a first attempt to correlate it to the rain rate is proposed. 5. Precipitation events monitoring by using GNSS radio occultations
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Concerning the LEO GNSS technique, the monitoring of rainy events has been performed on three different Italian areas (Trasimeno Lake, Tuscany and Genova) by using radio occultations corresponding to convective systems. The objective is to connect the convective system cloud top altitude detected by GNSS RO (Biondi et al., 2012) to the system intensity in terms of rain rate which can be measured by ground based stations. A single rain gauge sensor was used in Genova, a dense network of 7 weather stations in the Trasimeno Lake area and a network of 8 stations was used in the Tuscany region, well distributed on the region (Cortona, Firenze, Massa, Monterotondo, Pieve Santo Stefano, Poggibonsi, Pontedera e Scansano). The study area and the weather station locations are reported in Fig. 7. In totality, 17 ROs within 100 km from the target areas during convective systems in the period 2006-2013 were selected, as shown in Fig. 7, and one of them was also co-located with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Cloudsat tracks (6 hour time window and 100 km space window). CALIPSO and Cloudsat are two different satellites part of the A-Train constellation flying on the same orbit close in time and space. The synergy of the Cloudsat Cloud Profiling Radar (CPR) and the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) provides a very accurate description of the cloud structure through the 2B-GEOPROF-LIDAR product (http://cloudsat.cira.colostate.edu/dataSpecs.php?prodid=10). We have used the 2B-GEOPROF-LIDAR cloud mask version 3 for validating the cloud top altitude retrieved by the GPS RO profile. This co-located GNSS RO was acquired the 15th of May 2009 at 18:03 UTC on the Trasimeno Lake area and the correspondent CPR-CALIOP cloud mask was acquired at about 12:40 UTC, both reported in Fig. 8. In the left panel of Fig. 8 the RO temperature profile (red) and the climatological temperature profile (blue) on the Trasimeno Lake area are shown, while in the right panel the cloud mask from Cloudsat/Calipso co-located with the RO profile is reported. The cloud mask highlights clouds with a local average cloud top at 11.7 km of altitude and the temperature profile from the co-located RO shows a double tropopause with lowest coldest point at 11.4 km of altitude, much colder than the climatological temperature at the same altitude. According to Biondi et al. (2012) the system cloud top height should correspond to the lowest temperature inversion altitude (horizontal black line) and this agrees with the cloud mask. Considering the 17 selected ROs, 7 cases of were acquired in Genoa, 3 on the Trasimeno area and 7 in Tuscany. In Fig. 9 the scatter plot between the cloud top altitude evaluated with the Biondi et al. (2012) method and the rain rate measured by the weather stations is shown. Unfortunately the weather stations have different accuracy and temporal resolution, thus the rain rate computation can be affected by these uncertainties. The linear fitting (red line) of the points in Fig. 9 shows a slight increase of the rain rate with the cloud top altitude. However the limited number of samples, together with the previously mentioned uncertainties does not allow to provide a consistent trend and correlation between these two parameters. Anyway this first attempt to infer a trend between the cloud top altitude and the convective system rain rate seems to suggest the existence of a potential relationship that further investigation could clarify.
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Fig. 7. Locations of the weather stations used in this work for measuring the convective system rain rate, mean tangent points of the selected 17 GPS Radio Occultations (yellow dots) and CALIPSO track (light blue line) colocated with the Radio occultation analyzed in Fig. 8.
Fig. 8. Convective system selected on the Trasimeno Lake area, 15th of May 2009. Left panel: climatological temperature profile (blue), GNSS RO temperature profile (red). Right panel: the co-located Cloudsat/Calpso cloud mask with the same altitude range (0-20 km). The horizontal black line shows the altitude of the temperature inversion corresponding to the cloud top altitude according to Biondi et al. (2012).
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Fig. 9. Scatter plot between the cloud top altitude evaluated from GNSS RO data of the selected convective systems (period 2006-2013) and the correspondent rain rate measured by weather stations. 17 samples are shown, 3 from Trasimeno Lake area, 7 from Genoa and 7 from Tuscany. The red line shows the linear fitting. 6. Conclusions
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The ground-based and LEO GNSS sensing technique can be considered as a supplemental meteorological sensor in studying weather events, severe too. The advantage of using ground-based GNSS technique is that it is capable of providing continuous observations of the IPWV trend before, during and after the precipitation passage with high temporal resolution. The spatial resolution depends on the geographical location and density of the GNSS stations: to provide a greater horizontal inspection of the IPWV behavior a dense local network of receivers is required, properly distributing the GNSS stations. Therefore, ground-based GNSS receivers could help to characterize rainy events, taking into account that this methodology cannot directly predict precipitations, but can describe the tropospheric water vapor distribution including the short-term variability. RO technique using LEO GNSS data captures the vertical dynamics of the atmosphere during the precipitation event and it is able to detect the cloud top altitude of convective systems, but it cannot allow the high temporal resolution provided by the ground-based GNSS receivers. Also, only some events have RO profiles co-located in time and space. However, the analysis aimed to find a trend between the cloud top and the rain intensity seems to be promising. Considering the limited number of samples used for this work, the different accuracy and temporal resolution of the weather stations, the uncertainty ascribable to the cloud top detection method and to the colocation GNSS RO-weather station, large improvements are expected for the future studies. The different information provided on the same target area by ground based and LEO GNSS receivers could contribute to the development of an algorithm for nowcasting the intensity of the severe convection. For this reason the Atmospheric Radiation Measurement (ARM) Convective Radio Occultation Campaign (CROC) (http://www.arm.gov/campaigns/twp2012CROC) is already on going taking advantage of several instrument colocation.
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Acknowledgment
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We wish to thank the GeoDAF ASI for the availability of the geodetic data facility and prof. Patrizia Basili, prof. Piero Ciotti and ing. Vinia Mattioli for their contribution. Part of the research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n° 328233.
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Highlights
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- Global Navigation Satellite Systems (GNSS) for meteorological applications
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- Atmospheric parameters retrieved by ground-based and Low Earth Orbit receivers - Time series of integrated precipitable water vapor (IPWV) during rainy events
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- Retrieval of cloud top altitude of convective events with GNSS LEO receivers
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- Different spatial and temporal features depending on the GNSS receiver position
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Table 1. Classification of precipitation events for the four Italian stations MIN cumulated MAX cumulated Averaged Number of rain (mm) rain (mm) cumulated rain (mm) events Perugia 43.119 °N, 12.355 °E (from January 2000 to October 2001) >3 4.8 25.8 11.6 15 >1 and <3 0.8 38.2 4.70 94 <1 0.4 20.2 2.23 172 Genova 44.419 °N, 8.921 °E (from January 1999 to October 2001) >9 16.8 89.6 47.11 16 >3 and <9 1.6 116.4 15.80 94 >1 and <3 0.4 59.6 3.40 292 Cagliari 39.135 °N, 8.973 °E (from December 2000 to October 2001) >3 8.2 21 13.23 6 >1 and <3 1.2 19.6 5.02 27 <1 0.4 5.6 1.42 59 L’Aquila 42.368°N, 13.350°E (from December 2000 to October 2001) >8 10.2 25 17.6 5 >3 and <8 0.6 19.4 6.01 40 >1 and <2 0.03 7.4 0.71 98
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Duration (hours)
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Table 2. Percentage increase of the IPWV before the beginning of the precipitation (1.5 hours before for the duration classification, 3 hours before for the intensity classification). In the brackets the percentage of events having a positive IPWV gradient is reported. Duration and intensity refer to the classifications reported in Fig. 1 and Fig. 2. Duration Long Medium Short Intensity High Medium Low
Genova 2.3% (92%) 4.1% (84%) -2.5% (57%)
Perugia 12% (100%) 5.5% (78%) 5.4% (72%)
Cagliari -1.5% (0%) 5.4% (84%) 7% (86 %)
L’Aquila 1.5% (100%) 5.9% (90%) 2.2% (68%)
3.4% (53%) 2.2% (44%) 0.4% (43%)
16% (70%) -1.7% (60%) 10% (75%)
13.8% (78%) 3.6% (44%)
8.3% (79%) 13.7% (55%)
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S0169-8095(15)00223-9 doi: 10.1016/j.atmosres.2015.07.011 ATMOS 3458
To appear in:
Atmospheric Research
Received date: Revised date: Accepted date:
13 January 2015 13 July 2015 15 July 2015
Please cite this article as: Bonafoni, Stefania, Biondi, Riccardo, The usefulness of the Global Navigation Satellite Systems (GNSS) in the analysis of precipitation events, Atmospheric Research (2015), doi: 10.1016/j.atmosres.2015.07.011
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Dept. of Engineering, University of Perugia, via G. Duranti 93, 06125, Perugia, Italy; ph. +390755853663, [email protected] 2
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Stefania Bonafoni1*, Riccardo Biondi2,
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The usefulness of the Global Navigation Satellite Systems (GNSS) in the analysis of precipitation events
Wegener Center for Climate and Global Change, University of Graz, Graz, Austria
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*Corresponding author: [email protected]; Tel.: +390755853663,
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Abstract It is well known that the use of the Global Navigation Satellite Systems (GNSS), both with ground-based and Low Earth Orbit (LEO) receivers, allows to retrieve atmospheric parameters in all the weather conditions. Ground-based GNSS technique provides the integrated precipitable water vapor (IPWV) with temporal continuity at a specific receiver station, while the GNSS LEO technique allows for Radio Occultation (RO) observations of the atmosphere, providing a detailed atmospheric profiling but without temporal continuity at a specific site. In this work, several precipitation events occurred in Italy were analyzed exploiting the potential of the two GNSS techniques (i.e. ground-based and space-based GNSS receivers). From ground-based receivers, time series of IPWV were produced at specific locations with the purpose of analysing the water vapor behaviour during precipitation events. From LEO receivers, the profiling potential was exploited to retrieve the cloud top altitude of convective events, taking into account that although GNSS RO could capture the dynamics of the atmosphere with high vertical resolution, the temporal resolution is not enough to continuously monitor such an event in a local area. Therefore, the GNSS technique can be considered as a supplemental meteorological system useful in studying precipitation events, but with very different spatial and temporal features depending on the receiver positioning. Keywords: GNSS technique; precipitation events; integrated precipitable water vapor; cloud top altitude. 1. Introduction
It is well known that signals from the Global Navigation Satellite Systems (GNSS), which include for example the U.S. Global Positioning System (GPS), the Russian GLONASS, the future European Galileo, the Chinese COMPASS, commonly processed for navigation purposes, can also be used to characterize media where they propagate in. In the last decade, GNSS atmospheric and Earth’s surface remote sensing has become more and more important, thanks to technical improvements applied to the processing of such “free-of-charge”, everywhere available and weather insensitive signal. For example, the remote sensing of the wet part of the troposphere is possible “extracting” the atmospheric delays from the carrier phases of GNSS observations. Part of these delays, accumulated by the signal along its propagation path, can be associated to the water vapor. Considering the growing employment of ground-based GNSS receivers for the estimation of integrated precipitable water vapor (IPWV), time series of IPWV become available with high temporal and spatial resolution, taking into account that GNSS can be considered an all-weather system (Bevis et al., 1992, 1994; Koulali et al., 2012; Rohm et al., 2014). Concerning the impact of the rain on the GNSS signal, Solheim et al. (1999) proposed theoretical results
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on the microwave propagation delays induced by rain and other atmospheric constituents, while Champollion et al. (2004) and Brenot et al. (2006) analyzed the sensitivity of the total delay in the presence of severe precipitation events. Experimental measurements for monitoring the atmospheric water vapor are important for enabling reliable climate studies and to characterise the influence of the atmosphere on microwave propagation. For instance, the knowledge of water vapor field comes usually from radiosonde observations (RAOB’s), but the availability of data two or four times per day does not meet the requirement of frequent sampling of such parameter, taking into account the high degree of variability. Also, the ground-based microwave radiometers, that are able to work continuously for the retrieval the IPWV with a high temporal resolution, provide measurements not reliable during rainfall. Currently, different techniques employing in situ and remote sensing sensors are used to monitor the atmospheric water vapor, as reported and analyzed in Kämpfer (2013). More recently it was found that the GNSS satellite constellation also allows for Radio Occultation (RO) observations of the Earth’s atmosphere using one or more GNSS receivers onboard Low Earth Orbit (LEO) satellites. These space-based RO observations permit routine profiling of the atmospheric parameters in any meteorological condition with global coverage, high vertical resolution and precision based on limb sounding geometry (Kursinski et al., 1997, Steiner et al., 2013). Pilot study investigated the viability of using space-based GNSS RO technique to study and monitor precipitation events, especially severe thunderstorms and cyclones. Huang et al. (2005) demonstrated the improvement in forecasting the tropical cyclone (TC) best tracks using the RO refractivity; Biondi et al. (2012 and 2013) showed the thermal structure of convective systems and tropical cyclones developing a new technique for detecting their cloud top altitude; Vergados et al. (2014) estimated the wind intensity of hurricanes using temperature profiles from GPS RO measurements. Biondi et al. (2014) showed the different thermal structure of TCs reaching different altitudes according to the development ocean basin.With more than 10 years of GNSS RO observation availability, these acquisitions are important and well suited for climatological studies (Foelsche et al., 2008) thanks to the RO long-term stability and self-calibrated nature, improving operational analysis and global and regional weather forecast (Cardinali, 2009). However, the lower troposphere is a region of great uncertainty when using the GNSS RO technique to retrieve atmospheric profiles, due to the large amount of water vapor which introduces super-refraction and multipath effects (Kursinski et al., 1997). Therefore, GNSS RO allows a detailed atmospheric profiling excepting near the Earth’s surface, but without the temporal resolution required to continuously monitor precipitation events which could take place in a local area and over different period of time. Instead, the ground-based GNSS technique provides reliable integrated water vapor values with high temporal resolution at the receiver station. The spatial resolution of the distribution of the IPWV is dependent on the density of the GNSS stations. In this work, several precipitation events occurred in different zones of Italy and during different years are analyzed exploiting the potential of the two GNSS techniques, i.e. ground-based and LEO GNSS receivers. First, on the basis of previous experimental assessments of IPWV retrieval by GNSS measurements at specific receiver locations with high accuracy (Basili et al., 2001, Bonafoni et al., 2013), the detection of the behaviour of the IPWV during precipitative systems is proposed. For this purpose, a statistical characterization of the trend of the atmospheric water vapor content just before and during the precipitation events is provided. The precipitation systems have been selected during about two years of measurements at four Italian GNSS stations with available co-located automatic rainfall recorders. From literature, different works have been dealt with the analysis of the water vapor from ground-based GNSS during precipitation events, frequently single severe events, (Choy et al, 2013, Champollion et al., 2004; Koulali et al., 2012; Brenot, 2013; Rohm et al., 2014): in this work both a statistical study of events classified in terms of duration and intensity and a detailed study of single significant events is performed, considering four Italian stations with different climatological and geographical conditions.
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Concerning the GNSS LEO, the cloud top altitude from the RO profiles is retrieved during convective systems selected again in different zones of Italy and during different years: then, a first analysis aimed to find a link between the cloud top and the rain intensity is carried out.
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2. IPWV measurements using ground-based GNSS receiver
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In this study 4 Italian GNSS receiver stations, Perugia, Genova, Cagliari and L’Aquila, were selected (coordinates in Tab. 1) belonging to the Italian GNSS fiducial network managed by ASI/CGS (Agezia Spaziale Italiana/Centro di Geodesia Spaziale), with available co-located weather stations with rainfall measurement systems. ASI/CGS delivers GNSS data to the users through the EUREF (European Geodetic Reference Systems) local data center GeoDAF (Geodetic Data Archiving Facility, http://geodaf.mt.asi.it/). The use of ground-based GNSS receivers for IPWV estimation is a well established technique (Bevis et al., 1992, 1994; Rocken et al., 1995; Wolfe and Gutman, 2000; Basili et al., 2001; Bonafoni et al., 2013). It is based on measurements of the tropospheric delay affecting the signals during their propagation from the GNSS satellites to the receivers on ground. Basically, the Zenith Total Delay (ZTD), that is the excess path length due to the signal travel through the troposphere at zenith, is used to estimate the IPWV. The dispersive ionospheric effect is removed by a linear combination of dual frequency data (Klobuchar and Kunches, 2001). ASI/CGS provides to EUREF daily final Zenith Total Delay (ZTD) estimates in SINEX TRO files of the considered network. The delivered GNSS data are processed by using the JPL GIPSY-OASIS II software, with a time resolution of 15 minutes. The retrieval algorithm to estimate IPWV from the zenith total delay is based to the decomposition of ZTD into two components: the Zenith Hydrostatic Delay (ZHD), that is mainly dependent on the dry air gasses in the atmosphere and accounts for approximately 90% of the delay, and the Zenith Wet Delay (ZWD) that depends entirely on the moisture content of the atmosphere. Therefore, the ZWD is given by: ZWD = ZTD - ZHD
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Applying the well-known Saastamoinen model (Saastamoinen, 1972) to accurate surface pressure measurements, the ZHD can be predicted with high accuracy and compute the ZWD using (1). Then, it is possible to convert the ZWD values into IPWV ones by using the following relationship: IPWV = П·ZWD
(2)
where the factor П is a function of various physical constants and of the weighted mean temperature Tm of the whole atmosphere above the GNSS antenna (Davis et al., 1985; Askne and Nordius, 1987): ( e / T )dz Tm 2 (e / T )dz 10
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Rv [( k3 / Tm ) (k 2 mk 1 )]
(3)
(4)
where dz has units of length in the zenith, T is the air temperature (K), e is the partial pressure of water vapor (hPa), is the density of liquid water (g/m3), Rv is the specific gas constant for water vapor, m is the ratio of molar masses of water vapor and dry air, and k1, k2, k3 are the constants defined by Thayer (1974) as: k1 77.6036 ; k2 64.79; k3 3.776 10 5 .
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3. IPWV monitoring of precipitation events
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The transformation of ZWD into IPWV assumes that the wet path delay is entirely due to water vapor, and that liquid water and ice do not contribute significantly to it (Duan et al., 1996). The time-varying parameter can be estimated with such an accuracy that very little uncertainty is introduced during the computation of (2). Bevis et al. (1994) showed that in most practical conditions the uncertainty in the parameter estimationis essentially due to the uncertainty for Tm (predicted from the surface temperature Ts on the basis of a linear regression), leading to a relative error in on the order of 2%. In this work we have computed estimating Tm by linear regression on Ts (available from the weather stations) with coefficients determined using RAOB observations at seven Italian stations (12128 RAOB atmospheric profiles from 1999 to 2000): this lead to an estimate error on Tm having a standard deviation of 3.4 K, corresponding to a relative error on the value of slightly grater to 1%. Examples of different approaches and accuracies in the computation of and in the estimation of IPWV are shown in Basili et al. (2001). Concerning the prediction of ZHD with the Saastamoinen model, we have employed the surface pressure values corresponding in time to the ZTD data set of the GNSS stations available from the co-located weather stations.
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The automatic rain recorder systems of Perugia, Genova, Cagliari, L’Aquila, co-located near the GNSS receivers, provide values of cumulated rain on a time period of fifteen, thirty, fifteen and sixty minutes, respectively. For each rain event, an atmospheric water vapor monitoring has been performed exploiting approximately two years (2000 and 2001) of IPWV estimations from GNSS observations. Based on these rainfall measurements, taking into account that the four sites present different climatological and geographical conditions, a two-step classification of precipitation events has been performed on the basis of time duration and cumulated rain. First, for each station, the precipitation events have been classified into few classes of different duration, statistically significant for the site considered: for such classes a first study of the IPWV behaviour just before and during the rainfall has been performed. Table 1 synthesises the classification of the precipitation events for the four stations in terms of duration, reporting also the cumulated rain. Genova site exhibits more intense and frequent rainy phenomena.
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Table 1. Classification of precipitation events for the four Italian stations
Duration (hours)
MIN cumulated MAX cumulated Averaged Number of rain (mm) rain (mm) cumulated rain (mm) events Perugia 43.119 °N, 12.355 °E (from January 2000 to October 2001) >3 4.8 25.8 11.6 15 >1 and <3 0.8 38.2 4.70 94 <1 0.4 20.2 2.23 172 Genova 44.419 °N, 8.921 °E (from January 1999 to October 2001) >9 16.8 89.6 47.11 16 >3 and <9 1.6 116.4 15.80 94 >1 and <3 0.4 59.6 3.40 292 Cagliari 39.135 °N, 8.973 °E (from December 2000 to October 2001) >3 8.2 21 13.23 6 >1 and <3 1.2 19.6 5.02 27 <1 0.4 5.6 1.42 59
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L’Aquila 42.368°N, 13.350°E (from December 2000 to October 2001) >8 10.2 25 17.6 >3 and <8 0.6 19.4 6.01 >1 and <2 0.03 7.4 0.71
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Fig.1 shows the averaged IPWV time series for each station from one hour and half before until two hours after the beginning of the rainfall for the classified events (except for L’Aquila where the time intervals are greater since the cumulated rain is provided hourly). In these figures precipitation events of the same class (i.e. of the same time duration) with different cumulated rain are assembled together. Even if the mean level of IPWV is different at each sites, a general increase of IPWV field just before the rainfall can be noted. During the rainfall, the averaged IPWV evolution has not the same behaviour, but with the water vapor values reaching a plateau or decreasing: the only exception is for heavy rain of long duration, as the case of precipitation events longer than 9 hours, observed at Genova, or longer than 8 hours, at L’Aquila.
Fig. 1. Averaged IPWV (mm) values for rainy events classified in terms of duration (hours), for the four GNSS stations. The vertical line marks the beginning of the rainfall.
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Taking into account the statistically significant rain rate values for each site, the Fig. 2 shows the averaged IPWV time series three hours before the rainfall and for the first hour of precipitation for events classified in terms of rain rate. Again, a general increase of water vapor content can be recognized, but now is more evident the increased IPWV mean level for rain rate growing values. This fact suggests that most probably the convective systems are characterized by a high IPWV pattern.
Fig. 2. Averaged IPWV (mm) values for rainy events classified in terms of rain rate (mm/h), for the four GNSS stations. The vertical line marks the beginning of the rainfall. Due to the occurrence of less intense rainy events for Cagliari and L’Aquila sites the rain rate values of the selected classes are lower than the ones selected for Genova and Perugia, but the same average trend is noted. The results of Fig. 1 and Fig. 2 can be summarized in Table 2, where the percentage increase of the IPWV before the beginning of the precipitation and the percentage of events having a positive IPWV gradient are reported.
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Perugia 12% (100%) 5.5% (78%) 5.4% (72%)
Cagliari -1.5% (0%) 5.4% (84%) 7% (86 %)
L’Aquila 1.5% (100%) 5.9% (90%) 2.2% (68%)
3.4% (53%) 2.2% (44%) 0.4% (43%)
16% (70%) -1.7% (60%) 10% (75%)
13.8% (78%) 3.6% (44%)
8.3% (79%) 13.7% (55%)
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Genova 2.3% (92%) 4.1% (84%) -2.5% (57%)
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Duration Long Medium Short Intensity High Medium Low
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Table 2. Percentage increase of the IPWV before the beginning of the precipitation (1.5 hours before for the duration classification, 3 hours before for the intensity classification). In the brackets the percentage of events having a positive IPWV gradient is reported. Duration and intensity refer to the classifications reported in Fig. 1 and Fig. 2.
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Table 2 suggests that the IPWV average increase before the precipitation is variable, considering that the four stations have different climatological and geographical conditions, and that some events inside each class have a decreasing trend despite the mean behaviour. For instance, all the events with duration greater than 3 hours occurred at Cagliari have a decreasing IPWV trend before precipitation. Now, a monitoring of the IPWV for specific events is proposed, taking into account the intensity of the rainfall. In Fig. 3, the IPWV behaviour (blue line) and the correspondent rain rate (green line) are plotted together (before, during and after the precipitation), for a long rainy event occurred at Genova the 5 th November 2000. We can recognize the increase of IPWV before the rainfall, but also the continuous climbing trends until the maximum rain rate (16 mm/h), and after a decreasing. Also, a rapid upward trend just before the end of rainfall is present, where a relative peak of rainfall has been recorded. In general, a light lead time of the IPWV peaks with respect to to the rain rate peaks can be noted. This behaviour seems to suggest that the water vapor fuels the rain event and reveals a correlation between the IPWV trend and the amount of precipitation.
Fig. 3. Genova: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 5th November 2000, h.22.30, duration 20 hours.
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Fig. 4 reports the same analysis for the longest event occurred at Perugia the 15 th October 2000. The IPWV time series shows an upward trend before the rainfall and an evident decreasing until the end of the precipitation (IPWV difference before and after of about 10 mm). Here the peak during the rainfall are not so evident as in the previous case of Genova since the rain rate level are less discontinuous.
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Fig. 4. Perugia: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 15th November 2000, h.08.00, duration about 6 hours.
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Fig. 5 and 6 show the same analysis for the Cagliari and L’Aquila sites, where again the greater IPWV peak is in advance with respect to the rain rate peak.
Fig. 5. Cagliari: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 21st December 2000, h.15.30, duration 5.30 hours.
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Fig. 6. L’Aquila: IPWV values (blue line) and rain rate (green line) for the precipitation event of the 17th August 2000, h.17.00, duration 4 hours.
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This study proves that precipitable water vapor data from ground-based GNSS receivers can be useful in weather analysis and modelling applications (Pacione et al., 2001; De Pondeca et al., 2001; Nakamura et al., 2004; Seko et al., 2004; Zhang et al., 2007) taking into account that GNSS ground stations are in abundance and the system is not very sensitive to rain for intensities below 40 mm/h (Solheim et al., 1999). This preliminary results show that GNSS data could help in analysing some features of a rain event but to tune a “quantitative” criteria for the definition of a precipitation event other information are necessary, such as cloud profile, cloud liquid content, radar/lidar measurements and so on (Brenot et al., 2013; Champollion et al., 2004; Michaelides et al., 2009; Yan et al., 2014). In fact the IPWV average increase before the precipitation is variable and also depends on climatological and geographical conditions, with some events showing even a decreasing trend. Therefore, even if this methodology cannot directly predict precipitations, it can describe the IPWV behaviour in the troposphere including the short-term variability. Also, the IPWV was analyzed at a single station, providing information of the horizontal water vapor distribution only on the surrounding area of the station: to provide an enlarged horizontal inspection a dense local network of ground-based GNSS receivers is required. For instance, a suggestion of possible strategies to properly distribute the GNSS stations to achieve a desired spatial resolution in the IPWV estimation, crowding them in some prescribed regions, are provided in Bonafoni et al. (2013). 4. The GNSS radio occultation technique The radio occultation technique uses phase and amplitude of the two L-band signals transmitted from GNSS satellites and measured by the receivers on board of LEO satellites. When the signal passes through the atmosphere it is refracted due to refractive index gradients and the correspondent measurement can be characterized by the bending angle, , as a function of the impact parameter a. Assuming local spherical symmetry, the refractive index profile, n, related to a given impact parameter (rp ), can be derived from and a using the Abel inversion (Kursinski et al., 1997):
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where arp = n(rp)rp is the impact parameter for the ray whose tangent radius is rp. The refractivity profile is then N=(n-1)106, that at microwave wavelength is given by (Thayer, 1974): (6)
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where Pd is the pressure of dry air in hPa, Pw the partial pressure of water vapor in hPa, T is the atmospheric temperature in Kelvin. This equation is accurate to within 0.5% for frequencies up to 100 GHz. The water vapor term (third of eq. 6) is small in the upper troposphere and stratosphere, so the refractivity can be used to directly calculate the so-called dry temperature and dry pressure. Commonly eq. 6 can be solved to obtain estimates of Pw, Pd, and T by assimilating the refractivity together with first-guess profiles from, e.g., European Centre for Medium-Range Weather Forecasts (ECMWF), using a one-dimensional variational (1DVar) technique (Healy and Eyre, 2000). The vertical resolution provided by RO ranges from 60 m in the troposphere to 1 km in the stratosphere (Gorbunov et al., 2004) and the soundings have the highest accuracy between 5 and 25 km of altitude with observational errors, including measurement and representativeness errors, in the range 0.3% to 0.5% in refractivity (ScherllinPirscher et al., 2011). The RO horizontal resolution can range from 70 km up to about 300 km and this creates some uncertainty issues when studying small scale events. For this work we have used the GPS RO products Level 2 (L2) processed by the Wegener Center for Climate and Global Change (WEGC) through the new Occultation Processing System (OPS) version 5.6 (Kirchengast et al., 2007) based on University Corporation for Atmospheric Research (UCAR) orbit and excess phase data, version 2010.2640 (Schwaerz et al., 2013). The WEGC OPSv5.6 is based on a geometrics optics retrieval combined with a wave optics retrieval in the lower and middle troposphere. A bending angle optimization is performed at high altitudes with co-located short-range forecast profiles of the ECMWF. The physical temperature is retrieved on the basis of an optimal estimation with co-located ECMWF short-term forecast profiles as background data. Although the GNSS RO technique is exploited to obtain profiles of refractivity, temperature, pressure and humidity in the atmosphere at global scale, the integrated water vapor values (retrieved by GNSS ground-based receivers as previously shown) are not measurable with reliability by the LEO receivers due to spatial and temporal reasons: - GNSS RO allows a detailed atmospheric profiling, excepting near the Earth’s surface: since the lower troposphere is the region with the largest and variable amount of water vapor, the computation of the IPWV from the RO profiles would not be reliable; - ground-based GNSS technique provides the integrated water vapor retrieval with high temporal resolution, allowing the analysis of the IPWV behavior before, during and after the precipitation event at a specific site. GNSS RO can capture the vertical dynamics of the atmosphere at a random instant with respect to the temporal evolution of the precipitation event, preventing the continuous monitor of such an event in a local area. Also, only some events have RO profiles associated. Therefore, in the next section a useful atmospheric parameter achievable from the RO profiles during precipitation events, i.e. the cloud top, is retrieved, and a first attempt to correlate it to the rain rate is proposed. 5. Precipitation events monitoring by using GNSS radio occultations
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Concerning the LEO GNSS technique, the monitoring of rainy events has been performed on three different Italian areas (Trasimeno Lake, Tuscany and Genova) by using radio occultations corresponding to convective systems. The objective is to connect the convective system cloud top altitude detected by GNSS RO (Biondi et al., 2012) to the system intensity in terms of rain rate which can be measured by ground based stations. A single rain gauge sensor was used in Genova, a dense network of 7 weather stations in the Trasimeno Lake area and a network of 8 stations was used in the Tuscany region, well distributed on the region (Cortona, Firenze, Massa, Monterotondo, Pieve Santo Stefano, Poggibonsi, Pontedera e Scansano). The study area and the weather station locations are reported in Fig. 7. In totality, 17 ROs within 100 km from the target areas during convective systems in the period 2006-2013 were selected, as shown in Fig. 7, and one of them was also co-located with the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and Cloudsat tracks (6 hour time window and 100 km space window). CALIPSO and Cloudsat are two different satellites part of the A-Train constellation flying on the same orbit close in time and space. The synergy of the Cloudsat Cloud Profiling Radar (CPR) and the CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) provides a very accurate description of the cloud structure through the 2B-GEOPROF-LIDAR product (http://cloudsat.cira.colostate.edu/dataSpecs.php?prodid=10). We have used the 2B-GEOPROF-LIDAR cloud mask version 3 for validating the cloud top altitude retrieved by the GPS RO profile. This co-located GNSS RO was acquired the 15th of May 2009 at 18:03 UTC on the Trasimeno Lake area and the correspondent CPR-CALIOP cloud mask was acquired at about 12:40 UTC, both reported in Fig. 8. In the left panel of Fig. 8 the RO temperature profile (red) and the climatological temperature profile (blue) on the Trasimeno Lake area are shown, while in the right panel the cloud mask from Cloudsat/Calipso co-located with the RO profile is reported. The cloud mask highlights clouds with a local average cloud top at 11.7 km of altitude and the temperature profile from the co-located RO shows a double tropopause with lowest coldest point at 11.4 km of altitude, much colder than the climatological temperature at the same altitude. According to Biondi et al. (2012) the system cloud top height should correspond to the lowest temperature inversion altitude (horizontal black line) and this agrees with the cloud mask. Considering the 17 selected ROs, 7 cases of were acquired in Genoa, 3 on the Trasimeno area and 7 in Tuscany. In Fig. 9 the scatter plot between the cloud top altitude evaluated with the Biondi et al. (2012) method and the rain rate measured by the weather stations is shown. Unfortunately the weather stations have different accuracy and temporal resolution, thus the rain rate computation can be affected by these uncertainties. The linear fitting (red line) of the points in Fig. 9 shows a slight increase of the rain rate with the cloud top altitude. However the limited number of samples, together with the previously mentioned uncertainties does not allow to provide a consistent trend and correlation between these two parameters. Anyway this first attempt to infer a trend between the cloud top altitude and the convective system rain rate seems to suggest the existence of a potential relationship that further investigation could clarify.
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Fig. 7. Locations of the weather stations used in this work for measuring the convective system rain rate, mean tangent points of the selected 17 GPS Radio Occultations (yellow dots) and CALIPSO track (light blue line) colocated with the Radio occultation analyzed in Fig. 8.
Fig. 8. Convective system selected on the Trasimeno Lake area, 15th of May 2009. Left panel: climatological temperature profile (blue), GNSS RO temperature profile (red). Right panel: the co-located Cloudsat/Calpso cloud mask with the same altitude range (0-20 km). The horizontal black line shows the altitude of the temperature inversion corresponding to the cloud top altitude according to Biondi et al. (2012).
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Fig. 9. Scatter plot between the cloud top altitude evaluated from GNSS RO data of the selected convective systems (period 2006-2013) and the correspondent rain rate measured by weather stations. 17 samples are shown, 3 from Trasimeno Lake area, 7 from Genoa and 7 from Tuscany. The red line shows the linear fitting. 6. Conclusions
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The ground-based and LEO GNSS sensing technique can be considered as a supplemental meteorological sensor in studying weather events, severe too. The advantage of using ground-based GNSS technique is that it is capable of providing continuous observations of the IPWV trend before, during and after the precipitation passage with high temporal resolution. The spatial resolution depends on the geographical location and density of the GNSS stations: to provide a greater horizontal inspection of the IPWV behavior a dense local network of receivers is required, properly distributing the GNSS stations. Therefore, ground-based GNSS receivers could help to characterize rainy events, taking into account that this methodology cannot directly predict precipitations, but can describe the tropospheric water vapor distribution including the short-term variability. RO technique using LEO GNSS data captures the vertical dynamics of the atmosphere during the precipitation event and it is able to detect the cloud top altitude of convective systems, but it cannot allow the high temporal resolution provided by the ground-based GNSS receivers. Also, only some events have RO profiles co-located in time and space. However, the analysis aimed to find a trend between the cloud top and the rain intensity seems to be promising. Considering the limited number of samples used for this work, the different accuracy and temporal resolution of the weather stations, the uncertainty ascribable to the cloud top detection method and to the colocation GNSS RO-weather station, large improvements are expected for the future studies. The different information provided on the same target area by ground based and LEO GNSS receivers could contribute to the development of an algorithm for nowcasting the intensity of the severe convection. For this reason the Atmospheric Radiation Measurement (ARM) Convective Radio Occultation Campaign (CROC) (http://www.arm.gov/campaigns/twp2012CROC) is already on going taking advantage of several instrument colocation.
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Acknowledgment
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We wish to thank the GeoDAF ASI for the availability of the geodetic data facility and prof. Patrizia Basili, prof. Piero Ciotti and ing. Vinia Mattioli for their contribution. Part of the research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n° 328233.
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Highlights
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- Global Navigation Satellite Systems (GNSS) for meteorological applications
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- Atmospheric parameters retrieved by ground-based and Low Earth Orbit receivers - Time series of integrated precipitable water vapor (IPWV) during rainy events
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- Retrieval of cloud top altitude of convective events with GNSS LEO receivers
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- Different spatial and temporal features depending on the GNSS receiver position
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Table 1. Classification of precipitation events for the four Italian stations MIN cumulated MAX cumulated Averaged Number of rain (mm) rain (mm) cumulated rain (mm) events Perugia 43.119 °N, 12.355 °E (from January 2000 to October 2001) >3 4.8 25.8 11.6 15 >1 and <3 0.8 38.2 4.70 94 <1 0.4 20.2 2.23 172 Genova 44.419 °N, 8.921 °E (from January 1999 to October 2001) >9 16.8 89.6 47.11 16 >3 and <9 1.6 116.4 15.80 94 >1 and <3 0.4 59.6 3.40 292 Cagliari 39.135 °N, 8.973 °E (from December 2000 to October 2001) >3 8.2 21 13.23 6 >1 and <3 1.2 19.6 5.02 27 <1 0.4 5.6 1.42 59 L’Aquila 42.368°N, 13.350°E (from December 2000 to October 2001) >8 10.2 25 17.6 5 >3 and <8 0.6 19.4 6.01 40 >1 and <2 0.03 7.4 0.71 98
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Duration (hours)
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Table 2. Percentage increase of the IPWV before the beginning of the precipitation (1.5 hours before for the duration classification, 3 hours before for the intensity classification). In the brackets the percentage of events having a positive IPWV gradient is reported. Duration and intensity refer to the classifications reported in Fig. 1 and Fig. 2. Duration Long Medium Short Intensity High Medium Low
Genova 2.3% (92%) 4.1% (84%) -2.5% (57%)
Perugia 12% (100%) 5.5% (78%) 5.4% (72%)
Cagliari -1.5% (0%) 5.4% (84%) 7% (86 %)
L’Aquila 1.5% (100%) 5.9% (90%) 2.2% (68%)
3.4% (53%) 2.2% (44%) 0.4% (43%)
16% (70%) -1.7% (60%) 10% (75%)
13.8% (78%) 3.6% (44%)
8.3% (79%) 13.7% (55%)
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